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Posted to commits@mxnet.apache.org by gi...@apache.org on 2017/08/07 07:39:06 UTC
[incubator-mxnet-site] branch asf-site updated: Nightly build
This is an automated email from the ASF dual-hosted git repository.
git-site-role pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new f5f61c2 Nightly build
f5f61c2 is described below
commit f5f61c2f89a98c3234de25b29a7fdd930618c984
Author: jenkins <us...@infra.apache.org>
AuthorDate: Mon Aug 7 07:39:02 2017 +0000
Nightly build
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...1_1common_1_1ObjectPoolAllocatable-members.html | 60 +-
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versions/master/doxygen/utils_8h.html | 116 +-
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versions/master/doxygen/utils_8h_source.html | 155 +-
versions/master/genindex.html | 5128 +++++++++--------
versions/master/get_started/amazonlinux_setup.html | 42 +-
versions/master/get_started/build_from_source.html | 36 +-
versions/master/get_started/centos_setup.html | 42 +-
versions/master/get_started/index.html | 46 +-
versions/master/get_started/install.html | 44 +-
versions/master/get_started/osx_setup.html | 40 +-
versions/master/get_started/raspbian_setup.html | 42 +-
versions/master/get_started/tx2_setup.html | 42 +-
versions/master/get_started/ubuntu_setup.html | 70 +-
versions/master/get_started/why_mxnet.html | 50 +-
versions/master/get_started/windows_setup.html | 94 +-
versions/master/how_to/bucketing.html | 36 +-
versions/master/how_to/caffe.html | 36 +-
versions/master/how_to/cloud.html | 40 +-
versions/master/how_to/develop_and_hack.html | 36 +-
versions/master/how_to/env_var.html | 38 +-
versions/master/how_to/faq.html | 36 +-
versions/master/how_to/finetune.html | 42 +-
versions/master/how_to/finetune.ipynb | 2 +-
versions/master/how_to/index.html | 44 +-
versions/master/how_to/model_parallel_lstm.html | 38 +-
versions/master/how_to/multi_devices.html | 42 +-
versions/master/how_to/new_op.html | 66 +-
versions/master/how_to/nnpack.html | 128 +-
versions/master/how_to/perf.html | 176 +-
versions/master/how_to/recordio.html | 42 +-
versions/master/how_to/s3_integration.html | 89 +-
versions/master/how_to/smart_device.html | 46 +-
versions/master/how_to/torch.html | 36 +-
versions/master/how_to/visualize_graph.html | 36 +-
versions/master/index.html | 102 +-
versions/master/model_zoo/index.html | 76 +-
versions/master/objects.inv | Bin 9082 -> 7107 bytes
versions/master/py-modindex.html | 58 +-
versions/master/search.html | 36 +-
versions/master/searchindex.js | 2 +-
versions/master/tutorials/basic/data.html | 44 +-
versions/master/tutorials/basic/image_io.html | 36 +-
versions/master/tutorials/basic/module.html | 46 +-
versions/master/tutorials/basic/module.ipynb | 2 +-
versions/master/tutorials/basic/ndarray.html | 56 +-
versions/master/tutorials/basic/record_io.html | 36 +-
versions/master/tutorials/basic/symbol.html | 42 +-
versions/master/tutorials/c++/basics.html | 36 +-
.../master/tutorials/embedded/wine_detector.html | 44 +-
.../general_ml/recommendation_systems.html | 36 +-
versions/master/tutorials/gluon/autograd.html | 36 +-
versions/master/tutorials/gluon/customop.html | 36 +-
versions/master/tutorials/gluon/gluon.html | 36 +-
versions/master/tutorials/gluon/hybrid.html | 37 +-
versions/master/tutorials/gluon/hybrid.ipynb | 2 +-
versions/master/tutorials/gluon/mnist.html | 38 +-
versions/master/tutorials/gluon/ndarray.html | 38 +-
versions/master/tutorials/index.html | 44 +-
versions/master/tutorials/nlp/cnn.html | 121 +-
versions/master/tutorials/nlp/nce_loss.html | 44 +-
versions/master/tutorials/nlp/rnn.html | 36 +-
versions/master/tutorials/python/kvstore.html | 36 +-
.../master/tutorials/python/linear-regression.html | 42 +-
.../tutorials/python/matrix_factorization.html | 36 +-
versions/master/tutorials/python/mnist.html | 42 +-
.../master/tutorials/python/predict_image.html | 42 +-
.../tutorials/r/CallbackFunctionTutorial.html | 50 +-
.../master/tutorials/r/CustomIteratorTutorial.html | 36 +-
.../master/tutorials/r/CustomLossFunction.html | 238 +-
versions/master/tutorials/r/charRnnModel.html | 48 +-
.../r/classifyRealImageWithPretrainedModel.html | 46 +-
.../tutorials/r/fiveMinutesNeuralNetwork.html | 54 +-
versions/master/tutorials/r/mnistCompetition.html | 54 +-
versions/master/tutorials/r/ndarray.html | 52 +-
versions/master/tutorials/r/symbol.html | 46 +-
versions/master/tutorials/scala/char_lstm.html | 77 +-
versions/master/tutorials/scala/mnist.html | 36 +-
.../tutorials/scala/mxnet_scala_on_intellij.html | 64 +-
.../speech_recognition/baidu_warp_ctc.html | 60 +-
.../tutorials/speech_recognition/speech_lstm.html | 108 +-
.../unsupervised_learning/auto_encoders.html | 36 +-
.../tutorials/unsupervised_learning/gan.html | 36 +-
.../vision/large_scale_classification.html | 96 +-
995 files changed, 56255 insertions(+), 21098 deletions(-)
diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md
new file mode 100644
index 0000000..85720e6
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE.md
@@ -0,0 +1,42 @@
+For bugs or installation issues, please provide the following information.
+The more information you provide, the more likely people will be able to help you.
+
+## Environment info
+Operating System:
+
+Compiler:
+
+Package used (Python/R/Scala/Julia):
+
+MXNet version:
+
+Or if installed from source:
+
+MXNet commit hash (`git rev-parse HEAD`):
+
+If you are using python package, please provide
+
+Python version and distribution:
+
+If you are using R package, please provide
+
+R `sessionInfo()`:
+
+## Error Message:
+Please paste the full error message, including stack trace.
+
+## Minimum reproducible example
+if you are using your own code, please provide a short script that reproduces the error.
+
+## Steps to reproduce
+or if you are running standard examples, please provide the commands you have run that lead to the error.
+
+1.
+2.
+3.
+
+## What have you tried to solve it?
+
+1.
+2.
+3.
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..ca5d03b
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,108 @@
+sudo: false
+
+language: cpp
+
+os:
+ # - linux
+ - osx
+
+osx_image: xcode8
+
+env:
+ # code analysis
+ # - TASK=lint
+ # build mxnet.so with CUDA
+ # - TASK=build
+ # run tests/cpp
+ - TASK=cpp_test
+ # run tests/python
+ - TASK=python_test
+ - TASK=r_test
+ # - TASK=julia JULIA_VER=0.4
+ # - TASK=scala_test
+
+ # TODO, R test, distributed test, clang, more g++ versions
+
+matrix:
+ include:
+ - # os: linux
+ # dist: trusty
+ # env: TASK=perl_test
+ - os: osx
+ ## sudo is required because
+ ## prexexisting packages conflict
+ ## with new ones.
+ ## would be nice to have macports
+ ## on travis osx, it has all needed perl packages
+ sudo: required
+ env: TASK=perl_test
+# env: TASK=julia JULIA_VER=0.4
+# - os: linux
+# env: TASK=build
+# - os: linux
+# env: TASK=cpp_test
+# - os: linux
+# env: TASK=python_test
+# - os: linux
+# env: TASK=r_test
+# - os: linux
+# env: TASK=scala_test
+
+# dependent apt packages
+addons:
+ apt:
+ sources:
+ - ubuntu-toolchain-r-test
+ packages:
+ - doxygen
+ - wget
+ - git
+ - libcurl4-openssl-dev
+ - unzip
+ - libatlas-dev
+ - libopencv-dev
+ - gcc-4.8
+ - g++-4.8
+ - python-numpy
+ - python-nose
+ - python3-numpy
+ - python3-dev
+ - python3-nose
+ - python-h5py
+ - python3-h5py
+ - graphviz
+ - libmouse-perl
+ - pdl
+ - cpanminus
+ - swig
+ - libgraphviz-perl
+
+before_install:
+ - export NVCC_PREFIX=${HOME}
+ - source dmlc-core/scripts/travis/travis_setup_env.sh
+ - export PYTHONPATH=${PYTHONPATH}:${PWD}/python
+ - export MAVEN_SKIP_RC=true
+ - export MAVEN_OPTS="-Xmx512m -XX:MaxPermSize=256m -XX:-UseGCOverheadLimit -XX:+CMSClassUnloadingEnabled -XX:+UseConcMarkSweepGC"
+
+install:
+ - source tests/travis/setup.sh
+
+script:
+ - tests/travis/run_test.sh
+
+cache:
+ directories:
+ - ${HOME}/.cache/usr
+
+before_cache:
+ - dmlc-core/scripts/travis/travis_before_cache.sh
+
+after_failure:
+ - tests/travis/travis_after_failure.sh
+
+notifications:
+# Emails are sent to the committer's git-configured email address by default,
+ email:
+ on_success: change
+ on_failure: always
+ #slack: dmlc:NmroCzntCiWOuxUZpii40USd
diff --git a/tag.txt b/tag.txt
old mode 100755
new mode 100644
diff --git a/versions/master/.buildinfo b/versions/master/.buildinfo
index f3477d1..fb8e27d 100644
--- a/versions/master/.buildinfo
+++ b/versions/master/.buildinfo
@@ -1,4 +1,4 @@
# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 1a24bdda52b90f51d3f373db3de6f3eb
+config: 29665977988a3bfa9878234358b3266d
tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/versions/master/README.html b/versions/master/README.html
index 63ac8b2..7d9efa2 100644
--- a/versions/master/README.html
+++ b/versions/master/README.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet documentation — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="_static/basic.css" rel="stylesheet" type="text/css">
+<link href="_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: './',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
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-</form>
+</input></form>
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diff --git a/versions/master/_sources/README.txt b/versions/master/_sources/README.txt
new file mode 100644
index 0000000..7780f89
--- /dev/null
+++ b/versions/master/_sources/README.txt
@@ -0,0 +1,22 @@
+# MXNet documentation
+
+A built version of document is available at http://mxnet.io
+
+To build the documents locally, we need to first install [docker](docker.com).
+Then use the following commands to clone and
+build the documents.
+
+```bash
+git clone --recursive https://github.com/dmlc/mxnet
+cd mxnet && make docs
+```
+
+The results will be available at `docs/_build/html/`.
+
+Note:
+
+- If C++ codes have been changed, we suggest to remove the previous results to
+ trigger the rebuild for all pages, namely run `make clean_docs`.
+- If C++ codes are failed to build, run `make clean`
+- If CSS or javascript are changed, we often need to do a *force refresh* in the
+ browser to clear the cache.
diff --git a/versions/master/_sources/api/c++/index.txt b/versions/master/_sources/api/c++/index.txt
new file mode 100644
index 0000000..9a68cda
--- /dev/null
+++ b/versions/master/_sources/api/c++/index.txt
@@ -0,0 +1,8 @@
+# MXNet - C++ API
+
+For namespaces, classes, and code files for the MXNet C++ package, see the following:
+
+* [Namespaces](http://mxnet.io/doxygen/namespaces.html)
+* [Classes](http://mxnet.io/doxygen/annotated.html)
+* [Code Files](http://mxnet.io/doxygen/files.html)
+* [MXNet CPP Package](https://github.com/dmlc/mxnet/tree/master/cpp-package)
diff --git a/versions/master/_sources/api/julia/index.txt b/versions/master/_sources/api/julia/index.txt
new file mode 100644
index 0000000..ecd08bf
--- /dev/null
+++ b/versions/master/_sources/api/julia/index.txt
@@ -0,0 +1,13 @@
+MXNet - Julia API
+=================
+MXNet supports the Julia programming language. The MXNet Julia package brings flexible and efficient GPU
+computing and the state-of-art deep learning to Julia.
+
+- It enables you to write seamless tensor/matrix computation with multiple GPUs in Julia.
+- It also enables you to construct and customize the state-of-art deep learning models in Julia,
+ and apply them to tasks such as image classification and data science challenges.
+
+
+
+
+Julia documents are available at [http://dmlc.ml/MXNet.jl/latest/](http://dmlc.ml/MXNet.jl/latest/).
diff --git a/versions/master/_sources/api/perl/index.txt b/versions/master/_sources/api/perl/index.txt
new file mode 100644
index 0000000..19f70fa
--- /dev/null
+++ b/versions/master/_sources/api/perl/index.txt
@@ -0,0 +1,64 @@
+# MXNet - Perl API
+
+MXNet supports the Perl programming language. The MXNet Perl package brings flexible and efficient GPU
+computing and state-of-art deep learning to Perl. It enables you to write seamless tensor/matrix computation with multiple GPUs in Perl.
+It also lets you construct and customize the state-of-art deep learning models in Perl,
+ and apply them to tasks, such as image classification and data science challenges.
+
+One important thing to internalize is that Perl interface is written to be as close as possible to the Python's API,
+so most if not all of Python's documentation and examples should just work in Perl after making few
+changes in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ; and in 99% of cases
+that's all that is needed there.
+In addition please refer to [excellent metacpan doc interface](https://metacpan.org/release/AI-MXNet) and to very detailed
+[MXNet Python API Documentation](http://mxnet.io/api/python/index.html).
+
+AI::MXNet is seamlessly glued with PDL, the C++ level state can be easily initialized from PDL and the results can be
+transferred to PDL objects in order to allow you to use all the glory and power of the PDL!
+
+Here is how you can perform tensor or matrix computation in Perl with AI::MXNet and PDL:
+
+```perl
+pdl> use AI::MXNet qw(mx); # creates 'mx' module on the fly with the interface close to the Python's API
+
+pdl> print $arr = mx->nd->ones([2, 3])
+<AI::MXNet::NDArray 2x3 @cpu(0)>
+
+pdl> print Data::Dumper::Dumper($arr->shape)
+$VAR1 = [
+ 2,
+ 3
+ ];
+
+pdl> print (($arr*2)->aspdl) ## converts AI::MXNet::NDArray object to PDL object
+
+[
+ [2 2 2]
+ [2 2 2]
+]
+
+pdl> print $arr = mx->nd->array([[1,2],[3,4]]) ## init the NDArray from Perl array ref given in PDL::pdl constructor format
+<AI::MXNet::NDArray 2x2 @cpu(0)>
+pdl> print $arr->aspdl
+
+[
+ [1 2]
+ [3 4]
+]
+
+## init the NDArray from PDL but be aware that PDL methods expect the dimensions order in column major format
+## AI::MXNet::NDArray is row major
+pdl> print mx->nd->array(sequence(2,3))->aspdl ## 3 rows, 2 columns
+
+[
+ [0 1]
+ [2 3]
+ [4 5]
+]
+```
+ ## Perl API Reference
+ * [Module API](module.md) is a flexible high-level interface for training neural networks.
+ * [Symbolic API](symbol.md) performs operations on NDArrays to assemble neural networks from layers.
+ * [IO Data Loading API](io.md) performs parsing and data loading.
+ * [NDArray API](ndarray.md) performs vector/matrix/tensor operations.
+ * [KVStore API](kvstore.md) performs multi-GPU and multi-host distributed training.
+
diff --git a/versions/master/_sources/api/perl/io.txt b/versions/master/_sources/api/perl/io.txt
new file mode 100644
index 0000000..3310f26
--- /dev/null
+++ b/versions/master/_sources/api/perl/io.txt
@@ -0,0 +1,111 @@
+# Data Loading API
+
+## Overview
+
+A data iterator reads data batch by batch.
+
+```perl
+pdl> $data = mx->nd->ones([100,10])
+pdl> $nd_iter = mx->io->NDArrayIter($data, batch_size=>25)
+pdl> for my $batch (@{ $nd_iter }) { print $batch->data->[0],"\n" }
+<AI::MXNet::NDArray 25x10 @cpu(0)>
+<AI::MXNet::NDArray 25x10 @cpu(0)>
+<AI::MXNet::NDArray 25x10 @cpu(0)>
+<AI::MXNet::NDArray 25x10 @cpu(0)>
+```
+
+If `$nd_iter->reset()` is called, then reads the data again from beginning.
+
+In addition, an iterator provides information about the batch, including the
+shapes and name.
+
+```perl
+pdl> $nd_iter = mx->io->NDArrayIter(data=>{data => mx->nd->ones([100,10])}, label=>{softmax_label => mx->nd->ones([100])}, batch_size=>25)
+pdl> print($nd_iter->provide_data->[0],"\n")
+DataDesc[data,25x10,float32,NCHW]
+pdl> print($nd_iter->provide_label->[0],"\n")
+DataDesc[softmax_label,25,float32,NCHW]
+```
+
+So this iterator can be used to train a symbol whose input data variable has
+name `data` and input label variable has name `softmax_label`.
+
+
+```perl
+pdl> $data = mx->sym->Variable('data')
+pdl> $label = mx->sym->Variable('softmax_label')
+pdl> $fullc = mx->sym->FullyConnected(data=>$data, num_hidden=>1)
+pdl> $loss = mx->sym->SoftmaxOutput(data=>$data, label=>$label)
+pdl> $mod = mx->mod->Module($loss)
+pdl> print($mod->data_names->[0])
+data
+pdl> print($mod->label_names->[0])
+softmax_label
+pdl> $mod->bind(data_shapes=>$nd_iter->provide_data, label_shapes=>$nd_iter->provide_label)
+```
+
+Then we can call `$mod->fit($nd_iter, num_epoch=>2)` to train `loss` by 2 epochs.
+
+## Predefined Data iterators
+
+```perl
+mx->io->NDArrayIter
+mx->io->CSVIter
+mx->io->ImageRecordIter
+mx->io->ImageRecordUInt8Iter
+mx->io->MNISTIter
+mx->recordio->MXRecordIO
+mx->recordio->MXIndexedRecordIO
+mx->image->ImageIter
+```
+
+## Helper classes and functions
+
+Data structures and other iterators provided in the `AI::MXNet::IO` package.
+
+```perl
+AI::MXNet::DataDesc
+AI::MXNet::DataBatch
+AI::MXNet::DataIter
+AI::MXNet::ResizeIter
+AI::MXNet::MXDataIter
+```
+
+A list of image modification functions provided by `AI::MXNet::Image`.
+
+```perl
+mx->image->imdecode
+mx->image->scale_down
+mx->image->resize_short
+mx->image->fixed_crop
+mx->image->random_crop
+mx->image->center_crop
+mx->image->color_normalize
+mx->image->random_size_crop
+mx->image->ResizeAug
+mx->image->RandomCropAug
+mx->image->RandomSizedCropAug
+mx->image->CenterCropAug
+mx->image->RandomOrderAug
+mx->image->ColorJitterAug
+mx->image->LightingAug
+mx->image->ColorNormalizeAug
+mx->image->HorizontalFlipAug
+mx->image->CastAug
+mx->image->CreateAugmenter
+```
+
+Functions to read and write RecordIO files.
+
+```perl
+mx->recordio->pack
+mx->recordio->unpack
+mx->recordio->unpack_img
+```
+
+## Develop a new iterator
+
+Writing a new data iterator in Perl is straightforward. Most MXNet
+training/inference programs accept an object with ``provide_data``
+and ``provide_label`` properties.
+Please refer to AI-MXNet/examples for the examples of custom iterators.
diff --git a/versions/master/_sources/api/perl/kvstore.txt b/versions/master/_sources/api/perl/kvstore.txt
new file mode 100644
index 0000000..7ff73be
--- /dev/null
+++ b/versions/master/_sources/api/perl/kvstore.txt
@@ -0,0 +1,109 @@
+# KVStore API
+
+Topics:
+* [Basic Push and Pull](#basic-push-and-pull)
+* [List Key-Value Pairs](#list-key-value-pairs)
+
+## Basic Push and Pull
+
+Provides basic operation over multiple devices (GPUs) on a single device.
+
+### Initialization
+
+Let's consider a simple example. It initializes
+a (int, NDArray) pair into the store, and then pulls the value out.
+
+```perl
+pdl> $kv = mx->kv->create('local')
+pdl> $shape = [2,3]
+pdl> $kv->init(3, mx->nd->ones($shape)*2)
+pdl> $a = mx->nd->zeros($shape)
+pdl> $kv->pull(3, out => $a)
+pdl> print $a->aspdl
+[
+ [2 2 2]
+ [2 2 2]
+]
+```
+
+### Push, Aggregation, and Updater
+
+For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
+
+```perl
+pdl> $kv->push(3, mx->nd->ones($shape)*8)
+pdl> $a = mx->nd->zeros($shape)
+pdl> $kv->pull(3, out => $a)
+pdl> print $a->aspdl
+[
+ [8 8 8]
+ [8 8 8]
+]
+```
+
+The data that you want to push can be stored on any device. Furthermore, you can push multiple
+values into the same key, where KVStore first sums all of these
+values, and then you pull the aggregated value, as follows:
+
+```perl
+pdl> $kv->push(3, [mx->nd->ones($shape, ctx=>mx->cpu(0)), mx->nd->ones($shape, ctx=>mx->cpu(1))])
+pdl> $kv->pull(3, out => $a)
+pdl> print $a->aspdl
+[
+ [2 2 2]
+ [2 2 2]
+]
+```
+
+For each push command, KVStore applies the pushed value to the value stored by an
+`updater`. The default updater is `ASSIGN`. You can replace the default to
+control how data is merged.
+
+```perl
+pdl> $updater = sub { my ($key, $input, $stored) = @_; print "update on key: $key\n"; $stored += $input * 3; }
+pdl> $kv->_set_updater($updater)
+pdl> $kv->push(3, [mx->nd->ones($shape, ctx=>mx->cpu(0)), mx->nd->ones($shape, ctx=>mx->cpu(1))])
+update on key: 3
+pdl> $kv->pull(3, out => $a)
+pdl> print $a->aspdl
+[
+ [8 8 8]
+ [8 8 8]
+]
+```
+
+### Pull
+
+You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
+pull the value into several devices with a single call.
+
+```perl
+pdl> $b = [mx->nd->zeros($shape, ctx=>mx->cpu(0)), mx->nd->zeros($shape, ctx=>mx->cpu(1))]
+pdl> $kv->pull(3, out => $b)
+pdl> print $b->[1]->aspdl
+[
+ [8 8 8]
+ [8 8 8]
+]
+```
+
+## List Key-Value Pairs
+
+All of the operations that we've discussed so far are performed on a single key. KVStore also provides
+the interface for generating a list of key-value pairs. For a single device, use the following:
+
+```perl
+pdl> $keys = [5,7,9]
+pdl> $kv->init($keys, [map { mx->nd->ones($shape) } 0..@$keys-1])
+pdl> $kv->push($keys, [map { mx->nd->ones($shape) } 0..@$keys-1])
+update on key: 5
+update on key: 7
+update on key: 9
+pdl> $b = [map { mx->nd->ones($shape) } 0..@$keys-1]
+pdl> $kv->pull($keys, out => $b)
+pdl> print $b->[1]->aspdl
+[
+ [4 4 4]
+ [4 4 4]
+]
+```
diff --git a/versions/master/_sources/api/perl/module.txt b/versions/master/_sources/api/perl/module.txt
new file mode 100644
index 0000000..132e333
--- /dev/null
+++ b/versions/master/_sources/api/perl/module.txt
@@ -0,0 +1,45 @@
+# Module API
+
+## Overview
+
+The module API, defined in the `module` (or simply `mod`) package (`AI::MXNet::Module` under the hood), provides an
+intermediate and high-level interface for performing computation with a
+`AI::MXNet::Symbol` or just `mx->sym`. One can roughly think a module is a machine which can execute a
+program defined by a `Symbol`.
+
+The class `AI::MXNet::Module` is a commonly used module, which accepts a `AI::MXNet::Symbol` as
+the input:
+
+```perl
+pdl> $data = mx->symbol->Variable('data')
+pdl> $fc1 = mx->symbol->FullyConnected($data, name=>'fc1', num_hidden=>128)
+pdl> $act1 = mx->symbol->Activation($fc1, name=>'relu1', act_type=>"relu")
+pdl> $fc2 = mx->symbol->FullyConnected($act1, name=>'fc2', num_hidden=>10)
+pdl> $out = mx->symbol->SoftmaxOutput($fc2, name => 'softmax')
+pdl> $mod = mx->mod->Module($out) # create a module by given a Symbol
+```
+
+Assume there is a valid MXNet data iterator `data`. We can initialize the
+module:
+
+```perl
+pdl> $mod->bind(data_shapes=>$data->provide_data,
+ label_shapes=>$data->provide_label) # create memory by given input shapes
+pdl> $mod->init_params() # initial parameters with the default random initializer
+```
+
+Now the module is able to compute. We can call high-level API to train and
+predict:
+
+```perl
+pdl> $mod->fit($data, num_epoch=>10, ...) # train
+pdl> $mod->predict($new_data) # predict on new data
+```
+
+or use intermediate APIs to perform step-by-step computations
+
+```perl
+pdl> $mod->forward($data_batch, is_train => 1) # forward on the provided data batch
+pdl> $mod->backward() # backward to calculate the gradients
+pdl> $mod->update() # update parameters using the default optimizer
+```
diff --git a/versions/master/_sources/api/perl/ndarray.txt b/versions/master/_sources/api/perl/ndarray.txt
new file mode 100644
index 0000000..c7962b9
--- /dev/null
+++ b/versions/master/_sources/api/perl/ndarray.txt
@@ -0,0 +1,42 @@
+# NDArray API
+
+## Overview
+
+A `AI::MXNet::NDArray` is a multidimensional container of items of the same type and
+size. Various methods for data manipulation and computation are provided.
+
+```perl
+pdl> $x = mx->nd->array([[1, 2, 3], [4, 5, 6]])
+pdl> print $x->aspdl->shape
+[3, 2]
+pdl> $y = $x + mx->nd->ones($x->shape)*3
+pdl> print $y->aspdl
+[
+ [4 5 6]
+ [7 8 9]
+]
+pdl> $z = $y->as_in_context(mx->gpu(0))
+pdl> print $z,"\n"
+<AI::MXNet::NDArray 2x3 @gpu(0)>
+```
+
+A detailed tutorial is available at
+[http://mxnet.io/tutorials/basic/ndarray.html](http://mxnet.io/tutorials/basic/ndarray.html).
+
+Note: AI::MXNet::NDarray is similar to numpy.ndarray in some aspects. But the difference is not negligible. For example
+
+- AI::MXNet::NDArray->T does real data transpose to return new a copied array, instead
+ of returning a view of the input array.
+- AI::MXNet::NDArray->dot performs dot between the last axis of the first input array
+ and the first axis of the second input, while numpy.dot uses the second
+ last axis of the input array.
+
+In additional, NDArray supports GPU computation and various neural
+network layers.
+
+AI::MXNet::NDarray also provides almost same routines as AI::MXNet::symbol. Most
+routines between these two packages share the same C++ operator source
+codes. But AI::MXNet::NDarray differs from AI::MXNet::Symbol in several aspects:
+
+- AI::MXNet::NDArray adopts imperative programming, namely sentences are executed
+ step-by-step so that the results can be obtained immediately.
diff --git a/versions/master/_sources/api/perl/symbol.txt b/versions/master/_sources/api/perl/symbol.txt
new file mode 100644
index 0000000..bb93d3f
--- /dev/null
+++ b/versions/master/_sources/api/perl/symbol.txt
@@ -0,0 +1,127 @@
+# MXNet Perl Symbolic API
+
+Topics:
+
+* [How to Compose Symbols](#how-to-compose-symbols) introduces operator overloading of symbols.
+* [Symbol Attributes](#symbol-attributes) describes how to attach attributes to symbols.
+* [Serialization](#serialization) explains how to save and load symbols.
+* [Executing Symbols](#executing-symbols) explains how to evaluate the symbols with data.
+* [Multiple Outputs](#multiple-outputs) explains how to configure multiple outputs.
+
+## How to Compose Symbols
+
+The symbolic API provides a way to configure computation graphs.
+You can configure the graphs either at the level of neural network layer operations or as fine-grained operations.
+
+The following example configures a two-layer neural network.
+
+```perl
+pdl> use AI::MXNet qw(mx)
+pdl> $data = mx->symbold->Variable("data")
+pdl> $fc1 = mx->symbol->FullyConnected(data => $data, name => "fc1", num_hidden" -> 128)
+pdl> $act1 = mx->symbol->Activation(data => $fc1, name => "relu1", act_type => "relu")
+pdl> $fc2 = mx->symbol->FullyConnected(data => $act1, name => "fc2", num_hidden => 64)
+pdl> $net = mx->symbol->SoftmaxOutput(data => $fc2, name => "out")
+```
+
+The basic arithmetic operators (plus, minus, div, multiplication) are overloaded for
+*element-wise operations* of symbols.
+
+The following example creates a computation graph that adds two inputs together.
+
+```perl
+pdl> use AI::MXNet qw(mx)
+pdl> $a = mx->symbol->Variable("a")
+pdl> $b = mx->symbol->Variable("b")
+pdl> $c = $a + $b
+```
+
+## Symbol Attributes
+
+You can add an attribute to a symbol by providing an attribute hash when you create a symbol.
+
+```perl
+$data = mx->symbol->Variable("data", attr => { mood => "angry" })
+$op = mx->symbol->Convolution(data => $data, kernel => [1, 1], num_filter => 1, attr => { mood => "so so" })
+```
+
+For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use `->attr($key)`:
+
+```
+ $data->attr("mood")
+```
+
+To attach attributes, you can use ```AI::MXNet::AttrScope```. ```AI::MXNet::AttrScopeAttrScope``` automatically adds
+the specified attributes to all of the symbols created within that scope.
+The user can also inherit this object to change naming behavior. For example:
+
+```perl
+use AI::MXNet qw(mx);
+use Test::More tests => 3;
+my ($data, $gdata);
+{
+ local($mx::AttrScope) = mx->AttrScope(group=>4, data=>'great');
+ $data = mx->sym->Variable("data", attr => { dtype => "data", group => "1" });
+ $gdata = mx->sym->Variable("data2");
+}
+ok($gdata->attr("group") == 4);
+ok($data->attr("group") == 1);
+
+my $exceedScopeData = mx->sym->Variable("data3");
+ok((not defined $exceedScopeData->attr("group")), "No group attr in global attr scope");
+```
+
+## Serialization
+
+There are two ways to save and load the symbols. You can use the `mx->symbol->save` and `mxnet->symbol->load` functions to serialize the ```AI::MXNet::Symbol``` objects.
+The advantage of using `save` and `load` functions is that it is language agnostic and cloud friendly.
+The symbol is saved in JSON format. You can also get a JSON string directly using `$symbol->tojson`.
+
+The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string.
+
+```perl
+pdl> use AI::MXNet qw(mx)
+pdl> $a = mx->sym->Variable("a")
+pdl> $b = mx->sym->Variable("b")
+pdl> $c = $a + $b
+pdl> $c->save("s3://my-bucket/symbol-c.json")
+pdl> $c2 = $c->load("s3://my-bucket/symbol-c.json")
+pdl> ok($c->tojson eq $c2->tojson)
+ok 1
+```
+
+## Executing Symbols
+
+After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
+If you are training a neural network, this is typically
+handled by the high-level [AI::MXNet::Module package](module.md) and the [`fit()`] function.
+
+For neural networks used in "feed-forward", "prediction", or "inference" mode (all terms for the same
+thing: running a trained network), the input arguments are the
+input data, and the weights of the neural network that were learned during training.
+
+To manually execute a set of symbols, you need to create an [`AI::MXNet::Executor`] object,
+which is typically constructed by calling the [`simple_bind(<parameters>)`] method on a AI::MXNet::Symbol.
+
+## Multiple Outputs
+
+To group the symbols together, use the [AI::MXNet::Symbol->Group](#mxnet.symbol.Group) function.
+
+```perl
+pdl> use AI::MXNet qw(mx)
+pdl> use Data::Dumper
+pdl> $data = mx->sym->Variable("data")
+pdl> $fc1 = mx->sym->FullyConnected($data, name => "fc1", num_hidden => 128)
+pdl> $act1 = mx->sym->Activation($fc1, name => "relu1", act_type => "relu")
+pdl> $fc2 = mx->sym->FullyConnected($act1, name => "fc2", num_hidden => 64)
+pdl> $net = mx->sym->SoftmaxOutput($fc2, name => "softmax")
+pdl> $group = mx->sym->Group([$fc1, $net])
+pdl> print Dumper($group->list_outputs())
+$VAR1 = [
+ 'fc1_output',
+ 'softmax_output'
+];
+```
+
+After you get the ```Group```, you can bind on ```group``` instead.
+The resulting executor will have two outputs, one for fc1_output and one for softmax_output.
diff --git a/versions/master/_sources/api/python/autograd.txt b/versions/master/_sources/api/python/autograd.txt
new file mode 100644
index 0000000..440a1e4
--- /dev/null
+++ b/versions/master/_sources/api/python/autograd.txt
@@ -0,0 +1,32 @@
+# Autograd Package
+
+
+```eval_rst
+.. currentmodule:: mxnet.autograd
+```
+
+```eval_rst
+.. warning:: This package is currently experimental and may change in the near future.
+```
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+## Autograd
+
+```eval_rst
+.. currentmodule:: mxnet.autograd
+```
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ record
+ pause
+ mark_variables
+ backward
+ set_training
+ set_recording
+```
+
diff --git a/versions/master/_sources/api/python/callback.txt b/versions/master/_sources/api/python/callback.txt
new file mode 100644
index 0000000..c83816c
--- /dev/null
+++ b/versions/master/_sources/api/python/callback.txt
@@ -0,0 +1,27 @@
+# Callback API
+
+```eval_rst
+.. currentmodule:: mxnet.ndarray
+```
+
+## Overview
+
+This document lists the routines of the callback package
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ mxnet.callback
+```
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. automodule:: mxnet.callback
+ :members:
+```
+
+<script>auto_index("api-reference");</script>
\ No newline at end of file
diff --git a/versions/master/_sources/api/python/gluon.txt b/versions/master/_sources/api/python/gluon.txt
new file mode 100644
index 0000000..6e213bb
--- /dev/null
+++ b/versions/master/_sources/api/python/gluon.txt
@@ -0,0 +1,565 @@
+# Gluon Package
+
+
+```eval_rst
+.. currentmodule:: mxnet.gluon
+```
+
+```eval_rst
+.. warning:: This package is currently experimental and may change in the near future.
+```
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+## Overview
+
+Gluon package is a high-level interface for MXNet designed to be easy to use while
+keeping most of the flexibility of low level API. Gluon supports both imperative
+and symbolic programming, making it easy to train complex models imperatively
+in Python and then deploy with symbolic graph in C++ and Scala.
+
+## Parameter
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Parameter
+ ParameterDict
+```
+
+
+## Containers
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Block
+ HybridBlock
+ SymbolBlock
+```
+
+## Neural Network Layers
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.nn
+```
+
+### Containers
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Sequential
+ HybridSequential
+```
+
+
+### Basic Layers
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Dense
+ Activation
+ Dropout
+ BatchNorm
+ LeakyReLU
+ Embedding
+```
+
+
+### Convolutional Layers
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Conv1D
+ Conv2D
+ Conv3D
+ Conv1DTranspose
+ Conv2DTranspose
+ Conv3DTranspose
+```
+
+
+
+### Pooling Layers
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ MaxPool1D
+ MaxPool2D
+ MaxPool3D
+ AvgPool1D
+ AvgPool2D
+ AvgPool3D
+ GlobalMaxPool1D
+ GlobalMaxPool2D
+ GlobalMaxPool3D
+ GlobalAvgPool1D
+ GlobalAvgPool2D
+ GlobalAvgPool3D
+```
+
+
+
+## Recurrent Layers
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.rnn
+```
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ RecurrentCell
+ RNN
+ LSTM
+ GRU
+ RNNCell
+ LSTMCell
+ GRUCell
+ SequentialRNNCell
+ BidirectionalCell
+ DropoutCell
+ ZoneoutCell
+ ResidualCell
+```
+
+
+## Trainer
+
+```eval_rst
+.. currentmodule:: mxnet.gluon
+
+.. autosummary::
+ :nosignatures:
+
+ Trainer
+```
+
+
+## Loss functions
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.loss
+```
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ L2Loss
+ L1Loss
+ SoftmaxCrossEntropyLoss
+ KLDivLoss
+```
+
+## Utilities
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.utils
+```
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ split_data
+ split_and_load
+ clip_global_norm
+```
+
+## Data
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.data
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Dataset
+ ArrayDataset
+ RecordFileDataset
+ ImageRecordDataset
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Sampler
+ SequentialSampler
+ RandomSampler
+ BatchSampler
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ DataLoader
+```
+
+### Vision
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.data.vision
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ MNIST
+ CIFAR10
+```
+
+## Model Zoo
+
+Model zoo provides pre-defined and pre-trained models to help bootstrap machine learning applications.
+
+### Vision
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.model_zoo.vision
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ get_model
+```
+
+#### ResNet
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ resnet18_v1
+ resnet34_v1
+ resnet50_v1
+ resnet101_v1
+ resnet152_v1
+ resnet18_v2
+ resnet34_v2
+ resnet50_v2
+ resnet101_v2
+ resnet152_v2
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ ResNetV1
+ ResNetV2
+ BasicBlockV1
+ BasicBlockV2
+ BottleneckV1
+ BottleneckV2
+ get_resnet
+```
+
+#### VGG
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ vgg11
+ vgg13
+ vgg16
+ vgg19
+ vgg11_bn
+ vgg13_bn
+ vgg16_bn
+ vgg19_bn
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ VGG
+ get_vgg
+```
+
+#### Alexnet
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ alexnet
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ AlexNet
+```
+
+#### DenseNet
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ densenet121
+ densenet161
+ densenet169
+ densenet201
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ DenseNet
+```
+
+#### SqueezeNet
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ squeezenet1_0
+ squeezenet1_1
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ SqueezeNet
+```
+
+#### Inception
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ inception_v3
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Inception3
+```
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. autoclass:: mxnet.gluon.Parameter
+ :members:
+.. autoclass:: mxnet.gluon.ParameterDict
+ :members:
+
+.. autoclass:: mxnet.gluon.Block
+ :members:
+
+ .. automethod:: __call__
+.. autoclass:: mxnet.gluon.HybridBlock
+ :members:
+.. autoclass:: mxnet.gluon.SymbolBlock
+ :members:
+
+.. autoclass:: mxnet.gluon.nn.Sequential
+ :members:
+.. autoclass:: mxnet.gluon.nn.HybridSequential
+ :members:
+.. autoclass:: mxnet.gluon.nn.Dense
+ :members:
+.. autoclass:: mxnet.gluon.nn.Activation
+ :members:
+.. autoclass:: mxnet.gluon.nn.Dropout
+ :members:
+.. autoclass:: mxnet.gluon.nn.BatchNorm
+ :members:
+.. autoclass:: mxnet.gluon.nn.LeakyReLU
+ :members:
+.. autoclass:: mxnet.gluon.nn.Embedding
+ :members:
+.. autoclass:: mxnet.gluon.nn.Conv1D
+ :members:
+.. autoclass:: mxnet.gluon.nn.Conv2D
+ :members:
+.. autoclass:: mxnet.gluon.nn.Conv3D
+ :members:
+.. autoclass:: mxnet.gluon.nn.Conv1DTranspose
+ :members:
+.. autoclass:: mxnet.gluon.nn.Conv2DTranspose
+ :members:
+.. autoclass:: mxnet.gluon.nn.Conv3DTranspose
+ :members:
+.. autoclass:: mxnet.gluon.nn.MaxPool1D
+ :members:
+.. autoclass:: mxnet.gluon.nn.MaxPool2D
+ :members:
+.. autoclass:: mxnet.gluon.nn.MaxPool3D
+ :members:
+.. autoclass:: mxnet.gluon.nn.AvgPool1D
+ :members:
+.. autoclass:: mxnet.gluon.nn.AvgPool2D
+ :members:
+.. autoclass:: mxnet.gluon.nn.AvgPool3D
+ :members:
+.. autoclass:: mxnet.gluon.nn.GlobalMaxPool1D
+ :members:
+.. autoclass:: mxnet.gluon.nn.GlobalMaxPool2D
+ :members:
+.. autoclass:: mxnet.gluon.nn.GlobalMaxPool3D
+ :members:
+.. autoclass:: mxnet.gluon.nn.GlobalAvgPool1D
+ :members:
+.. autoclass:: mxnet.gluon.nn.GlobalAvgPool2D
+ :members:
+.. autoclass:: mxnet.gluon.nn.GlobalAvgPool3D
+ :members:
+
+.. autoclass:: mxnet.gluon.rnn.RecurrentCell
+ :members:
+
+ .. automethod:: __call__
+.. autoclass:: mxnet.gluon.rnn.RNN
+ :members:
+.. autoclass:: mxnet.gluon.rnn.LSTM
+ :members:
+.. autoclass:: mxnet.gluon.rnn.GRU
+ :members:
+.. autoclass:: mxnet.gluon.rnn.RNNCell
+ :members:
+.. autoclass:: mxnet.gluon.rnn.LSTMCell
+ :members:
+.. autoclass:: mxnet.gluon.rnn.GRUCell
+ :members:
+.. autoclass:: mxnet.gluon.rnn.SequentialRNNCell
+ :members:
+.. autoclass:: mxnet.gluon.rnn.BidirectionalCell
+ :members:
+.. autoclass:: mxnet.gluon.rnn.DropoutCell
+ :members:
+.. autoclass:: mxnet.gluon.rnn.ZoneoutCell
+ :members:
+.. autoclass:: mxnet.gluon.rnn.ResidualCell
+ :members:
+
+.. autoclass:: mxnet.gluon.Trainer
+ :members:
+
+.. autoclass:: mxnet.gluon.loss.L2Loss
+ :members:
+.. autoclass:: mxnet.gluon.loss.L1Loss
+ :members:
+.. autoclass:: mxnet.gluon.loss.SoftmaxCrossEntropyLoss
+ :members:
+.. autoclass:: mxnet.gluon.loss.KLDivLoss
+ :members:
+.. automethod:: mxnet.gluon.utils.split_data
+
+.. automethod:: mxnet.gluon.utils.split_and_load
+
+.. automethod:: mxnet.gluon.utils.clip_global_norm
+
+.. autoclass:: mxnet.gluon.data.Dataset
+ :members:
+.. autoclass:: mxnet.gluon.data.ArrayDataset
+ :members:
+.. autoclass:: mxnet.gluon.data.RecordFileDataset
+ :members:
+.. autoclass:: mxnet.gluon.data.ImageRecordDataset
+ :members:
+.. autoclass:: mxnet.gluon.data.Sampler
+ :members:
+.. autoclass:: mxnet.gluon.data.SequentialSampler
+ :members:
+.. autoclass:: mxnet.gluon.data.RandomSampler
+ :members:
+.. autoclass:: mxnet.gluon.data.BatchSampler
+ :members:
+.. autoclass:: mxnet.gluon.data.DataLoader
+ :members:
+.. automodule:: mxnet.gluon.data.vision
+ :members:
+
+.. automodule:: mxnet.gluon.model_zoo.vision
+ :members:
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet18_v1
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet34_v1
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet50_v1
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet101_v1
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet152_v1
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet18_v2
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet34_v2
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet50_v2
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet101_v2
+.. automethod:: mxnet.gluon.model_zoo.vision.resnet152_v2
+.. automethod:: mxnet.gluon.model_zoo.vision.get_resnet
+.. autoclass:: mxnet.gluon.model_zoo.vision.ResNetV1
+ :members:
+.. autoclass:: mxnet.gluon.model_zoo.vision.BasicBlockV1
+ :members:
+.. autoclass:: mxnet.gluon.model_zoo.vision.BottleneckV1
+ :members:
+.. autoclass:: mxnet.gluon.model_zoo.vision.ResNetV2
+ :members:
+.. autoclass:: mxnet.gluon.model_zoo.vision.BasicBlockV2
+ :members:
+.. autoclass:: mxnet.gluon.model_zoo.vision.BottleneckV2
+ :members:
+.. automethod:: mxnet.gluon.model_zoo.vision.vgg11
+.. automethod:: mxnet.gluon.model_zoo.vision.vgg13
+.. automethod:: mxnet.gluon.model_zoo.vision.vgg16
+.. automethod:: mxnet.gluon.model_zoo.vision.vgg19
+.. automethod:: mxnet.gluon.model_zoo.vision.vgg11_bn
+.. automethod:: mxnet.gluon.model_zoo.vision.vgg13_bn
+.. automethod:: mxnet.gluon.model_zoo.vision.vgg16_bn
+.. automethod:: mxnet.gluon.model_zoo.vision.vgg19_bn
+.. automethod:: mxnet.gluon.model_zoo.vision.get_vgg
+.. autoclass:: mxnet.gluon.model_zoo.vision.VGG
+ :members:
+.. automethod:: mxnet.gluon.model_zoo.vision.alexnet
+.. autoclass:: mxnet.gluon.model_zoo.vision.AlexNet
+ :members:
+.. automethod:: mxnet.gluon.model_zoo.vision.densenet121
+.. automethod:: mxnet.gluon.model_zoo.vision.densenet161
+.. automethod:: mxnet.gluon.model_zoo.vision.densenet169
+.. automethod:: mxnet.gluon.model_zoo.vision.densenet201
+.. autoclass:: mxnet.gluon.model_zoo.vision.DenseNet
+ :members:
+.. automethod:: mxnet.gluon.model_zoo.vision.squeezenet1_0
+.. automethod:: mxnet.gluon.model_zoo.vision.squeezenet1_1
+.. autoclass:: mxnet.gluon.model_zoo.vision.SqueezeNet
+ :members:
+.. automethod:: mxnet.gluon.model_zoo.vision.inception_v3
+.. autoclass:: mxnet.gluon.model_zoo.vision.Inception3
+ :members:
+```
+
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/image.txt b/versions/master/_sources/api/python/image.txt
new file mode 100644
index 0000000..23b5ee3
--- /dev/null
+++ b/versions/master/_sources/api/python/image.txt
@@ -0,0 +1,206 @@
+# Image API
+
+## Overview
+This document summarizes supporting functions and iterators to read and process
+images provided in
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ mxnet.image
+```
+
+## Image processing functions
+```eval_rst
+.. currentmodule:: mxnet
+.. autosummary::
+ :nosignatures:
+
+ image.imdecode
+ image.scale_down
+ image.resize_short
+ image.fixed_crop
+ image.random_crop
+ image.center_crop
+ image.color_normalize
+ image.random_size_crop
+```
+
+## Image iterators
+Iterators support loading image from binary `Record IO` and raw image files.
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ image.ImageIter
+```
+```python
+>>> data_iter = mx.image.ImageIter(batch_size=4, data_shape=(3, 224, 224), label_width=1,
+ path_imglist='data/custom.lst')
+>>> data_iter.reset()
+>>> for data in data_iter:
+... d = data.data[0]
+... print(d.shape)
+>>> # we can apply lots of augmentations as well
+>>> data_iter = mx.image.ImageIter(4, (3, 224, 224), path_imglist='data/custom.lst',
+ rand_crop=resize=True, rand_mirror=True, mean=True,
+ brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1,
+ pca_noise=0.1, rand_gray=0.05)
+>>> data = data_iter.next()
+>>> # specify augmenters manually is also supported
+>>> data_iter = mx.image.ImageIter(32, (3, 224, 224), path_rec='data/caltech.rec',
+ path_imgidx='data/caltech.idx', shuffle=True,
+ aug_list=[mx.image.HorizontalFlipAug(0.5),
+ mx.image.ColorJitterAug(0.1, 0.1, 0.1)])
+```
+
+We use helper function to initialize augmenters
+```eval_rst
+ .. currentmodule:: mxnet
+.. autosummary::
+ :nosignatures:
+
+ image.CreateAugmenter
+```
+
+A list of supporting augmenters
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ image.Augmenter
+ image.ResizeAug
+ image.ForceResizeAug
+ image.RandomCropAug
+ image.RandomSizedCropAug
+ image.CenterCropAug
+ image.RandomOrderAug
+ image.BrightnessJitterAug
+ image.ContrastJitterAug
+ image.SaturationJitterAug
+ image.HueJitterAug
+ image.ColorJitterAug
+ image.LightingAug
+ image.ColorNormalizeAug
+ image.RandomGrayAug
+ image.HorizontalFlipAug
+ image.CastAug
+```
+
+Similar to `ImageIter`, `ImageDetIter` is designed for `Object Detection` tasks.
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ image.ImageDetIter
+```
+
+```python
+>>> data_iter = mx.image.ImageDetIter(batch_size=4, data_shape=(3, 224, 224),
+ path_imglist='data/train.lst')
+>>> data_iter.reset()
+>>> for data in data_iter:
+... d = data.data[0]
+... l = data.label[0]
+... print(d.shape)
+... print(l.shape)
+```
+
+Unlike object classification with fixed label_width, object count may vary from
+image to image. Thus we have special format for object detection labels.
+Usually the `lst` file generated by `tools/im2rec.py` is a list of
+```
+index_0 label_0 image_path_0
+index_1 label_1 image_path_1
+```
+Where `label_N` is a number a of fixed-width vector.
+The format of label used in object detection is a variable length vector
+```
+A B [header] [(object0), (object1), ... (objectN)]
+```
+Where A is the width of header, B is the width of each object.
+Header is optional and used for inserting helper information such as (width, height).
+Each object is usually 5 or 6 numbers describing the object properties, for example:
+[id, xmin, ymin, xmax, ymax, difficulty]
+Putting all together, we have a `lst` file for object detection:
+```
+0 2 5 640 480 1 0.1 0.2 0.8 0.9 2 0.5 0.3 0.6 0.8 data/xxx.jpg
+1 2 5 480 640 3 0.05 0.16 0.75 0.9 data/xxx.jpg
+2 2 5 500 600 2 0.6 0.1 0.7 0.5 0 0.1 0.3 0.2 0.4 3 0.25 0.25 0.3 0.3 data/xxx.jpg
+...
+```
+
+A helper function to initialize Augmenters for `Object detection` task
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ image.CreateDetAugmenter
+ ```
+
+Since `Detection` task is sensitive to object localization, any modification
+to image that introduced localization shift will require correction to label,
+and a list of augmenters specific for `Object detection` is provided
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ image.DetBorrowAug
+ image.DetRandomSelectAug
+ image.DetHorizontalFlipAug
+ image.DetRandomCropAug
+ image.DetRandomPadAug
+```
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. automodule:: mxnet.image
+.. autoclass:: mxnet.image.ImageIter
+ :members:
+
+.. automethod:: mxnet.image.imdecode
+.. automethod:: mxnet.image.scale_down
+.. automethod:: mxnet.image.resize_short
+.. automethod:: mxnet.image.fixed_crop
+.. automethod:: mxnet.image.random_crop
+.. automethod:: mxnet.image.center_crop
+.. automethod:: mxnet.image.color_normalize
+.. automethod:: mxnet.image.random_size_crop
+
+.. autoclass:: mxnet.image.Augmenter
+ :members:
+.. autoclass:: mxnet.image.ResizeAug
+.. autoclass:: mxnet.image.ForceResizeAug
+.. autoclass:: mxnet.image.RandomCropAug
+.. autoclass:: mxnet.image.RandomSizedCropAug
+.. autoclass:: mxnet.image.CenterCropAug
+.. autoclass:: mxnet.image.RandomOrderAug
+.. autoclass:: mxnet.image.BrightnessJitterAug
+.. autoclass:: mxnet.image.ContrastJitterAug
+.. autoclass:: mxnet.image.SaturationJitterAug
+.. autoclass:: mxnet.image.HueJitterAug
+.. autoclass:: mxnet.image.ColorJitterAug
+.. autoclass:: mxnet.image.LightingAug
+.. autoclass:: mxnet.image.ColorNormalizeAug
+.. autoclass:: mxnet.image.RandomGrayAug
+.. autoclass:: mxnet.image.HorizontalFlipAug
+.. autoclass:: mxnet.image.CastAug
+
+.. automethod:: mxnet.image.CreateAugmenter
+
+.. autoclass:: mxnet.image.ImageDetIter
+ :members:
+.. autoclass:: mxnet.image.DetAugmenter
+ :members:
+.. autoclass:: mxnet.image.DetBorrowAug
+.. autoclass:: mxnet.image.DetRandomSelectAug
+.. autoclass:: mxnet.image.DetHorizontalFlipAug
+.. autoclass:: mxnet.image.DetRandomCropAug
+.. autoclass:: mxnet.image.DetRandomPadAug
+
+.. automethod:: mxnet.image.CreateDetAugmenter
+```
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/index.txt b/versions/master/_sources/api/python/index.txt
new file mode 100644
index 0000000..964ccde
--- /dev/null
+++ b/versions/master/_sources/api/python/index.txt
@@ -0,0 +1,40 @@
+# MXNet - Python API
+
+MXNet provides a rich Python API to serve a broad community of Python developers.
+In this section, we provide an in-depth discussion of the functionality provided by
+various MXNet Python packages. We have included code samples for most of the APIs
+for improved clarity. These code samples will run as-is as long as MXNet is first
+imported by running:
+
+```python
+>>> import mxnet as mx
+```
+
+```eval_rst
+
+.. note:: A convenient way to execute examples is the ``%doctest_mode`` mode of
+ Jupyter notebook, which allows for pasting multi-line examples containing
+ ``>>>`` while preserving indentation. Run ``%doctest_mode?`` in Jupyter notebook
+ for more details.
+
+```
+
+## Table of contents
+
+```eval_rst
+.. toctree::
+ :maxdepth: 2
+
+ ndarray
+ symbol
+ module
+ autograd
+ gluon
+ rnn
+ kvstore
+ io
+ image
+ optimization
+ callback
+ metric
+```
diff --git a/versions/master/_sources/api/python/io.txt b/versions/master/_sources/api/python/io.txt
new file mode 100644
index 0000000..15f8aa3
--- /dev/null
+++ b/versions/master/_sources/api/python/io.txt
@@ -0,0 +1,159 @@
+# Data Loading API
+
+## Overview
+
+This document summarizes supported data formats and iterator APIs to read the
+data including
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ mxnet.io
+ mxnet.recordio
+ mxnet.image
+```
+
+First, let's see how to write an iterator for a new data format.
+The following iterator can be used to train a symbol whose input data variable has
+name `data` and input label variable has name `softmax_label`.
+The iterator also provides information about the batch, including the
+shapes and name.
+
+```python
+>>> nd_iter = mx.io.NDArrayIter(data={'data':mx.nd.ones((100,10))},
+... label={'softmax_label':mx.nd.ones((100,))},
+... batch_size=25)
+>>> print(nd_iter.provide_data)
+[DataDesc[data,(25, 10L),<type 'numpy.float32'>,NCHW]]
+>>> print(nd_iter.provide_label)
+[DataDesc[softmax_label,(25,),<type 'numpy.float32'>,NCHW]]
+```
+
+Let's see a complete example of how to use data iterator in model training.
+```python
+>>> data = mx.sym.Variable('data')
+>>> label = mx.sym.Variable('softmax_label')
+>>> fullc = mx.sym.FullyConnected(data=data, num_hidden=1)
+>>> loss = mx.sym.SoftmaxOutput(data=data, label=label)
+>>> mod = mx.mod.Module(loss, data_names=['data'], label_names=['softmax_label'])
+>>> mod.bind(data_shapes=nd_iter.provide_data, label_shapes=nd_iter.provide_label)
+>>> mod.fit(nd_iter, num_epoch=2)
+```
+
+A detailed tutorial is available at
+[Iterators - Loading data](http://mxnet.io/tutorials/basic/data.html).
+
+## Data iterators
+
+```eval_rst
+ .. currentmodule:: mxnet
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ io.NDArrayIter
+ io.CSVIter
+ io.ImageRecordIter
+ io.ImageRecordUInt8Iter
+ io.MNISTIter
+ recordio.MXRecordIO
+ recordio.MXIndexedRecordIO
+ image.ImageIter
+ image.ImageDetIter
+```
+
+## Helper classes and functions
+
+
+Data structures and other iterators provided in the ``mxnet.io`` packages.
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ io.DataDesc
+ io.DataBatch
+ io.DataIter
+ io.ResizeIter
+ io.PrefetchingIter
+ io.MXDataIter
+```
+
+Functions to read and write RecordIO files.
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ recordio.pack
+ recordio.unpack
+ recordio.unpack_img
+ recordio.pack_img
+```
+
+## Develop a new iterator
+
+Writing a new data iterator in Python is straightforward. Most MXNet
+training/inference programs accept an iterable object with ``provide_data``
+and ``provide_label`` properties.
+This [tutorial](http://mxnet.io/tutorials/basic/data.html) explains how to
+write an iterator from scratch.
+
+The following example demonstrates how to combine
+multiple data iterators into a single one. It can be used for multiple
+modality training such as image captioning, in which images are read by
+``ImageRecordIter`` while documents are read by ``CSVIter``
+
+```python
+class MultiIter:
+ def __init__(self, iter_list):
+ self.iters = iter_list
+ def next(self):
+ batches = [i.next() for i in self.iters]
+ return DataBatch(data=[*b.data for b in batches],
+ label=[*b.label for b in batches])
+ def reset(self):
+ for i in self.iters:
+ i.reset()
+ @property
+ def provide_data(self):
+ return [*i.provide_data for i in self.iters]
+ @property
+ def provide_label(self):
+ return [*i.provide_label for i in self.iters]
+
+iter = MultiIter([mx.io.ImageRecordIter('image.rec'), mx.io.CSVIter('txt.csv')])
+```
+
+Parsing and performing another pre-processing such as augmentation may be expensive.
+If performance is critical, we can implement a data iterator in C++. Refer to
+[src/io](https://github.com/dmlc/mxnet/tree/master/src/io) for examples.
+
+### Change batch layout
+
+By default, the backend engine treats the first dimension of each data and label variable in data
+iterators as the batch size (i.e. `NCHW` or `NT` layout). In order to override the axis for batch size,
+the `provide_data` (and `provide_label` if there is label) properties should include the layouts. This
+is especially useful in RNN since `TNC` layouts are often more efficient. For example:
+
+```python
+@property
+def provide_data(self):
+ return [DataDesc(name='seq_var', shape=(seq_length, batch_size), layout='TN')]
+```
+The backend engine will recognize the index of `N` in the `layout` as the axis for batch size.
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. automodule:: mxnet.io
+ :members:
+.. automodule:: mxnet.recordio
+ :members:
+```
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/kvstore.txt b/versions/master/_sources/api/python/kvstore.txt
new file mode 100644
index 0000000..28297fa
--- /dev/null
+++ b/versions/master/_sources/api/python/kvstore.txt
@@ -0,0 +1,127 @@
+# KVStore API
+
+## Basic Push and Pull
+
+Provides basic operation over multiple devices (GPUs) on a single device.
+
+### Initialization
+
+Let's consider a simple example. It initializes
+a (`int`, `NDArray`) pair into the store, and then pulls the value out.
+
+```python
+>>> kv = mx.kv.create('local') # create a local kv store.
+>>> shape = (2,3)
+>>> kv.init(3, mx.nd.ones(shape)*2)
+>>> a = mx.nd.zeros(shape)
+>>> kv.pull(3, out = a)
+>>> print a.asnumpy()
+[[ 2. 2. 2.]
+ [ 2. 2. 2.]]
+```
+
+### Push, Aggregation, and Updater
+
+For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
+
+```python
+>>> kv.push(3, mx.nd.ones(shape)*8)
+>>> kv.pull(3, out = a) # pull out the value
+>>> print a.asnumpy()
+[[ 8. 8. 8.]
+ [ 8. 8. 8.]]
+```
+
+The data that you want to push can be stored on any device. Furthermore, you can push multiple
+values into the same key, where KVStore first sums all of these
+values, and then pushes the aggregated value, as follows:
+
+```python
+>>> gpus = [mx.gpu(i) for i in range(4)]
+>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
+>>> kv.push(3, b)
+>>> kv.pull(3, out = a)
+>>> print a.asnumpy()
+[[ 4. 4. 4.]
+ [ 4. 4. 4.]]
+```
+
+For each push command, KVStore applies the pushed value to the value stored by an
+`updater`. The default updater is `ASSIGN`. You can replace the default to
+control how data is merged.
+
+```python
+>>> def update(key, input, stored):
+>>> print "update on key: %d" % key
+>>> stored += input * 2
+>>> kv._set_updater(update)
+>>> kv.pull(3, out=a)
+>>> print a.asnumpy()
+[[ 4. 4. 4.]
+ [ 4. 4. 4.]]
+>>> kv.push(3, mx.nd.ones(shape))
+update on key: 3
+>>> kv.pull(3, out=a)
+>>> print a.asnumpy()
+[[ 6. 6. 6.]
+ [ 6. 6. 6.]]
+```
+
+### Pull
+
+You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
+pull the value into several devices with a single call.
+
+```python
+>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
+>>> kv.pull(3, out = b)
+>>> print b[1].asnumpy()
+[[ 6. 6. 6.]
+ [ 6. 6. 6.]]
+```
+
+## List Key-Value Pairs
+
+All of the operations that we've discussed so far are performed on a single key. KVStore also provides
+the interface for generating a list of key-value pairs. For a single device, use the following:
+
+```python
+>>> keys = [5, 7, 9]
+>>> kv.init(keys, [mx.nd.ones(shape)]*len(keys))
+>>> kv.push(keys, [mx.nd.ones(shape)]*len(keys))
+update on key: 5
+update on key: 7
+update on key: 9
+>>> b = [mx.nd.zeros(shape)]*len(keys)
+>>> kv.pull(keys, out = b)
+>>> print b[1].asnumpy()
+[[ 3. 3. 3.]
+ [ 3. 3. 3.]]
+```
+
+For multiple devices:
+
+```python
+>>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
+>>> kv.push(keys, b)
+update on key: 5
+update on key: 7
+update on key: 9
+>>> kv.pull(keys, out = b)
+>>> print b[1][1].asnumpy()
+[[ 11. 11. 11.]
+ [ 11. 11. 11.]]
+```
+
+
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. automodule:: mxnet.kvstore
+ :members:
+```
+
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/metric.txt b/versions/master/_sources/api/python/metric.txt
new file mode 100644
index 0000000..50a4a9b
--- /dev/null
+++ b/versions/master/_sources/api/python/metric.txt
@@ -0,0 +1,28 @@
+# Evaluation Metric API
+
+```eval_rst
+.. currentmodule:: mxnet.metric
+```
+
+## Overview
+
+This document lists all the evaluation metrics available to evaluate
+the performance of a learned model.
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ mxnet.metric
+```
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. automodule:: mxnet.metric
+ :members:
+```
+
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/model.txt b/versions/master/_sources/api/python/model.txt
new file mode 100644
index 0000000..9640958
--- /dev/null
+++ b/versions/master/_sources/api/python/model.txt
@@ -0,0 +1,158 @@
+# Model API
+
+The model API provides a simplified way to train neural networks using common best practices.
+It's a thin wrapper built on top of the [ndarray](ndarray.md) and [symbolic](symbol.md)
+modules that make neural network training easy.
+
+Topics:
+
+* [Train a Model](#train-a-model)
+* [Save the Model](#save-the-model)
+* [Periodic Checkpoint](#periodic-checkpointing)
+* [Initializer API Reference](#initializer-api-reference)
+* [Evaluation Metric API Reference](#evaluation-metric-api-reference)
+* [Optimizer API Reference](#optimizer-api-reference)
+* [Model API Reference](#model-api-reference)
+
+## Train the Model
+
+To train a model, perform two steps: configure the model using the symbol parameter,
+then call ```model.Feedforward.create``` to create the model.
+The following example creates a two-layer neural network.
+
+```python
+ # configure a two layer neuralnetwork
+ data = mx.symbol.Variable('data')
+ fc1 = mx.symbol.FullyConnected(data, name='fc1', num_hidden=128)
+ act1 = mx.symbol.Activation(fc1, name='relu1', act_type='relu')
+ fc2 = mx.symbol.FullyConnected(act1, name='fc2', num_hidden=64)
+ softmax = mx.symbol.SoftmaxOutput(fc2, name='sm')
+ # create a model
+ model = mx.model.FeedForward.create(
+ softmax,
+ X=data_set,
+ num_epoch=num_epoch,
+ learning_rate=0.01)
+```
+You can also use the `scikit-learn-style` construct and `fit` function to create a model.
+
+```python
+ # create a model using sklearn-style two-step way
+ model = mx.model.FeedForward(
+ softmax,
+ num_epoch=num_epoch,
+ learning_rate=0.01)
+
+ model.fit(X=data_set)
+```
+For more information, see [Model API Reference](#model-api-reference).
+
+## Save the Model
+
+After the job is done, save your work.
+To save the model, you can directly pickle it with Python.
+We also provide `save` and `load` functions.
+
+```python
+ # save a model to mymodel-symbol.json and mymodel-0100.params
+ prefix = 'mymodel'
+ iteration = 100
+ model.save(prefix, iteration)
+
+ # load model back
+ model_loaded = mx.model.FeedForward.load(prefix, iteration)
+```
+The advantage of these two `save` and `load` functions are that they are language agnostic.
+You should be able to save and load directly into cloud storage, such as Amazon S3 and HDFS.
+
+## Periodic Checkpointing
+
+We recommend checkpointing your model after each iteration.
+To do this, add a checkpoint callback ```do_checkpoint(path)``` to the function.
+The training process automatically checkpoints the specified location after
+each iteration.
+
+```python
+ prefix='models/chkpt'
+ model = mx.model.FeedForward.create(
+ softmax,
+ X=data_set,
+ iter_end_callback=mx.callback.do_checkpoint(prefix),
+ ...)
+```
+You can load the model checkpoint later using ```Feedforward.load```.
+
+## Use Multiple Devices
+
+Set ```ctx``` to the list of devices that you want to train on.
+
+```python
+ devices = [mx.gpu(i) for i in range(num_device)]
+ model = mx.model.FeedForward.create(
+ softmax,
+ X=dataset,
+ ctx=devices,
+ ...)
+```
+Training occurs in parallel on the GPUs that you specify.
+
+```eval_rst
+ .. raw:: html
+
+ <script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+```
+
+
+## Initializer API Reference
+
+
+```eval_rst
+ .. automodule:: mxnet.initializer
+ :members:
+
+ .. raw:: html
+
+ <script>auto_index("initializer-api-reference");</script>
+```
+
+## Evaluation Metric API Reference
+
+
+```eval_rst
+ .. automodule:: mxnet.metric
+ :members:
+
+ .. raw:: html
+
+ <script>auto_index("evaluation-metric-api-reference");</script>
+```
+
+## Optimizer API Reference
+
+
+```eval_rst
+ .. automodule:: mxnet.optimizer
+ :members:
+
+ .. raw:: html
+
+ <script>auto_index("optimizer-api-reference");</script>
+```
+
+## Model API Reference
+
+
+```eval_rst
+ .. automodule:: mxnet.model
+ :members:
+
+ .. raw:: html
+
+ <script>auto_index("model-api-reference");</script>
+```
+
+## Next Steps
+* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
+* See [IO Data Loading API](io.md) for parsing and loading data.
+* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/versions/master/_sources/api/python/module.txt b/versions/master/_sources/api/python/module.txt
new file mode 100644
index 0000000..73110a4
--- /dev/null
+++ b/versions/master/_sources/api/python/module.txt
@@ -0,0 +1,210 @@
+# Module API
+
+```eval_rst
+.. currentmodule:: mxnet.module
+```
+
+## Overview
+
+The module API, defined in the `module` (or simply `mod`) package, provides an
+intermediate and high-level interface for performing computation with a
+`Symbol`. One can roughly think a module is a machine which can execute a
+program defined by a `Symbol`.
+
+The `module.Module` accepts a `Symbol` as the input.
+```python
+>>> data = mx.sym.Variable('data')
+>>> fc1 = mx.sym.FullyConnected(data, name='fc1', num_hidden=128)
+>>> act1 = mx.sym.Activation(fc1, name='relu1', act_type="relu")
+>>> fc2 = mx.sym.FullyConnected(act1, name='fc2', num_hidden=10)
+>>> out = mx.sym.SoftmaxOutput(fc2, name = 'softmax')
+>>> mod = mx.mod.Module(out) # create a module by given a Symbol
+```
+
+Assume there is a valid MXNet data iterator `nd_iter`. We can initialize the
+module:
+```python
+>>> mod.bind(data_shapes=nd_iter.provide_data,
+>>> label_shapes=nd_iter.provide_label) # create memory by given input shapes
+>>> mod.init_params() # initial parameters with the default random initializer
+```
+
+Now the module is able to compute. We can call high-level API to train and
+predict:
+```python
+>>> mod.fit(nd_iter, num_epoch=10, ...) # train
+>>> mod.predict(new_nd_iter) # predict on new data
+```
+
+or use intermediate APIs to perform step-by-step computations
+```python
+>>> mod.forward(data_batch) # forward on the provided data batch
+>>> mod.backward() # backward to calculate the gradients
+>>> mod.update() # update parameters using the default optimizer
+```
+
+A detailed tutorial is available at
+[Module - Neural network training and inference](http://mxnet.io/tutorials/basic/module.html).
+
+The `module` package provides several modules:
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BaseModule
+ Module
+ SequentialModule
+ BucketingModule
+ PythonModule
+ PythonLossModule
+```
+
+We summarize the interface for each class in the following sections.
+
+## The `BaseModule` class
+
+The `BaseModule` is the base class for all other module classes. It defines the
+interface each module class should provide.
+
+### Initialize memory
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BaseModule.bind
+```
+
+### Get and set parameters
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BaseModule.init_params
+ BaseModule.set_params
+ BaseModule.get_params
+ BaseModule.save_params
+ BaseModule.load_params
+```
+
+### Train and predict
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BaseModule.fit
+ BaseModule.score
+ BaseModule.iter_predict
+ BaseModule.predict
+```
+
+### Forward and backward
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BaseModule.forward
+ BaseModule.backward
+ BaseModule.forward_backward
+```
+
+### Update parameters
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BaseModule.init_optimizer
+ BaseModule.update
+ BaseModule.update_metric
+```
+
+### Input and output
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BaseModule.data_names
+ BaseModule.output_names
+ BaseModule.data_shapes
+ BaseModule.label_shapes
+ BaseModule.output_shapes
+ BaseModule.get_outputs
+ BaseModule.get_input_grads
+```
+
+### Others
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BaseModule.get_states
+ BaseModule.set_states
+ BaseModule.install_monitor
+ BaseModule.symbol
+```
+
+
+## Other build-in modules
+
+Besides the basic interface defined in `BaseModule`, each module class supports
+additional functionality. We summarize them in this section.
+
+### Class `Module`
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Module.load
+ Module.save_checkpoint
+ Module.reshape
+ Module.borrow_optimizer
+ Module.save_optimizer_states
+ Module.load_optimizer_states
+```
+
+### Class `BucketModule`
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BucketModule.switch_bucket
+```
+
+### Class `SequentialModule`
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ SequentialModule.add
+```
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. autoclass:: mxnet.module.BaseModule
+ :members:
+.. autoclass:: mxnet.module.Module
+ :members:
+.. autoclass:: mxnet.module.BucketingModule
+ :members:
+.. autoclass:: mxnet.module.SequentialModule
+ :members:
+.. autoclass:: mxnet.module.PythonModule
+ :members:
+.. autoclass:: mxnet.module.PythonLossModule
+ :members:
+```
+
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/ndarray.txt b/versions/master/_sources/api/python/ndarray.txt
new file mode 100644
index 0000000..5e9f7e1
--- /dev/null
+++ b/versions/master/_sources/api/python/ndarray.txt
@@ -0,0 +1,513 @@
+# NDArray API
+
+```eval_rst
+.. currentmodule:: mxnet.ndarray
+```
+
+## Overview
+
+This document lists the routines of the *n*-dimensional array package:
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ mxnet.ndarray
+```
+
+The `NDArray` API, defined in the `ndarray` (or simply `nd`) package, provides
+imperative tensor operations on CPU/GPU.
+An `NDArray` represents a multi-dimensional, fixed-size homogenous array.
+
+```python
+>>> x = mx.nd.array([[1, 2, 3], [4, 5, 6]])
+>>> type(x)
+<class 'mxnet.ndarray.NDArray'>
+>>> x.shape
+(2, 3)
+>>> y = x + mx.nd.ones(x.shape)*3
+>>> print(y.asnumpy())
+[[ 4. 5. 6.]
+ [ 7. 8. 9.]]
+>>> z = y.as_in_context(mx.gpu(0))
+>>> print(z)
+<NDArray 2x3 @gpu(0)>
+```
+
+A detailed tutorial is available at
+[NDArray - Imperative tensor operations on CPU/GPU](http://mxnet.io/tutorials/basic/ndarray.html).
+<br><br>
+
+```eval_rst
+
+.. note:: ``mxnet.ndarray`` is similar to ``numpy.ndarray`` in some aspects. But the differences are not negligible. For instance:
+
+ - ``mxnet.ndarray.NDArray.T`` does real data transpose to return new a copied
+ array, instead of returning a view of the input array.
+ - ``mxnet.ndarray.dot`` performs dot product between the last axis of the
+ first input array and the first axis of the second input, while `numpy.dot`
+ uses the second last axis of the input array.
+
+ In addition, ``mxnet.ndarray.NDArray`` supports GPU computation and various neural
+ network layers.
+
+.. note:: ``ndarray`` provides almost the same routines as ``symbol``. Most
+ routines between these two packages share the source code. But ``ndarray``
+ differs from ``symbol`` in few aspects:
+
+ - ``ndarray`` adopts imperative programming, namely sentences are executed
+ step-by-step so that the results can be obtained immediately whereas
+ ``symbol`` adopts declarative programming.
+
+ - Most binary operators in ``ndarray`` such as ``+`` and ``>`` have
+ broadcasting enabled by default.
+```
+
+In the rest of this document, we first overview the methods provided by the
+`ndarray.NDArray` class, and then list other routines provided by the
+`ndarray` package.
+
+
+## The `NDArray` class
+
+### Array attributes
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ NDArray.shape
+ NDArray.size
+ NDArray.context
+ NDArray.dtype
+```
+
+### Array conversion
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ NDArray.copy
+ NDArray.copyto
+ NDArray.as_in_context
+ NDArray.asnumpy
+ NDArray.asscalar
+ NDArray.astype
+```
+
+### Array change shape
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ NDArray.T
+ NDArray.reshape
+ NDArray.broadcast_to
+```
+
+### Arithmetic operations
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ NDArray.__add__
+ NDArray.__sub__
+ NDArray.__rsub__
+ NDArray.__neg__
+ NDArray.__mul__
+ NDArray.__div__
+ NDArray.__rdiv__
+ NDArray.__mod__
+ NDArray.__rmod__
+ NDArray.__pow__
+```
+
+### In-place arithmetic operations
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ NDArray.__iadd__
+ NDArray.__isub__
+ NDArray.__imul__
+ NDArray.__idiv__
+ NDArray.__imod__
+```
+
+### Comparison operators
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ NDArray.__lt__
+ NDArray.__le__
+ NDArray.__gt__
+ NDArray.__ge__
+ NDArray.__eq__
+ NDArray.__ne__
+```
+
+### Indexing
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ NDArray.__getitem__
+ NDArray.__setitem__
+```
+
+### Lazy evaluation
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ NDArray.wait_to_read
+```
+
+## Array creation routines
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ array
+ empty
+ zeros
+ ones
+ full
+ arange
+ load
+ save
+```
+
+## Array manipulation routines
+
+### Changing array shape and type
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ cast
+ reshape
+ flatten
+ expand_dims
+```
+
+### Expanding array elements
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ broadcast_to
+ broadcast_axes
+ repeat
+ tile
+ pad
+```
+
+### Rearranging elements
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ transpose
+ swapaxes
+ flip
+```
+
+### Joining and splitting arrays
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ concat
+ split
+```
+
+### Indexing routines
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ slice
+ slice_axis
+ take
+ batch_take
+ one_hot
+ pick
+```
+
+## Mathematical functions
+
+### Arithmetic operations
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ add
+ subtract
+ negative
+ multiply
+ divide
+ modulo
+ dot
+ batch_dot
+ add_n
+```
+
+### Trigonometric functions
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ sin
+ cos
+ tan
+ arcsin
+ arccos
+ arctan
+ degrees
+ radians
+```
+
+### Hyperbolic functions
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ sinh
+ cosh
+ tanh
+ arcsinh
+ arccosh
+ arctanh
+```
+
+### Reduce functions
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ sum
+ nansum
+ prod
+ nanprod
+ mean
+ max
+ min
+ norm
+```
+
+### Rounding
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ round
+ rint
+ fix
+ floor
+ ceil
+ trunc
+```
+
+
+### Exponents and logarithms
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ exp
+ expm1
+ log
+ log10
+ log2
+ log1p
+```
+
+### Powers
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ power
+ sqrt
+ rsqrt
+ square
+```
+
+### Logic functions
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ equal
+ not_equal
+ greater
+ greater_equal
+ lesser
+ lesser_equal
+```
+### Random sampling
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ random_uniform
+ random_normal
+ random_gamma
+ random_exponential
+ random_poisson
+ random_negative_binomial
+ random_generalized_negative_binomial
+ mxnet.random.seed
+```
+
+### Sorting and searching
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ sort
+ topk
+ argsort
+ argmax
+ argmin
+```
+
+### Miscellaneous
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ maximum
+ minimum
+ clip
+ abs
+ sign
+ gamma
+ gammaln
+```
+
+## Neural network
+
+### Basic
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ FullyConnected
+ Convolution
+ Activation
+ BatchNorm
+ Pooling
+ SoftmaxOutput
+ softmax
+ log_softmax
+```
+
+### More
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Correlation
+ Deconvolution
+ RNN
+ Embedding
+ LeakyReLU
+ InstanceNorm
+ L2Normalization
+ LRN
+ ROIPooling
+ SoftmaxActivation
+ Dropout
+ BilinearSampler
+ GridGenerator
+ UpSampling
+ SpatialTransformer
+ LinearRegressionOutput
+ LogisticRegressionOutput
+ MAERegressionOutput
+ SVMOutput
+ softmax_cross_entropy
+ smooth_l1
+ IdentityAttachKLSparseReg
+ MakeLoss
+ BlockGrad
+ Custom
+```
+
+## Contrib
+
+```eval_rst
+.. warning:: This package contains experimental APIs and may change in the near future.
+```
+
+The `contrib.ndarray` module contains many useful experimental APIs for new features. This is a place for the community to try out the new features, so that feature contributors can receive feedback.
+
+```eval_rst
+.. currentmodule:: mxnet.contrib.ndarray
+
+.. autosummary::
+ :nosignatures:
+
+ CTCLoss
+ DeformableConvolution
+ DeformablePSROIPooling
+ MultiBoxDetection
+ MultiBoxPrior
+ MultiBoxTarget
+ MultiProposal
+ PSROIPooling
+ Proposal
+ count_sketch
+ ctc_loss
+ dequantize
+ fft
+ ifft
+ quantize
+```
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. automodule:: mxnet.ndarray
+ :members:
+
+.. automodule:: mxnet.random
+ :members:
+
+.. automodule:: mxnet.contrib.ndarray
+ :members:
+
+```
+
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/optimization.txt b/versions/master/_sources/api/python/optimization.txt
new file mode 100644
index 0000000..e333b00
--- /dev/null
+++ b/versions/master/_sources/api/python/optimization.txt
@@ -0,0 +1,157 @@
+# Optimization: initialize and update weights
+
+## Overview
+
+This document summaries the APIs used to initialize and update the model weights
+during training
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ mxnet.initializer
+ mxnet.optimizer
+ mxnet.lr_scheduler
+```
+
+and how to develop a new optimization algorithm in MXNet.
+
+Assume there there is a pre-defined ``Symbol`` and a ``Module`` is created for
+it
+
+```python
+>>> data = mx.symbol.Variable('data')
+>>> label = mx.symbol.Variable('softmax_label')
+>>> fc = mx.symbol.FullyConnected(data, name='fc', num_hidden=10)
+>>> loss = mx.symbol.SoftmaxOutput(fc, label, name='softmax')
+>>> mod = mx.mod.Module(loss)
+>>> mod.bind(data_shapes=[('data', (128,20))], label_shapes=[('softmax_label', (128,))])
+```
+
+Next we can initialize the weights with values sampled uniformly from
+``[-1,1]``:
+
+```python
+>>> mod.init_params(mx.initializer.Uniform(scale=1.0))
+```
+
+Then we will train a model with standard SGD which decreases the learning rate
+by multiplying 0.9 for each 100 batches.
+
+```python
+>>> lr_sch = mx.lr_scheduler.FactorScheduler(step=100, factor=0.9)
+>>> mod.init_optimizer(
+... optimizer='sgd', optimizer_params=(('learning_rate', 0.1), ('lr_scheduler', lr_sch)))
+```
+
+Finally run ``mod.fit(...)`` to start training.
+
+## The ``mxnet.initializer`` package
+
+```eval_rst
+.. currentmodule:: mxnet.initializer
+```
+
+The base class ``Initializer`` defines the default behaviors to initialize
+various parameters, such as set bias to 1, except for the weight. Other classes
+then defines how to initialize the weight.
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Initializer
+ Uniform
+ Normal
+ Load
+ Mixed
+ Zero
+ One
+ Constant
+ Orthogonal
+ Xavier
+ MSRAPrelu
+ Bilinear
+ FusedRNN
+```
+
+## The ``mxnet.optimizer`` package
+
+```eval_rst
+.. currentmodule:: mxnet.optimizer
+```
+
+The base class ``Optimizer`` accepts commonly shared arguments such as
+``learning_rate`` and defines the interface. Each other class in this package
+implements one weight updating function.
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Optimizer
+ SGD
+ NAG
+ RMSProp
+ Adam
+ AdaGrad
+ AdaDelta
+ DCASGD
+ SGLD
+```
+
+## The ``mxnet.lr_scheduler`` package
+
+```eval_rst
+.. currentmodule:: mxnet.lr_scheduler
+```
+
+The base class ``LRScheduler`` defines the interface, while other classes
+implement various schemes to change the learning rate during training.
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ LRScheduler
+ FactorScheduler
+ MultiFactorScheduler
+```
+
+## Implement a new algorithm
+
+Most classes listed in this document are implemented in Python by using ``NDArray``.
+So implementing new weight updating or initialization functions is
+straightforward.
+
+For `initializer`, create a subclass of ``Initializer`` and define the
+`_init_weight` method. We can also change the default behaviors to initialize
+other parameters such as `_init_bias`. See
+[`initializer.py`](https://github.com/dmlc/mxnet/blob/master/python/mxnet/initializer.py)
+for examples.
+
+For ``optimizer``, create a subclass of ``Optimizer``
+and implement two methods ``create_state`` and ``update``. Also add
+``@mx.optimizer.Optimizer.register`` before this class. See
+[`optimizer.py`](https://github.com/dmlc/mxnet/blob/master/python/mxnet/optimizer.py)
+for examples.
+
+For `lr_scheduler`, create a subclass of `LRScheduler` and then implement the
+`__call__` method. See
+[`lr_scheduler.py`](https://github.com/dmlc/mxnet/blob/master/python/mxnet/lr_scheduler.py)
+for examples.
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. automodule:: mxnet.optimizer
+ :members:
+.. automodule:: mxnet.lr_scheduler
+ :members:
+.. automodule:: mxnet.initializer
+ :members:
+```
+
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/rnn.txt b/versions/master/_sources/api/python/rnn.txt
new file mode 100644
index 0000000..4021b26
--- /dev/null
+++ b/versions/master/_sources/api/python/rnn.txt
@@ -0,0 +1,361 @@
+# RNN Cell API
+
+```eval_rst
+.. currentmodule:: mxnet.rnn
+```
+
+```eval_rst
+.. warning:: This package is currently experimental and may change in the near future.
+```
+
+## Overview
+
+The `rnn` module includes the recurrent neural network (RNN) cell APIs, a suite of tools for building an RNN's symbolic graph.
+```eval_rst
+.. note:: The `rnn` module offers higher-level interface while `symbol.RNN` is a lower-level interface. The cell APIs in `rnn` module are easier to use in most cases.
+```
+
+## The `rnn` module
+
+### Cell interfaces
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BaseRNNCell.__call__
+ BaseRNNCell.unroll
+ BaseRNNCell.reset
+ BaseRNNCell.begin_state
+ BaseRNNCell.unpack_weights
+ BaseRNNCell.pack_weights
+```
+
+When working with the cell API, the precise input and output symbols
+depend on the type of RNN you are using. Take Long Short-Term Memory (LSTM) for example:
+
+```python
+import mxnet as mx
+# Shape of 'step_data' is (batch_size,).
+step_input = mx.symbol.Variable('step_data')
+
+# First we embed our raw input data to be used as LSTM's input.
+embedded_step = mx.symbol.Embedding(data=step_input, \
+ input_dim=input_dim, \
+ output_dim=embed_dim)
+
+# Then we create an LSTM cell.
+lstm_cell = mx.rnn.LSTMCell(num_hidden=50)
+# Initialize its hidden and memory states.
+# 'begin_state' method takes an initialization function, and uses 'zeros' by default.
+begin_state = lstm_cell.begin_state()
+```
+
+The LSTM cell and other non-fused RNN cells are callable. Calling the cell updates it's state once. This transformation depends on both the current input and the previous states. See this [blog post](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) for a great introduction to LSTM and other RNN.
+```python
+# Call the cell to get the output of one time step for a batch.
+output, states = lstm_cell(embedded_step, begin_state)
+
+# 'output' is lstm_t0_out_output of shape (batch_size, hidden_dim).
+
+# 'states' has the recurrent states that will be carried over to the next step,
+# which includes both the "hidden state" and the "cell state":
+# Both 'lstm_t0_out_output' and 'lstm_t0_state_output' have shape (batch_size, hidden_dim).
+```
+
+Most of the time our goal is to process a sequence of many steps. For this, we need to unroll the LSTM according to the sequence length.
+```python
+# Embed a sequence. 'seq_data' has the shape of (batch_size, sequence_length).
+seq_input = mx.symbol.Variable('seq_data')
+embedded_seq = mx.symbol.Embedding(data=seq_input, \
+ input_dim=input_dim, \
+ output_dim=embed_dim)
+```
+```eval_rst
+.. note:: Remember to reset the cell when unrolling/stepping for a new sequence by calling `lstm_cell.reset()`.
+```
+```python
+# Note that when unrolling, if 'merge_outputs' is set to True, the 'outputs' is merged into a single symbol
+# In the layout, 'N' represents batch size, 'T' represents sequence length, and 'C' represents the
+# number of dimensions in hidden states.
+outputs, states = lstm_cell.unroll(length=sequence_length, \
+ inputs=embedded_seq, \
+ layout='NTC', \
+ merge_outputs=True)
+# 'outputs' is concat0_output of shape (batch_size, sequence_length, hidden_dim).
+# The hidden state and cell state from the final time step is returned:
+# Both 'lstm_t4_out_output' and 'lstm_t4_state_output' have shape (batch_size, hidden_dim).
+
+# If merge_outputs is set to False, a list of symbols for each of the time steps is returned.
+outputs, states = lstm_cell.unroll(length=sequence_length, \
+ inputs=embedded_seq, \
+ layout='NTC', \
+ merge_outputs=False)
+# In this case, 'outputs' is a list of symbols. Each symbol is of shape (batch_size, hidden_dim).
+```
+
+```eval_rst
+.. note:: Loading and saving models that are built with RNN cells API requires using
+ `mx.rnn.load_rnn_checkpoint`, `mx.rnn.save_rnn_checkpoint`, and `mx.rnn.do_rnn_checkpoint`.
+ The list of all the used cells should be provided as the first argument to those functions.
+```
+
+### Basic RNN cells
+
+`rnn` module supports the following RNN cell types.
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ LSTMCell
+ GRUCell
+ RNNCell
+```
+
+### Modifier cells
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BidirectionalCell
+ DropoutCell
+ ZoneoutCell
+ ResidualCell
+```
+
+A modifier cell takes in one or more cells and transforms the output of those cells.
+`BidirectionalCell` is one example. It takes two cells for forward unroll and backward unroll
+respectively. After unrolling, the outputs of the forward and backward pass are concatenated.
+```python
+# Bidirectional cell takes two RNN cells, for forward and backward pass respectively.
+# Having different types of cells for forward and backward unrolling is allowed.
+bi_cell = mx.rnn.BidirectionalCell(
+ mx.rnn.LSTMCell(num_hidden=50),
+ mx.rnn.GRUCell(num_hidden=75))
+outputs, states = bi_cell.unroll(length=sequence_length, \
+ inputs=embedded_seq, \
+ merge_outputs=True)
+# The output feature is the concatenation of the forward and backward pass.
+# Thus, the number of output dimensions is the sum of the dimensions of the two cells.
+# 'outputs' is the symbol 'bi_out_output' of shape (batch_size, sequence_length, 125L)
+
+# The states of the BidirectionalCell is a list of two lists, corresponding to the
+# states of the forward and backward cells respectively.
+```
+```eval_rst
+.. note:: BidirectionalCell cannot be called or stepped, because the backward unroll requires the output of
+ future steps, and thus the whole sequence is required.
+```
+
+Dropout and zoneout are popular regularization techniques that can be applied to RNN. `rnn`
+module provides `DropoutCell` and `ZoneoutCell` for regularization on the output and recurrent
+states of RNN. `ZoneoutCell` takes one RNN cell in the constructor, and supports unrolling like
+other cells.
+```python
+zoneout_cell = mx.rnn.ZoneoutCell(lstm_cell, zoneout_states=0.5)
+outputs, states = zoneout_cell.unroll(length=sequence_length, \
+ inputs=embedded_seq, \
+ merge_outputs=True)
+```
+`DropoutCell` performs dropout on the input sequence. It can be used in a stacked
+multi-layer RNN setting, which we will cover next.
+
+Residual connection is a useful technique for training deep neural models because it helps the
+propagation of gradients by shortening the paths. `ResidualCell` provides such functionality for
+RNN models.
+```python
+residual_cell = mx.rnn.ResidualCell(lstm_cell)
+outputs, states = residual_cell.unroll(length=sequence_length, \
+ inputs=embedded_seq, \
+ merge_outputs=True)
+```
+The `outputs` are the element-wise sum of both the input and the output of the LSTM cell.
+
+### Multi-layer cells
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ SequentialRNNCell
+ SequentialRNNCell.add
+```
+
+The `SequentialRNNCell` allows stacking multiple layers of RNN cells to improve the expressiveness
+and performance of the model. Cells can be added to a `SequentialRNNCell` in order, from bottom to
+top. When unrolling, the output of a lower-level cell is automatically passed to the cell above.
+
+```python
+stacked_rnn_cells = mx.rnn.SequentialRNNCell()
+stacked_rnn_cells.add(mx.rnn.BidirectionalCell(
+ mx.rnn.LSTMCell(num_hidden=50),
+ mx.rnn.LSTMCell(num_hidden=50)))
+
+# Dropout the output of the bottom layer BidirectionalCell with a retention probability of 0.5.
+stacked_rnn_cells.add(mx.rnn.DropoutCell(0.5))
+
+stacked_rnn_cells.add(mx.rnn.LSTMCell(num_hidden=50))
+outputs, states = stacked_rnn_cells.unroll(length=sequence_length, \
+ inputs=embedded_seq, \
+ merge_outputs=True)
+
+# The output of SequentialRNNCell is the same as that of the last layer.
+# In this case 'outputs' is the symbol 'concat6_output' of shape (batch_size, sequence_length, hidden_dim)
+# The states of the SequentialRNNCell is a list of lists, with each list
+# corresponding to the states of each of the added cells respectively.
+```
+
+### Fused RNN cell
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ FusedRNNCell
+ FusedRNNCell.unfuse
+```
+
+The computation of an RNN for an input sequence consists of many GEMM and point-wise operations with
+temporal dependencies dependencies. This could make the computation memory-bound especially on GPU,
+resulting in longer wall-time. By combining the computation of many small matrices into that of
+larger ones and streaming the computation whenever possible, the ratio of computation to memory I/O
+can be increased, which results in better performance on GPU. Such optimization technique is called
+"fusing".
+[This post](https://devblogs.nvidia.com/parallelforall/optimizing-recurrent-neural-networks-cudnn-5/)
+talks in greater detail.
+
+The `rnn` module includes a `FusedRNNCell`, which provides the optimized fused implementation.
+The FusedRNNCell supports bidirectional RNNs and dropout.
+
+```python
+fused_lstm_cell = mx.rnn.FusedRNNCell(num_hidden=50, \
+ num_layers=3, \
+ mode='lstm', \
+ bidirectional=True, \
+ dropout=0.5)
+outputs, _ = fused_lstm_cell.unroll(length=sequence_length, \
+ inputs=embedded_seq, \
+ merge_outputs=True)
+# The 'outputs' is the symbol 'lstm_rnn_output' that has the shape
+# (batch_size, sequence_length, forward_backward_concat_dim)
+```
+```eval_rst
+.. note:: `FusedRNNCell` supports GPU-only. It cannot be called or stepped.
+.. note:: When `dropout` is set to non-zero in `FusedRNNCell`, the dropout is applied to the
+ output of all layers except the last layer. If there is only one layer in the `FusedRNNCell`, the
+ dropout rate is ignored.
+.. note:: Similar to `BidirectionalCell`, when `bidirectional` flag is set to `True`, the output
+ of `FusedRNNCell` is twice the size specified by `num_hidden`.
+```
+
+When training a deep, complex model *on multiple GPUs* it's recommended to stack
+fused RNN cells (one layer per cell) together instead of one with all layers.
+The reason is that fused RNN cells don't set gradients to be ready until the
+computation for the entire layer is completed. Breaking a multi-layer fused RNN
+cell into several one-layer ones allows gradients to be processed ealier. This
+reduces communication overhead, especially with multiple GPUs.
+
+The `unfuse()` method can be used to convert the `FusedRNNCell` into an equivalent
+and CPU-compatible `SequentialRNNCell` that mirrors the settings of the `FusedRNNCell`.
+```python
+unfused_lstm_cell = fused_lstm_cell.unfuse()
+unfused_outputs, _ = unfused_lstm_cell.unroll(length=sequence_length, \
+ inputs=embedded_seq, \
+ merge_outputs=True)
+# The 'outputs' is the symbol 'lstm_bi_l2_out_output' that has the shape
+# (batch_size, sequence_length, forward_backward_concat_dim)
+```
+
+### RNN checkpoint methods and parameters
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ save_rnn_checkpoint
+ load_rnn_checkpoint
+ do_rnn_checkpoint
+```
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ RNNParams
+ RNNParams.get
+```
+
+The model parameters from the training with fused cell can be used for inference with unfused cell,
+and vice versa. As the parameters of fused and unfused cells are organized differently, they need to
+be converted first. `FusedRNNCell`'s parameters are merged and flattened. In the fused example above,
+the mode has `lstm_parameters` of shape `(total_num_params,)`, whereas the
+equivalent SequentialRNNCell's parameters are separate:
+```python
+'lstm_l0_i2h_weight': (out_dim, embed_dim)
+'lstm_l0_i2h_bias': (out_dim,)
+'lstm_l0_h2h_weight': (out_dim, hidden_dim)
+'lstm_l0_h2h_bias': (out_dim,)
+'lstm_r0_i2h_weight': (out_dim, embed_dim)
+...
+```
+
+All cells in the `rnn` module support the method `unpack_weights()` for converting `FusedRNNCell`
+parameters to the unfused format and `pack_weights()` for fusing the parameters. The RNN-specific
+checkpointing methods (`load_rnn_checkpoint, save_rnn_checkpoint, do_rnn_checkpoint`) handle the
+conversion transparently based on the provided cells.
+
+### I/O utilities
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ BucketSentenceIter
+ encode_sentences
+```
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. autoclass:: mxnet.rnn.BaseRNNCell
+ :members:
+
+ .. automethod:: __call__
+.. autoclass:: mxnet.rnn.LSTMCell
+ :members:
+.. autoclass:: mxnet.rnn.GRUCell
+ :members:
+.. autoclass:: mxnet.rnn.RNNCell
+ :members:
+.. autoclass:: mxnet.rnn.FusedRNNCell
+ :members:
+.. autoclass:: mxnet.rnn.SequentialRNNCell
+ :members:
+.. autoclass:: mxnet.rnn.BidirectionalCell
+ :members:
+.. autoclass:: mxnet.rnn.DropoutCell
+ :members:
+.. autoclass:: mxnet.rnn.ZoneoutCell
+ :members:
+.. autoclass:: mxnet.rnn.ResidualCell
+ :members:
+.. autoclass:: mxnet.rnn.RNNParams
+ :members:
+
+
+.. autoclass:: mxnet.rnn.BucketSentenceIter
+ :members:
+.. automethod:: mxnet.rnn.encode_sentences
+
+.. automethod:: mxnet.rnn.save_rnn_checkpoint
+
+.. automethod:: mxnet.rnn.load_rnn_checkpoint
+
+.. automethod:: mxnet.rnn.do_rnn_checkpoint
+
+```
+
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/symbol.txt b/versions/master/_sources/api/python/symbol.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/api/python/symbol.txt
@@ -0,0 +1,527 @@
+# Symbol API
+
+```eval_rst
+ .. currentmodule:: mxnet.symbol
+```
+
+## Overview
+
+This document lists the routines of the symbolic expression package:
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ mxnet.symbol
+```
+
+The `Symbol` API, defined in the `symbol` (or simply `sym`) package, provides
+neural network graphs and auto-differentiation.
+A symbol represents a multi-output symbolic expression.
+They are composited by operators, such as simple matrix operations (e.g. “+”),
+or a neural network layer (e.g. convolution layer).
+An operator can take several input variables,
+produce more than one output variables, and have internal state variables.
+A variable can be either free, which we can bind with value later,
+or an output of another symbol.
+
+```python
+>>> a = mx.sym.Variable('a')
+>>> b = mx.sym.Variable('b')
+>>> c = 2 * a + b
+>>> type(c)
+<class 'mxnet.symbol.Symbol'>
+>>> e = c.bind(mx.cpu(), {'a': mx.nd.array([1,2]), 'b':mx.nd.array([2,3])})
+>>> y = e.forward()
+>>> y
+[<NDArray 2 @cpu(0)>]
+>>> y[0].asnumpy()
+array([ 4., 7.], dtype=float32)
+```
+
+A detailed tutorial is available at [Symbol - Neural network graphs and auto-differentiation](http://mxnet.io/tutorials/basic/symbol.html).
+<br><br>
+
+```eval_rst
+
+.. note:: most operators provided in ``symbol`` are similar to those in ``ndarray``
+ although there are few differences:
+
+ - ``symbol`` adopts declarative programming. In other words, we need to first
+ compose the computations, and then feed it with data for execution whereas
+ ndarray adopts imperative programming.
+
+ - Most binary operators in ``symbol`` such as ``+`` and ``>`` don't broadcast.
+ We need to call the broadcast version of the operator such as ``broadcast_plus``
+ explicitly.
+```
+
+In the rest of this document, we first overview the methods provided by the
+`symbol.Symbol` class, and then list other routines provided by the
+`symbol` package.
+
+## The `Symbol` class
+
+### Composition
+
+Composite multiple symbols into a new one by an operator.
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Symbol.__call__
+```
+
+#### Arithmetic operations
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Symbol.__add__
+ Symbol.__sub__
+ Symbol.__rsub__
+ Symbol.__neg__
+ Symbol.__mul__
+ Symbol.__div__
+ Symbol.__rdiv__
+ Symbol.__mod__
+ Symbol.__rmod__
+ Symbol.__pow__
+```
+
+#### Comparison operators
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Symbol.__lt__
+ Symbol.__le__
+ Symbol.__gt__
+ Symbol.__ge__
+ Symbol.__eq__
+ Symbol.__ne__
+```
+
+### Query information
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Symbol.name
+ Symbol.list_arguments
+ Symbol.list_outputs
+ Symbol.list_auxiliary_states
+ Symbol.list_attr
+ Symbol.attr
+ Symbol.attr_dict
+```
+
+### Get internal and output symbol
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Symbol.__getitem__
+ Symbol.__iter__
+ Symbol.get_internals
+ Symbol.get_children
+```
+
+### Inference type and shape
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Symbol.infer_type
+ Symbol.infer_shape
+ Symbol.infer_shape_partial
+```
+
+
+### Bind
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Symbol.bind
+ Symbol.simple_bind
+```
+
+### Save
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Symbol.save
+ Symbol.tojson
+ Symbol.debug_str
+```
+
+## Symbol creation routines
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ var
+ zeros
+ ones
+ arange
+```
+
+## Symbol manipulation routines
+
+### Changing shape and type
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ cast
+ reshape
+ flatten
+ expand_dims
+```
+
+### Expanding elements
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ broadcast_to
+ broadcast_axes
+ repeat
+ tile
+ pad
+```
+
+### Rearranging elements
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ transpose
+ swapaxes
+ flip
+```
+
+### Joining and splitting symbols
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ concat
+ split
+```
+
+### Indexing routines
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ slice
+ slice_axis
+ take
+ batch_take
+ one_hot
+```
+
+## Mathematical functions
+
+### Arithmetic operations
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ broadcast_add
+ broadcast_sub
+ broadcast_mul
+ broadcast_div
+ broadcast_mod
+ negative
+ reciprocal
+ dot
+ batch_dot
+ add_n
+```
+
+### Trigonometric functions
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ sin
+ cos
+ tan
+ arcsin
+ arccos
+ arctan
+ hypot
+ broadcast_hypot
+ degrees
+ radians
+```
+
+### Hyperbolic functions
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ sinh
+ cosh
+ tanh
+ arcsinh
+ arccosh
+ arctanh
+```
+
+### Reduce functions
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ sum
+ nansum
+ prod
+ nanprod
+ mean
+ max
+ min
+ norm
+```
+
+### Rounding
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ round
+ rint
+ fix
+ floor
+ ceil
+ trunc
+```
+
+
+### Exponents and logarithms
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ exp
+ expm1
+ log
+ log10
+ log2
+ log1p
+```
+
+### Powers
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ broadcast_power
+ sqrt
+ rsqrt
+ square
+```
+
+### Logic functions
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ broadcast_equal
+ broadcast_not_equal
+ broadcast_greater
+ broadcast_greater_equal
+ broadcast_lesser
+ broadcast_lesser_equal
+```
+### Random sampling
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ random_uniform
+ random_normal
+ random_gamma
+ random_exponential
+ random_poisson
+ random_negative_binomial
+ random_generalized_negative_binomial
+ sample_uniform
+ sample_normal
+ sample_gamma
+ sample_exponential
+ sample_poisson
+ sample_negative_binomial
+ sample_generalized_negative_binomial
+ mxnet.random.seed
+```
+
+### Sorting and searching
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ sort
+ topk
+ argsort
+ argmax
+ argmin
+```
+
+### Linear Algebra
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ linalg_gemm
+ linalg_gemm2
+ linalg_potrf
+ linalg_potri
+ linalg_trmm
+ linalg_trsm
+ linalg_sumlogdiag
+```
+
+### Miscellaneous
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ maximum
+ minimum
+ broadcast_maximum
+ broadcast_minimum
+ clip
+ abs
+ sign
+ gamma
+ gammaln
+```
+
+## Neural network
+
+### Basic
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ FullyConnected
+ Convolution
+ Activation
+ BatchNorm
+ Pooling
+ SoftmaxOutput
+ softmax
+ log_softmax
+```
+
+### More
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Correlation
+ Deconvolution
+ RNN
+ Embedding
+ LeakyReLU
+ InstanceNorm
+ L2Normalization
+ LRN
+ ROIPooling
+ SoftmaxActivation
+ Dropout
+ BilinearSampler
+ GridGenerator
+ UpSampling
+ SpatialTransformer
+ LinearRegressionOutput
+ LogisticRegressionOutput
+ MAERegressionOutput
+ SVMOutput
+ softmax_cross_entropy
+ smooth_l1
+ IdentityAttachKLSparseReg
+ MakeLoss
+ BlockGrad
+ Custom
+```
+
+## Contrib
+
+```eval_rst
+.. warning:: This package contains experimental APIs and may change in the near future.
+```
+
+The `contrib.symbol` module contains many useful experimental APIs for new features. This is a place for the community to try out the new features, so that feature contributors can receive feedback.
+
+```eval_rst
+.. currentmodule:: mxnet.contrib.symbol
+
+.. autosummary::
+ :nosignatures:
+
+ CTCLoss
+ DeformableConvolution
+ DeformablePSROIPooling
+ MultiBoxDetection
+ MultiBoxPrior
+ MultiBoxTarget
+ MultiProposal
+ PSROIPooling
+ Proposal
+ count_sketch
+ ctc_loss
+ dequantize
+ fft
+ ifft
+ quantize
+```
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+.. automodule:: mxnet.symbol
+ :members:
+
+.. automodule:: mxnet.contrib.symbol
+ :members:
+
+```
+
+<script>auto_index("api-reference");</script>
diff --git a/versions/master/_sources/api/python/symbol_in_pictures.txt b/versions/master/_sources/api/python/symbol_in_pictures.txt
new file mode 100644
index 0000000..4fdde75
--- /dev/null
+++ b/versions/master/_sources/api/python/symbol_in_pictures.txt
@@ -0,0 +1,77 @@
+# Symbolic Configuration and Execution in Pictures
+
+This topic explains symbolic construction and execution in pictures.
+We recommend that you also read [Symbolic API](symbol.md).
+
+## Compose Symbols
+
+Symbols are a description of the computation that you want to perform. The symbolic construction API generates the computation
+graph that describes the computation. The following picture shows how you compose symbols to describe basic computations.
+
+
+
+- The ```mxnet.symbol.Variable``` function creates argument nodes that represent input to the computation.
+- The symbol is overloaded with basic element-wise mathematical operations.
+
+## Configure Neural Networks
+
+In addition to supporting fine-grained operations, MXNet provides a way to perform big operations that are analogous to layers in neural networks.
+You can use operators to describe the configuration of a neural network.
+
+
+
+
+## Example of a Multi-Input Network
+
+The following example shows how to configure multiple input neural networks.
+
+
+
+
+## Bind and Execute Symbol
+
+When you need to execute a symbol graph, you call the bind function to bind ```NDArrays``` to the argument nodes
+in order to obtain an ```Executor```.
+
+
+
+To get the output results, given the bound NDArrays as input, you can call ```Executor.Forward```.
+
+
+
+
+## Bind Multiple Outputs
+
+To group symbols, then bind them to
+get the outputs of both, use ```mx.symbol.Group```.
+
+
+
+Remember: Bind only what you need, so that the system can perform more optimizations.
+
+
+## Calculate the Gradient
+
+In the bind function, you can specify NDArrays that will hold gradients. Calling ```Executor.backward``` after ```Executor.forward``` gives you the corresponding gradients.
+
+
+
+
+## Simple Bind Interface for Neural Networks
+
+It can be tedious to pass the argument NDArrays to the bind function, especially when you are binding a big
+graph. ```Symbol.simple_bind``` provides a way to simplify
+the procedure. You need to specify only input data shapes. The function allocates the arguments, and binds
+the Executor for you.
+
+
+
+## Auxiliary States
+
+Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states might not be part of the computation, they can be helpful for tracking. You can pass auxiliary states in the same way that you pass arguments.
+
+
+
+## Next Steps
+
+See [Symbolic API](symbol.md) and [Python Documentation](index.md).
diff --git a/versions/master/_sources/api/r/index.txt b/versions/master/_sources/api/r/index.txt
new file mode 100644
index 0000000..7b57947
--- /dev/null
+++ b/versions/master/_sources/api/r/index.txt
@@ -0,0 +1,28 @@
+# MXNet - R API
+
+See the [MXNet R Reference Manual](https://media.readthedocs.org/pdf/mxnet-test/latest/mxnet-test.pdf).
+
+MXNet supports the R programming language. The MXNet R package brings flexible and efficient GPU
+computing and state-of-art deep learning to R. It enables you to write seamless tensor/matrix computation with multiple GPUs in R. It also lets you construct and customize the state-of-art deep learning models in R,
+ and apply them to tasks, such as image classification and data science challenges.
+
+You can perform tensor or matrix computation in R:
+
+```r
+ > require(mxnet)
+ Loading required package: mxnet
+ > a <- mx.nd.ones(c(2,3))
+ > a
+ [,1] [,2] [,3]
+ [1,] 1 1 1
+ [2,] 1 1 1
+ > a + 1
+ [,1] [,2] [,3]
+ [1,] 2 2 2
+ [2,] 2 2 2
+```
+## Resources
+
+* [MXNet R Reference Manual](https://media.readthedocs.org/pdf/mxnet-test/latest/mxnet-test.pdf)
+* [MXNet for R Tutorials](http://mxnet.io/tutorials/index.html#R-Tutorials)
+
diff --git a/versions/master/_sources/api/scala/index.txt b/versions/master/_sources/api/scala/index.txt
new file mode 100644
index 0000000..2d5a047
--- /dev/null
+++ b/versions/master/_sources/api/scala/index.txt
@@ -0,0 +1,42 @@
+# MXNet - Scala API
+
+See the [MXNet Scala API Documentation](http://mxnet.io/api/scala/docs/index.html).
+
+MXNet supports the Scala programming language. The MXNet Scala package brings flexible and efficient GPU
+computing and state-of-art deep learning to Scala. It enables you to write seamless tensor/matrix computation with multiple GPUs in Scala. It also lets you construct and customize the state-of-art deep learning models in Scala,
+ and apply them to tasks, such as image classification and data science challenges.
+
+You can perform tensor or matrix computation in pure Scala:
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ import ml.dmlc.mxnet._
+
+ scala> val arr = NDArray.ones(2, 3)
+ arr: ml.dmlc.mxnet.NDArray = ml.dmlc.mxnet.NDArray@f5e74790
+
+ scala> arr.shape
+ res0: ml.dmlc.mxnet.Shape = (2,3)
+
+ scala> (arr * 2).toArray
+ res2: Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
+
+ scala> (arr * 2).shape
+ res3: ml.dmlc.mxnet.Shape = (2,3)
+```
+
+ ## Scala API Reference
+ * [Module API](module.md) is a flexible high-level interface for training neural networks.
+ * [Model API](model.md) is an alternate simple high-level interface for training neural networks.
+ * [Symbolic API](symbol.md) performs operations on NDArrays to assemble neural networks from layers.
+ * [IO Data Loading API](io.md) performs parsing and data loading.
+ * [NDArray API](ndarray.md) performs vector/matrix/tensor operations.
+ * [KVStore API](kvstore.md) performs multi-GPU and multi-host distributed training.
+
+
+## Resources
+
+* [MXNet Scala API Documentation](http://mxnet.io/api/scala/docs/index.html)
+* [Handwritten Digit Classification in Scala](http://mxnet.io/tutorials/scala/mnist.html)
+* [Neural Style in Scala on MXNet](https://github.com/dmlc/mxnet/blob/master/scala-package/examples/src/main/scala/ml/dmlc/mxnetexamples/neuralstyle/NeuralStyle.scala)
+* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/src/main/scala/ml/dmlc/mxnetexamples)
diff --git a/versions/master/_sources/api/scala/io.txt b/versions/master/_sources/api/scala/io.txt
new file mode 100644
index 0000000..316a1ee
--- /dev/null
+++ b/versions/master/_sources/api/scala/io.txt
@@ -0,0 +1,165 @@
+# MXNet Scala Data Loading API
+This topic introduces the data input method for MXNet. MXNet uses an iterator to provide data to the neural network. Iterators do some preprocessing and generate batches for the neural network.
+
+MXNet provides basic iterators for MNIST and RecordIO images. To hide the cost of I/O, MXNet uses a prefetch strategy that enables parallelism for the learning process and data fetching. Data is automatically fetched by an independent thread.
+
+Topics:
+
+* [Data Iterator Parameters](#parameters-for-data-iterator) clarifies the different usages for dataiter parameters.
+* [Create a Data Iterator](#create-a-data-iterator) introduces how to create a data iterator in MXNet for Scala.
+* [How to Get Data](#how-to-get-data) introduces the data resource and data preparation tools.
+* [IO API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.IO$) explains the IO API.
+
+
+## Data Iterator Parameters
+
+To create a data iterator, you typically need to provide five parameters:
+
+* **Dataset Param** provides basic information about the dataset, e.g., file path, input shape.
+* **Batch Param** provides information required to form a batch, e.g., batch size.
+* **Augmentation Param** tells MXNet which augmentation operations (e.g., crop or mirror) to perform on an input image.
+* **Backend Param** controls the behavior of the back-end threads to hide the cost of data loading.
+* **Auxiliary Param** provides options for checking and debugging.
+
+You *must* provide the **Dataset Param** and **Batch Param**, otherwise MXNet can't create the data batch. Provide other parameters as required by your algorithm and performance needs. We provide a detailed explanation and examples of the options later.
+
+## Create a Data Iterator
+
+The IO API provides a simple way to create a data iterator in Scala.
+The following example code shows how to create a CIFAR data iterator.
+
+```scala
+ val dataiter = IO.ImageRecordIter(Map(
+ // Utility Parameter
+ // Optional
+ // Name of the data, should match the name of the data input of the network
+ // data_name='data',
+ // Utility Parameter
+ // Optional
+ // Name of the label, should match the name of the label parameter of the network
+ // Usually, if the loss layer is named 'foo', then the label input has the name
+ // 'foo_label', unless overwritten
+ // label_name='softmax_label',
+ // Dataset Parameter
+ // Impulsary
+ // indicating the data file, please check the data is already there
+ "path_imgrec" -> "data/cifar/train.rec",
+ // Dataset Parameter
+ // Impulsary
+ // indicating the image size after preprocessing
+ "data_shape" -> "(3,28,28)",
+ // Batch Parameter
+ // Impulsary
+ // tells how many images in a batch
+ "batch_size" -> "100",
+ // Augmentation Parameter
+ // Optional
+ // when offers mean_img, each image will subtract the mean value at each pixel
+ "mean_img" -> "data/cifar/cifar10_mean.bin",
+ // Augmentation Parameter
+ // Optional
+ // randomly crop a patch of the data_shape from the original image
+ "rand_crop" -> "True",
+ // Augmentation Parameter
+ // Optional
+ // randomly mirror the image horizontally
+ "rand_mirror" -> "True",
+ // Augmentation Parameter
+ // Optional
+ // randomly shuffle the data
+ "shuffle" -> "False",
+ // Backend Parameter
+ // Optional
+ // Preprocessing thread number
+ "preprocess_threads" -> "4",
+ // Backend Parameter
+ // Optional
+ // Prefetch buffer size
+ "prefetch_buffer" = "1"))
+```
+
+First, explicitly specify the kind of data (MNIST, ImageRecord, etc.) to fetch. Then, provide the options for the dataset, batching, image augmentation, multi-tread processing, and prefetching operations. The code automatically validates the parameters. If a required parameter is missing, MXNet returns an error.
+
+## How to Get Data
+
+
+We provide [scripts](https://github.com/dmlc/mxnet/tree/master/scala-package/core/scripts) to download MNIST data and CIFAR10 ImageRecord data. If you want to create your own dataset, we recommend using the Image RecordIO data format.
+
+## Create a Dataset Using RecordIO
+
+RecordIO implements a file format for a sequence of records. We recommend storing images as records and packing them together. The benefits include:
+
+* Storing images in a compact format--e.g., JPEG, for records--greatly reduces the size of the dataset on the disk.
+* Packing data together allows continuous reading on the disk.
+* RecordIO has a simple way to partition, simplifying distributed setting. We provide an example later.
+
+We provide the [im2rec tool](https://github.com/dmlc/mxnet/blob/master/tools/im2rec.cc) so you can create an Image RecordIO dataset by yourself. The following walkthrough shows you how.
+
+### Prerequisites
+Download the data. You don't need to resize the images manually. You can use `im2rec` to resize them automatically. For details, see "Extension: Using Multiple Labels for a Single Image," later in this topic.
+
+### Step 1. Make an Image List File
+After you download the data, you need to make an image list file. The format is:
+
+```
+ integer_image_index \t label_index \t path_to_image
+```
+Typically, the program takes the list of names of all of the images, shuffles them, then separates them into two lists: a training filename list and a testing filename list. Write the list in the right format.
+
+This is an example file:
+
+```bash
+ 95099 464 n04467665_17283.JPEG
+ 10025081 412 ILSVRC2010_val_00025082.JPEG
+ 74181 789 n01915811_2739.JPEG
+ 10035553 859 ILSVRC2010_val_00035554.JPEG
+ 10048727 929 ILSVRC2010_val_00048728.JPEG
+ 94028 924 n01980166_4956.JPEG
+ 1080682 650 n11807979_571.JPEG
+ 972457 633 n07723039_1627.JPEG
+ 7534 11 n01630670_4486.JPEG
+ 1191261 249 n12407079_5106.JPEG
+
+```
+
+### Step 2. Create the Binary File
+To generate a binary image, use `im2rec` in the tool folder. `im2rec` takes the path of the `_image list file_` you generated, the `_root path_` of the images, and the `_output file path_` as input. This process usually takes several hours, so be patient.
+
+A sample command:
+
+```bash
+ ./bin/im2rec image.lst image_root_dir output.bin resize=256
+```
+For more details, run ```./bin/im2rec```.
+
+### Extension: Multiple Labels for a Single Image
+
+The `im2rec` tool and `IO.ImageRecordIter` have multi-label support for a single image.
+For example, if you have four labels for a single image, you can use the following procedure to use the RecordIO tools.
+
+1. Write the image list files as follows:
+
+ ```
+ integer_image_index \t label_1 \t label_2 \t label_3 \t label_4 \t path_to_image
+ ```
+
+2. Run `im2rec`, adding a 'label_width=4' to the command argument, for example:
+
+ ```bash
+ ./bin/im2rec image.lst image_root_dir output.bin resize=256 label_width=4
+ ```
+
+3. In the iterator generation code, set `label_width=4` and `path_imglist=<<The PATH TO YOUR image.lst>>`, for example:
+
+ ```scala
+ val dataiter = IO.ImageRecordIter(Map(
+ "path_imgrec" -> "data/cifar/train.rec",
+ "data_shape" -> "(3,28,28)",
+ "path_imglist" -> "data/cifar/image.lst",
+ "label_width" -> "4"
+ ))
+ ```
+
+## Next Steps
+* [NDArray API](ndarray.md) for vector/matrix/tensor operations
+* [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training
diff --git a/versions/master/_sources/api/scala/kvstore.txt b/versions/master/_sources/api/scala/kvstore.txt
new file mode 100644
index 0000000..70b317a
--- /dev/null
+++ b/versions/master/_sources/api/scala/kvstore.txt
@@ -0,0 +1,108 @@
+# KVStore API
+
+Topics:
+* [Basic Push and Pull](#basic-push-and-pull)
+* [List Key-Value Pairs](#list-key-value-pairs)
+* [API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.KVStore)
+
+
+## Basic Push and Pull
+
+Provides basic operation over multiple devices (GPUs) on a single device.
+
+### Initialization
+
+Let's consider a simple example. It initializes
+a (`int`, `NDArray`) pair into the store, and then pulls the value out.
+
+```scala
+ scala> val kv = KVStore.create("local") // create a local kv store.
+ scala> val shape = Shape(2,3)
+ scala> kv.init(3, NDArray.ones(shape)*2)
+ scala> val a = NDArray.zeros(shape)
+ scala> kv.pull(3, out = a)
+ scala> a.toArray
+ Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
+```
+
+### Push, Aggregation, and Updater
+
+For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
+
+```scala
+ scala> kv.push(3, NDArray.ones(shape)*8)
+ scala> kv.pull(3, out = a) // pull out the value
+ scala> a.toArray
+ Array[Float] = Array(8.0, 8.0, 8.0, 8.0, 8.0, 8.0)
+```
+
+The data that you want to push can be stored on any device. Furthermore, you can push multiple
+values into the same key, where KVStore first sums all of these
+values, and then pushes the aggregated value, as follows:
+
+```scala
+ scala> val gpus = Array(Context.gpu(0), Context.gpu(1), Context.gpu(2), Context.gpu(3))
+ scala> val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)), \
+ scala> NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
+ scala> kv.push(3, b)
+ scala> kv.pull(3, out = a)
+ scala> a.toArray
+ Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
+```
+
+For each push command, KVStore applies the pushed value to the value stored by an
+`updater`. The default updater is `ASSIGN`. You can replace the default to
+control how data is merged.
+
+```scala
+ scala> val updater = new MXKVStoreUpdater {
+ override def update(key: Int, input: NDArray, stored: NDArray): Unit = {
+ println(s"update on key $key")
+ stored += input * 2
+ }
+ override def dispose(): Unit = {}
+ }
+ scala> kv.setUpdater(updater)
+ scala> kv.pull(3, a)
+ scala> a.toArray
+ Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
+ scala> kv.push(3, NDArray.ones(shape))
+ update on key 3
+ scala> kv.pull(3, a)
+ scala> a.toArray
+ Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
+```
+
+### Pull
+
+You've already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can
+pull the value into several devices with a single call.
+
+```scala
+ scala> val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)),\
+ scala> NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
+ scala> kv.pull(3, outs = b)
+ scala> b(1).toArray
+ Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
+```
+
+## List Key-Value Pairs
+
+All of the operations that we've discussed so far are performed on a single key. KVStore also provides
+the interface for generating a list of key-value pairs. For a single device, use the following:
+
+```scala
+ scala> val keys = Array(5, 7, 9)
+ scala> kv.init(keys, Array.fill(keys.length)(NDArray.ones(shape)))
+ scala> kv.push(keys, Array.fill(keys.length)(NDArray.ones(shape)))
+ update on key: 5
+ update on key: 7
+ update on key: 9
+ scala> val b = Array.fill(keys.length)(NDArray.zeros(shape))
+ scala> kv.pull(keys, outs = b)
+ scala> b(1).toArray
+ Array[Float] = Array(3.0, 3.0, 3.0, 3.0, 3.0, 3.0)
+```
+
+## Next Steps
+* [Scala Tutorials](http://mxnet.io/tutorials/index.html#Python-Tutorials)
diff --git a/versions/master/_sources/api/scala/model.txt b/versions/master/_sources/api/scala/model.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/api/scala/model.txt
@@ -0,0 +1,118 @@
+# MXNet Scala Model API
+
+The model API provides a simplified way to train neural networks using common best practices.
+It's a thin wrapper built on top of the [ndarray](ndarray.md) and [symbolic](symbol.md)
+modules that make neural network training easy.
+
+Topics:
+
+* [Train a Model](#train-a-model)
+* [Save the Model](#save-the-model)
+* [Periodic Checkpoint](#periodic-checkpointing)
+* [Multiple Devices](#use-multiple-devices)
+* [Model API Reference](#http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.Model)
+
+## Train the Model
+
+To train a model, perform two steps: configure the model using the symbol parameter,
+then call ```model.Feedforward.create``` to create the model.
+The following example creates a two-layer neural network.
+
+```scala
+ // configure a two layer neuralnetwork
+ val data = Symbol.Variable("data")
+ val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
+ val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
+ val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
+ val softmax = Symbol.SoftmaxOutput(name = "sm")()(Map("data" -> fc2))
+
+ // Construct the FeedForward model and fit on the input training data
+ val model = FeedForward.newBuilder(softmax)
+ .setContext(Context.cpu())
+ .setNumEpoch(num_epoch)
+ .setOptimizer(new SGD(learningRate = 0.01f, momentum = 0.9f, wd = 0.0001f))
+ .setTrainData(trainDataIter)
+ .setEvalData(valDataIter)
+ .build()
+```
+You can also use the `scikit-learn-style` construct and `fit` function to create a model.
+
+```scala
+ // create a model using sklearn-style two-step way
+ val model = new FeedForward(softmax,
+ numEpoch = numEpochs,
+ argParams = argParams,
+ auxParams = auxParams,
+ beginEpoch = beginEpoch,
+ epochSize = epochSize)
+
+ model.fit(trainData = train)
+```
+For more information, see [API Reference](http://mxnet.io/api/scala/docs/index.html).
+
+## Save the Model
+
+After the job is done, save your work.
+We also provide `save` and `load` functions. You can use the `load` function to load a model checkpoint from a file.
+
+```scala
+ // checkpoint the model data into file,
+ // save a model to modelPrefix-symbol.json and modelPrefix-0100.params
+ val modelPrefix: String = "checkpt"
+ val num_epoch = 100
+ Model.saveCheckpoint(modelPrefix, epoch + 1, symbol, argParams, auxStates)
+
+ // load model back
+ val model_loaded = FeedForward.load(modelPrefix, num_epoch)
+```
+The advantage of these two `save` and `load` functions is that they are language agnostic.
+You should be able to save and load directly into cloud storage, such as Amazon S3 and HDFS.
+
+## Periodic Checkpointing
+
+We recommend checkpointing your model after each iteration.
+To do this, use ```EpochEndCallback``` to add a ```Model.saveCheckpoint(<parameters>)``` checkpoint callback to the function after each iteration .
+
+```scala
+ // modelPrefix-symbol.json will be saved for symbol.
+ // modelPrefix-epoch.params will be saved for parameters.
+ // Checkpoint the model into file. Can specify parameters.
+ // For more information, check API doc.
+ val modelPrefix: String = "checkpt"
+ val checkpoint: EpochEndCallback =
+ if (modelPrefix == null) null
+ else new EpochEndCallback {
+ override def invoke(epoch: Int, symbol: Symbol,
+ argParams: Map[String, NDArray],
+ auxStates: Map[String, NDArray]): Unit = {
+ Model.saveCheckpoint(modelPrefix, epoch + 1, symbol, argParams, auxParams)
+ }
+ }
+
+ // Load model checkpoint from file. Returns symbol, argParams, auxParams.
+ val (_, argParams, _) = Model.loadCheckpoint(modelPrefix, num_epoch)
+
+```
+You can load the model checkpoint later using ```Model.loadCheckpoint(modelPrefix, num_epoch)```.
+
+## Use Multiple Devices
+
+Set ```ctx``` to the list of devices that you want to train on. You can create a list of devices in any way you want.
+
+```scala
+ val devices = Array(Context.gpu(0), Context.gpu(1))
+
+ val model = new FeedForward(ctx = devices,
+ symbol = network,
+ numEpoch = numEpochs,
+ optimizer = optimizer,
+ epochSize = epochSize,
+ ...)
+```
+Training occurs in parallel on the GPUs that you specify.
+
+## Next Steps
+* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
+* See [IO Data Loading API](io.md) for parsing and loading data.
+* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/versions/master/_sources/api/scala/module.txt b/versions/master/_sources/api/scala/module.txt
new file mode 100644
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+++ b/versions/master/_sources/api/scala/module.txt
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+# Module API
+The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. A *module* is an instance of subclasses of the `BaseModule`. The most widely used module class is called `Module`. Module wraps a `Symbol` and one or more `Executors`. For a full list of functions, see `BaseModule`.
+A subclass of modules might have extra interface functions. This topic provides some examples of common use cases. All of the module APIs are in the `Module` namespace.
+
+## Preparing a Module for Computation
+
+To construct a module, refer to the constructors for the module class. For example, the `Module` class accepts a `Symbol` as input:
+
+```scala
+ import ml.dmlc.mxnet._
+ import ml.dmlc.mxnet.module.{FitParams, Module}
+
+ // construct a simple MLP
+ val data = Symbol.Variable("data")
+ val fc1 = Symbol.FullyConnected(name = "fc1")(data)(Map("num_hidden" -> 128))
+ val act1 = Symbol.Activation(name = "relu1")(fc1)(Map("act_type" -> "relu"))
+ val fc2 = Symbol.FullyConnected(name = "fc2")(act1)(Map("num_hidden" -> 64))
+ val act2 = Symbol.Activation(name = "relu2")(fc2)(Map("act_type" -> "relu"))
+ val fc3 = Symbol.FullyConnected(name = "fc3")(act2)(Map("num_hidden" -> 10))
+ val out = Symbol.SoftmaxOutput(name = "softmax")(fc3)()
+
+ // construct the module
+ val mod = new Module(out)
+```
+
+By default, `context` is the CPU. If you need data parallelization, you can specify a GPU context or an array of GPU contexts.
+
+Before you can compute with a module, you need to call `bind()` to allocate the device memory and `initParams()` or `SetParams()` to initialize the parameters.
+If you simply want to fit a module, you don't need to call `bind()` and `initParams()` explicitly, because the fit() function automatically calls them if they are needed.
+
+```scala
+ mod.bind(dataShapes = train_dataiter.provideData, labelShapes = Some(train_dataiter.provideLabel))
+ mod.initParams()
+```
+
+Now you can compute with the module using functions like `forward()`, `backward()`, etc.
+
+## Training, Predicting, and Evaluating
+
+Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the `fit()` function with some `DataIter`s:
+
+```scala
+ import ml.dmlc.mxnet.optimizer.SGD
+ val mod = new Module(softmax)
+
+ mod.fit(train_dataiter, evalData = scala.Option(eval_dataiter), \
+ numEpoch = n_epoch, fitParams = new FitParams()\
+ .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f)))
+```
+
+The interface is very similar to the old `FeedForward` class. You can pass in batch-end callbacks using `setBatchEndCallback` and epoch-end callbacks using `setEpochEndCallback`. You can also set parameters using methods like `setOptimizer` and `setEvalMetric`. To learn more about the `FitParams()`, see the [API page](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.module.FitParams). To predict with a module, call `predict()` with a `DataIter`:
+
+```scala
+ mod.predict(val_dataiter)
+```
+
+The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict()` function](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.module.BaseModule).
+
+When prediction results might be too large to fit in memory, use the `predictEveryBatch` API:
+
+```scala
+ val preds = mod.predictEveryBatch(val_dataiter)
+ val_dataiter.reset()
+ var i = 0
+ while (val_dataiter.hasNext) {
+ val batch = val_dataiter.next()
+ val predLabel: Array[Int] = NDArray.argmax_channel(preds(i)(0)).toArray.map(_.toInt)
+ val label = batch.label(0).toArray.map(_.toInt)
+ //do something...
+ i += 1
+ }
+```
+
+If you need to evaluate on a test set and don't need the prediction output, call the `score()` function with a `DataIter` and an `EvalMetric`:
+
+```scala
+ mod.score(val_dataiter, metric)
+```
+
+This runs predictions on each batch in the provided `DataIter` and computes the evaluation score using the provided `EvalMetric`. The evaluation results are stored in `metric` so that you can query later.
+
+## Saving and Loading Module Parameters
+
+To save the module parameters in each training epoch, use a `checkpoint` callback:
+
+```scala
+ val modelPrefix: String = "mymodel"
+
+ for (epoch <- 0 until 5) {
+ while(train_dataiter.hasNext){
+ // forward backward pass
+ //do something...
+ }
+ val checkpoint = mod.saveCheckpoint(modelPrefix, epoch, saveOptStates = true)
+
+ }
+```
+
+To load the saved module parameters, call the `loadCheckpoint` function:
+
+```scala
+ val mod = Module.loadCheckpoint(modelPrefix, loadModelEpoch, loadOptimizerStates = true)
+```
+
+To initialize parameters, Bind the symbols to construct executors first with `bind` method. Then, initialize the parameters and auxiliary states by calling `initParams()` method.
+
+```scala
+ mod.bind(dataShapes = train_dataiter.provideData, labelShapes = Some(train_dataiter.provideLabel))
+ mod.initParams()
+```
+
+To get current parameters, use `getParams` method.
+
+```scala
+ val (argParams, auxParams) = mod.getParams
+```
+
+To assign parameter and aux state values, use `setParams` method.
+
+```scala
+ mod.setParams(argParams, auxParams)
+```
+
+To resume training from a saved checkpoint, instead of calling `setParams()`, directly call `fit()`, passing the loaded parameters, so that `fit()` knows to start from those parameters instead of initializing randomly:
+
+```scala
+ mod.fit(..., fitParams=new FitParams().setArgParams(argParams).\
+ setAuxParams(auxParams).setBeginEpoch(beginEpoch))
+```
+
+Create an object of the `FitParams()` class, and then use it to call the `setBeginEpoch()` method to pass `beginEpoch` so that `fit()` knows to resume from a saved epoch.
+
+## Next Steps
+* See [Model API](model.md) for an alternative simple high-level interface for training neural networks.
+* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
+* See [IO Data Loading API](io.md) for parsing and loading data.
+* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/versions/master/_sources/api/scala/ndarray.txt b/versions/master/_sources/api/scala/ndarray.txt
new file mode 100644
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+++ b/versions/master/_sources/api/scala/ndarray.txt
@@ -0,0 +1,157 @@
+# NDArray API
+
+
+The NDArray package (`mxnet.ndarray`) contains tensor operations similar to `numpy.ndarray`. The syntax is also similar, except for some additional calls for dealing with I/O and multiple devices.
+
+Topics:
+
+* [Create NDArray](#create-ndarray)
+* [NDArray Operations](#ndarray-operations)
+* [NDArray API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.NDArray)
+
+## Create NDArray
+
+Create `mxnet.ndarray` as follows:
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> // all-zero array of dimension 100x50
+ scala> val a = NDArray.zeros(100, 50)
+ scala> // all-one array of dimension 256x32x128x1
+ scala> val b = NDArray.ones(256, 32, 128, 1)
+ scala> // initialize array with contents, you can specify dimensions of array using Shape parameter while creating array.
+ scala> val c = NDArray.array(Array(1, 2, 3, 4, 5, 6), shape = Shape(2, 3))
+```
+This is similar to the way you use `numpy`.
+## NDArray Operations
+
+We provide some basic ndarray operations, like arithmetic and slice operations.
+
+### Arithmetic Operations
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val a = NDArray.zeros(100, 50)
+ scala> a.shape
+ ml.dmlc.mxnet.Shape = (100,50)
+ scala> val b = NDArray.ones(100, 50)
+ scala> // c and d will be calculated in parallel here!
+ scala> val c = a + b
+ scala> val d = a - b
+ scala> // inplace operation, b's contents will be modified, but c and d won't be affected.
+ scala> b += d
+```
+
+### Multiplication/Division Operations
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ //Multiplication
+ scala> val ndones = NDArray.ones(2, 1)
+ scala> val ndtwos = ndones * 2
+ scala> ndtwos.toArray
+ Array[Float] = Array(2.0, 2.0)
+ scala> (ndones * ndones).toArray
+ Array[Float] = Array(1.0, 1.0)
+ scala> (ndtwos * ndtwos).toArray
+ Array[Float] = Array(4.0, 4.0)
+ scala> ndtwos *= ndtwos // inplace
+ scala> ndtwos.toArray
+ Array[Float] = Array(4.0, 4.0)
+
+ //Division
+ scala> val ndones = NDArray.ones(2, 1)
+ scala> val ndzeros = ndones - 1f
+ scala> val ndhalves = ndones / 2
+ scala> ndhalves.toArray
+ Array[Float] = Array(0.5, 0.5)
+ scala> (ndhalves / ndhalves).toArray
+ Array[Float] = Array(1.0, 1.0)
+ scala> (ndones / ndones).toArray
+ Array[Float] = Array(1.0, 1.0)
+ scala> (ndzeros / ndones).toArray
+ Array[Float] = Array(0.0, 0.0)
+ scala> ndhalves /= ndhalves
+ scala> ndhalves.toArray
+ Array[Float] = Array(1.0, 1.0)
+```
+
+### Slice Operations
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val a = NDArray.array(Array(1f, 2f, 3f, 4f, 5f, 6f), shape = Shape(3, 2))
+ scala> val a1 = a.slice(1)
+ scala> assert(a1.shape === Shape(1, 2))
+ scala> assert(a1.toArray === Array(3f, 4f))
+
+ scala> val a2 = arr.slice(1, 3)
+ scala> assert(a2.shape === Shape(2, 2))
+ scala> assert(a2.toArray === Array(3f, 4f, 5f, 6f))
+```
+
+### Dot Product
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val arr1 = NDArray.array(Array(1f, 2f), shape = Shape(1, 2))
+ scala> val arr2 = NDArray.array(Array(3f, 4f), shape = Shape(2, 1))
+ scala> val res = NDArray.dot(arr1, arr2)
+ scala> res.shape
+ ml.dmlc.mxnet.Shape = (1,1)
+ scala> res.toArray
+ Array[Float] = Array(11.0)
+```
+
+### Save and Load NDArray
+
+You can use MXNet functions to save and load a list or dictionary of NDArrays from file systems, as follows:
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val a = NDArray.zeros(100, 200)
+ scala> val b = NDArray.zeros(100, 200)
+ scala> // save list of NDArrays
+ scala> NDArray.save("/path/to/array/file", Array(a, b))
+ scala> // save dictionary of NDArrays to AWS S3
+ scala> NDArray.save("s3://path/to/s3/array", Map("A" -> a, "B" -> b))
+ scala> // save list of NDArrays to hdfs.
+ scala> NDArray.save("hdfs://path/to/hdfs/array", Array(a, b))
+ scala> val from_file = NDArray.load("/path/to/array/file")
+ scala> val from_s3 = NDArray.load("s3://path/to/s3/array")
+ scala> val from_hdfs = NDArray.load("hdfs://path/to/hdfs/array")
+```
+The good thing about using the `save` and `load` interface is that you can use the format across all `mxnet` language bindings. They also already support Amazon S3 and HDFS.
+
+### Multi-Device Support
+
+Device information is stored in the `mxnet.Context` structure. When creating NDArray in MXNet, you can use the context argument (the default is the CPU context) to create arrays on specific devices as follows:
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val cpu_a = NDArray.zeros(100, 200)
+ scala> cpu_a.context
+ ml.dmlc.mxnet.Context = cpu(0)
+ scala> val ctx = Context.gpu(0)
+ scala> val gpu_b = NDArray.zeros(Shape(100, 200), ctx)
+ scala> gpu_b.context
+ ml.dmlc.mxnet.Context = gpu(0)
+```
+
+Currently, we *do not* allow operations among arrays from different contexts. To manually enable this, use the `copyto` member function to copy the content to different devices, and continue computation:
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val x = NDArray.zeros(100, 200)
+ scala> val ctx = Context.gpu(0)
+ scala> val y = NDArray.zeros(Shape(100, 200), ctx)
+ scala> val z = x + y
+ mxnet.base.MXNetError: [13:29:12] src/ndarray/ndarray.cc:33:
+ Check failed: lhs.ctx() == rhs.ctx() operands context mismatch
+ scala> val cpu_y = NDArray.zeros(100, 200)
+ scala> y.copyto(cpu_y)
+ scala> val z = x + cpu_y
+```
+
+## Next Steps
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/versions/master/_sources/api/scala/symbol.txt b/versions/master/_sources/api/scala/symbol.txt
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+# MXNet Scala Symbolic API
+
+Topics:
+
+* [How to Compose Symbols](#overloaded-operators) introduces operator overloading of symbols.
+* [Symbol Attributes](#symbol-attributes) describes how to attach attributes to symbols.
+* [Serialization](#serialization) explains how to save and load symbols.
+* [Executing Symbols](#executing-symbols) explains how to evaluate the symbols with data.
+* [Execution API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.Executor) documents the execution APIs.
+* [Multiple Outputs](#multiple-outputs) explains how to configure multiple outputs.
+* [Symbol Creation API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.Symbol) documents functions.
+
+We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures.md).
+
+## How to Compose Symbols
+
+The symbolic API provides a way to configure computation graphs.
+You can configure the graphs either at the level of neural network layer operations or as fine-grained operations.
+
+The following example configures a two-layer neural network.
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val data = Symbol.Variable("data")
+ scala> val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
+ scala> val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
+ scala> val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
+ scala> val net = Symbol.SoftmaxOutput(name = "out")()(Map("data" -> fc2))
+ scala> :type net
+ ml.dmlc.mxnet.Symbol
+```
+
+The basic arithmetic operators (plus, minus, div, multiplication) are overloaded for
+*element-wise operations* of symbols.
+
+The following example creates a computation graph that adds two inputs together.
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val a = Symbol.Variable("a")
+ scala> val b = Symbol.Variable("b")
+ scala> val c = a + b
+```
+
+## Symbol Attributes
+
+You can add an attribute to a symbol by providing an attribute dictionary when you create a symbol.
+
+```scala
+ val data = Symbol.Variable("data", Map("mood"-> "angry"))
+ val op = Symbol.Convolution()()(
+ Map("data" -> data, "kernel" -> "(1, 1)", "num_filter" -> 1, "mood"-> "so so"))
+```
+For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use `attr(key)`:
+
+```
+ data.attr("mood")
+ res6: Option[String] = Some(angry)
+```
+
+To attach attributes, you can use ```AttrScope```. ```AttrScope``` automatically adds the specified attributes to all of the symbols created within that scope. The user can also inherit this object to change naming behavior. For example:
+
+```scala
+ val (data, gdata) =
+ AttrScope(Map("group" -> "4", "data" -> "great")).withScope {
+ val data = Symbol.Variable("data", attr = Map("dtype" -> "data", "group" -> "1"))
+ val gdata = Symbol.Variable("data2")
+ (data, gdata)
+ }
+ assert(gdata.attr("group").get === "4")
+ assert(data.attr("group").get === "1")
+
+ val exceedScopeData = Symbol.Variable("data3")
+ assert(exceedScopeData.attr("group") === None, "No group attr in global attr scope")
+```
+
+## Serialization
+
+There are two ways to save and load the symbols. You can use the `mxnet.Symbol.save` and `mxnet.Symbol.load` functions to serialize the ```Symbol``` objects.
+The advantage of using `save` and `load` functions is that it is language agnostic and cloud friendly.
+The symbol is saved in JSON format. You can also get a JSON string directly using `mxnet.Symbol.toJson`.
+Refer to [API documentation](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.Symbol) for more details.
+
+The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string.
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val a = Symbol.Variable("a")
+ scala> val b = Symbol.Variable("b")
+ scala> val c = a + b
+ scala> c.save("s3://my-bucket/symbol-c.json")
+ scala> val c2 = Symbol.load("s3://my-bucket/symbol-c.json")
+ scala> c.toJson == c2.toJson
+ Boolean = true
+```
+
+## Executing Symbols
+
+After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
+If you are training a neural network, this is typically
+handled by the high-level [Model class](model.md) and the [`fit()`] function.
+
+For neural networks used in "feed-forward", "prediction", or "inference" mode (all terms for the same
+thing: running a trained network), the input arguments are the
+input data, and the weights of the neural network that were learned during training.
+
+To manually execute a set of symbols, you need to create an [`Executor`] object,
+which is typically constructed by calling the [`simpleBind(<parameters>)`] method on a symbol.
+
+## Multiple Outputs
+
+To group the symbols together, use the [mxnet.symbol.Group](#mxnet.symbol.Group) function.
+
+```scala
+ scala> import ml.dmlc.mxnet._
+ scala> val data = Symbol.Variable("data")
+ scala> val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
+ scala> val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
+ scala> val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
+ scala> val net = Symbol.SoftmaxOutput(name = "out")()(Map("data" -> fc2))
+ scala> val group = Symbol.Group(fc1, net)
+ scala> group.listOutputs()
+ IndexedSeq[String] = ArrayBuffer(fc1_output, out_output)
+```
+
+After you get the ```group```, you can bind on ```group``` instead.
+The resulting executor will have two outputs, one for fc1_output and one for softmax_output.
+
+## Next Steps
+* See [IO Data Loading API](io.md) for parsing and loading data.
+* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
+* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.
diff --git a/versions/master/_sources/api/scala/symbol_in_pictures.txt b/versions/master/_sources/api/scala/symbol_in_pictures.txt
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+# Symbolic Configuration and Execution in Pictures
+
+This topic explains symbolic construction and execution in pictures.
+We recommend that you also read [Symbolic API](symbol.md).
+
+## Compose Symbols
+
+Symbols are a description of the computation that you want to perform. The symbolic construction API generates the computation
+graph that describes the computation. The following picture shows how you compose symbols to describe basic computations.
+
+
+
+- The ```mxnet.Symbol.Variable``` function creates argument nodes that represent input to the computation.
+- The symbol is overloaded with basic element-wise mathematical operations.
+
+## Configure Neural Networks
+
+In addition to supporting fine-grained operations, MXNet provides a way to perform big operations that are analogous to layers in neural networks.
+You can use operators to describe the configuration of a neural network.
+
+
+
+
+## Example of a Multi-Input Network
+
+The following example shows how to configure multiple input neural networks.
+
+
+
+
+## Bind and Execute Symbol
+
+When you need to execute a symbol graph, you call the bind function to bind ```NDArrays``` to the argument nodes
+in order to obtain an ```Executor```.
+
+
+
+To get the output results, given the bound NDArrays as input, you can call ```Executor.Forward```.
+
+
+
+
+## Bind Multiple Outputs
+
+To group symbols, then bind them to get outputs of both, use ```mx.symbol.Group```.
+
+
+
+Remember: Bind only what you need, so that the system can perform more optimizations.
+
+
+## Calculate the Gradient
+
+In the bind function, you can specify NDArrays that will hold gradients. Calling ```Executor.backward``` after ```Executor.forward``` gives you the corresponding gradients.
+
+
+
+
+## Simple Bind Interface for Neural Networks
+
+It can be tedious to pass the argument NDArrays to the bind function, especially when you are binding a big
+graph. ```Symbol.simple_bind``` provides a way to simplify
+the procedure. You need to specify only input data shapes. The function allocates the arguments, and binds
+the Executor for you.
+
+
+
+## Auxiliary States
+
+Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states might not be part of the computation, they can be helpful for tracking. You can pass auxiliary states in the same way that you pass arguments.
+
+
+
+## Next Steps
+
+See [Symbolic API](symbol.md) and [Python Documentation](index.md).
diff --git a/versions/master/_sources/architecture/index.txt b/versions/master/_sources/architecture/index.txt
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+# MXNet Architecture
+
+Building a high-performance deep learning library
+requires many systems-level design decisions.
+In this design note, we share the rationale
+for the specific choices made when designing _MXNet_.
+We imagine that these insights may be useful
+to both deep learning practitioners
+and builders of other deep learning systems.
+
+## Deep Learning System Design Concepts
+
+The following pages address general design concepts for deep learning systems.
+Mainly, they focus on the following 3 areas:
+abstraction, optimization, and trade-offs between efficiency and flexibility.
+Additionally, we provide an overview of the complete MXNet system.
+
+* [MXNet System Overview](http://mxnet.io/architecture/overview.html)
+* [Deep Learning Programming Style: Symbolic vs Imperative](http://mxnet.io/architecture/program_model.html)
+* [Dependency Engine for Deep Learning](http://mxnet.io/architecture/note_engine.html)
+* [Optimizing the Memory Consumption in Deep Learning](http://mxnet.io/architecture/note_memory.html)
+* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
diff --git a/versions/master/_sources/architecture/note_data_loading.txt b/versions/master/_sources/architecture/note_data_loading.txt
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+# Designing Efficient Data Loaders for Deep Learning
+
+Data loading is an important component of any machine learning system.
+When we work with tiny datasets, we can get away with loading an entire dataset into GPU memory.
+With larger datasets, we must store examples in main memory.
+And when datasets grow too large to fit into main memory,
+data loading can become performance-critical.
+In designing a data loader,
+we aim to achieve more efficient data loading,
+to spend less effort on data preparation,
+and to present a clean and flexible interface.
+
+We organize this design note as follows:
+
+* **IO Design Insight:** Guiding principles in data loading design.
+* **Data Format:** Our solution using dmlc-core's binary recordIO implementation.
+* **Data Loading:** Our method to reduce IO cost by utilizing the threaded iterator provided by dmlc-core.
+* **Interface Design:** Our approach to facilitate writing MXNet data iterators in just a few lines of Python.
+* **Future Extension:** Prospective ideas for making data loading more flexible.
+
+Our analysis will motivate several requirements that an effective IO system should fulfill.
+
+***List of Key Requirements***
+- Small file size.
+- Parallel (distributed) packing of data.
+- Fast data loading and online augmentation.
+- Quick reads from arbitrary parts of the dataset in the distributed setting.
+
+## Design Insight
+To design an IO system, we must address two kinds of tasks:
+data preparation and data loading.
+Data preparation is usually performed offline,
+whereas data loading influences the online performance.
+In this section, we will introduce our insight of IO design involving the two phases.
+
+### Data Preparation
+Data preparation describes the process of packing data
+into a desired format for later processing.
+When working with large datasets like ImageNet, this process can be time-consuming.
+In these cases, there are several heuristics we ought to follow:
+
+- Pack the dataset into small numbers of files. A dataset may contain millions of data instances. Packed data distributes easily from machine to machine.
+- Do the packing once. We don't want to repack data every time run-time settings, like the number of machines, are changed.
+- Process the packing in parallel to save time.
+- Be able to access arbitrary parts of the data easily. This is crucial for distributed machine learning when data parallelism is introduced. Things may get tricky when the data has been packed into several physical data files. The desired behavior could be: the packed data can be logically separated into arbitrary numbers of partitions, no matter how many physical data files there are. For example, if we pack 1000 images into 4 physical files, then each file contains 250 images. If we t [...]
+
+### Data Loading
+The next step to consider is how to load the packed data into RAM.
+Our goal is to load the data as quickly as possible.
+There are several heuristics we try to follow:
+- **Read continuously:** We can read faster when reading from contiguous locations on disk.
+- **Reduce the bytes to be loaded:** We can achieve this by storing data in a compact way, e.g. saving images in JPEG format.
+- **Load and train in different threads:** This avoids computational bottlenecks while loading data.
+- **Save RAM:** Judiciously decide whether to load entire files into RAM.
+
+## Data Format
+
+Since the training of deep neural network often involves large amounts of data,
+the format we choose should be both efficient and convenient.
+To achieve our goals, we need to pack binary data into a splittable format.
+In MXNet, we rely on the binary recordIO format implemented in dmlc-core.
+
+### Binary Record
+
+
+In MXNet's binary RecordIO, we store each data instance as a record.
+**kMagic** is a *magic number* indicating the start of a record.
+**Lrecord** encodes length and a continue flag.
+In lrecord,
+- cflag == 0: this is a complete record
+- cflag == 1: start of a multiple-records
+- cflag == 2: middle of multiple-records
+- cflag == 3: end of multiple-records
+
+**Data** is the space to save data content.
+**Pad** is simply a padding space to make record align to 4 bytes.
+
+After we pack the data, each file contains multiple records.
+Then, loading can be continuous.
+This avoids the low performance that can result
+from reading random locations on disk.
+
+One advantage of storing data via records
+is that each record can vary in length.
+This allows us to save data compactly
+when good compression algorithms are available for our data.
+For example, we can use JPEG format to save image data.
+The packed data will be much smaller
+compared with storing uncompressed RGB values for each pixel.
+
+Take ImageNet_1K dataset as an example.
+If we store the data as 3 * 256 * 256 array of raw RGB values,
+the dataset would occupy more than **200G**.
+But after compressing the images using JPEG,
+they only occupy about **35G** of disk space.
+This significantly reduces the cost owing to reading from disk.
+
+Here's an example of binary recordIO:
+
+We first resize the image into 256 * 256,
+then compress into JPEG format.
+After that, we save a header that indicates the index and label
+for that image to be used when constructing the *Data* field for that record.
+We then pack several images together into a file.
+
+### Access Arbitrary Parts Of Data
+
+One desirable property for a data loader might be:
+The packed data can be logically sliced into an arbitrary number of partitions,
+no matter how many physical packed data files there are.
+Since binary recordIO can easily locate
+the start and end of a record using the Magic Number,
+we can achieve the above goal using the InputSplit
+functionality provided by dmlc-core.
+
+InputSplit takes the following parameters:
+- FileSystem *filesys*: dmlc-core wrapper around the IO operations for different file systems, like hdfs, s3, local. User shouldn't need to worry about the difference between file systems anymore.
+- Char *uri*: The URI of files. Note that it could be a list of files because we may pack the data into several physical parts. File URIs are separated by ';'.
+- Unsigned *nsplit*: The number of logical splits. *nsplit* could be different from the number of physical files.
+- Unsigned *rank*: Which split to load in this process.
+
+The splitting process is demonstrated below:
+- Determine the size of each partition.
+
+
+
+- Approximately partition the records according to file size. Note that the boundary of each part may be located in the middle of a record.
+
+
+
+- Set the beginning of partitions in such a way as to avoid splitting records across partitions.
+
+
+
+By conducting the above operations,
+we now identify the records belong to each part,
+and the physical data files needed by each logical part.
+InputSplit greatly simplifies data parallelism,
+where each process only reads part of the data.
+
+Since our partitioning scheme does not depend on the number of physical data files,
+we can process a huge dataset like ImageNet_22K in parallel fashion as illustrated below.
+We don't need to consider distributed loading issue at the preparation time,
+just select the most efficient physical file number
+according to the dataset size and computing resources available.
+
+
+## Data Loading and Preprocessing
+
+When the speed of loading and preprocessing can't keep up
+with the speed of training or evaluation,
+IO can bottleneck the speed of the whole system.
+In this section, we will introduce a few tricks
+to achieve greater efficiency when loading
+and preprocessing data packed in binary recordIO format.
+When applied to the ImageNet dataset, our approach achieves
+the IO speed of **3000** images/sec **with a normal HDD**.
+
+### Loading and preprocessing on the fly
+
+When training deep neural networks,
+we sometimes must load and preprocess the data
+while simultaneously training for the following reasons:
+- When the whole size of the dataset exceeds available RAM size, we can't load it in advance;
+- Sometimes, to make models robust to things like translations, rotations, and small amounts of color shift of noise, we introduce randomness into the training process. In these cases we must re-preprocess the data each time we revisit an example.
+
+In service of efficiency, we also address multi-threading techniques. Taking Imagenet training as an example, after loading a bunch of image records, we can start multiple threads to simultaneously perform image decoding and image augmentation. We depict this process in the following illustration:
+
+
+### Hide IO Cost Using Threadediter
+
+One way to lower IO cost is to pre-fetch the data for next batch on one thread,
+while the main thread performs the forward and backward passes for training.
+To support more complicated training schemes,
+MXNet provides a more general IO processing pipeline
+using *threadediter* provided by dmlc-core.
+The key of *threadediter* is to start a stand-alone thread that acts as a data provider,
+while the main thread acts as a data consumer as illustrated below.
+
+The threadediter maintains a buffer of a certain size
+and automatically fills the buffer when it's not full.
+And after the consumer finishes consuming part of the data in the buffer,
+threadediter will reuse the space to save the next part of data.
+
+
+## MXNet IO Python Interface
+We make the IO object as an iterator in numpy.
+By achieving that, the user can easily access the data
+using a for-loop or calling next() function.
+Defining a data iterator is very similar to defining a symbolic operator in MXNet.
+
+The following example code demonstrates a Cifar data iterator.
+
+```python
+dataiter = mx.io.ImageRecordIter(
+ # Dataset Parameter, indicating the data file, please check the data is already there
+ path_imgrec="data/cifar/train.rec",
+ # Dataset Parameter, indicating the image size after preprocessing
+ data_shape=(3,28,28),
+ # Batch Parameter, tells how many images in a batch
+ batch_size=100,
+ # Augmentation Parameter, when offers mean_img, each image will subtract the mean value at each pixel
+ mean_img="data/cifar/cifar10_mean.bin",
+ # Augmentation Parameter, randomly crop a patch of the data_shape from the original image
+ rand_crop=True,
+ # Augmentation Parameter, randomly mirror the image horizontally
+ rand_mirror=True,
+ # Augmentation Parameter, randomly shuffle the data
+ shuffle=False,
+ # Backend Parameter, preprocessing thread number
+ preprocess_threads=4,
+ # Backend Parameter, prefetch buffer size
+ prefetch_buffer=1)
+```
+
+Generally, to create a data iterator, you need to provide five kinds of parameters:
+
+* **Dataset Param:** Information needed to access the dataset, e.g. file path, input shape.
+* **Batch Param:** Specifies how to form a batch, e.g. batch size.
+* **Augmentation Param:** Which augmentation operations (e.g. crop, mirror) should be taken on an input image.
+* **Backend Param:** Controls the behavior of the backend threads to hide data loading cost.
+* **Auxiliary Param:** Provides options to help with debugging.
+
+Usually, **Dataset Param** and **Batch Param** MUST be given,
+otherwise the data batch can't be created.
+Other parameters can be given as needed.
+Ideally, we should separate the MX Data IO into modules,
+some of which might be useful to expose to users, for example:
+
+* **Efficient prefetcher:** allows the user to write a data loader that reads their customized binary format that automatically gets multi-threaded prefetcher support.
+* **Data transformer:** image random cropping, mirroring, etc. Allows the users to use those tools, or plug in their own customized transformers (maybe they want to add some specific kind of coherent random noise to data, etc.)
+
+## Future Extensions
+
+In the future, there are some extensions to our data IO
+that we might consider adding.
+Specifically, we might add specialized support
+for applications including image segmentation, object localization, and speech recognition.
+More detail will be provided when such applications have been running on MXNet.
diff --git a/versions/master/_sources/architecture/note_engine.txt b/versions/master/_sources/architecture/note_engine.txt
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+# Dependency Engine for Deep Learning
+
+We always want deep learning libraries
+to run faster and scale to larger datasets.
+One natural approach is to see if we can benefit
+from throwing more hardware at the problem,
+as by using multiple GPUs simultaneously.
+
+Library designers then ask:
+How can we *parallelize* computation across devices?
+And, more importantly, how can we *synchronize* computation
+when we introduce multi-threading?
+A runtime dependency engine is a generic solution to these problems.
+
+In this document, we examine approaches for using
+runtime dependency scheduling to accelerate deep learning.
+We aim to explain how runtime dependency scheduling
+can both speed up and simplify multi-device deep learning.
+We also explore potential designs for a generic dependency engine
+that could be both library- and operation-independent.
+
+Most of the discussion of on this page draws inspiration
+from the MXNet dependency engine.
+The dependency tracking algorithm we discuss
+was primarily developed by [Yutian Li](https://github.com/hotpxl)
+and [Mingjie Wang](https://github.com/jermainewang).
+
+## Dependency Scheduling
+
+Although most users want to take advantage of parallel computation,
+most of us are more familiar with serial programs.
+So one natural question is: how can we write serial programs
+and build a library to automatically parallelize our programs
+in an asynchronous way?
+
+For example, in the following code, we can run `B = A + 1`
+and `C = A + 2` in any order, or in parallel:
+
+```python
+ A = 2
+ B = A + 1
+ C = A + 2
+ D = B * C
+```
+
+However, it's quite hard to code the sequence manually
+because the last operation, `D = B * C`, needs to wait
+for both of the preceding operations to complete before it starts.
+The following dependency graph/data flow graph illustrates this.
+
+
+
+
+A dependency engine is a library that takes a sequence of operations
+and schedules them according to the dependency pattern, potentially in parallel.
+So in this example, a dependency library
+could run ```B = A + 1``` and ```C = A + 2``` in parallel,
+and run ```D = B * C``` after those operations complete.
+
+## Problems in Dependency Scheduling
+
+A dependency engine relieves the burden of writing concurrent programs.
+However, as operations become parallelized,
+new dependency tracking problems arise.
+In this section, we discuss those problems.
+
+### Data Flow Dependency
+Data flow dependency describes how the outcome of one computation
+can be used in other computations.
+Every dependency engine has to solve the data flow dependency problem.
+
+
+
+Because we discussed this issue in the preceding section,
+we include the same figure here. Libraries that have
+data flow tracking engines include Minerva and Purine2.
+
+### Memory Recycling
+When should we recycle the memory that we allocated to the arrays?
+In serial processing, this is easy to determine.
+We simply recycle the memory after the variable goes out of scope.
+However, as the following figure shows, this is a bit harder in parallel processing.
+
+
+
+In this example, because both computations need to use values from A,
+we can't recycle the memory until both complete.
+The engine must schedule the memory recycling operations according to the dependencies,
+and ensure that they are executed after both ```B = A + 1``` and ```C = A + 2``` complete.
+
+
+### Random Number Generation
+Random number generators, which are commonly used in machine learning,
+pose interesting challenges for dependency engines.
+Consider the following example:
+
+
+
+In this example, we are generating random numbers in a sequence.
+Although it seems that the two random number generations can be parallelized,
+this is usually not the case. A pseudo-random number generator (PRNG)
+is not thread-safe because it might cause some internal state
+to mutate when generating a new number.
+Even if the PRNG is thread-safe,
+it is preferable to serialize number generation,
+so we can get reproducible random numbers.
+
+## Case Study: A Dependency Engine for a Multi-GPU Neural Network
+
+In the last section, we discussed the problems
+we might face in designing a dependency engine.
+Before thinking about how to design a generic engine to solve those problems,
+let's consider how a dependency engine can help in multi-GPU training of a neural network.
+The following pseudocode Python program illustrates
+training one batch on a two-layer neural network.
+
+```python
+ # Example of one iteration Two GPU neural Net
+ data = next_batch()
+ data[gpu0].copyfrom(data[0:50])
+ data[gpu1].copyfrom(data[50:100])
+ # forward, backprop on GPU 0
+ fc1[gpu0] = FullcForward(data[gpu0], fc1_weight[gpu0])
+ fc2[gpu0] = FullcForward(fc1[gpu0], fc2_weight[gpu0])
+ fc2_ograd[gpu0] = LossGrad(fc2[gpu0], label[0:50])
+ fc1_ograd[gpu0], fc2_wgrad[gpu0] =
+ FullcBackward(fc2_ograd[gpu0] , fc2_weight[gpu0])
+ _, fc1_wgrad[gpu0] = FullcBackward(fc1_ograd[gpu0] , fc1_weight[gpu0])
+ # forward, backprop on GPU 1
+ fc1[gpu1] = FullcForward(data[gpu1], fc1_weight[gpu1])
+ fc2[gpu1] = FullcForward(fc1[gpu1], fc2_weight[gpu1])
+ fc2_ograd[gpu1] = LossGrad(fc2[gpu1], label[50:100])
+ fc1_ograd[gpu1], fc2_wgrad[gpu1] =
+ FullcBackward(fc2_ograd[gpu1] , fc2_weight[gpu1])
+ _, fc1_wgrad[gpu1] = FullcBackward(fc1_ograd[gpu1] , fc1_weight[gpu1])
+ # aggregate gradient and update
+ fc1_wgrad[cpu] = fc1_wgrad[gpu0] + fc1_wgrad[gpu1]
+ fc2_wgrad[cpu] = fc2_wgrad[gpu0] + fc2_wgrad[gpu1]
+ fc1_weight[cpu] -= lr * fc1_wgrad[cpu]
+ fc2_weight[cpu] -= lr * fc2_wgrad[cpu]
+ fc1_weight[cpu].copyto(fc1_weight[gpu0] , fc1_weight[gpu1])
+ fc2_weight[cpu].copyto(fc2_weight[gpu0] , fc2_weight[gpu1])
+```
+In this program, the data 0 to 50 is copied to GPU 0,
+and the data 50 to 100 is copied to GPU 1.
+The calculated gradients are aggregated in the CPU,
+which then performs a simple SGD update,
+and copies the updated weight back to each GPU.
+This is a common way to write a parallel program in a serial manner.
+The following dependency graph shows how it can be parallelized:
+
+
+
+***Notes:***
+
+- The gradient can be copied to the CPU as soon as we get the gradient of a layer.
+- The weight can be copied back soon as the weight is updated.
+- In the forward pass, we have a dependency on ```fc1_weight[cpu].copyto(fc1_weight[gpu0] , fc1_weight[gpu1])```
+ from the previous iteration.
+- There is a delay in computation between the last backward pass to layer k and the next forward call to layer k. We can synchronize the weight of layer k *in parallel* with other computation during this delay.
+
+This approach to optimization is used by multi-GPU deep learning libraries, such as CXXNet.
+The point is to overlap weight synchronization (communication) with computation.
+However, it's not easy to do that, because the copy operation needs to be triggered
+as soon as the backward pass of the layer completes,
+which then triggers the reduction, updates, etc.
+
+A dependency engine can schedule these operations and perform multi-threading
+and dependency tracking.
+
+## Designing a Generic Dependency Engine
+
+We hope that you're convinced that a dependency engine is useful
+for scaling deep learning programs to multiple devices.
+Now let's discuss how we can design and implement
+a generic interface for a dependency engine.
+This solution isn't the only possible design for a dependency engine.
+It's an example that we think is useful in most cases.
+
+Our goal is to create a dependency engine that is *generic* and *lightweight*.
+Ideally, we'd like the engine that easily plugs into existing deep learning code,
+and that can scale up to multiple machines with minor modifications.
+To do that, we need to focus only on dependency tracking,
+not on assumptions about what users can or can't do.
+
+Here's a summary of goals for the engine:
+
+- The engine should not be aware of what operations it performs, so that users can perform any operations they define.
+- It should not be restricted in what type of objects it can schedule.
+ - We should be able to schedule dependencies on GPU and CPU memory.
+ - We should be able to track dependencies on the random number generator, etc.
+- The engine should not allocate resources. It should only track dependencies. Users can allocate their own memory, PRNG, etc.
+
+The following Python snippet provides an engine interface that might help us reach our goal. Note that a real implementation will be closer to the metal, typically in C++.
+
+```python
+ class DepEngine(object):
+ def new_variable():
+ """Return a new variable tag
+ Returns
+ -------
+ vtag : Variable Tag
+ The token of the engine to represent dependencies.
+ """
+ pass
+
+ def push(exec_func, read_vars, mutate_vars):
+ """Push the operation to the engine.
+
+ Parameters
+ ----------
+ exec_func : callable
+ The real operation to be performed.
+
+ read_vars : list of Variable Tags
+ The list of variables this operation will read from.
+
+ mutate_vars : list of Variable Tags
+ The list of variables this operation will mutate.
+ """
+ pass
+```
+
+Because we can't make assumptions about what objects we are scheduling, we ask the user to allocate a
+_virtual tag_ that is associated with each object to represent what we need to schedule.
+So, at the beginning, the user can allocate the variable tag,
+and attach it to each of the objects that we want to schedule.
+
+
+
+The user then calls `push` to tell the engine about the function to execute.
+The user also needs to specify the dependencies of the operation,
+using `read_vars` and `write_vars`:
+
+- `read_vars` are variable tags for objects that the operation will _read from_, without changing their internal state.
+- `mutate_vars` are variable tags for objects whose internal states the operation will mutate.
+
+
+
+The preceding figure shows how to push operation `B = A + 1` to the dependency engine. `B.data` and
+`A.data` are the allocated space. Note that the engine is *only aware of variable tags*.
+Any execution function can be processed.
+This interface is generic for the operations and resources we want to schedule.
+
+For fun, let's look at how the engine internals work with the tags by considering the following code snippet:
+
+```
+ B = A + 1
+ C = A + 2
+ A = C * 2
+ D = A + 3
+```
+
+The first line reads variable `A` and mutates variable `B`. The second line reads variable `A` and mutates variable `C`. And so on.
+
+The engine maintains a queue for each variable, as the following animation shows for each of the four lines. Green blocks represents a read action, while red blocks represent mutations.
+
+
+
+Upon building this queue, the engine sees that the first two green blocks at the beginning of `A`'s queue could actually be run in parallel because they are both read actions and won't conflict with each other. The following graph illustrates this point.
+
+
+
+One cool thing about all this scheduling is that it's not confined to numerical calculations.
+Because everything that is scheduled is only a tag, the engine could schedule everything!
+
+The following figure gives a complete push sequence of the programs we mentioned in previous sections.
+
+
+
+### Porting Existing Code to the Dependency Engine
+Because the generic interface doesn't control things like memory allocation and which operation to execute,
+most existing code can be scheduled by the dependency engine in two steps:
+
+
+1. Allocate the variable tags associated with resources like memory blob, PRNGS.
+ - Call `push` with the execution function as the original code to execute, and put the variable tags of
+ corresponding resources correctly in `read_vars` and `mutate_vars`.
+
+## Implementing the Generic Dependency Engine
+
+We have described the generic engine interface and
+how it can be used to schedule various operations.
+In this section, we provide a high-level discussion
+of how to implement such an engine.
+
+The general idea is as follows:
+
+- Use a queue to track all of the pending dependencies on each variable tag.
+- Use a counter on each operation to track how many dependencies are yet to be fulfilled.
+- When operations are completed, update the state of the queue and dependency counters to schedule new operations.
+
+The following figure illustrates the scheduling algorithm
+and might give you a better sense of what is going on in the engine.
+
+
+
+Below, we show another example involving random number generators.
+
+
+
+As you can see, the purpose of the algorithm is to update pending queues
+of operations and to make the right state transition when an operation has completed.
+More care should be taken to make sure the state transitions
+are done in a way that's safe for threads.
+
+### Separate Dependency Tracking with Running Policy
+If you're reading carefully, you might have noticed
+that the preceding section shows only the algorithm
+for deciding when an operation can be executed.
+We didn't show how to actually run an operation.
+In practice, there can be many different policies.
+For example, we can either use a global thread-pool to run all operations,
+or use a specific thread to run operations on each device.
+
+This running policy is usually independent of dependency tracking,
+and can be separated out as either an independent module
+or a virtual interface of base-dependency tracking modules.
+Developing an elegant runtime policy that is fair
+to all operations and schedules is an interesting systems problem itself.
+
+## Discussion
+
+The design that we discussed in this article
+isn't the only solution to the dependency tracking problem.
+It's just one example of how we might approach this.
+To be sure, some of these design choices are debatable.
+We'll discuss some of them in this section.
+
+### Dynamic vs. Static
+The dependency engine interface discussed in this topic is somewhat dynamic
+in the sense that the user can push operations one by one,
+instead of declaring the entire dependency graph (static).
+Dynamic scheduling can require more overhead
+than static declarations, in terms of data structure.
+However, it also enables more flexibility, such as supporting auto parallelism
+for imperative programs or a mixture of imperative and symbolic programs.
+You can also add some level of predeclared operations
+to the interface to enable data structure reuse.
+
+### Mutation vs. Immutable
+The generic engine interface presented in this page
+supports explicit scheduling for mutation.
+In a typical data flow engine, the data are usually immutable.
+Working with immutable data has a lot of benefits.
+For example, immutable data is generally more suitable for parallelization,
+and facilitates better fault tolerance in a distributed setting (by way of re-computation).
+
+However, immutability presents several challenges:
+
+- It's harder to schedule resource contention problems, as arise when dealing with random numbers and deletion.
+- The engine usually needs to manage resources (memory, random number) to avoid conflicts. It's harder to plug in user-allocated space, etc.
+- Preallocated static memory isn't available, again because the usual pattern is to write to a preallocated layer space, which is not supported if data is immutable.
+
+Allowing mutation mitigates these issues.
+
+
+## Source Code of the Generic Dependency Engine
+[MXNet](https://github.com/dmlc/mxnet) provides an implementation
+of the generic dependency engine described in this page.
+You can find more details in [this topic](http://mxnet.io/architecture/note_engine.html).
+We welcome your contributions.
+
+## Next Steps
+
+* [Squeeze the Memory Consumption of Deep Learning](http://mxnet.io/architecture/note_memory.html)
+* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
+* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/versions/master/_sources/architecture/note_memory.txt b/versions/master/_sources/architecture/note_memory.txt
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+# Optimizing Memory Consumption in Deep Learning
+
+Over the last ten years, a constant trend in deep learning
+is towards deeper and larger networks.
+Despite rapid advances in hardware performance,
+cutting-edge deep learning models continue to push the limits of GPU RAM.
+So even today, it's always desirable to find ways
+to train larger models while consuming less memory.
+Doing so enables us to train faster, using larger batch sizes,
+and consequently achieving a higher GPU utilization rate.
+
+In this document, we explore techniques for optimizing
+memory allocation for deep neural networks.
+We discuss a few candidate solutions.
+While our proposals are by no means exhaustive,
+these solutions are instructive and allow us to
+introduce the major design issues at play.
+
+## Computation Graph
+
+First, let's revisit the idea of the computation graph.
+A computation graph describes the (data flow) dependencies
+between the operations in the deep network.
+The operations performed in the graph
+can be either fine-grained or coarse-grained.
+The following figure shows two examples of computation graphs.
+
+
+
+The concept of a computation graph is explicitly encoded in packages like Theano and CGT.
+In other libraries, computation graphs appear implicitly as network configuration files.
+The major difference in these libraries comes down to how they calculate gradients.
+There are mainly two ways: performing back-propagation on the _same_ graph
+or explicitly representing a _backwards path_ to calculate the required gradients.
+
+
+
+Libraries like Caffe, CXXNet, and Torch take the former approach,
+performing back-prop on the original graph.
+Libraries like Theano and CGT take the latter approach,
+explicitly representing the backward path.
+In this discussion, we adopt the *explicit backward path* approach
+because it has several advantages for optimization.
+
+However, we should emphasize that choosing the explicit backward path approach doesn't restrict us
+to symbolic libraries, such as Theano and CGT. We can also use the explicit backward path for gradient calculation of
+layer-based (which ties forward and backward together) libraries. The following graph shows how to do this.
+Basically, we introduce a backward node that links to the forward node of the graph and calls the ```layer.backward```
+in the backward operations.
+
+
+
+This discussion applies to almost all existing deep learning libraries.
+(There are differences between libraries, e.g., higher-order differentiation, which is beyond the scope of this topic.)
+
+Why is the explicit backward path better? Let's explain it with two examples.
+The first reason is that the explicit backward path
+clearly describes the dependency between computations.
+Consider the following case, where we want to get
+the gradient of A and B. As we can see clearly from the graph,
+the computation of the ```d(C)``` gradient doesn't depend on F.
+This means that we can free the memory of ```F```
+right after the forward computation is done.
+Similarly, the memory of ```C``` can be recycled.
+
+
+
+Another advantage of the explicit backward path
+is the ability to have a different backward path,
+instead of a mirror of forward one.
+A common example is the split connection case,
+as shown in the following figure.
+
+
+
+In this example, the output of B is referenced by two operations.
+If we want to do the gradient calculation in the same
+network, we need to introduce an explicit split layer.
+This means we need to do the split for the forward pass, too.
+In this figure, the forward pass doesn't contain a split layer,
+but the graph will automatically insert a gradient
+aggregation node before passing the gradient back to B.
+This helps us to save the memory cost of allocating the output of the split layer,
+and the operation cost of replicating the data in the forward pass.
+
+If we adopt the explicit backward approach,
+there's no difference between the forward pass and the backward pass.
+We simply step through the computation graph in chronological order
+and carry out computations.
+This makes the explicit backward approach easy to analyze.
+We just need to answer the question:
+how do we allocate memory for each output node of a computation graph?
+
+
+## What Can Be Optimized?
+
+As you can see, the computation graph is a useful way
+to discuss memory allocation optimization techniques.
+Already, we've shown how you can save some memory
+by using the explicit backward graph.
+Now let's explore further optimizations,
+and see how we might determine reasonable baselines for benchmarking.
+
+Assume that we want to build a neural network with `n` layers.
+Typically, when implementing a neural network,
+we need to allocate node space for both the output of each layer
+and the gradient values used during back-propagation.
+This means we need roughly `2 n` memory cells.
+We face the same requirement when using the explicit backward graph approach
+because the number of nodes in a backward pass
+is roughly the same as in a forward pass.
+
+### In-place Operations
+One of the simplest techniques we can employ
+is _in-place memory sharing_ across operations.
+For neural networks, we can usually apply this technique
+for the operations corresponding to activation functions.
+Consider the following case, where we want
+to compute the value of three chained sigmoid functions.
+
+
+
+Because we can compute sigmoid ```in-place```,
+using the same memory for input and output,
+we can compute an arbitrary-length chain
+of sigmoid functions using constant memory.
+
+Note: it's easy to make mistakes when implementing in-place optimization.
+Consider the following case, where the value of B is used not only by C, but also by F.
+
+
+
+We can't perform in-place optimization because the value of B
+is still needed after ```C=sigmoid(B)``` is computed.
+An algorithm that simply does in-place optimization
+for every sigmoid operation might fall into such trap,
+so we need to be careful about when we can use it.
+
+### Standard Memory Sharing
+In-place operations are not the only places where we can share memory.
+In the following example, because the value of B is no longer needed
+after we compute E, we can reuse B's memory to hold the result of E.
+
+
+
+*Memory sharing doesn't necessarily require the same data shape*.
+Note that in the preceding example, the shapes of `B` and `E` can differ.
+To handle such a situation, we can allocate a memory region
+of size equal to the maximum of that required by `B` and `E` and share it between them.
+
+### Example of Real Neural Network Allocation
+Of course, these are only toy examples and they address only the computation of the forward pass.
+But the same ideas apply to real neural networks.
+The following figure shows an allocation plan for a two-layer perceptron.
+
+
+
+In this example:
+
+- In-place optimization is applied when computing ```act1```, ```d(fc1)```, ```out``` and ```d(fc2)```.
+- Memory is shared between ```d(act1)``` and ```d(A)```.
+
+## Memory Allocation Algorithm
+
+So far, we've discussed general techniques for optimizing memory allocation.
+We've seen that there are traps to avoid,
+as demonstrated in the case of in-place memory optimization.
+So, how can we allocate memory correctly?
+This is not a new problem.
+For example, it is very similar
+to the problem with register allocation in compilers.
+There might be techniques that we can borrow.
+We're not attempting to give a comprehensive review of techniques here,
+but rather to introduce some simple
+but useful tricks to attack the problem.
+
+The key problem is that we need to place resources
+so that they don't conflict with each other.
+More specifically, each variable has a *life time*
+between the time it gets computed until the last time it is used.
+In the case of the multi-layer perceptron,
+the *life time* of ```fc1``` ends after ```act1``` get computed.
+
+
+
+The principle is *to allow memory sharing only between variables whose lifetimes don't overlap*.
+There are multiple ways to do this.
+You can construct the conflicting graph
+with each variable as a node and link the edge
+between variables with overlapping lifespans,
+and then run a graph-coloring algorithm.
+This likely has ```$O(n^2)$``` complexity,
+where ```n``` is the number of nodes in the graph.
+This might be too costly.
+
+Let's consider another simple heuristic.
+The idea is to simulate the procedure of traversing the graph,
+and keep a count of future operations that depends on the node.
+
+
+
+- An in-place optimization can be performed when only the current operation depends on the source (i.e., ```count==1```).
+- Memory can be recycled into the box on the upper right corner when the ```count``` goes to 0.
+- When we need new memory, we can either get it from the box or allocate a new one.
+
+***Note:*** During the simulation, no memory is allocated.
+Instead, we keep a record of how much memory each node needs,
+and allocate the maximum of the shared parts in the final memory plan.
+
+## Static vs. Dynamic Allocation
+
+The preceding strategy exactly simulates
+the dynamic memory allocation procedure
+in imperative languages, such as Python.
+The ```count``` is the reference counter for each memory object,
+and the object gets garbage collected
+when the reference counter goes to 0.
+In that sense,
+we are simulating dynamic memory allocation once
+to create a static allocation plan.
+Can we simply use an imperative language
+that dynamically allocates and deallocates memory?
+
+The major difference is that static allocation is only done once,
+so we can afford to use more complicated algorithms.
+For example, we can search for memory sizes
+that are similar to the required memory block.
+The Allocation can also be made graph aware.
+We'll talk about that in the next section.
+Dynamic allocation puts more pressure
+on fast memory allocation and garbage collection.
+
+There is also one takeaway for users
+who want to rely on dynamic memory allocations:
+*do not unnecessarily reference objects*.
+For example, if we organize all of the nodes in a list
+and store then in a Net object,
+these nodes will never get dereferenced, and we gain no space.
+Unfortunately, this is a common way to organize code.
+
+
+## Memory Allocation for Parallel Operations
+
+In the previous section, we discussed
+how we can *simulate* running the procedure
+for a computation graph to get a static allocation plan.
+However, optimizing for parallel computation presents other challenges
+because resource sharing and parallelization are on the two ends of a balance.
+Let's look at the following two allocation plans for the same graph:
+
+
+
+Both allocation plans are valid
+if we run the computation serially,
+from ```A[1]``` to ```A[8]```.
+However, the allocation plan on the left
+introduces additional dependencies,
+which means we can't run computation of ```A[2]``` and ```A[5]``` in parallel.
+The plan on the right can.
+To parallelize computation, we need to take greater care.
+
+### Be Correct and Safe First
+Being correct is our first principle.
+This means to execute in a way that takes implicit dependency
+memory sharing into consideration.
+You can do this by adding the implicit dependency edge to the execution graph.
+Or, even simpler, if the execution engine is mutation aware,
+as described in [our discussion of dependency engine design](http://mxnet.io/architecture/note_engine.html),
+push the operation in sequence
+and write to the same variable tag
+that represents the same memory region.
+
+Always produce a safe memory allocation plan.
+This means never allocate the same memory
+to nodes that can be parallelized.
+This might not be ideal when memory reduction is more desirable,
+and we don't gain too much when we can get benefit
+from multiple computing streams simultaneously executing on the same GPU.
+
+### Try to Allow More Parallelization
+Now we can safely perform some optimizations.
+The general idea is to try and encourage memory sharing between nodes that can't be parallelized.
+You can do this by creating an ancestor relationship
+graph and querying it during allocation,
+which costs approximately ```$O(n^2)$``` in time to construct.
+We can also use a heuristic here,
+for example, color the path in the graph.
+As shown in the following figure,
+when you try to find the longest paths in the graph,
+color them the same color and continue.
+
+
+
+After you get the color of the node,
+you allow sharing (or encourage sharing)
+only between nodes of the same color.
+This is a stricter version of the ancestor relationship,
+but it costs only `$O(n)$` of time
+if you search for only the first `k` path.
+
+This is by no means the only solution.
+More sophisticated approaches might exist:
+
+## How Much Can you Save?
+
+We've discussed the techniques and algorithms you can use
+to squeeze memory usage for deep learning.
+How much can you really save by using these techniques?
+
+On coarse-grained operation graphs
+that are already optimized for big operations,
+you can reduce memory consumption roughly *by half*.
+You can reduce memory usage even more
+if you are optimizing a fine-grained computation network
+used by symbolic libraries, such as Theano.
+
+Most of the ideas in this article inspired the design of _MXNet_.
+We've also provided a [Memory Cost Estimation Script](https://github.com/dmlc/mxnet/tree/master/example/memcost),
+which you can use to see how much memory you need under different scenarios.
+
+The script has an option called `forward_only`,
+which shows the cost of running only the forward pass.
+You will find that cost when using this option
+is extremely low compared to others.
+This is simply because there's more memory reuse
+if you run only the forward pass.
+
+So here are two takeaways:
+
+- Use a computation graph to allocate memory.
+- For deep learning models, prediction consumes much less memory than training.
+
+
+## Next Steps
+
+* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
+* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/versions/master/_sources/architecture/overview.txt b/versions/master/_sources/architecture/overview.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/architecture/overview.txt
@@ -0,0 +1,857 @@
+# MXNet System Architecture
+
+
+
+This figure shows the major modules and components of the MXNet system and their interaction. The modules are:
+
+- Runtime Dependency Engine: Schedules and executes the
+ operations according to their read/write dependency.
+- Storage Allocator: Efficiently allocates and recycles memory blocks
+ on host (CPU) and devices (GPUs).
+- Resource Manager: Manages global resources, such as the random number generator
+ and temporal space.
+- NDArray: Dynamic, asynchronous n-dimensional arrays,
+ which provide flexible imperative programs for MXNet.
+- Symbolic Execution: Static symbolic graph executor,
+ which provides efficient symbolic graph execution and optimization.
+- Operator: Operators that define static forward and gradient
+ calculation (backprop).
+- SimpleOp: Operators that extend NDArray operators and symbolic operators
+ in a unified fashion.
+- Symbol Construction: Symbolic construction, which provides a way to construct
+ a computation graph (net configuration).
+- KVStore: Key-value store interface for efficient parameter synchronization.
+- Data Loading(IO): Efficient distributed data loading and augmentation.
+
+# MXNet System Components
+
+## Execution Engine
+
+You can use MXNet's engine not only for deep learning,
+but for any domain-specific problem.
+It's designed to solve a general problem:
+execute a bunch of functions following their dependencies.
+Execution of any two functions with dependencies should be serialized.
+To boost performance, functions with no dependencies *can* be executed in parallel.
+For a general discussion of this topic,
+see our [notes on the dependency engine](note_engine.md).
+
+### Interface
+
+The following API is the core interface for the execution engine:
+
+```c++
+ virtual void PushSync(Fn exec_fun, Context exec_ctx,
+ std::vector<VarHandle> const& const_vars,
+ std::vector<VarHandle> const& mutate_vars) = 0;
+```
+This API allows you to push a function (`exec_fun`),
+along with its context information and dependencies, to the engine.
+`exec_ctx` is the context information in which the `exec_fun` should be executed,
+`const_vars` denotes the variables that the function reads from,
+and `mutate_vars` are the variables to be modified.
+The engine provides the following guarantee:
+
+>*The execution of any two functions
+that modify a common variable
+is serialized in their push order.*
+
+### Function
+
+The function type of the engine is:
+
+```c++
+ using Fn = std::function<void(RunContext)>;
+```
+`RunContext` contains runtime information, which is determined by the engine:
+
+```c++
+ struct RunContext {
+ // stream pointer which could be safely cast to
+ // cudaStream_t* type
+ void *stream;
+ };
+```
+Alternatively, you could use `mxnet::engine::DAGEngine::Fn`, which has the same type definition.
+
+All of the functions are executed by the engine's internal threads.
+In such a model, it's usually not a good idea to push *blocking* functions
+to the engine (usually for dealing with I/O tasks like disk, web service, UI, etc.)
+because it will occupy the execution thread and reduce total throughput.
+In that case, we provide another *asynchronous* function type:
+
+```c++
+ using Callback = std::function<void()>;
+ using AsyncFn = std::function<void(RunContext, Callback)>;
+```
+In the `AsyncFn` function, you can pass the heavy part to your own threads
+and safely exit the body of the function.
+The engine doesn't consider the function finished
+until the `Callback` function is called.
+
+### Context
+
+You can specify the `Context` of the function to be executed within.
+This usually includes whether the function should be run on a CPU or a GPU,
+and if you specify a GPU, which GPU to use.
+`Context` is different from `RunContext`.
+`Context` contains device type (GPU/CPU) and device id,
+ while `RunContext` contains information that can be decided only during runtime,
+ for example, on which stream the function should be executed.
+
+### VarHandle
+
+`VarHandle` is used to specify the dependencies of functions.
+The MXNet engine is designed to be decoupled from other MXNet modules.
+So `VarHandle` is like an engine-provided token you use
+to represent the external resources the functions can use or modify.
+It's designed to be lightweight, so creating,
+deleting, or copying a variable incurs little overhead.
+Upon pushing the functions, you need to specify the variables
+that will be used (immutable) in the `const_vars` vector,
+and the variables that will be modified (mutable) in the `mutate_vars` vector.
+The engine uses one rule for resolving the dependencies among functions:
+
+>*The execution of any two functions when one of them modifies at least one common variable is serialized in their push order.*
+
+For example, if `Fn1` and `Fn2` both mutate `V2` then `Fn2`
+is guaranteed to be executed after `Fn1`
+if `Fn2` is pushed after `Fn1`.
+On the other hand, if `Fn1` and `Fn2` both use `V2`,
+their actual execution order could be random.
+
+This design allows the engine to schedule *state-mutating* operations in a manner
+that minimizes calls to allocate new memory.
+For example, the weight update function in DNN
+can now use the `+=` operator
+to update the weights in place,
+rather than generating a new weight array each time.
+
+To create a variable, use the `NewVar()` API.
+To delete a variable, use the `PushDelete` API.
+
+### Push and Wait
+
+*All `Push` APIs are asynchronous.* The API call returns immediately
+regardless of whether the pushed `Fn` is finished or not.
+This allows the engine to start computing at the same time
+as the user thread is pushing functions.
+`Push` APIs are not thread-safe.
+To be specific, only one thread should make engine API calls at a time.
+
+If you want to wait for a specific `Fn` to finish,
+include a callback function in the closure,
+and call the function at the end of your `Fn`.
+
+If you want to wait for all `Fn`s
+that involve (use or mutate) a certain variable to finish,
+use the `WaitForVar(var)` API.
+
+If you want to wait for all pushed `Fn`s to finish,
+use the `WaitForAll()` API.
+
+### Save Object Creation Cost
+
+In some cases, you need to push several functions to the engine for a long period of time.
+If the computation of these functions is light,
+the overhead of copying lambdas and creating use/mutate variable lists becomes relatively high.
+We provide an API to create an `OprHandle` beforehand:
+
+```c++
+ virtual OprHandle NewOperator(AsyncFn fn,
+ std::vector<VarHandle> const& const_vars,
+ std::vector<VarHandle> const& mutate_vars) = 0;
+```
+You can keep pushing the `OprHandle` without repeatedly creating them:
+
+```c++
+ virtual void Push(OprHandle op, Context exec_ctx) = 0;
+```
+To delete it, call the `DeleteOperator(OprHandle op)` API.
+Ensure that the operator has finished computing before calling this API.
+
+
+### API Reference
+
+```eval_rst
+ .. doxygenclass:: mxnet::Engine
+ :members:
+```
+
+## Operators in MXNet
+
+In MXNet, an operator is a class that contains both actual computation logic
+and auxiliary information that can aid the system in performing optimizations,
+like in-place updates and auto-derivatives.
+To understand the remainder of the document,
+we recommend that you familiarize yourself with the `mshadow` library,
+because all operators compute on the tensor-like structure `mshadow::TBlob`
+provided by the system during runtime.
+
+MXNet's operator interface allows you to:
+
+* Reduce memory allocation cost by specifying in-place updates.
+* Hide some internal arguments from Python to make it cleaner.
+* Define the relationships among input tensors and output tensors,
+which allows the system to perform shape checking for you.
+* Acquire additional temporary spaces from the system
+to perform computation (e.g., calling `cudnn` routines).
+
+### Operator Interface
+
+`Forward` is the core operator interface:
+
+```c++
+ virtual void Forward(const OpContext &ctx,
+ const std::vector<TBlob> &in_data,
+ const std::vector<OpReqType> &req,
+ const std::vector<TBlob> &out_data,
+ const std::vector<TBlob> &aux_states) = 0;
+```
+The `OpContext` structure is:
+
+```c++
+ struct OpContext {
+ int is_train;
+ RunContext run_ctx;
+ std::vector<Resource> requested;
+ }
+```
+It describes whether the operator is in the train or test phase,
+which device the operator should be run on (in `run_ctx`),
+and requested resources (covered in the following sections).
+
+- `in_data` and `out_data` represent the input and output tensors, respectively.
+All of the tensor spaces have been allocated by the system.
+- `req` denotes how the computation results are written into the `out_data`.
+In other words, `req.size() == out_data.size()` and `req[i]`
+correspond to the write type of `out_data[i]`.
+
+- The `OpReqType` is defined as:
+
+```c++
+ enum OpReqType {
+ kNullOp,
+ kWriteTo,
+ kWriteInplace,
+ kAddTo
+ };
+```
+ Normally, the types of all `out_data` should be `kWriteTo`,
+ meaning that the provided `out_data` tensor is a *raw* memory block,
+ so the operator should write results directly into it.
+ In some cases, for example when calculating the `gradient` tensor,
+ it would be great if we could accumulate the result,
+ rather than directly overwrite the tensor contents
+ so that no extra space needs to be created each time.
+ In such a case, the corresponding `req` type is set as `kAddTo`,
+ indicating that a `+=` should be called.
+
+- `aux_states` is intentionally designed for auxiliary tensors used to help computation. Currently, it is useless.
+
+Aside from the `Forward` operator, you could optionally implement the `Backward` interface:
+
+```c++
+ virtual void Backward(const OpContext &ctx,
+ const std::vector<TBlob> &out_grad,
+ const std::vector<TBlob> &in_data,
+ const std::vector<TBlob> &out_data,
+ const std::vector<OpReqType> &req,
+ const std::vector<TBlob> &in_grad,
+ const std::vector<TBlob> &aux_states);
+```
+This interface follows the same design principle as the `Forward` interface,
+except that `out_grad`, `in_data`, and `out_data` are given,
+and the operator computes `in_grad` as the results.
+ The naming strategy is similar to Torch's convention,
+ and can be summarized in following figure:
+
+[input/output semantics figure]
+
+Some operators might not require all of the following:
+`out_grad`, `in_data` and `out_data`.
+You can specify these dependencies with the `DeclareBackwardDependency` interface in `OperatorProperty`.
+
+### Operator Property
+
+One convolution might have several implementations,
+and you might want to switch among them to achieve the best performance.
+Therefore, we separate the operator *semantic* interfaces
+from the implementation interface (`Operator` class)
+into the `OperatorProperty` class.
+The `OperatorProperty` interface consists of:
+
+* **InferShape:**
+
+```c++
+ virtual bool InferShape(std::vector<TShape> *in_shape,
+ std::vector<TShape> *out_shape,
+ std::vector<TShape> *aux_shape) const = 0;
+```
+
+This interface has two purposes:
+* Tell the system the size of each input and output tensor,
+ so it can allocate space for them before the `Forward` and `Backward` call.
+* Perform a size check to make sure that there isn't an obvious error before running.
+ The shape in `in_shape` is set by the system
+ (from the `out_shape` of the previous operators).
+ It returns `false` when there is not enough information
+ to infer shapes or throws an error when the shape is inconsistent.
+
+* **Request Resources:** Operations like `cudnnConvolutionForward` need a work space for computation.
+If the system can manage that, it could then perform optimizations,
+like reuse the space, and so on.
+MXNet defines two interfaces to achieve this:
+
+```c++
+ virtual std::vector<ResourceRequest> ForwardResource(
+ const std::vector<TShape> &in_shape) const;
+ virtual std::vector<ResourceRequest> BackwardResource(
+ const std::vector<TShape> &in_shape) const;
+```
+ The `ResourceRequest` structure (in `resource.h`) currently contains only a type flag:
+
+```c++
+ struct ResourceRequest {
+ enum Type {
+ kRandom, // get a mshadow::Random<xpu> object
+ kTempSpace, // request temporary space
+ };
+ Type type;
+ };
+```
+ If `ForwardResource` and `BackwardResource` return non-empty arrays,
+ the system offers the corresponding resources through the `ctx` parameter
+ in the `Forward` and `Backward` interface of `Operator`.
+ Basically, to access those resources, simply write:
+
+```c++
+ auto tmp_space_res = ctx.requested[kTempSpace].get_space(some_shape, some_stream);
+ auto rand_res = ctx.requested[kRandom].get_random(some_stream);
+```
+ For an example, see `src/operator/cudnn_convolution-inl.h`.
+
+* **Backward dependency:** Let's look at two different operator signatures
+(we name all of the arguments for demonstration purposes):
+
+```c++
+ void FullyConnectedForward(TBlob weight, TBlob in_data, TBlob out_data);
+ void FullyConnectedBackward(TBlob weight, TBlob in_data, TBlob out_grad, TBlob in_grad);
+
+ void PoolingForward(TBlob in_data, TBlob out_data);
+ void PoolingBackward(TBlob in_data, TBlob out_data, TBlob out_grad, TBlob in_grad);
+```
+ Note that `out_data` in `FullyConnectedForward`
+ is not used by `FullyConnectedBackward`,
+ while `PoolingBackward` requires all of the arguments of `PoolingForward`.
+ Therefore, for `FullyConnectedForward`,
+ the `out_data` tensor once consumed could be safely freed
+ because the backward function will not need it.
+ This provides a chance for the system to collect some tensors
+ as garbage as soon as possible.
+ To specify this situation, we provide an interface:
+
+```c++
+ virtual std::vector<int> DeclareBackwardDependency(
+ const std::vector<int> &out_grad,
+ const std::vector<int> &in_data,
+ const std::vector<int> &out_data) const;
+```
+ The `int` element of the argument vector is an ID
+ to distinguish different arrays.
+ Let's see how this interface specifies different dependencies
+ for `FullyConnected` and `Pooling`:
+
+ ```c++
+ std::vector<int> FullyConnectedProperty::DeclareBackwardDependency(
+ const std::vector<int> &out_grad,
+ const std::vector<int> &in_data,
+ const std::vector<int> &out_data) const {
+ return {out_grad[0], in_data[0]}; // NOTE: out_data[0] is NOT included
+ }
+ std::vector<int> PoolingProperty::DeclareBackwardDependency(
+ const std::vector<int> &out_grad,
+ const std::vector<int> &in_data,
+ const std::vector<int> &out_data) const {
+ return {out_grad[0], in_data[0], out_data[0]};
+ }
+```
+
+* **In place Option:** To further save the cost of memory allocation,
+you can use in-place updates.
+They are appropriate for element-wise operations
+when the input tensor and output tensor have the same shape.
+You specify and in-place update with the following interface:
+
+```c++
+ virtual std::vector<std::pair<int, void*>> ElewiseOpProperty::ForwardInplaceOption(
+ const std::vector<int> &in_data,
+ const std::vector<void*> &out_data) const {
+ return { {in_data[0], out_data[0]} };
+ }
+ virtual std::vector<std::pair<int, void*>> ElewiseOpProperty::BackwardInplaceOption(
+ const std::vector<int> &out_grad,
+ const std::vector<int> &in_data,
+ const std::vector<int> &out_data,
+ const std::vector<void*> &in_grad) const {
+ return { {out_grad[0], in_grad[0]} }
+ }
+```
+ This tells the system that the `in_data[0]` and `out_data[0]` tensors could share the same memory spaces during `Forward`, and so do `out_grad[0]` and `in_grad[0]` during `Backward`.
+
+ >**Important:** Even if you use the preceding specification, it's *not* guaranteed that the input and output tensors will share the same space. In fact, this is only a suggestion for the system, which makes the final decision. However, in either case, the decision is completely transparent to you, so the actual `Forward` and `Backward` implementation does not need to consider that.
+
+* **Expose Operator to Python:** Because of the restrictions of C++, you need user to implement following interfaces:
+
+```c++
+ // initial the property class from a list of key-value string pairs
+ virtual void Init(const vector<pair<string, string>> &kwargs) = 0;
+ // return the parameters in a key-value string map
+ virtual map<string, string> GetParams() const = 0;
+ // return the name of arguments (for generating signature in python)
+ virtual vector<string> ListArguments() const;
+ // return the name of output values
+ virtual vector<string> ListOutputs() const;
+ // return the name of auxiliary states
+ virtual vector<string> ListAuxiliaryStates() const;
+ // return the number of output values
+ virtual int NumOutputs() const;
+ // return the number of visible outputs
+ virtual int NumVisibleOutputs() const;
+```
+
+### Create an Operator from the Operator Property
+
+ `OperatorProperty` includes all *semantic* attributes of an operation. It's also responsible for creating the `Operator` pointer for actual computation.
+
+#### Create Operator
+Implement the following interface in `OperatorProperty`:
+
+```c++
+ virtual Operator* CreateOperator(Context ctx) const = 0;
+```
+For example:
+
+```c++
+ class ConvolutionOp {
+ public:
+ void Forward( ... ) { ... }
+ void Backward( ... ) { ... }
+ };
+ class ConvolutionOpProperty : public OperatorProperty {
+ public:
+ Operator* CreateOperator(Context ctx) const {
+ return new ConvolutionOp;
+ }
+ };
+```
+
+#### Parametrize Operator
+When implementing a convolution operator, you need to know the kernel size,
+the stride size, padding size, and so on.
+These parameters should be passed to the operator
+before any `Forward` or `Backward` interface is called.
+To do so, you could define a `ConvolutionParam` structure, as follows:
+
+```c++
+ #include <dmlc/parameter.h>
+ struct ConvolutionParam : public dmlc::Parameter<ConvolutionParam> {
+ TShape kernel, stride, pad;
+ uint32_t num_filter, num_group, workspace;
+ bool no_bias;
+ };
+```
+Put it in `ConvolutionOpProperty`, and pass it to the operator class during construction:
+
+```c++
+ class ConvolutionOp {
+ public:
+ ConvolutionOp(ConvolutionParam p): param_(p) {}
+ void Forward( ... ) { ... }
+ void Backward( ... ) { ... }
+ private:
+ ConvolutionParam param_;
+ };
+ class ConvolutionOpProperty : public OperatorProperty {
+ public:
+ void Init(const vector<pair<string, string>& kwargs) {
+ // initialize param_ using kwargs
+ }
+ Operator* CreateOperator(Context ctx) const {
+ return new ConvolutionOp(param_);
+ }
+ private:
+ ConvolutionParam param_;
+ };
+```
+
+#### Register the Operator Property Class and the Parameter Class to MXNet
+Use the following macros to register the parameter structure and the operator property class to MXNet:
+
+```c++
+ DMLC_REGISTER_PARAMETER(ConvolutionParam);
+ MXNET_REGISTER_OP_PROPERTY(Convolution, ConvolutionOpProperty);
+```
+The first argument is the name string, the second is the property class name.
+
+### Interface Summary
+
+We've almost covered the entire interface required to define a new operator. Let's do a recap:
+
+* Use the `Operator` interface to write your computation logic (`Forward` and `Backward`).
+* Use the `OperatorProperty` interface to:
+ - Pass the parameter to the operator class (you can use the `Init` interface).
+ - Create an operator using the `CreateOperator` interface.
+ - Correctly implement the operator description interface, such as the names of arguments, etc.
+ - Correctly implement the `InferShape` interface to set the output tensor shape.
+ - [Optional] If additional resources are needed, check `ForwardResource` and `BackwardResource`.
+ - [Optional] If `Backward` doesn't need all of the input and output of `Forward`, check `DeclareBackwardDependency`.
+ - [Optional] If in-place update is supported, check `ForwardInplaceOption` and `BackwardInplaceOption`.
+* Register the `OperatorProperty` class and the parameter class.
+
+## Unifying the NDArray Operator and Symbolic Operator
+NDArray operations are similar to symbolic operations,
+except that sometimes you can't write in place to the operands
+without a complete dependency graph.
+However, the logic underlying NDArray and symbolic operations are almost identical.
+*SimpleOp*, a new unified operator API,
+unifies different invoking processes
+and returns to the fundamental elements of operators.
+Because most mathematical operators attend to one or two operands,
+and more operands make dependency-related optimization useful,
+the unified operator is specifically designed for unary and binary operations.
+
+Consider the elements of an operation.
+Ideally, you need only functions and derivatives
+to describe an operation.
+Let's restrict that to the space of unary and binary operations.
+How do we classify all operations to maximize the possibility
+of in-place write optimization?
+Note that you can separate functions by the number of operands.
+Derivatives are a bit more complex.
+To construct a dependency graph, you need to know whether output value,
+input data, or neither are needed alongside head gradient.
+Gradient functions in the unified API are differentiated
+by the types of operands it takes for calculation.
+
+Before you learn more about the SimpleOp interface,
+ we recommend that you review the
+ [mshadow library guide](https://github.com/dmlc/mshadow/tree/master/guide)
+ because calculations will be done in the `mshadow::TBlob` structure.
+
+In the following example, we'll create an operator
+functioning as a smooth l1 loss,
+which is a mixture of l1 loss and l2 loss. The loss itself can be written as:
+
+```
+ loss = outside_weight .* f(inside_weight .* (data - label))
+ grad = outside_weight .* inside_weight .* f'(inside_weight .* (data - label))
+```
+ `.*` stands for element-wise multiplication, and `f`, `f'` is the smooth l1 loss function,
+which we are assuming is in `mshadow` for now.
+At first glance, it's impossible to implement
+this particular loss as a unary or binary operator.
+But we have automatic differentiation in symbolic execution.
+That simplifies the loss to `f` and `f'` directly.
+This loss is no more complex than a `sin` or an `abs` function,
+and can certainly be implemented as a unary operator.
+
+## SimpleOp: The Unified Operator API
+### Define Shapes
+The `mshadow` library requires explicit memory allocation.
+As a consequence, all data shapes
+must be provided before any calculation occurs.
+ Before we proceed with defining functions and gradient,
+let's check input data shape consistency and provide output shape.
+
+```cpp
+ typedef TShape (*UnaryShapeFunction)(const TShape& src,
+ const EnvArguments& env);
+ typedef TShape (*BinaryShapeFunction)(const TShape& const TShape& rhs,lhs,
+
+ const EnvArguments& env);
+```
+You can use `mshadow::TShape` to check input data shape and designate output data shape.
+If you don't define this function, the default output shape is the same as the input shape.
+In the case of a binary operator, the shape of `lhs` and `rhs` is checked as the same by default.
+
+You can also use shape functions to check if any additional arguments and resources are present.
+Refer to the additional usages of `EnvArguments` to accomplish this.
+
+Before we start on our smooth l1 loss example, we define a `XPU` to `cpu` or `gpu` in the header
+`smooth_l1_unary-inl.h` implementation so that we reuse the same code in `smooth_l1_unary.cc` and
+`smooth_l1_unary.cu`.
+
+```cpp
+ #include <mxnet/operator_util.h>
+ #if defined(__CUDACC__)
+ #define XPU gpu
+ #else
+ #define XPU cpu
+ #endif
+```
+In our smooth l1 loss example, it's okay to use the default behavior whereby the output has the same shape as the source.
+Written explicitly, it is:
+
+```cpp
+ inline TShape SmoothL1Shape_(const TShape& src,
+ const EnvArguments& env) {
+ return TShape(src);
+```
+
+### Define Functions
+Create a unary or binary function with one output: `mshadow::TBlob`.
+
+```cpp
+ typedef void (*UnaryFunction)(const TBlob& src,
+ const EnvArguments& env,
+ TBlob* ret,
+ OpReqType req,
+ RunContext ctx);
+ typedef void (*BinaryFunction)(const TBlob& lhs,
+ const TBlob& rhs,
+ const EnvArguments& env,
+ TBlob* ret,
+ OpReqType req,
+ RunContext ctx);
+```
+* Functions are differentiated by the types of input arguments.
+* `RunContext ctx` contains information needed during runtime for execution.
+
+```cpp
+ struct RunContext {
+ void *stream; // the stream of the device, can be NULL or Stream<gpu>* in GPU mode
+ template<typename xpu> inline mshadow::Stream<xpu>* get_stream() // get mshadow stream from Context
+ } // namespace mxnet
+```
+ `mshadow::stream<xpu> *s = ctx.get_stream<xpu>();` is an example of obtaining a stream from `ctx`.
+* `OpReqType req` denotes how computation results are written into `ret`.
+
+```cpp
+ enum OpReqType {
+ kNullOp, // no operation, do not write anything
+ kWriteTo, // write gradient to provided space
+ kWriteInplace, // perform an in-place write
+ kAddTo // add to the provided space
+ };
+```
+ A macro is defined in `operator_util.h` for a simplified use of `OpReqType`.
+ `ASSIGN_DISPATCH(out, req, exp)` checks `req` and performs an assignment.
+
+In our smooth l1 loss example, we use `UnaryFunction` to define the function of this operator.
+
+```cpp
+ template<typename xpu>
+ void SmoothL1Forward_(const TBlob& src,
+ const EnvArguments& env,
+ TBlob *ret,
+ OpReqType req,
+ RunContext ctx) {
+ using namespace mshadow;
+ using namespace mshadow::expr;
+ mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+ real_t sigma2 = env.scalar * env.scalar;
+ MSHADOW_TYPE_SWITCH(ret->type_flag_, DType, {
+ mshadow::Tensor<xpu, 2, DType> out = ret->get<xpu, 2, DType>(s);
+ mshadow::Tensor<xpu, 2, DType> in = src.get<xpu, 2, DType>(s);
+ ASSIGN_DISPATCH(out, req,
+ F<mshadow_op::smooth_l1_loss>(in, ScalarExp<DType>(sigma2)));
+ });
+ }
+```
+After obtaining `mshadow::Stream` from `RunContext`, we get `mshadow::Tensor` from `mshadow::TBlob`.
+`mshadow::F` is a shortcut to initiate a `mshadow` expression. The macro `MSHADOW_TYPE_SWITCH(type, DType, ...)`
+handles details on different types, and the macro `ASSIGN_DISPATCH(out, req, exp)` checks `OpReqType` and
+performs actions accordingly. `sigma2` is a special parameter in this loss, which we will cover later.
+
+### Define Gradients (Optional)
+Create a gradient function with various types of inputs.
+
+```cpp
+ // depending only on out_grad
+ typedef void (*UnaryGradFunctionT0)(const OutputGrad& out_grad,
+ const EnvArguments& env,
+ TBlob* in_grad,
+ OpReqType req,
+ RunContext ctx);
+ // depending only on out_value
+ typedef void (*UnaryGradFunctionT1)(const OutputGrad& out_grad,
+ const OutputValue& out_value,
+ const EnvArguments& env,
+ TBlob* in_grad,
+ OpReqType req,
+ RunContext ctx);
+ // depending only on in_data
+ typedef void (*UnaryGradFunctionT2)(const OutputGrad& out_grad,
+ const Input0& in_data0,
+ const EnvArguments& env,
+ TBlob* in_grad,
+ OpReqType req,
+ RunContext ctx);
+```
+Gradient functions of binary operators have similar structures, except that `Input`, `TBlob`, and `OpReqType`
+are doubled.
+
+`GradFunctionArgument`
+
+ `Input0`, `Input`, `OutputValue`, and `OutputGrad` all share the structure of `GradFunctionArgument`,
+ which is defined as:
+
+ ```cpp
+ struct GradFunctionArgument {
+ TBlob data;
+ }
+ ```
+
+In our smooth l1 loss example, note that it's an `f'(x)`,
+which utilizes input for the gradient calculation,
+so the `UnaryGradFunctionT2` is suitable.
+To enable the chain rule of the gradient,
+we also need to multiply `out_grad` from the top to the result of `in_grad`.
+
+```cpp
+ template<typename xpu>
+ void SmoothL1BackwardUseIn_(const OutputGrad& out_grad,
+ const Input0& in_data0,
+ const EnvArguments& env,
+ TBlob *in_grad,
+ OpReqType req,
+ RunContext ctx) {
+ using namespace mshadow;
+ using namespace mshadow::expr;
+ mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+ real_t sigma2 = env.scalar * env.scalar;
+ MSHADOW_TYPE_SWITCH(in_grad->type_flag_, DType, {
+ mshadow::Tensor<xpu, 2, DType> src = in_data0.data.get<xpu, 2, DType>(s);
+ mshadow::Tensor<xpu, 2, DType> ograd = out_grad.data.get<xpu, 2, DType>(s);
+ mshadow::Tensor<xpu, 2, DType> igrad = in_grad->get<xpu, 2, DType>(s);
+ ASSIGN_DISPATCH(igrad, req,
+ ograd * F<mshadow_op::smooth_l1_gradient>(src, ScalarExp<DType>(sigma2)));
+ });
+ }
+```
+
+### Register SimpleOp to MXNet
+After creating the shape, function, and gradient, restore them into both an NDArray operator and
+a symbolic operator. To simplify this process, use the registration macro defined in `operator_util.h`.
+
+```cpp
+ MXNET_REGISTER_SIMPLE_OP(Name, DEV)
+ .set_shape_function(Shape)
+ .set_function(DEV::kDevMask, Function<XPU>, SimpleOpInplaceOption)
+ .set_gradient(DEV::kDevMask, Gradient<XPU>, SimpleOpInplaceOption)
+ .describe("description");
+```
+`SimpleOpInplaceOption` is defined as:
+
+```cpp
+ enum SimpleOpInplaceOption {
+ kNoInplace, // do not allow inplace in arguments
+ kInplaceInOut, // allow inplace in with out (unary)
+ kInplaceOutIn, // allow inplace out_grad with in_grad (unary)
+ kInplaceLhsOut, // allow inplace left operand with out (binary)
+ kInplaceOutLhs // allow inplace out_grad with lhs_grad (binary)
+ };
+```
+
+In our example, we have a gradient function that relies on input data, so the function can't be written in
+place. The output gradient has no purpose after gradient computation, so the gradient can be written in place.
+
+```cpp
+ MXNET_REGISTER_SIMPLE_OP(smooth_l1, XPU)
+ .set_function(XPU::kDevMask, SmoothL1Forward_<XPU>, kNoInplace)
+ .set_gradient(XPU::kDevMask, SmoothL1BackwardUseIn_<XPU>, kInplaceOutIn)
+ .set_enable_scalar(true)
+ .describe("Calculate Smooth L1 Loss(lhs, scalar)");
+```
+Remember from the discussion of shape functions that a default behavior without `set_shape_function` forces the inputs
+(if they're binary) to be the same shape and yield the same shape for output. We'll discuss `set_enable_scalar` later.
+
+### NDArray Operator Summary
+* Create a shape function for determining the output shape.
+* Create a function as the forward routine by choosing a suitable function type.
+* Create a gradient as the backward routine by choosing a suitable gradient type.
+* Register the operator using the registration process.
+
+## Additional Information on SimpleOp
+### Using SimpleOp on EnvArguments
+Some operations might need a scalar as input, such as a gradient scale, a set of keyword arguments
+controlling behavior, or a temporary space to speed up calculations.`EnvArguments` provides additional arguments and resources to make calculations more scalable
+and efficient.
+
+```cpp
+ struct EnvArguments {
+ real_t scalar; // scalar argument, if enabled
+ std::vector<std::pair<std::string, std::string> > kwargs; // keyword arguments
+ std::vector<Resource> resource; // pointer to the resources requested
+ };
+```
+
+More registration parameters are required to enable these additional features. To prevent confusion on parameters, `scalar` and `kwargs`
+can't be present at the same time. To enable `scalar`, use
+`set_enable_scalar(bool enable_scalar)` in registration. Then, in forward functions and gradients, the `scalar` can be accessed from `env.scalar` as in the function parameter `EnvArguments env`.
+
+To enable `kwargs`, use `set_enable_kwargs(bool enable_kwargs)` in registration. Then, in forward
+functions and gradients, additional arguments are contained in `env.kwarg`, which is defined as
+`std::vector<std::pair<std::string, std::string> >`. Use the DMLC parameter structure to
+simplify parsing keyword arguments. For more details, see the [guide on parameter structure](https://github.com/dmlc/dmlc-core/blob/master/doc/parameter.md).
+
+Additional resources like `mshadow::Random<xpu>` and temporary memory space can also be requested and
+accessed from `EnvArguments.resource`. The registration routine is `set_resource_request(ResourceRequest req)`
+or `set_resource_request(const std::vector<ResourceRequest>)`, where `mxnet::ResourceRequest` is defined as:
+
+```cpp
+ struct ResourceRequest {
+ enum Type { // Resource type, indicating what the pointer type is
+ kRandom, // mshadow::Random<xpu> object
+ kTempSpace // A dynamic temp space that can be arbitrary size
+ };
+ Type type; // type of resources
+ };
+```
+Registration will request the declared resource requests from `mxnet::ResourceManager`, and place resources
+in `std::vector<Resource> resource` in `EnvArguments`. To access resources, use the following:
+
+```cpp
+ auto tmp_space_res = env.resources[0].get_space(some_shape, some_stream);
+ auto rand_res = env.resources[0].get_random(some_stream);
+```
+For an example, see `src/operator/loss_binary_op-inl.h`.
+
+In our smooth l1 loss example, a scalar input is needed to mark the turning point of a loss function. Therefore,
+in the registration process, we use `set_enable_scalar(true)`, and use `env.scalar` in function and gradient
+declarations.
+
+### Crafting a Tensor Operation
+Because computation utilizes the `mshadow` library and we sometimes don't have functions readily available, we
+can craft tensor operations in operator implementations. If you define such functions as element-wise, you
+can implement them as a `mxnet::op::mshadow_op`. `src/operator/mshadow_op.h` that contains a lot of `mshadow_op`,
+for example. `mshadow_op` are expression mappers. They deal with the scalar case of desired functions. For details, see
+[mshadow expression API guide](https://github.com/dmlc/mshadow/tree/master/doc).
+
+If an operation can't be done in an element-wise way, like the softmax loss and gradient, then you need to create a new tensor operation. You need to create as `mshadow` function and as `mshadow::cuda`
+function directly. For details, see the `mshadow` library. For an example, see `src/operator/roi_pooling.cc`.
+
+In our smooth l1 loss example, we create two mappers, namely the scalar cases of smooth l1 loss and gradient.
+
+```cpp
+ namespace mshadow_op {
+ struct smooth_l1_loss {
+ // a is x, b is sigma2
+ MSHADOW_XINLINE static real_t Map(real_t a, real_t b) {
+ if (a > 1.0f / b) {
+ return a - 0.5f / b;
+ } else if (a < -1.0f / b) {
+ return -a - 0.5f / b;
+ } else {
+ return 0.5f * a * a * b;
+ }
+ }
+ };
+ }
+```
+The gradient, which can be found in `src/operator/smooth_l1_unary-inl.h`, is similar.
+
+### Beyond Two Operands
+The new unified API is designed to fulfill the fundamentals of an operation. For operators with more than two inputs,
+more than one output, or that need more features, see the original [Operator API](http://mxnet.io/architecture/overview.html#operators-in-mxnet).
diff --git a/versions/master/_sources/architecture/program_model.txt b/versions/master/_sources/architecture/program_model.txt
new file mode 100644
index 0000000..380990e
--- /dev/null
+++ b/versions/master/_sources/architecture/program_model.txt
@@ -0,0 +1,605 @@
+# Deep Learning Programming Style
+
+However much we might ultimately care about performance,
+we first need working code before we can start worrying about optimization.
+Writing clear, intuitive deep learning code can be challenging,
+and the first thing any practitioner must deal with is the language syntax itself.
+Complicating matters, of the many deep learning libraries out there,
+each has its own approach to programming style.
+
+In this document, we focus on two of the most important high-level design decisions:
+1. Whether to embrace the _symbolic_ or _imperative_ paradigm for mathematical computation.
+2. Whether to build networks with bigger (more abstract) or more atomic operations.
+
+Throughout, we'll focus on the programming models themselves.
+When programming style decisions may impact performance, we point this out,
+but we don't dwell on specific implementation details.
+
+
+## Symbolic vs. Imperative Programs
+
+If you are a Python or C++ programmer, then you're already familiar with imperative programs.
+Imperative-style programs perform computation as you run them.
+Most code you write in Python is imperative, as is the following NumPy snippet.
+
+```python
+ import numpy as np
+ a = np.ones(10)
+ b = np.ones(10) * 2
+ c = b * a
+ d = c + 1
+```
+When the program executes ```c = b * a```, it runs the actual numerical computation.
+
+Symbolic programs are a bit different. With symbolic-style programs,
+we first define a (potentially complex) function abstractly.
+When defining the function, no actual numerical computation takes place.
+We define the abstract function in terms of placeholder values.
+Then we can compile the function, and evaluate it given real inputs.
+In the following example, we rewrite the imperative program from above
+as a symbolic-style program:
+
+```python
+ A = Variable('A')
+ B = Variable('B')
+ C = B * A
+ D = C + Constant(1)
+ # compiles the function
+ f = compile(D)
+ d = f(A=np.ones(10), B=np.ones(10)*2)
+```
+As you can see, in the symbolic version, when ```C = B * A``` is executed, no computation occurs.
+Instead, this operation generates a _computation graph_ (also called a _symbolic graph_)
+that represents the computation.
+The following figure shows a computation graph to compute ```D```.
+
+
+
+Most symbolic-style programs contain, either explicitly or implicitly, a *compile* step.
+This converts the computation graph into a function that we can later call.
+In the above example, numerical computation only occurs in the last line of code.
+The defining characteristic of symbolic programs is their clear separation
+between building the computation graph and executing it.
+For neural networks, we typically define the entire model as a single compute graph.
+
+Among other popular deep learning libraries, Torch, Chainer, and Minerva embrace the imperative style.
+Examples of symbolic-style deep learning libraries include Theano, CGT, and TensorFlow.
+We might also view libraries like CXXNet and Caffe, which rely on configuration files, as symbolic-style libraries.
+In this interpretation, we'd consider the content of the configuration file as defining the computation graph.
+
+Now that you understand the difference between these two programming models, let's compare the advantages of each.
+
+
+### Imperative Programs Tend to be More Flexible
+
+When you're using an imperative-style library from Python, you are writing in Python.
+Nearly anything that would be intuitive to write in Python, you could accelerate by calling down in the appropriate places to the imperative deep learning library.
+On the other hand, when you write a symbolic program, you may not have access to all the familiar Python constructs, like iteration.
+Consider the following imperative program, and think about how you can translate this into a symbolic program.
+
+```python
+ a = 2
+ b = a + 1
+ d = np.zeros(10)
+ for i in range(d):
+ d += np.zeros(10)
+```
+This wouldn't be so easy if the Python for-loop weren't supported by the symbolic API.
+When you write a symbolic program in Python, you're *not* writing in Python.
+Instead, you're writing in a domain-specific language (DSL) defined by the symbolic API.
+The symbolic APIs found in deep learning libraries
+are powerful DSLs that generate callable computation graphs for neural networks.
+<!-- In that sense, config-file input libraries are all symbolic. -->
+
+Intuitively, you might say that imperative programs
+are more *native* than symbolic programs.
+It's easier to use native language features.
+For example, it's straightforward to print out the values
+in the middle of computation or to use native control flow and loops
+at any point in the flow of computation.
+
+### Symbolic Programs Tend to be More Efficient
+
+As we've seen, imperative programs tend to be flexible
+and fit nicely into the programming flow of a host language.
+So you might wonder, why do so many deep learning libraries
+embrace the symbolic paradigm?
+The main reason is efficiency, both in terms of memory and speed.
+Let's revisit our toy example from before.
+
+```python
+ import numpy as np
+ a = np.ones(10)
+ b = np.ones(10) * 2
+ c = b * a
+ d = c + 1
+ ...
+```
+
+
+
+Assume that each cell in the array occupies 8 bytes of memory.
+How much memory do you need to execute this program in the Python console?
+
+As an imperative program we need to allocate memory at each line.
+That leaves us allocating 4 arrays of size 10.
+So we'll need `4 * 10 * 8 = 320` bytes.
+On the other hand, if we built a computation graph,
+and knew in advance that we only needed `d`,
+we could reuse the memory originally allocated for intermediate values.
+For example, by performing computations in-place,
+we might recycle the bits allocated for ```b``` to store `c`.
+And we might recycle the bits allocated for `c` to store `d`.
+In the end we could cut our memory requirement in half,
+requiring just `2 * 10 * 8 = 160` bytes.
+
+Symbolic programs are more *restricted*.
+When we call `compile` on D, we tell the system
+that only the value of `d` is needed.
+The intermediate values of the computation,
+in this case ```c```, is then invisible to us.
+
+We benefit because the symbolic programs
+can then safely reuse the memory for in-place computation.
+But on the other hand, if we later decide that we need to access `c`, we're out of luck.
+So imperative programs are better prepared to encounter all possible demands.
+If we ran the imperative version of the code in a Python console,
+we could inspect any of the intermediate variables in the future.
+
+<!-- Of course, this is somewhat misleading, because garbage collection can occur in imperative programs and memory could then be reused.
+However, imperative programs do need to be "prepared to encounter all possible demands," and this limits the optimization you can perform. This is true for non-trivial cases, such
+as gradient calculation, which we discuss in next section. -->
+
+Symbolic programs can also perform another kind of optimization, called operation folding.
+Returning to our toy example, the multiplication and addition operations
+can be folded into one operation, as shown in the following graph.
+If the computation runs on a GPU processor,
+one GPU kernel will be executed, instead of two.
+In fact, this is one way we hand-craft operations
+in optimized libraries, such as CXXNet and Caffe.
+Operation folding improves computation efficiency.
+
+
+
+Note, you can't perform operation folding in imperative programs,
+because the intermediate values might be referenced in the future.
+Operation folding is possible in symbolic programs
+because you get the entire computation graph,
+and a clear specification of which values will be needed and which are not.
+
+
+### Case Study: Backprop and AutoDiff
+
+In this section, we compare the two programming models
+on the problem of auto differentiation, or backpropagation.
+Differentiation is of vital importance in deep learning
+because it's the mechanism by which we train our models.
+In any deep learning model, we define a _loss function_.
+A _loss function_ measures how far the model is from the desired output.
+We then typically pass over training examples (pairs of inputs and ground-truth outputs).
+At each step we update the model's _parameters_ to minimize the loss.
+To determine the direction in which to update the parameters,
+we need to take the derivative of the loss function with respect to the parameters.
+
+In the past, whenever someone defined a new model,
+they had to work out the derivative calculations by hand.
+While the math is reasonably straightforward,
+for complex models, it can be time-consuming and tedious work.
+All modern deep learning libraries make the practitioner/researcher's job
+much easier, by automatically solving the problem of gradient calculation.
+
+Both imperative and symbolic programs can perform gradient calculation.
+So let's take a look at how you might perform automatic differentiation with each.
+
+Let's start with imperative programs.
+The following example Python code performs automatic differentiation using our toy example:
+
+```python
+ class array(object) :
+ """Simple Array object that support autodiff."""
+ def __init__(self, value, name=None):
+ self.value = value
+ if name:
+ self.grad = lambda g : {name : g}
+
+ def __add__(self, other):
+ assert isinstance(other, int)
+ ret = array(self.value + other)
+ ret.grad = lambda g : self.grad(g)
+ return ret
+
+ def __mul__(self, other):
+ assert isinstance(other, array)
+ ret = array(self.value * other.value)
+ def grad(g):
+ x = self.grad(g * other.value)
+ x.update(other.grad(g * self.value))
+ return x
+ ret.grad = grad
+ return ret
+
+ # some examples
+ a = array(1, 'a')
+ b = array(2, 'b')
+ c = b * a
+ d = c + 1
+ print d.value
+ print d.grad(1)
+ # Results
+ # 3
+ # {'a': 2, 'b': 1}
+```
+
+In this code, each array object contains a grad function (it is actually a closure).
+When you run ```d.grad```, it recursively invokes the grad function of its inputs,
+backprops the gradient value back, and
+returns the gradient value of each input.
+
+This might look a bit complicated, so let's consider
+the gradient calculation for symbolic programs.
+The following program performs symbolic gradient calculation for the same task.
+
+```python
+ A = Variable('A')
+ B = Variable('B')
+ C = B * A
+ D = C + Constant(1)
+ # get gradient node.
+ gA, gB = D.grad(wrt=[A, B])
+ # compiles the gradient function.
+ f = compile([gA, gB])
+ grad_a, grad_b = f(A=np.ones(10), B=np.ones(10)*2)
+```
+
+The grad function of ```D``` generates a backward computation graph,
+and returns a gradient node, ```gA, gB```,
+which correspond to the red nodes in the following figure.
+
+
+
+The imperative program actually does the same thing as the symbolic program.
+It implicitly saves a backward computation graph in the grad closure.
+When you invoked ```d.grad```, you start from ```d(D)```,
+backtrack through the graph to compute the gradient, and collect the results.
+
+The gradient calculations in both symbolic
+and imperative programming follow the same pattern.
+What's the difference then?
+Recall the *be prepared to encounter all possible demands* requirement of imperative programs.
+If you are creating an array library that supports automatic differentiation,
+you have to keep the grad closure along with the computation.
+This means that none of the history variables can be
+garbage-collected because they are referenced by variable `d` by way of function closure.
+
+What if you want to compute only the value of `d`,
+and don't want the gradient value?
+In symbolic programming, you declare this with `f=compiled([D])`.
+This also declares the boundary of computation,
+telling the system that you want to compute only the forward pass.
+As a result, the system can free the memory of previous results,
+and share the memory between inputs and outputs.
+
+Imagine running a deep neural network with ```n``` layers.
+If you are running only the forward pass,
+not the backward(gradient) pass,
+you need to allocate only two copies of
+temporal space to store the values of the intermediate layers,
+instead of ```n``` copies of them.
+However, because imperative programs need to be prepared
+to encounter all possible demands of getting the gradient,
+they have to store the intermediate values,
+which requires ```n``` copies of temporal space.
+
+As you can see, the level of optimization depends
+on the restrictions on what you can do.
+Symbolic programs ask you to clearly specify
+these restrictions when you compile the graph.
+One the other hand, imperative programs
+must be prepared for a wider range of demands.
+Symbolic programs have a natural advantage
+because they know more about what you do and don't want.
+
+There are ways in which we can modify imperative programs
+to incorporate similar restrictions.
+For example, one solution to the preceding
+problem is to introduce a context variable.
+You can introduce a no-gradient context variable
+to turn gradient calculation off.
+<!-- This provides an imperative program with the ability to impose some restrictions, but reduces efficiency. -->
+
+```python
+ with context.NoGradient():
+ a = array(1, 'a')
+ b = array(2, 'b')
+ c = b * a
+ d = c + 1
+```
+
+However, this example still must be prepared to encounter all possible demands,
+which means that you can't perform the in-place calculation
+to reuse memory in the forward pass (a trick commonly used to reduce GPU memory usage).
+The techniques we've discussed generate an explicit backward pass.
+Some of the libraries such as Caffe and CXXNet perform backprop implicitly on the same graph.
+The approach we've discussed in this section also applies to them.
+
+Most configuration-file-based libraries,
+such as CXXNet and Caffe are designed
+to meet one or two generic requirements:
+get the activation of each layer,
+or get the gradient of all of the weights.
+These libraries have the same problem:
+the more generic operations the library has to support,
+the less optimization (memory sharing) you can do,
+based on the same data structure.
+
+As you can see, the trade-off between restriction
+and flexibility is the same for most cases.
+
+### Model Checkpoint
+
+It's important to able to save a model and load it back later.
+There are different ways to *save* your work.
+Normally, to save a neural network,
+you need to save two things: a net configuration
+for the structure of the neural network and the weights of the neural network.
+
+The ability to check the configuration is a plus for symbolic programs.
+Because the symbolic construction phase does not perform computation,
+you can directly serialize the computation graph, and load it back later.
+This solves the problem of saving the configuration
+without introducing an additional layer.
+
+```python
+ A = Variable('A')
+ B = Variable('B')
+ C = B * A
+ D = C + Constant(1)
+ D.save('mygraph')
+ ...
+ D2 = load('mygraph')
+ f = compile([D2])
+ # more operations
+ ...
+```
+
+Because an imperative program executes as it describes the computation,
+you have to save the code itself as the ```configuration```,
+or build another configuration layer on top of the imperative language.
+
+### Parameter Updates
+
+Most symbolic programs are data flow (computation) graphs.
+Data flow graphs describe computation.
+But it's not obvious how to use graphs to describe parameter updates.
+That's because parameter updates introduce mutation,
+which is not a data flow concept.
+Most symbolic programs introduce a special update statement
+to update persistent state in the programs.
+
+It's usually easier to write parameter updates in an imperative style,
+especially when you need multiple updates that relate to each other.
+For symbolic programs, the update statement is also executed as you call it.
+So in that sense, most symbolic deep learning libraries
+fall back on the imperative approach to perform updates,
+while using the symbolic approach to perform gradient calculation.
+
+### There Is No Strict Boundary
+
+In comparing the two programming styles,
+some of our arguments might not be strictly true,
+i.e., it's possible to make an imperative program
+more like a traditional symbolic program or vice versa.
+However, the two archetypes are useful abstractions,
+especially for understanding the differences between deep learning libraries.
+We might reasonably conclude that there is no clear boundary between programming styles.
+For example, you can create a just-in-time (JIT) compiler in Python
+to compile imperative Python programs,
+which provides some of the advantages of global
+information held in symbolic programs.
+
+
+## Big vs. Small Operations
+
+When designing a deep learning library, another important programming model decision
+is precisely what operations to support.
+In general, there are two families of operations supported by most deep learning libraries:
+
+- Big operations - typically for computing neural network layers (e.g. FullyConnected and BatchNormalize).
+- Small operations - mathematical functions like matrix multiplication and element-wise addition.
+
+Libraries like CXXNet and Caffe support layer-level operations.
+Libraries like Theano and Minerva support fine-grained operations.
+
+### Smaller Operations Can Be More Flexible
+It's quite natural to use smaller operations to compose bigger operations.
+For example, the sigmoid unit can simply be composed of division, addition and an exponentiation:
+
+```python
+ sigmoid(x) = 1.0 / (1.0 + exp(-x))
+```
+Using smaller operations as building blocks, you can express nearly anything you want.
+If you're more familiar with CXXNet- or Caffe-style layers,
+note that these operations don't differ from a layer, except that they are smaller.
+
+```python
+ SigmoidLayer(x) = EWiseDivisionLayer(1.0, AddScalarLayer(ExpLayer(-x), 1.0))
+```
+This expression composes three layers,
+with each defining its forward and backward (gradient) function.
+Using smaller operations gives you the advantage of building new layers quickly,
+because you only need to compose the components.
+
+### Big Operations Are More Efficient
+Directly composing sigmoid layers requires three layers of operation, instead of one.
+
+```python
+ SigmoidLayer(x) = EWiseDivisionLayer(1.0, AddScalarLayer(ExpLayer(-x), 1.0))
+```
+This code creates overhead for computation and memory (which could be optimized, with cost).
+
+Libraries like CXXNet and Caffe take a different approach.
+To support coarse-grained operations,
+such as BatchNormalization and the SigmoidLayer directly,
+in each layer, the calculation kernel is hand crafted
+with one or only some CUDA kernel launches.
+This makes these implementations more efficient.
+
+### Compilation and Optimization
+
+Can small operations be optimized? Of course, they can.
+Let's look at the system optimization part of the compilation engine.
+Two types of optimization can be performed on the computation graph:
+
+- Memory allocation optimization, to reuse the memory of the intermediate computations.
+- Operator fusion, to detect sub-graph patterns, such as the sigmoid, and fuse them into a bigger operation kernel.
+
+Memory allocation optimization isn't restricted to small operations graphs.
+You can use it with bigger operations graph, too.
+However, optimization might not be essential
+for bigger operation libraries like CXXNet and Caffe,
+because you can't find the compilation step in them.
+However, there's a (dumb) ```compilation step``` in these libraries,
+that basically translates the layers into a fixed forward,
+backprop execution plan, by running each operation one by one.
+
+For computation graphs with smaller operations,
+these optimizations are crucial to performance.
+Because the operations are small,
+there are many sub-graph patterns that can be matched.
+Also, because the final, generated operations
+might not enumerable,
+an explicit recompilation of the kernels is required,
+as opposed to the fixed amount of precompiled kernels
+in the big operation libraries.
+This creates compilation overhead for the symbolic libraries
+that support small operations.
+Requiring compilation optimization also creates engineering overhead
+for the libraries that solely support smaller operations.
+
+As in the case of symbolic vs imperative,
+the bigger operation libraries "cheat"
+by asking you to provide restrictions (to the common layer),
+so that you actually perform the sub-graph matching.
+This moves the compilation overhead to the real brain, which is usually not too bad.
+
+### Expression Template and Statically Typed Language
+You always have a need to write small operations and compose them.
+Libraries like Caffe use hand-crafted kernels to build these bigger blocks.
+Otherwise, you would have to compose smaller operations using Python.
+
+There's a third choice that works pretty well.
+This is called the expression template.
+Basically, you use template programming to
+generate generic kernels from an expression tree at compile time.
+For details, see [Expression Template Tutorial](https://github.com/dmlc/mshadow/blob/master/guide/exp-template/README.md).
+CXXNet makes extensive use of an expression template,
+which enables creating much shorter and more readable code that matches
+the performance of hand-crafted kernels.
+
+The difference between using an expression template and Python kernel generation
+is that expression evaluation is done at compile time for C++ with an existing type,
+so there is no additional runtime overhead.
+In principle, this is also possible with other statically typed languages that support templates,
+but we've seen this trick used only in C++.
+
+Expression template libraries create a middle ground between Python operations
+and hand-crafted big kernels by allowing C++ users to craft efficient big
+operations by composing smaller operations. It's an option worth considering.
+
+## Mix the Approaches
+
+Now that we've compared the programming models, which one should you choose?
+Before delving into that, we should emphasize that depending on the problems you're trying to solve,
+our comparison might not necessarily have a big impact.
+
+Remember [Amdahl's law](https://en.wikipedia.org/wiki/Amdahl%27s_law):
+If you are optimizing a non-performance-critical part of your problem,
+you won't get much of a performance gain.
+
+As you've seen, there usually is a trade-off between efficiency,
+flexibility, and engineering complexity.
+The more suitable programming style depends on the problem you are trying to solve.
+For example, imperative programs are better for parameter updates,
+and symbolic programs for gradient calculation.
+
+We advocate *mixing* the approaches.
+Sometimes the part that we want to be flexible
+isn't crucial to performance.
+In these cases, it's okay to leave some efficiency on the table
+to support more flexible interfaces.
+In machine learning, combining methods usually works better than using just one.
+
+If you can combine the programming models correctly,
+you can get better results than when using a single programming model.
+In this section, we discuss how to do so.
+
+### Symbolic and Imperative Programs
+There are two ways to mix symbolic and imperative programs:
+
+- Use imperative programs within symbolic programs as callbacks
+- Use symbolic programs as part of imperative programs
+
+We've observed that it's usually helpful to write parameter updates imperatively,
+and perform gradient calculations in symbolic programs.
+
+Symbolic libraries already mix programs because Python itself is imperative.
+For example, the following program mixes the symbolic approach with NumPy, which is imperative.
+
+```python
+ A = Variable('A')
+ B = Variable('B')
+ C = B * A
+ D = C + Constant(1)
+ # compiles the function
+ f = compile(D)
+ d = f(A=np.ones(10), B=np.ones(10)*2)
+ d = d + 1.0
+```
+The symbolic graphs are compiled into a function that can be executed imperatively.
+The internals are a black box to the user.
+This is exactly like writing C++ programs and exposing them to Python, which we commonly do.
+
+Because parameter memory resides on the GPU,
+you might not want to use NumPy as an imperative component.
+Supporting a GPU-compatible imperative library
+that interacts with symbolic compiled functions
+or provides a limited amount of updating syntax
+in the update statement in symbolic program execution
+might be a better choice.
+
+### Small and Big Operations
+
+There might be a good reason to combine small and big operations.
+Consider applications that perform tasks such as changing
+a loss function or adding a few customized layers to an existing structure.
+Usually, you can use big operations to compose existing
+components, and use smaller operations to build the new parts.
+
+Recall Amdahl's law. Often, the new components
+are not the cause of the computation bottleneck.
+Because the performance-critical part is already optimized by
+the bigger operations, it's okay to forego optimizing the additional small operations,
+or to do a limited amount of memory optimization instead
+of operation fusion and directly running them.
+
+### Choose Your Own Approach
+
+In this document, we compared multiple approaches
+to developing programming environments for deep learning.
+We compared both the usability and efficiency implications of each,
+finding that many of these trade-offs (like imperative vs symbolic aren't necessarily black and white).
+You can choose your approach, or combine the approaches
+to create more interesting and intelligent deep learning libraries.
+
+## Contribute to MXNet
+
+This document is part of our effort to provide [open-source system design notes](index.md)
+for deep learning libraries. If you're interested in contributing to _MXNet_ or its
+documentation, [fork us on GitHub](http://github.com/dmlc/mxnet).
+
+## Next Steps
+
+* [Dependency Engine for Deep Learning](http://mxnet.io/architecture/note_engine.html)
+* [Squeeze the Memory Consumption of Deep Learning](http://mxnet.io/architecture/note_memory.html)
+* [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html)
+* [Survey of RNN Interface](http://mxnet.io/architecture/rnn_interface.html)
diff --git a/versions/master/_sources/architecture/release_note_0_9.txt b/versions/master/_sources/architecture/release_note_0_9.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/architecture/release_note_0_9.txt
@@ -0,0 +1,49 @@
+# MXNet 0.9 (NNVM) Release Note
+
+Version 0.9 brings a number of important features and changes, including a back-end refactor to adopt the [NNVM](https://github.com/dmlc/nnvm) framework, a profiler for analyzing performance, a fast image IO and augmentation module that bypasses GIL, and various other changes.
+
+## NNVM Refactor
+
+NNVM is a library for neural network graph construction, optimization, and operator registration. It serves as an intermediary layer between the front-end (MXNet user API) and the back-end (computation on the device). After version 0.9, MXNet fully adopts the NNVM framework. Now it's easier to create operators. You can also register "pass"es that process and optimizes the graph when `bind` is called on the symbol. For more discussion on how to create operators with NNVM, please refer to [...]
+
+Other changes brought by NNVM include:
+- Backward shape inference is now supported
+- All operators can now be used with both symbolic and ndarray API. For example, `mx.nd.Activation(x, act_type='relu')` works now.
+- Optional cython API for mx.symbol and mx.ndarray is now available. Use `make cython` to activate it for accelerated communication with the back-end.
+
+## Profiler
+
+
+
+MXNet now provides a native profiler for analyzing the performance of operators. This feature compliments general profiling tools like nvprof and gprof by summarizing at the operator level, instead of function, kernel, or instruction level.
+
+To use this feature, first set `USE_PROFILER = 1` in `config.mk` and rebuild mxnet. Then add three lines at the beginning and end of the section of your program you want to profile:
+```python
+mx.profiler.profiler_set_config(mode=scope, filename=fname)
+profiler.profiler_set_state('run')
+
+# do computation ...
+
+profiler.profiler_set_state('stop')
+```
+`scope` can be 'symbolic' (to only include symbolic operations) or 'all' (to include all operations), and `fname` is the path to save profiler output.
+
+After program finishes, navigate to [chrome://tracing](chrome://tracing) in a Chrome browser and load profiler output to see the results.
+
+## Image IO
+
+MXNet already has `mx.io.ImageRecordIter` for loading and preprocessing images. However, some tasks need more flexible image processing API. Detection, for example, requires transforming labels together with images. Usually, people write custom data iterators in python to handle this. But due to the infamous Global Interpreter Lock (GIL), python scripts cannot use multithreading to speed up processing.
+
+`mx.image` provides a set of fast image processing API that leverage MXNet Engine to automatically parallelize processing. You can write
+```python
+imgs = [mx.image.imdecode(open(f).read()) for f in img_paths]
+```
+and decoding will be automatically run in parallel.
+
+## Miscellaneous
+
+- sgd and adam optimizer are now implemented with a single imperative call. They should be as fast and memory efficient as cc optimizers. ccsgd is now deprecated and redirects to sgd.
+- Layout support is added. Use `mx.io.DataDesc(..., layout='NHWC')` in provide_data to specify data layout. use `mx.sym.YourSymbol(..., __layout__='NHWC')` to specify output layout. `layout` option is now available for Convolution layer.
+- element_mask is removed. Please use src*mask.reshape((mask.size, 1, 1, ..., 1)) directly as binary ops now support broadcasting.
+- sum_axis, max_axis, and min_axis are deprecated. Please use mx.nd.max(src, axis=n) instead.
+- symbol attributes are now limited to ctx_group, lr_mult, wd_mult, force_mirroring. All other custom attributes need to be in __xxx__ format (start and end with double underscore) or an error will be triggered during attribute parsing.
diff --git a/versions/master/_sources/architecture/rnn_interface.txt b/versions/master/_sources/architecture/rnn_interface.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/architecture/rnn_interface.txt
@@ -0,0 +1,100 @@
+# Survey of Existing Interfaces and Implementations
+
+Commonly used deep learning libraries with good RNN/LSTM support include [Theano](http://deeplearning.net/software/theano/library/scan.html) and its wrappers [Lasagne](http://lasagne.readthedocs.org/en/latest/modules/layers/recurrent.html) and [Keras](http://keras.io/layers/recurrent/); [CNTK](https://cntk.codeplex.com/); [TensorFlow](https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html); and various implementations in Torch, such as [this well-known character-level [...]
+
+In this document, we present a comparative analysis of the approaches taken by these libraries.
+
+## Theano
+
+In Theano, RNN support comes via its [scan operator](http://deeplearning.net/software/theano/library/scan.html),
+which allows construction of a loop where the number of iterations is specified
+as a runtime value of a symbolic variable.
+You can find an official example of an LSTM implementation with scan
+[here](http://deeplearning.net/tutorial/lstm.html).
+
+### Implementation
+
+I'm not very familiar with the Theano internals,
+but it seems from [theano/scan_module/scan_op.py#execute](https://github.com/Theano/Theano/blob/master/theano/scan_module/scan_op.py#L1225)
+that the scan operator is implemented with a loop in Python
+that performs one iteration at a time:
+
+```python
+ fn = self.fn.fn
+
+ while (i < n_steps) and cond:
+ # ...
+ fn()
+```
+
+The `grad` function in Theano constructs a symbolic graph for computing gradients. So the `grad` for the scan operator is actually implemented by [constructing another scan operator](https://github.com/Theano/Theano/blob/master/theano/scan_module/scan_op.py#L2527):
+
+```python
+ local_op = Scan(inner_gfn_ins, inner_gfn_outs, info)
+ outputs = local_op(*outer_inputs)
+```
+
+The [performance guide](http://deeplearning.net/software/theano/library/scan.html#optimizing-scan-s-performance) for Theano's scan operator suggests minimizing the usage of the scan. This might be due to the fact that the loop is executed in Python, which might be a bit slow (due to context switching and the performance of Python itself). Moreover, because no unrolling is performed, the graph optimizer can't see the big picture.
+
+If I understand correctly, when multiple RNN/LSTM layers are stacked, instead of a single loop with each iteration computing the whole feedforward network operation, the computation sequentially does a separate loop for each layer that uses the scan operator. If all of the intermediate values are stored to support computing the gradients, this is fine. Otherwise, using a single loop could be more memory efficient.
+
+### Lasagne
+
+The documentation for RNN in Lasagne can be found [here](http://lasagne.readthedocs.org/en/latest/modules/layers/recurrent.html). In Lasagne, a recurrent layer is just like a standard layer, except that the input shape is expected to be `(batch_size, sequence_length, feature_dimension)`. The output shape is then `(batch_size, sequence_length, output_dimension)`.
+
+Both `batch_size` and `sequence_length` are specified as `None`, and inferred from the data. Alternatively, when memory is sufficient and the (maximum) sequence length is known beforehand, you can set `unroll_scan` to `False`. Then Lasagne will unroll the graph explicitly, instead of using the Theano `scan` operator. Explicitly unrolling is implemented in [utils.py#unroll_scan](https://github.com/Lasagne/Lasagne/blob/master/lasagne/utils.py#L340).
+
+The recurrent layer also accepts a `mask_input`, to support variable length sequences (e.g., when sequences within a mini-batch have different lengths. The mask has the shape `(batch_size, sequence_length)`.
+
+### Keras
+
+The documentation for RNN in Keras can be found [here](http://keras.io/layers/recurrent/). The interface in Keras is similar to the interface in Lasagne. The input is expected to be of shape `(batch_size, sequence_length, feature_dimension)`, and the output shape (if `return_sequences` is `True`) is `(batch_size, sequence_length, feature_dimension)`.
+
+Keras currently supports both a Theano and a TensorFlow back end. RNN for the Theano back end is [implemented with the scan operator](https://github.com/fchollet/keras/blob/master/keras/backend/theano_backend.py#L432). For TensorFlow, it seems to be [implemented via explicitly unrolling](https://github.com/fchollet/keras/blob/master/keras/backend/tensorflow_backend.py#L396). The documentation says that for the TensorFlow back end, the sequence length must be specified beforehand, and mas [...]
+
+## Torch
+
+[karpathy/char-rnn](https://github.com/karpathy/char-rnn) is implemented by [explicitly unrolling](https://github.com/karpathy/char-rnn/blob/master/model/RNN.lua#L15). On the contrary, [Element-Research/rnn](https://github.com/Element-Research/rnn) runs sequence iteration in Lua. It actually has a very modular design:
+
+* The basic RNN/LSTM modules run only *one* time step per one call of `forward` (and accumulate/store necessary information to support backward computation, if needed). You could have detailed control when using this API directly.
+* A collection of `Sequencer`s are defined to model common scenarios, like forwarding sequence, bi-directional sequence, attention models, etc.
+* There are other utility modules, like masking to support variable length sequences, etc.
+
+## CNTK
+
+CNTK looks quite different from other common deep learning libraries. I don't understand it very well. I will talk with Yu to get more details.
+
+It seems that the basic data types are matrices (although there is also a `TensorView` utility class). The mini-batch data for sequence data is packed in a matrix with N-row being `feature_dimension` and N-column being `sequence_length * batch_size` (see Figure 2.9 on page 50 of the [CNTKBook](http://research.microsoft.com/pubs/226641/CNTKBook-20151201.pdf)).
+
+Recurrent networks are first-class citizens in CNTK. In section 5.2.1.8 of the CNTKBook, you can see an example of a customized computation node. The node needs to explicitly define the functions for standard forward and forward with a time index, which is used for RNN evaluation:
+
+```cpp
+ virtual void EvaluateThisNode()
+ {
+ EvaluateThisNodeS(FunctionValues(), Inputs(0)->
+ FunctionValues(), Inputs(1)->FunctionValues());
+ }
+ virtual void EvaluateThisNode(const size_t timeIdxInSeq)
+ {
+ Matrix<ElemType> sliceInputValue = Inputs(1)->
+ FunctionValues().ColumnSlice(timeIdxInSeq *
+ m_samplesInRecurrentStep, m_samplesInRecurrentStep);
+ Matrix<ElemType> sliceOutputValue = m_functionValues.
+ ColumnSlice(timeIdxInSeq * m_samplesInRecurrentStep,
+ m_samplesInRecurrentStep);
+ EvaluateThisNodeS(sliceOutputValue, Inputs(0)->
+ FunctionValues(), sliceInput1Value);
+ }
+```
+
+The function `ColumnSlice(start_col, num_col)` takes out the packed data for that time index, as described above (here `m_samplesInRecurrentStep` must be the mini-batch size).
+
+The low-level API for recurrent connection seem to be a *delay node*. But I'm not sure how to use this low-level API. The [example of PTB language model](https://cntk.codeplex.com/SourceControl/latest#Examples/Text/PennTreebank/Config/rnn.config) uses a very high-level API (simply setting `recurrentLayer = 1` in the config).
+
+## TensorFlow
+
+The [current example of RNNLM](https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html#recurrent-neural-networks) in TensorFlow uses explicit unrolling for a predefined number of time steps. The white-paper mentions that an advanced control flow API (Theano's scan-like) is planned.
+
+## Next Steps
+
+* [MXNet System Overview](http://mxnet.io/architecture/overview.html)
diff --git a/versions/master/_sources/community/contribute.txt b/versions/master/_sources/community/contribute.txt
new file mode 100644
index 0000000..3a39743
--- /dev/null
+++ b/versions/master/_sources/community/contribute.txt
@@ -0,0 +1,181 @@
+# Contribute to MXNet
+
+MXNet has been developed and is used by a group of active community members.
+Please contribute to improve the project.
+After your patch has been merged, remember to add your name to [CONTRIBUTORS.md](https://github.com/dmlc/mxnet/blob/master/CONTRIBUTORS.md).
+
+## Code Contribution
+
+### Core Library
+
+- Follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html) for C++ code.
+- Use doxygen to document all of the interface code.
+- To reproduce the linter checks, type ```make lint```.
+
+### Python Package
+
+- Always add docstring to the new functions in numpydoc format.
+- To reproduce the linter checks, type ```make lint```.
+
+### R Package
+
+#### Code Style
+- Most of the C++ code in the R package relies heavily on [Rcpp](https://github.com/RcppCore/Rcpp).
+- We follow the Google C++ Style Guide for C++ code. This allows us to maintain consistency with the rest of the project. It also allows us to check style automatically with a linter.
+- To check the code style, type the following command at the root folder:
+```bash
+make rcpplint
+```
+- If necessary, disable the linter warning on certain lines with ```// NOLINT(*)``` comments.
+
+#### Auto-Generated API
+- Many MXNet APIs are exposed dynamically from Rcpp.
+- mxnet_generated.R is the auto-generated API and documents for these functions.
+- Remake the file by typing the following command at root folder:
+```bash
+make rcppexport
+```
+- Use this command only when there is an update to dynamic functions.
+
+#### API Document
+The document is generated using roxygen2. To remake the documents in the root folder, use the following command:
+```bash
+make roxygen.
+```
+
+#### R Markdown Vignettes
+R Markdown vignettes are located on GitHub in [R-package/vignettes](https://github.com/dmlc/mxnet/tree/master/R-package/vignettes).
+These R Markdown files aren't compiled. We host the compiled version on [doc/R-package](https://github.com/dmlc/mxnet/tree/master/R-package/).
+
+To add a new R Markdown vignettes:
+
+* Add the original R Markdown file to ```R-package/vignettes```
+* Modify ```doc/R-package/Makefile``` to add the Markdown files to be built.
+* Clone the [dmlc/web-data](https://github.com/dmlc/web-data) repo to the ```doc``` folder.
+* Type the following command for the ```doc/R-package``` package:
+```bash
+make the-markdown-to-make.md
+```
+* This generates the markdown and the figures and places them into ```doc/web-data/mxnet/knitr```.
+* Modify the ```doc/R-package/index.md``` to point to the generated markdown.
+* Add the generated figure to the ```dmlc/web-data``` repo.
+ * If you have already cloned the repo to doc, use ```git add```.
+* Create a pull request for both the markdown and ```dmlc/web-data```.
+* You can also build the document locally with the following command: ```doc```
+```bash
+make html
+```
+
+### Test Cases
+
+* All of our tests can be found in the GitHub repo in [this directory](https://github.com/dmlc/mxnet/tree/master/tests).
+* We use Python nose for python test cases, and gtest for C++ unit tests.
+
+### Examples
+
+* Use cases and examples are on GitHub in [examples](https://github.com/dmlc/mxnet/tree/master/example)
+* If you write a blog post or tutorial about or using MXNet, please tell us by creating an issue
+in our github repo. We regularly feature high-quality contributed content from the community.
+
+## Standard for Contributing APIs
+
+Make sure to add documentation with any code you contribute. Follow these standards:
+
+### API Documentation
+* Document are created with Sphinx and [recommonmark](http://recommonmark.readthedocs.org/en/latest/).
+* Follow [numpy doc standards](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt#docstring-standard) and
+some changes we made [MXNet doc standards](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt#docstring-standard).
+* If an API is implemented in Python or has a wrapper defined, the documentation and the examples reside
+where the function is defined in `.py` file in [python/mxnet](https://github.com/dmlc/mxnet/tree/master/python/mxnet) folder. Same goes for other languages.
+* If the API is dynamically generated from the MXNet backend, the documentation is in the C++ code(.cc
+file) where the operator is registered in describe method of the `NNVM_REGISTER_OP`. The file and line
+number for the function is usually printed with the API documentation on mxnet.io.
+* A clear and concise description of the function and its behavior.
+* List and describe each parameter with the valid input values, whether it is required or optional,
+and the default value if the parameter is optional.
+* Add an example to help the user understand the API better. If the example can be language-neutral
+or is conceptual, add it in the C++ documentation. Make sure your example works
+by running a Python version of the example.
+ * If a concrete and simple language-specific example can further clarify the API and the API arguments, add the
+example in language-specific files.
+* Refer to these examples for guidance:- [Embedding](http://mxnet.io/api/python/ndarray.html#mxnet.ndarray.Embedding) , [ROIPooling](http://mxnet.io/api/python/ndarray.html#mxnet.ndarray.ROIPooling) , [Reshape](http://mxnet.io/api/python/ndarray.html#mxnet.ndarray.Reshape).
+
+### Testing and Rendering
+* Make sure not to break any coding standards. Run
+```bash
+make lint
+```
+* You can build documents locally to proof them.
+
+## Guidelines to submit a Pull Request
+* Before submitting your contribution, rebase your code on the most recent version of the master:
+
+```bash
+ git remote add upstream https://github.com/dmlc/mxnet
+ git fetch upstream
+ git rebase upstream/master
+```
+* If you have multiple small commits,
+ merge them into meaningful groups (use ```git rebase``` then ```squash```).
+* Send the pull request.
+* Fix problems reported by automatic checks.
+* If you are contributing a new module, consider adding a test case in [tests](https://github.com/dmlc/mxnet/tree/master/tests).
+
+### Resolving a Conflict with the Master
+
+* Rebase to the current master:
+
+ ```bash
+ # The first two steps can be skipped after you do it once.
+ git remote add upstream https://github.com/dmlc/mxnet
+ git fetch upstream
+ git rebase upstream/master
+ ```
+
+* Git might show some conflicts that prevent merging, for example, ```conflicted.py```.
+ * Manually modify the file to resolve the conflict.
+ * After you resolve the conflict, mark it as resolved by using:
+
+```bash
+git add conflicted.py.
+```
+
+* Continue rebasing by using this command:
+
+ ```bash
+ git rebase --continue
+ ```
+
+* Finally push to your fork. You might need to force the push:
+
+ ```bash
+ git push --force
+ ```
+
+### Combining Multiple Commits
+If you are submitting multiple commits with later commits that are just fixes to previous ones, you can combine commits into meaningful groups before creating a push request.
+
+* Before doing so, configure Git's default editor if you haven't already done that:
+
+```bash
+git config core.editor the-editor-you-like
+```
+* Assuming that you want to merge last the last three commits, type the following commands:
+
+```bash
+git rebase -i HEAD~3
+```
+
+* In the text editor that appears, set the first commit as ```pick```, and change later ones to ```squash```.
+
+* After you save the file, another text editor will appear and ask you to modify the combined commit message.
+
+* Push the changes to your fork by forcing a push:
+
+```bash
+git push --force.
+```
+
+### What Is the Consequence of Forcing a Push?
+The previous two tips require forcing a push because we altered the path of the commits.
+It's fine to force a push to your own fork, as long as only your commits are changed.
diff --git a/versions/master/_sources/community/index.txt b/versions/master/_sources/community/index.txt
new file mode 100644
index 0000000..ccfba2c
--- /dev/null
+++ b/versions/master/_sources/community/index.txt
@@ -0,0 +1,13 @@
+# MXNet Community
+## Questions about Using MXNet
+If you are not sure of how to use MXNet for something, or have questions about applying it to a particular kind of problem, please post a question at [Stackoverflow](http://stackoverflow.com/) with tag - ```mxnet```. You can view StackOverflow questions about mxnet [here](http://stackoverflow.com/questions/tagged/mxnet).
+
+## Issue Tracker
+We track bugs and new feature requests in the MXNet Github repo in the issues folder: [mxnet/issues](https://github.com/dmlc/mxnet/issues).
+
+## Contributors
+MXNet has been developed and is used by a group of active community members. Contribute to improving it! For more information, see [contributions](http://mxnet.io/community/contribute.html).
+
+## Roadmap
+
+MXNet is evolving fast. To see what's next and what we are working on internally, go to the [MXNet Roadmap](https://github.com/dmlc/mxnet/labels/Roadmap).
diff --git a/versions/master/_sources/community/mxnet_channels.txt b/versions/master/_sources/community/mxnet_channels.txt
new file mode 100644
index 0000000..c938bd0
--- /dev/null
+++ b/versions/master/_sources/community/mxnet_channels.txt
@@ -0,0 +1,6 @@
+# Join MXNet Development Discussion
+
+Converse with the MXNet community via the following channels:
+
+- [MXNet Apache mailing list](https://lists.apache.org/list.html?dev@mxnet.apache.org) (dev@mxnet.apache.org): To subscribe, send an email to <a href="mailto:dev-subscribe@mxnet.apache.org">dev-subscribe@mxnet.apache.org</a>.
+- [MXNet Slack channel](https://apache-mxnet.slack.com): To request an invitation to the channel please subscribe to the mailing list above and then email: <a href="mailto:dev@mxnet.apache.org">dev@mxnet.apache.org</a>. Note: if you have an email address with apache.org, you do not need an approval to join the MXNet Slack channel.
diff --git a/versions/master/_sources/get_started/amazonlinux_setup.txt b/versions/master/_sources/get_started/amazonlinux_setup.txt
new file mode 100644
index 0000000..054e030
--- /dev/null
+++ b/versions/master/_sources/get_started/amazonlinux_setup.txt
@@ -0,0 +1,8 @@
+<!-- This page should be deleted after sometime (Allowing search engines
+to update links) -->
+<meta http-equiv="refresh" content="3; url=http://mxnet.io/get_started/install.html" />
+<!-- Just in case redirection does not work -->
+<p>
+ <a href="http://mxnet.io/get_started/install.html">
+ This content is moved to a new MXNet install page. Redirecting... </a>
+</p>
diff --git a/versions/master/_sources/get_started/build_from_source.txt b/versions/master/_sources/get_started/build_from_source.txt
new file mode 100644
index 0000000..4ff2cc0
--- /dev/null
+++ b/versions/master/_sources/get_started/build_from_source.txt
@@ -0,0 +1,450 @@
+# Build MXNet from Source
+
+**NOTE:** For MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/get_started/install.html).
+
+This document explains how to build MXNet from sources. Building MXNet from sources is a 2 step process.
+
+1. Build the MXNet shared library, `libmxnet.so`, from [C++ source files](#build-the-shared-library)
+2. Install the language binding for MXNet. MXNet supports -
+ [C++](#build-the-c-package),
+ [Scala](#build-the-scala-package), [R](#build-the-r-package), and
+ [Julia](#build-the-julia-package).
+
+## Build the shared library
+
+### Prerequisites
+
+You need C++ build tools and BLAS library to build MXNet shared library. If you want to run MXNet on GPUs, you need to install CUDA and CuDNN.
+
+#### C++ build tools
+
+1. A C++ compiler that supports C++ 11.
+[G++ (4.8 or later)](https://gcc.gnu.org/gcc-4.8/) or
+[Clang](http://clang.llvm.org/) is required.
+
+2. [Git](https://git-scm.com/downloads) for downloading the sources from Github repository.
+
+3. [GNU Make](https://www.gnu.org/software/make/) ([cmake](https://cmake.org/)
+ for Windows) to build the library.
+
+
+Select your preferences and follow the instructions to install MXNet from sources.
+<div class="btn-group opt-group" role="group">
+<button type="button" class="btn btn-default opt active">Linux</button>
+<button type="button" class="btn btn-default opt">macOS</button>
+<button type="button" class="btn btn-default opt">Windows</button>
+</div>
+<script type="text/javascript" src='../../_static/js/options.js'></script>
+
+<div class="linux">
+
+Then select the Linux distribution:
+<div class="btn-group opt-group" role="group">
+<button type="button" class="btn btn-default opt active">Ubuntu</button>
+<button type="button" class="btn btn-default opt">CentOS</button>
+<button type="button" class="btn btn-default opt">Others</button>
+</div>
+
+- **Ubuntu** for systems supporting the `apt-get`
+ package management program
+- **CentOS** for systems supporting the `yum` package
+ management program
+- **Others** for general Linux-like systems building dependencies from scratch.
+
+<div class="ubuntu">
+
+Install build tools and git on `Ubuntu >= 13.10` and `Debian >= 8`.
+
+```bash
+sudo apt-get update && sudo apt-get install build-essential git
+```
+
+</div>
+
+<div class="centos">
+
+Install build tools and git on `CentOS >= 7` and `Fedora >= 19`.
+
+```bash
+sudo yum groupinstall -y "Development Tools" && sudo yum install -y git
+```
+
+</div>
+
+<div class="others">
+
+Installing both `git` and `make` by following instructions on the websites is
+straightforward. Here we provide the instructions to build `gcc-4.8` from source codes.
+
+1. Install the 32-bit `libc` with one of the following system-specific commands:
+
+ ```bash
+ sudo apt-get install libc6-dev-i386 # In Ubuntu
+ sudo yum install glibc-devel.i686 # In RHEL (Red Hat Linux)
+ sudo yum install glibc-devel.i386 # In CentOS 5.8
+ sudo yum install glibc-devel.i686 # In CentOS 6/7
+ ```
+
+2. Download and extract the `gcc` source code with the prerequisites:
+
+ ```bash
+ wget http://mirrors.concertpass.com/gcc/releases/gcc-4.8.5/gcc-4.8.5.tar.gz
+ tar -zxf gcc-4.8.5.tar.gz
+ cd gcc-4.8.5
+ ./contrib/download_prerequisites
+ ```
+
+3. Build `gcc` by using 10 threads and then install to `/usr/local`
+
+ ```bash
+ mkdir release && cd release
+ ../configure --prefix=/usr/local --enable-languages=c,c++
+ make -j10
+ sudo make install
+ ```
+
+4. Add the lib path to your configure file such as `~/.bashrc`:
+
+ ```bash
+ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib64
+ ```
+
+</div>
+</div> <!-- linux -->
+
+<div class="windows">
+
+1. If [Microsoft Visual Studio 2015](https://www.visualstudio.com/downloads/) is not already installed, download and install it. You can download and install the free community edition.
+2. Download and Install [CMake](https://cmake.org/) if it is not already installed.
+
+</div>
+
+<div class="macos">
+
+Install [Xcode](https://developer.apple.com/xcode/).
+
+</div>
+
+#### BLAS library
+
+MXNet relies on the
+[BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (Basic
+Linear Algebra Subprograms) library for numerical computations. You can install
+any one among [ATLAS](http://math-atlas.sourceforge.net/),
+[OpenBLAS](http://www.openblas.net/) and
+[MKL](https://software.intel.com/en-us/intel-mkl).
+
+<div class="linux">
+<div class="ubuntu">
+
+```bash
+sudo apt-get install libatlas-base-dev
+```
+
+</div>
+
+<div class="centos">
+
+```bash
+sudo yum install atlas-devel
+```
+
+</div>
+
+<div class="linux">
+
+You can follow this link to build
+[OpenBlas from source](https://github.com/xianyi/OpenBLAS#installation-from-source).
+
+</div>
+</div>
+
+<div class="macos">
+
+macOS users can skip this step as `xcode` ships with a BLAS library.
+
+</div>
+
+<div class="windows">
+
+1. Download pre-built binaries for [OpenBLAS](https://sourceforge.net/projects/openblas/files/)
+2. Set the environment variable `OpenBLAS_HOME` to point to the OpenBLAS
+ directory that contains the `include/` and `lib/` directories. Typically, you
+ can find the directory in `C:\Program files (x86)\OpenBLAS\`.
+
+</div>
+
+#### Optional: [OpenCV](http://opencv.org/) for Image Loading and Augmentation
+
+<div class="linux">
+<div class="ubuntu">
+
+```bash
+sudo apt-get install libopencv-dev
+```
+
+</div>
+
+<div class="centos">
+
+```bash
+sudo apt-get install opencv-devel
+```
+
+</div>
+
+<div class="others">
+
+To build OpenCV from source code, you need the [cmake](https://cmake.org) library.
+
+1. If you don't have cmake or if your version of cmake is earlier than 3.6.1, run the following commands to install a newer version of cmake:
+
+ ```bash
+ wget https://cmake.org/files/v3.6/cmake-3.6.1-Linux-x86_64.tar.gz
+ tar -zxvf cmake-3.6.1-Linux-x86_64.tar.gz
+ alias cmake="cmake-3.6.1-Linux-x86_64/bin/cmake"
+ ```
+
+2. To download and extract the OpenCV source code, run the following commands:
+
+ ```bash
+ wget https://codeload.github.com/opencv/opencv/zip/2.4.13
+ unzip 2.4.13
+ cd opencv-2.4.13
+ mkdir release
+ cd release/
+ ```
+
+3. Build OpenCV. The following commands build OpenCV with 10 threads. We
+ disabled GPU support, which might significantly slow down an MXNet program
+ running on a GPU processor. It also disables 1394 which might generate a
+ warning. Then install it on `/usr/local`.
+
+ ```bash
+ cmake -D BUILD_opencv_gpu=OFF -D WITH_CUDA=OFF -D WITH_1394=OFF -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
+ make -j10
+ sudo make install
+ ```
+
+4. Add the lib path to your configuration such as `~/.bashrc`.
+
+ ```bash
+ export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig/
+ ```
+
+</div>
+</div>
+
+<div class="windows">
+
+First download and install [OpenCV](http://opencv.org/releases.html), then set
+the environment variable `OpenCV_DIR` to point to the OpenCV build directory.
+
+</div>
+
+#### Optional: [CUDA](https://developer.nvidia.com/cuda-downloads)/[cuDNN](https://developer.nvidia.com/cudnn) for Nvidia GPUs
+
+MXNet is compatible with both CUDA 7.5 and 8.0. It is recommended to use cuDNN 5.
+
+<div class="linux">
+<div class="ubuntu">
+
+Install CUDA 7.5 and cuDNN 5 on Ubuntu 14.04
+
+```bash
+wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_7.5-18_amd64.deb
+sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb
+echo "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64 /" | sudo tee /etc/apt/sources.list.d/nvidia-ml.list
+sudo apt-get update
+sudo apt-get install -y linux-image-extra-`uname -r` linux-headers-`uname -r` linux-image-`uname -r`
+sudo apt-get install -y cuda libcudnn5-dev=5.0.5-1+cuda7.5
+```
+
+</div>
+</div>
+
+### Build
+
+<div class="linux macos">
+
+First clone the recent codes
+
+```bash
+git clone --recursive https://github.com/dmlc/mxnet
+cd mxnet
+```
+
+File
+[`make/config.mk`](https://github.com/dmlc/mxnet/blob/master/make/config.mk)
+contains all the compilation options. You can edit it and then `make`. There are
+some example build options
+
+If you want to build MXNet with C++ language binding, please make sure you read [Build the C++ package](#build-the-c-package) first.
+
+</div>
+
+<div class="linux">
+
+- Build without using OpenCV. `-j` runs multiple jobs against multi-core CPUs.
+
+ ```bash
+ make -j USE_OPENCV=0
+ ```
+
+- Build with both GPU and OpenCV support
+
+ ```bash
+ make -j USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
+ ```
+
+</div>
+
+<div class="macos">
+
+- Build with the default BLAS library and clang installed with `xcode` (OPENMP
+ is disabled because it is not supported in default by clang).
+
+ ```bash
+ make -j USE_BLAS=apple USE_OPENCV=0 USE_OPENMP=0
+ ```
+
+</div>
+
+<div class="windows">
+
+Use [CMake](https://cmake.org/) to create a Visual Studio solution in ```./build```.
+
+In Visual Studio, open the solution file,```.sln```, and compile it.
+These commands produce a library called ```mxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.
+
+</div>
+
+## Build the C++ package
+The C++ package has the same prerequisites as the MXNet library, you should also have `python` installed. (Both `python` 2 and 3 are supported)
+
+To enable C++ package, just add `USE_CPP_PACKAGE=1` in the build options when building the MXNet shared library.
+
+## Build the R package
+
+The R package requires `R` to be installed.
+
+<div class="ubuntu">
+
+Follow the below instructions to install the latest R on Ubuntu 14.04 (trusty) and also the libraries used
+to build other R package dependencies.
+
+```bash
+echo "deb http://cran.rstudio.com/bin/linux/ubuntu trusty/" >> /etc/apt/sources.list
+gpg --keyserver keyserver.ubuntu.com --recv-key E084DAB9
+gpg -a --export E084DAB9 | apt-key add -
+
+apt-get update
+apt-get install -y r-base r-base-dev libxml2-dev libxt-dev libssl-dev
+```
+
+</div>
+
+Install the required R package dependencies:
+
+```bash
+cd R-package
+Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
+Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
+```
+
+Next, build and install the MXNet R package:
+
+```bash
+cd ..
+make rpkg
+R CMD INSTALL mxnet_current_r.tar.gz
+```
+
+## Build the Scala package
+
+Both JDK and Maven are required to build the Scala package.
+
+<div class="ubuntu">
+
+```bash
+sudo apt-get install -y maven default-jdk
+```
+
+</div>
+
+The following command builds the `.jar` package:
+
+```bash
+make scalapkg
+```
+
+which can be found by `ls scala-package/assembly/*/target/*SNAPSHOT.jar`.
+
+Optionally, we can install Scala for the interactive interface.
+
+<div class="ubuntu">
+
+```bash
+wget http://downloads.lightbend.com/scala/2.11.8/scala-2.11.8.deb
+dpkg -i scala-2.11.8.deb
+rm scala-2.11.8.deb
+```
+
+</div>
+
+Then we can start `scala` with `mxnet` imported by
+
+```bash
+scala -cp scala-package/assembly/*/target/*SNAPSHOT.jar
+```
+
+## Build the Julia package
+
+We need to first install Julia.
+
+<div class="ubuntu centos linux">
+
+The following commands install Julia 0.5.1
+
+```bash
+wget -q https://julialang.s3.amazonaws.com/bin/linux/x64/0.5/julia-0.5.1-linux-x86_64.tar.gz
+tar -zxf julia-0.5.1-linux-x86_64.tar.gz
+rm julia-0.5.1-linux-x86_64.tar.gz
+ln -s $(pwd)/julia-6445c82d00/bin/julia /usr/bin/julia
+```
+
+</div>
+
+Next set the environment variable `MXNET_HOME=/path/to/mxnet` so that Julia
+can find the pre-built library.
+
+Install the Julia package for MXNet with:
+
+```bash
+julia -e 'Pkg.add("MXNet")'
+```
+
+### Build the Perl package
+
+Run the following command from the MXNet source root directory to build the MXNet Perl package:
+
+```bash
+ sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
+ cpanm -q -L "${HOME}/perl5" Function::Parameters
+
+ MXNET_HOME=${PWD}
+ export LD_LIBRARY_PATH=${MXNET_HOME}/lib
+ export PERL5LIB=${HOME}/perl5/lib/perl5
+
+ cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
+ perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+ make install
+
+ cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
+ perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+ make install
+
+ cd ${MXNET_HOME}/perl-package/AI-MXNet/
+ perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+ make install
+```
diff --git a/versions/master/_sources/get_started/centos_setup.txt b/versions/master/_sources/get_started/centos_setup.txt
new file mode 100644
index 0000000..054e030
--- /dev/null
+++ b/versions/master/_sources/get_started/centos_setup.txt
@@ -0,0 +1,8 @@
+<!-- This page should be deleted after sometime (Allowing search engines
+to update links) -->
+<meta http-equiv="refresh" content="3; url=http://mxnet.io/get_started/install.html" />
+<!-- Just in case redirection does not work -->
+<p>
+ <a href="http://mxnet.io/get_started/install.html">
+ This content is moved to a new MXNet install page. Redirecting... </a>
+</p>
diff --git a/versions/master/_sources/get_started/index.txt b/versions/master/_sources/get_started/index.txt
new file mode 100644
index 0000000..1112c9d
--- /dev/null
+++ b/versions/master/_sources/get_started/index.txt
@@ -0,0 +1,8 @@
+
+<html lang="en-US">
+ <head>
+ <meta charset="UTF-8">
+ <meta http-equiv="refresh" content="0; url=http://mxnet.io/get_started/why_mxnet.html">
+ <title>Page Redirection</title>
+ </head>
+</html>
diff --git a/versions/master/_sources/get_started/install.txt b/versions/master/_sources/get_started/install.txt
new file mode 100644
index 0000000..063d419
--- /dev/null
+++ b/versions/master/_sources/get_started/install.txt
@@ -0,0 +1,1464 @@
+# Installing MXNet
+
+Indicate your preferred configuration. Then, follow the customized commands to install *MXNet*.
+
+<script type="text/javascript" src='../../_static/js/options.js'></script>
+
+<!-- START - OS Menu -->
+
+<div class="btn-group opt-group" role="group">
+ <button type="button" class="btn btn-default opt active">Linux</button>
+ <button type="button" class="btn btn-default opt">MacOS</button>
+ <button type="button" class="btn btn-default opt">Windows</button>
+ <button type="button" class="btn btn-default opt">Cloud</button>
+ <button type="button" class="btn btn-default opt">Devices</button>
+</div>
+
+<!-- START - Language Menu -->
+
+<div class="linux macos windows">
+<div class="btn-group opt-group" role="group">
+ <button type="button" class="btn btn-default opt active">Python</button>
+ <button type="button" class="btn btn-default opt">Scala</button>
+ <button type="button" class="btn btn-default opt">R</button>
+ <button type="button" class="btn btn-default opt">Julia</button>
+ <button type="button" class="btn btn-default opt">Perl</button>
+</div>
+</div>
+
+<!-- No CPU GPU for other Devices -->
+<div class="linux macos windows cloud">
+<div class="btn-group opt-group" role="group">
+ <button type="button" class="btn btn-default opt active">CPU</button>
+ <button type="button" class="btn btn-default opt">GPU</button>
+</div>
+</div>
+
+<!-- other devices -->
+<div class="devices">
+<div class="btn-group opt-group" role="group">
+ <button type="button" class="btn btn-default opt active">Raspberry Pi</button>
+ <button type="button" class="btn btn-default opt">NVIDIA Jetson TX2</button>
+</div>
+</div>
+
+<!-- Linux Python GPU Options -->
+
+<div class="linux macos">
+<div class="python">
+<div class="cpu gpu">
+<div class="btn-group opt-group" role="group">
+ <button type="button" class="btn btn-default opt active">Pip</button>
+ <button type="button" class="btn btn-default opt">Virtualenv</button>
+ <button type="button" class="btn btn-default opt">Docker</button>
+ <button type="button" class="btn btn-default opt">Build from Source</button>
+</div>
+</div>
+</div>
+</div>
+
+<!-- END - Main Menu -->
+
+<!-- START - Linux Python CPU Installation Instructions -->
+
+<div class="linux">
+ <div class="python">
+ <div class="cpu">
+
+The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
+
+<div class="virtualenv">
+<br/>
+
+**Step 1** Install virtualenv for Ubuntu.
+
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y python-dev python-virtualenv
+```
+
+**Step 2** Create and activate virtualenv environment for MXNet.
+
+Following command creates a virtualenv environment at `~/mxnet` directory. However, you can choose any directory by replacing `~/mxnet` with a directory of your choice.
+
+```bash
+$ virtualenv --system-site-packages ~/mxnet
+```
+
+Activate the virtualenv environment created for *MXNet*.
+
+```bash
+$ source ~/mxnet/bin/activate
+```
+
+After activating the environment, you should see the prompt as below.
+
+```bash
+(mxnet)$
+```
+
+**Step 3** Install MXNet in the active virtualenv environment.
+
+Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command.
+
+```bash
+$ pip install --upgrade pip
+```
+
+Install *MXNet* with OpenBLAS acceleration.
+
+```bash
+$ pip install mxnet
+```
+
+**Step 4** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+sudo apt-get install graphviz
+pip install graphviz
+```
+
+**Step 5** Validate the installation by running simple *MXNet* code described [here](#validate-mxnet-installation).
+
+**Note** You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).
+
+</div>
+
+<div class="pip">
+<br/>
+
+**Step 1** Install prerequisites - wget and latest pip.
+
+Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command in the terminal.
+
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y wget python gcc
+$ wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py
+```
+
+**Step 2** Install MXNet with OpenBLAS acceleration.
+
+```bash
+$ pip install mxnet
+```
+
+**Step 3** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+sudo apt-get install graphviz
+pip install graphviz
+```
+
+**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+</div>
+
+<div class="docker">
+<br/>
+
+Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
+
+**Step 1** Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
+
+*Note* - You can install Community Edition (CE) to get started with *MXNet*.
+
+**Step 2** [Optional] Post installation steps to manage Docker as a non-root user.
+
+Follow the four steps in this [docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user) to allow managing docker containers without *sudo*.
+
+If you skip this step, you need to use *sudo* each time you invoke Docker.
+
+**Step 2** Pull the MXNet docker image.
+
+```bash
+$ docker pull mxnet/python # Use sudo if you skip Step 2
+```
+
+You can list docker images to see if mxnet/python docker image pull was successful.
+
+```bash
+$ docker images # Use sudo if you skip Step 2
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
+```
+
+**Step 3** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+</div>
+
+<div class="build-from-source">
+<br/>
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
+2. Build the language specific bindings. Example - Python bindings, Scala bindings.
+
+**Minimum Requirements**
+1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
+2. [GNU Make](https://www.gnu.org/software/make/)
+
+<br/>
+
+**Build the MXNet core shared library**
+
+**Step 1** Install build tools and git.
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y build-essential git
+```
+
+**Step 2** Install OpenBLAS.
+
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+```bash
+$ sudo apt-get install -y libopenblas-dev liblapack-dev
+```
+
+**Step 3** Install OpenCV.
+
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+```bash
+$ sudo apt-get install -y libopencv-dev
+```
+
+**Step 4** Download MXNet sources and build MXNet core shared library.
+
+```bash
+$ git clone --recursive https://github.com/dmlc/mxnet
+$ cd mxnet
+$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas
+```
+
+*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in `make/config.mk`.
+
+<br/>
+
+**Build the MXNet Python binding**
+
+**Step 1** Install prerequisites - python, setup-tools, python-pip and numpy.
+
+```bash
+$ sudo apt-get install -y python-dev python-setuptools python-numpy python-pip
+```
+
+**Step 2** Install the MXNet Python binding.
+
+```bash
+$ cd python
+$ pip install --upgrade pip
+$ pip install -e .
+```
+
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
+
+**Step 3** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+sudo apt-get install graphviz
+pip install graphviz
+```
+
+**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+</div>
+
+</div>
+</div>
+</div>
+<!-- END - Linux Python CPU Installation Instructions -->
+
+<!-- START - Linux Python GPU Installation Instructions -->
+
+<div class="linux">
+<div class="python">
+<div class="gpu">
+
+The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
+
+
+**Prerequisites**
+
+Install the following NVIDIA libraries to setup *MXNet* with GPU support:
+
+1. Install CUDA 8.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
+2. Install cuDNN 5 for CUDA 8.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
+
+**Note:** Make sure to add CUDA install path to `LD_LIBRARY_PATH`.
+
+Example - *export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH*
+
+<div class="pip">
+<br/>
+
+**Step 1** Install prerequisites - wget and latest pip.
+
+Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command in the terminal.
+
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y wget python
+$ wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py
+```
+
+**Step 2** Install *MXNet* with GPU support using CUDA 8.0
+
+```bash
+$ pip install mxnet-cu80
+```
+
+**Step 3** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+sudo apt-get install graphviz
+pip install graphviz
+```
+
+**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+</div>
+
+<div class="virtualenv">
+
+<br/>
+
+**Step 1** Install virtualenv for Ubuntu.
+
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y python-dev python-virtualenv
+```
+
+**Step 2** Create and activate virtualenv environment for MXNet.
+
+Following command creates a virtualenv environment at `~/mxnet` directory. However, you can choose any directory by replacing `~/mxnet` with a directory of your choice.
+
+```bash
+$ virtualenv --system-site-packages ~/mxnet
+```
+
+Activate the virtualenv environment created for *MXNet*.
+
+```bash
+$ source ~/mxnet/bin/activate
+```
+
+After activating the environment, you should see the prompt as below.
+
+```bash
+(mxnet)$
+```
+
+**Step 3** Install MXNet in the active virtualenv environment.
+
+Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command.
+
+```bash
+(mxnet)$ pip install --upgrade pip
+```
+
+Install *MXNet* with GPU support using CUDA 8.0.
+
+```bash
+(mxnet)$ pip install mxnet-cu80
+```
+
+**Step 4** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+sudo apt-get install graphviz
+pip install graphviz
+```
+
+**Step 5** Validate the installation by running simple *MXNet* code described [here](#validate-mxnet-installation).
+
+**Note** You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).
+
+</div>
+
+<div class="docker">
+
+<br/>
+
+Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
+
+**Step 1** Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
+
+*Note* - You can install Community Edition (CE) to get started with *MXNet*.
+
+**Step 2** [Optional] Post installation steps to manage Docker as a non-root user.
+
+Follow the four steps in this [docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user) to allow managing docker containers without *sudo*.
+
+If you skip this step, you need to use *sudo* each time you invoke Docker.
+
+**Step 3** Install *nvidia-docker-plugin* following the [installation instructions](https://github.com/NVIDIA/nvidia-docker/wiki/Installation). *nvidia-docker-plugin* is required to enable the usage of GPUs from the docker containers.
+
+**Step 4** Pull the MXNet docker image.
+
+```bash
+$ docker pull mxnet/python:gpu # Use sudo if you skip Step 2
+```
+
+You can list docker images to see if mxnet/python docker image pull was successful.
+
+```bash
+$ docker images # Use sudo if you skip Step 2
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python gpu 493b2683c269 3 weeks ago 4.77 GB
+```
+
+**Step 5** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+</div>
+
+<div class="build-from-source">
+
+<br/>
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
+2. Build the language specific bindings. Example - Python bindings, Scala bindings.
+
+**Minimum Requirements**
+1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
+2. [GNU Make](https://www.gnu.org/software/make/)
+
+<br/>
+
+**Build the MXNet core shared library**
+
+**Step 1** Install build tools and git.
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y build-essential git
+```
+**Step 2** Install OpenBLAS.
+
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+```bash
+$ sudo apt-get install -y libopenblas-dev liblapack-dev
+```
+
+**Step 3** Install OpenCV.
+
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+```bash
+$ sudo apt-get install -y libopencv-dev
+```
+
+**Step 4** Download MXNet sources and build MXNet core shared library.
+
+```bash
+$ git clone --recursive https://github.com/dmlc/mxnet
+$ cd mxnet
+$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
+```
+
+*Note* - USE_OPENCV, USE_BLAS, USE_CUDA, USE_CUDA_PATH AND USE_CUDNN are make file flags to set compilation options to use OpenCV, OpenBLAS, CUDA and cuDNN libraries. You can explore and use more compilation options in `make/config.mk`. Make sure to set USE_CUDA_PATH to right CUDA installation path. In most cases it is - */usr/local/cuda*.
+
+<br/>
+
+**Install the MXNet Python binding**
+
+**Step 1** Install prerequisites - python, setup-tools, python-pip and numpy.
+
+```bash
+$ sudo apt-get install -y python-dev python-setuptools python-numpy python-pip
+```
+
+**Step 2** Install the MXNet Python binding.
+
+```bash
+$ cd python
+$ pip install --upgrade pip
+$ pip install -e .
+```
+
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
+
+**Step 3** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+sudo apt-get install graphviz
+pip install graphviz
+```
+
+**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+</div>
+
+</div>
+</div>
+</div>
+<!-- END - Linux Python GPU Installation Instructions -->
+
+<!-- START - MacOS Python CPU Installation Instructions -->
+
+<div class="macos">
+ <div class="python">
+ <div class="cpu">
+
+The following installation instructions have been tested on OSX Sierra and El Capitan.
+
+**Prerequisites**
+
+If not already installed, [download and install Xcode](https://developer.apple.com/xcode/) (or [insall it from the App Store](https://itunes.apple.com/us/app/xcode/id497799835)) for macOS. [Xcode](https://en.wikipedia.org/wiki/Xcode) is an integrated development environment for macOS containing a suite of software development tools like C/C++ compilers, BLAS library and more.
+
+<div class="virtualenv">
+<br/>
+
+**Step 1** Install prerequisites - Homebrew, python development tools.
+
+```bash
+# Install Homebrew
+$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+$ export PATH=/usr/local/bin:/usr/local/sbin:$PATH
+
+# Install python development tools - python2.7, pip, python-setuptools
+$ brew install python
+```
+
+**Step 2** Install virtualenv for macOS.
+
+```bash
+$ pip install virtualenv
+```
+
+**Step 3** Create and activate virtualenv environment for MXNet.
+
+Following command creates a virtualenv environment at `~/mxnet` directory. However, you can choose any directory by replacing `~/mxnet` with a directory of your choice.
+
+```bash
+$ virtualenv --system-site-packages ~/mxnet
+```
+
+Activate the virtualenv environment created for *MXNet*.
+
+```bash
+$ source ~/mxnet/bin/activate
+```
+
+After activating the environment, you should see the prompt as below.
+
+```bash
+(mxnet)$
+```
+
+**Step 4** Install MXNet in the active virtualenv environment.
+
+Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command.
+
+```bash
+(mxnet)$ pip install --upgrade pip
+(mxnet)$ pip install --upgrade setuptools
+```
+
+Install *MXNet* with OpenBLAS acceleration.
+
+```bash
+(mxnet)$ pip install mxnet
+```
+
+**Step 5** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+$ brew install graphviz
+(mxnet)$ pip install graphviz
+```
+
+**Step 6** Validate the installation by running simple *MXNet* code described [here](#validate-mxnet-installation).
+
+**Note** You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).
+
+</div>
+
+<div class="pip">
+<br/>
+
+**Step 1** Install prerequisites - Homebrew, python development tools.
+
+```bash
+# Install Homebrew
+$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+$ export PATH=/usr/local/bin:/usr/local/sbin:$PATH
+
+# Install python development tools - python2.7, pip, python-setuptools
+$ brew install python
+```
+
+**Step 2** Install MXNet with OpenBLAS acceleration.
+
+Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command.
+
+```bash
+$ pip install --upgrade pip
+$ pip install --upgrade setuptools
+```
+
+```bash
+$ pip install mxnet
+```
+
+**Step 3** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+$ brew install graphviz
+$ pip install graphviz
+```
+
+**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+</div>
+
+<div class="docker">
+<br/>
+
+Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
+
+**Step 1** Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/docker-for-mac/install/#install-and-run-docker-for-mac).
+
+*Note* - You can install Community Edition (CE) to get started with *MXNet*.
+
+**Step 2** Pull the MXNet docker image.
+
+```bash
+$ docker pull mxnet/python
+```
+
+You can list docker images to see if mxnet/python docker image pull was successful.
+
+```bash
+$ docker images
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
+mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
+```
+
+**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+</div>
+
+<div class="build-from-source">
+<br/>
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
+2. Build the language specific bindings. Example - Python bindings, Scala bindings.
+
+Make sure you have installed Xcode before proceeding further.
+
+<br/>
+
+All the instructions to build *MXNet* core shared library and *MXNet* Python bindings are compiled as one helper *bash* script. You can use [this bash script](https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-osx-python.sh) to build *MXNet* for Python, from source, on macOS.
+
+**Step 1** Download the bash script for building MXNet from source.
+
+```bash
+$ curl -O https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-osx-python.sh
+```
+
+**Step 2** Run the script to get latest MXNet source and build.
+
+```bash
+# Make the script executable
+$ chmod 744 install-mxnet-osx-python.sh
+
+# Run the script. It takes around 5 mins.
+$ bash install-mxnet-osx-python.sh
+```
+
+**Step 3** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+</div> <!-- End of source build -->
+
+</div>
+</div>
+</div>
+
+<!-- END - Mac OS Python CPU Installation Instructions -->
+
+<!-- START - Mac OS Python GPU Installation Instructions -->
+
+<div class="macos">
+ <div class="python">
+ <div class="gpu">
+
+More details and verified installation instructions for macOS, with GPUs, coming soon.
+
+
+*MXNet* is expected to be compatible on macOS with NVIDIA GPUs. Please install CUDA 8.0 and cuDNN 5.0, prior to installing GPU version of *MXNet*.
+
+</div>
+</div>
+</div>
+
+<!-- END - Mac OS Python GPU Installation Instructions -->
+
+<!-- START - Cloud Python Installation Instructions -->
+
+<div class="cloud">
+
+AWS Marketplace distributes AMIs (Amazon Machine Image) with MXNet pre-installed. You can launch an Amazon EC2 instance with one of the below AMIs:
+1. Deep Learning AMI (Amazon Machine Image) for [Ubuntu](https://aws.amazon.com/marketplace/pp/B06VSPXKDX)
+2. Deep Learning AMI for [Amazon Linux](https://aws.amazon.com/marketplace/pp/B01M0AXXQB)
+
+You could also run distributed deeplearning with *MXNet* on AWS using [Cloudformation Template](https://github.com/awslabs/deeplearning-cfn/blob/master/README.md).
+
+</div>
+
+<!-- END - Cloud Python Installation Instructions -->
+
+
+<!-- START - MacOS R CPU Installation Instructions -->
+
+<div class="macos">
+ <div class="r">
+ <div class="cpu">
+
+The CPU version of MXNet R package can be installed in R like other packages
+
+```r
+cran <- getOption("repos")
+cran["dmlc"] <- "https://s3-us-west-2.amazonaws.com/apache-mxnet/R/CRAN/"
+options(repos = cran)
+install.packages("mxnet")
+```
+
+
+</div>
+
+
+<div class="gpu">
+
+Will be available soon.
+
+</div>
+
+</div>
+</div>
+<!-- END - MacOS R CPU Installation Instructions -->
+
+
+<div class="linux">
+ <div class="r">
+ <div class="cpu">
+<br/>
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
+2. Build the language specific bindings.
+
+**Minimum Requirements**
+1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
+2. [GNU Make](https://www.gnu.org/software/make/)
+
+<br/>
+
+**Build the MXNet core shared library**
+
+**Step 1** Install build tools and git.
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y build-essential git
+```
+
+**Step 2** Install OpenBLAS.
+
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+```bash
+$ sudo apt-get install -y libopenblas-dev liblapack-dev
+```
+
+**Step 3** Install OpenCV.
+
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+```bash
+$ sudo apt-get install -y libopencv-dev
+```
+
+**Step 4** Download MXNet sources and build MXNet core shared library.
+
+```bash
+$ git clone --recursive https://github.com/dmlc/mxnet
+$ cd mxnet
+$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas
+```
+
+*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in `make/config.mk`.
+
+<br/>
+
+**Build and install the MXNet R binding**
+
+
+```bash
+$ make rpkg
+$ R CMD INSTALL mxnet_current_r.tar.gz
+```
+
+
+</div>
+
+<div class="gpu">
+
+The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
+
+
+**Prerequisites**
+
+Install the following NVIDIA libraries to setup *MXNet* with GPU support:
+
+1. Install CUDA 8.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
+2. Install cuDNN 5 for CUDA 8.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
+
+**Note:** Make sure to add CUDA install path to `LD_LIBRARY_PATH`.
+
+Example - *export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH*
+
+<br/>
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
+2. Build the language specific bindings.
+
+**Minimum Requirements**
+1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
+2. [GNU Make](https://www.gnu.org/software/make/)
+
+<br/>
+
+**Build the MXNet core shared library**
+
+**Step 1** Install build tools and git.
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y build-essential git
+```
+**Step 2** Install OpenBLAS.
+
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+```bash
+$ sudo apt-get install -y libopenblas-dev liblapack-dev
+```
+
+**Step 3** Install OpenCV.
+
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+```bash
+$ sudo apt-get install -y libopencv-dev
+```
+
+**Step 4** Download MXNet sources and build MXNet core shared library.
+
+```bash
+$ git clone --recursive https://github.com/dmlc/mxnet
+$ cd mxnet
+$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
+```
+
+*Note* - USE_OPENCV, USE_BLAS, USE_CUDA, USE_CUDA_PATH AND USE_CUDNN are make file flags to set compilation options to use OpenCV, OpenBLAS, CUDA and cuDNN libraries. You can explore and use more compilation options in `make/config.mk`. Make sure to set USE_CUDA_PATH to right CUDA installation path. In most cases it is - */usr/local/cuda*.
+
+<br/>
+
+**Build and install the MXNet R binding**
+
+```bash
+$ make rpkg
+$ R CMD INSTALL mxnet_current_r.tar.gz
+```
+
+</div>
+
+</div>
+</div>
+
+
+<!-- START - Windows R CPU Installation Instructions -->
+
+<div class="windows">
+ <div class="r">
+ <div class="cpu">
+
+The CPU version of MXNet R package can be installed in R like other packages
+
+
+```r
+cran <- getOption("repos")
+cran["dmlc"] <- "https://s3-us-west-2.amazonaws.com/apache-mxnet/R/CRAN/"
+options(repos = cran)
+install.packages("mxnet")
+```
+
+</div>
+
+<!-- END - Windows R CPU Installation Instructions -->
+
+<div class="gpu">
+
+The GPU version of MXNet R package can be installed in R like other packages
+
+
+```r
+cran <- getOption("repos")
+cran["dmlc"] <- "https://s3-us-west-2.amazonaws.com/apache-mxnet/R/CRAN/GPU"
+options(repos = cran)
+install.packages("mxnet")
+```
+
+Alternatively, You can also follow the installation instructions [in this guide](./windows_setup.md) to build MXNet from source.
+
+</div>
+</div>
+</div>
+
+<!-- END - Windows R GPU Installation Instructions -->
+
+<div class="linux">
+ <div class="scala julia perl">
+ <div class="cpu gpu">
+
+Follow the installation instructions [in this guide](./ubuntu_setup.md) to set up MXNet.
+
+</div>
+</div>
+</div>
+
+<div class="macos">
+ <div class="scala julia perl">
+ <div class="cpu gpu">
+
+Follow the installation instructions [in this guide](./osx_setup.md) to set up MXNet.
+
+</div>
+</div>
+</div>
+
+<div class="windows">
+ <div class="python scala julia perl">
+ <div class="gpu">
+
+Follow the installation instructions [in this guide](./windows_setup.md) to set up MXNet.
+
+</div>
+</div>
+</div>
+
+<div class="devices">
+ <div class="raspberry-pi">
+
+MXNet supports the Debian based Raspbian ARM based operating system so you can run MXNet on Raspberry Pi Devices.
+
+These instructions will walk through how to build MXNet for the Raspberry Pi and install the Python bindings for the library.
+
+The complete MXNet library and its requirements can take almost 200MB of RAM, and loading large models with the library can take over 1GB of RAM. Because of this, we recommend running MXNet on the Raspberry Pi 3 or an equivalent device that has more than 1 GB of RAM and a Secure Digital (SD) card that has at least 4 GB of free memory.
+
+**Install MXNet**
+
+Installing MXNet is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. Install the supported language-specific packages for MXNet.
+
+**Step 1** Build the Shared Library
+
+On Raspbian versions Wheezy and later, you need the following dependencies:
+
+- Git (to pull code from GitHub)
+
+- libblas (for linear algebraic operations)
+
+- libopencv (for computer vision operations. This is optional if you want to save RAM and Disk Space)
+
+- A C++ compiler that supports C++ 11. The C++ compiler compiles and builds MXNet source code. Supported compilers include the following:
+
+- [G++ (4.8 or later)](https://gcc.gnu.org/gcc-4.8/)
+
+Install these dependencies using the following commands in any directory:
+
+```bash
+ sudo apt-get update
+ sudo apt-get -y install git cmake build-essential g++-4.8 c++-4.8 liblapack* libblas* libopencv*
+```
+
+Clone the MXNet source code repository using the following ```git``` command in your home directory:
+```bash
+ git clone https://github.com/dmlc/mxnet.git --recursive
+ cd mxnet
+```
+
+If you aren't processing images with MXNet on the Raspberry Pi, you can minimize the size of the compiled library by building MXNet without the Open Source Computer Vision (OpenCV) library with the following commands:
+```bash
+ export USE_OPENCV = 0
+ make
+```
+
+Otherwise, you can build the complete MXNet library with the following command:
+```bash
+ make
+```
+
+Executing either of these commands start the build process, which can take up to a couple hours, and creates a file called ```libmxnet.so``` in the mxnet/lib directory.
+
+If you are getting build errors in which the compiler is being killed, it is likely that the compiler is running out of memory (especially if you are on Raspberry Pi 1, 2 or Zero, which have less than 1GB of RAM), this can often be rectified by increasing the swapfile size on the Pi by editing the file /etc/dphys-swapfile and changing the line CONF_SWAPSIZE=100 to CONF_SWAPSIZE=1024, then running:
+```bash
+ sudo /etc/init.d/dphys-swapfile stop
+ sudo /etc/init.d/dphys-swapfile start
+ free -m # to verify the swapfile size has been increased
+```
+
+**Step 2** Install MXNet Python Bindings
+
+To install python bindings run the following commands in the MXNet directory:
+
+```bash
+ cd python
+ pip install --upgrade pip
+ pip install -e .
+```
+
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
+
+You are now ready to run MXNet on your Raspberry Pi device. You can get started by following the tutorial on [Real-time Object Detection with MXNet On The Raspberry Pi](http://mxnet.io/tutorials/embedded/wine_detector.html).
+
+*Note - Because the complete MXNet library takes up a significant amount of the Raspberry Pi's limited RAM, when loading training data or large models into memory, you might have to turn off the GUI and terminate running processes to free RAM.*
+
+</div>
+
+
+<div class="nvidia-jetson-tx2">
+
+MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices.
+
+These instructions will walk through how to build MXNet for the Pascal based [NVIDIA Jetson TX2](http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html) and install the corresponding python language bindings.
+
+For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device.
+
+**Install MXNet**
+
+Installing MXNet is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. Install the supported language-specific packages for MXNet.
+
+**Step 1** Build the Shared Library
+
+You need the following additional dependencies:
+
+- Git (to pull code from GitHub)
+
+- libatlas (for linear algebraic operations)
+
+- libopencv (for computer vision operations)
+
+- python pip (to load relevant python packages for our language bindings)
+
+Install these dependencies using the following commands in any directory:
+
+```bash
+ sudo apt-get update
+ sudo apt-get -y install git build-essential libatlas-base-dev libopencv-dev graphviz python-pip
+ sudo pip install pip --upgrade
+ sudo pip install setuptools numpy --upgrade
+ sudo pip install graphviz jupyter
+```
+
+Clone the MXNet source code repository using the following ```git``` command in your home directory:
+```bash
+ git clone https://github.com/dmlc/mxnet.git --recursive
+ cd mxnet
+```
+
+Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
+```bash
+ cp make/config.mk .
+ echo "USE_CUDA=1" >> config.mk
+ echo "USE_CUDA_PATH=/usr/local/cuda" >> config.mk
+ echo "USE_CUDNN=1" >> config.mk
+```
+
+Edit the Mshadow Makefile to ensure MXNet builds with Pascal's hardware level low precision acceleration by editing mshadow/make/mshadow.mk and adding the following after line 122:
+```bash
+MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1
+```
+
+Now you can build the complete MXNet library with the following command:
+```bash
+ make -j $(nproc)
+```
+
+Executing this command creates a file called ```libmxnet.so``` in the mxnet/lib directory.
+
+**Step 2** Install MXNet Python Bindings
+
+To install python bindings run the following commands in the MXNet directory:
+
+```bash
+ cd python
+ pip install --upgrade pip
+ pip install -e .
+```
+
+Note that the `-e` flag is optional. It is equivalent to `--editable` and means that if you edit the source files, these changes will be reflected in the package installed.
+
+Add the mxnet folder to the path:
+
+```bash
+ cd ..
+ export MXNET_HOME=$(pwd)
+ echo "export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH" >> ~/.bashrc
+ source ~/.bashrc
+```
+
+You are now ready to run MXNet on your NVIDIA Jetson TX2 device.
+
+</div>
+</div>
+
+<br/>
+
+# Validate MXNet Installation
+
+<div class="linux macos">
+ <div class="python">
+ <div class="cpu">
+
+<div class="pip build-from-source">
+
+Start the python terminal.
+
+```bash
+$ python
+```
+</div>
+
+<div class="docker">
+
+Launch a Docker container with `mxnet/python` image and run example *MXNet* python program on the terminal.
+
+```bash
+$ docker run -it mxnet/python bash # Use sudo if you skip Step 2 in the installation instruction
+
+# Start a python terminal
+root@4919c4f58cac:/# python
+```
+</div>
+
+<div class="virtualenv">
+
+Activate the virtualenv environment created for *MXNet*.
+
+```bash
+$ source ~/mxnet/bin/activate
+```
+
+After activating the environment, you should see the prompt as below.
+
+```bash
+(mxnet)$
+```
+
+Start the python terminal.
+
+```bash
+$ python
+```
+
+</div>
+
+Run a short *MXNet* python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
+
+```python
+>>> import mxnet as mx
+>>> a = mx.nd.ones((2, 3))
+>>> b = a * 2 + 1
+>>> b.asnumpy()
+array([[ 3., 3., 3.],
+ [ 3., 3., 3.]], dtype=float32)
+```
+</div>
+</div>
+</div>
+
+<!-- Validation for GPU machines -->
+
+<div class="linux">
+ <div class="python">
+ <div class="gpu">
+
+<div class="pip build-from-source">
+
+Start the python terminal.
+
+```bash
+$ python
+```
+</div>
+
+<div class="docker">
+
+Launch a NVIDIA Docker container with `mxnet/python:gpu` image and run example *MXNet* python program on the terminal.
+
+```bash
+$ nvidia-docker run -it mxnet/python:gpu bash # Use sudo if you skip Step 2 in the installation instruction
+
+# Start a python terminal
+root@4919c4f58cac:/# python
+```
+</div>
+
+<div class="virtualenv">
+
+Activate the virtualenv environment created for *MXNet*.
+
+```bash
+$ source ~/mxnet/bin/activate
+```
+
+After activating the environment, you should see the prompt as below.
+
+```bash
+(mxnet)$
+```
+
+Start the python terminal.
+
+```bash
+$ python
+```
+
+</div>
+
+Run a short *MXNet* python program to create a 2X3 matrix of ones *a* on a *GPU*, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use *mx.gpu()*, to set *MXNet* context to be GPUs.
+
+```python
+>>> import mxnet as mx
+>>> a = mx.nd.ones((2, 3), mx.gpu())
+>>> b = a * 2 + 1
+>>> b.asnumpy()
+array([[ 3., 3., 3.],
+ [ 3., 3., 3.]], dtype=float32)
+```
+</div>
+</div>
+</div>
+
+<div class="macos">
+ <div class="python">
+ <div class="gpu">
+
+More details and verified validation instructions for macOS, with GPUs, coming soon.
+
+</div>
+</div>
+</div>
+
+<!-- Linux Clean up -->
+<div class="linux">
+ <div class="python">
+ <div class="cpu">
+
+<div class="pip build-from-source">
+
+Exit the Python terminal.
+
+```python
+>>> exit()
+$
+```
+</div>
+
+<div class="virtualenv">
+
+Exit the Python terminal and Deactivate the virtualenv *MXNet* environment.
+```python
+>>> exit()
+(mxnet)$ deactivate
+$
+```
+
+</div>
+
+<div class="docker">
+
+Exit the Python terminal and mxnet/python docker container.
+```python
+>>> exit()
+root@4919c4f58cac:/# exit
+```
+
+</div>
+
+</div>
+</div>
+</div>
+
+<!-- MacOS Clean up -->
+<div class="macos">
+ <div class="python">
+ <div class="cpu">
+
+<div class="pip build-from-source">
+
+Exit the Python terminal.
+
+```python
+>>> exit()
+$
+```
+</div>
+
+<div class="virtualenv">
+
+Exit the Python terminal and Deactivate the virtualenv *MXNet* environment.
+```python
+>>> exit()
+(mxnet)$ deactivate
+$
+```
+
+</div>
+
+<div class="docker">
+
+Exit the Python terminal and then the docker container.
+```python
+>>> exit()
+root@4919c4f58cac:/# exit
+```
+
+</div>
+
+</div>
+</div>
+</div>
+
+<!-- Validation for cloud installation -->
+
+<div class="cloud">
+
+Login to the cloud instance you launched, with pre-installed *MXNet*, following the guide by corresponding cloud provider.
+
+
+Start the python terminal.
+
+```bash
+$ python
+```
+<!-- Example Python code for CPU -->
+
+<div class="cpu">
+
+Run a short *MXNet* python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
+
+```python
+>>> import mxnet as mx
+>>> a = mx.nd.ones((2, 3))
+>>> b = a * 2 + 1
+>>> b.asnumpy()
+array([[ 3., 3., 3.],
+ [ 3., 3., 3.]], dtype=float32)
+ ```
+
+Exit the Python terminal.
+
+```python
+>>> exit()
+$
+```
+
+</div>
+
+<!-- Example Python code for CPU -->
+
+<div class="gpu">
+
+Run a short *MXNet* python program to create a 2X3 matrix of ones *a* on a *GPU*, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use *mx.gpu()*, to set *MXNet* context to be GPUs.
+
+```python
+>>> import mxnet as mx
+>>> a = mx.nd.ones((2, 3), mx.gpu())
+>>> b = a * 2 + 1
+>>> b.asnumpy()
+array([[ 3., 3., 3.],
+ [ 3., 3., 3.]], dtype=float32)
+```
+
+</div>
+
+</div>
+
+<!-- Example R code for CPU -->
+
+<div class="linux macos windows">
+ <div class="r">
+ <div class="cpu">
+
+Run a short *MXNet* R program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
+
+```r
+library(mxnet)
+a <- mx.nd.ones(c(2,3), ctx = mx.cpu())
+b <- a * 2 + 1
+b
+```
+
+</div>
+</div>
+</div>
+
+<!-- Example R code for GPU -->
+
+<div class="linux macos windows">
+ <div class="r">
+ <div class="gpu">
+
+Run a short *MXNet* R program to create a 2X3 matrix of ones *a* on a *GPU*, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use *mx.gpu()*, to set *MXNet* context to be GPUs.
+
+```r
+library(mxnet)
+a <- mx.nd.ones(c(2,3), ctx = mx.gpu())
+b <- a * 2 + 1
+b
+```
+
+</div>
+</div>
+</div>
+
+
+<div class="linux">
+ <div class="scala julia perl">
+ <div class="cpu gpu">
+
+Will be available soon.
+
+</div>
+</div>
+</div>
+
+<div class="macos">
+ <div class="scala julia perl">
+ <div class="cpu gpu">
+
+Will be available soon.
+
+</div>
+</div>
+</div>
+
+<div class="windows">
+ <div class="python scala julia perl">
+ <div class="cpu gpu">
+
+Will be available soon.
+
+</div>
+</div>
+</div>
+
+<div class="devices">
+ <div class="raspberry-pi">
+
+Will be available soon.
+
+</div>
+<div class="nvidia-jetson-tx2">
+
+Will be available soon.
+
+</div>
+</div>
diff --git a/versions/master/_sources/get_started/osx_setup.txt b/versions/master/_sources/get_started/osx_setup.txt
new file mode 100644
index 0000000..8e54394
--- /dev/null
+++ b/versions/master/_sources/get_started/osx_setup.txt
@@ -0,0 +1,221 @@
+# Installing MXNet froum source on OS X (Mac)
+
+**NOTE:** For prebuild MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/get_started/install.html).
+
+Installing MXNet is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. Install the supported language-specific packages for MXNet.
+
+**Note:** To change the compilation options for your build, edit the ```make/config.mk``` file and submit a build request with the ```make``` command.
+
+## Prepare Environment for GPU Installation
+
+This section is optional. Skip to next section if you don't plan to use GPUs. If you plan to build with GPU, you need to set up the environment for CUDA and cuDNN.
+
+First, download and install [CUDA 8 toolkit](https://developer.nvidia.com/cuda-toolkit).
+
+Once you have the CUDA Toolkit installed you will need to set up the required environment variables by adding the following to your ~/.bash_profile file:
+
+```bash
+ export CUDA_HOME=/usr/local/cuda
+ export DYLD_LIBRARY_PATH="$CUDA_HOME/lib:$DYLD_LIBRARY_PATH"
+ export PATH="$CUDA_HOME/bin:$PATH"
+```
+
+Reload ~/.bash_profile file and install dependencies:
+```bash
+ . ~/.bash_profile
+ brew install coreutils
+ brew tap caskroom/cask
+```
+
+Then download [cuDNN 5](https://developer.nvidia.com/cudnn).
+
+Unzip the file and change to the cudnn root directory. Move the header files and libraries to your local CUDA Toolkit folder:
+
+```bash
+ $ sudo mv include/cudnn.h /Developer/NVIDIA/CUDA-8.0/include/
+ $ sudo mv lib/libcudnn* /Developer/NVIDIA/CUDA-8.0/lib
+ $ sudo ln -s /Developer/NVIDIA/CUDA-8.0/lib/libcudnn* /usr/local/cuda/lib/
+```
+
+Now we can start to build MXNet.
+
+## Build the Shared Library
+
+### Install MXNet dependencies
+Install the dependencies, required for MXNet, with the following commands:
+- [Homebrew](http://brew.sh/)
+- OpenBLAS and homebrew/science (for linear algebraic operations)
+- OpenCV (for computer vision operations)
+
+```bash
+ # Paste this command in Mac terminal to install Homebrew
+ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+
+ # Insert the Homebrew directory at the top of your PATH environment variable
+ export PATH=/usr/local/bin:/usr/local/sbin:$PATH
+```
+
+```bash
+ brew update
+ brew install pkg-config
+ brew install graphviz
+ brew install openblas
+ brew tap homebrew/science
+ brew install opencv
+ # For getting pip
+ brew install python
+ # For visualization of network graphs
+ pip install graphviz
+ # Jupyter notebook
+ pip install jupyter
+```
+
+### Build MXNet Shared Library
+After you have installed the dependencies, pull the MXNet source code from Git and build MXNet to produce an MXNet library called ```libmxnet.so```.
+
+The file called ```osx.mk``` has the configuration required for building MXNet on OS X. First copy ```make/osx.mk``` into ```config.mk```, which is used by the ```make``` command:
+
+```bash
+ git clone --recursive https://github.com/dmlc/mxnet ~/mxnet
+ cd ~/mxnet
+ cp make/osx.mk ./config.mk
+ echo "USE_BLAS = openblas" >> ./config.mk
+ echo "ADD_CFLAGS += -I/usr/local/opt/openblas/include" >> ./config.mk
+ echo "ADD_LDFLAGS += -L/usr/local/opt/openblas/lib" >> ./config.mk
+ echo "ADD_LDFLAGS += -L/usr/local/lib/graphviz/" >> ./config.mk
+ make -j$(sysctl -n hw.ncpu)
+```
+
+If building with ```GPU``` support, add the following configuration to config.mk and build:
+```bash
+ echo "USE_CUDA = 1" >> ./config.mk
+ echo "USE_CUDA_PATH = /usr/local/cuda" >> ./config.mk
+ echo "USE_CUDNN = 1" >> ./config.mk
+ make
+```
+**Note:** To change build parameters, edit ```config.mk```.
+
+
+
+
+We have installed MXNet core library. Next, we will install MXNet interface package for the programming language of your choice:
+- [R](#install-the-mxnet-package-for-r)
+- [Julia](#install-the-mxnet-package-for-julia)
+- [Scala](#install-the-mxnet-package-for-scala)
+- [Perl](#install-the-mxnet-package-for-perl)
+
+## Install the MXNet Package for R
+You have 2 options:
+1. Building MXNet with the Prebuilt Binary Package
+2. Building MXNet from Source Code
+
+### Building MXNet with the Prebuilt Binary Package
+
+For OS X (Mac) users, MXNet provides a prebuilt binary package for CPUs. The prebuilt package is updated weekly. You can install the package directly in the R console using the following commands:
+
+```r
+ cran <- getOption("repos")
+ cran["dmlc"] <- "https://s3-us-west-2.amazonaws.com/apache-mxnet/R/CRAN/"
+ options(repos = cran)
+ install.packages("mxnet")
+```
+
+### Building MXNet from Source Code
+
+Run the following commands to install the MXNet dependencies and build the MXNet R package.
+
+```r
+ Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
+```
+```bash
+ cd R-package
+ Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
+ cd ..
+ make rpkg
+```
+
+**Note:** R-package is a folder in the MXNet source.
+
+These commands create the MXNet R package as a tar.gz file that you can install as an R package. To install the R package, run the following command, use your MXNet version number:
+
+```bash
+ R CMD INSTALL mxnet_current_r.tar.gz
+```
+
+## Install the MXNet Package for Julia
+The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:
+
+```bash
+ export MXNET_HOME=/<path to>/libmxnet
+```
+
+The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the ```libmxnet.so``` file at ```$MXNET_HOME/lib```. For example, if the root directory of libmxnet is ```~```, you would run the following command:
+
+```bash
+ export MXNET_HOME=/~/libmxnet
+```
+
+You might want to add this command to your ```~/.bashrc``` file. If you do, you can install the Julia package in the Julia console using the following command:
+
+```julia
+ Pkg.add("MXNet")
+```
+
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+
+## Install the MXNet Package for Scala
+Before you build MXNet for Scala from source code, you must complete [building the shared library](#build-the-shared-library). After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Scala package:
+
+```bash
+ make scalapkg
+```
+
+This command creates the JAR files for the assembly, core, and example modules. It also creates the native library in the ```native/{your-architecture}/target directory```, which you can use to cooperate with the core module.
+
+To install the MXNet Scala package into your local Maven repository, run the following command from the MXNet source root directory:
+
+```bash
+ make scalainstall
+```
+
+## Install the MXNet Package for Perl
+Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library).
+After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:
+
+```bash
+ brew install swig
+ sudo sh -c 'curl -L https://cpanmin.us | perl - App::cpanminus'
+ sudo cpanm -q -n PDL Mouse Function::Parameters
+
+ MXNET_HOME=${PWD}
+ export PERL5LIB=${HOME}/perl5/lib/perl5
+
+ cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
+ perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+ make
+ install_name_tool -change lib/libmxnet.so \
+ ${MXNET_HOME}/lib/libmxnet.so \
+ blib/arch/auto/AI/MXNetCAPI/MXNetCAPI.bundle
+ make install
+
+ cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
+ perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+ make
+ install_name_tool -change lib/libmxnet.so \
+ ${MXNET_HOME}/lib/libmxnet.so \
+ blib/arch/auto/AI/NNVMCAPI/NNVMCAPI.bundle
+ make install
+
+ cd ${MXNET_HOME}/perl-package/AI-MXNet/
+ perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+ make install
+```
+
+## Next Steps
+
+* [Tutorials](http://mxnet.io/tutorials/index.html)
+* [How To](http://mxnet.io/how_to/index.html)
+* [Architecture](http://mxnet.io/architecture/index.html)
diff --git a/versions/master/_sources/get_started/raspbian_setup.txt b/versions/master/_sources/get_started/raspbian_setup.txt
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+++ b/versions/master/_sources/get_started/raspbian_setup.txt
@@ -0,0 +1,8 @@
+<!-- This page should be deleted after sometime (Allowing search engines
+to update links) -->
+<meta http-equiv="refresh" content="3; url=http://mxnet.io/get_started/install.html" />
+<!-- Just in case redirection does not work -->
+<p>
+ <a href="http://mxnet.io/get_started/install.html">
+ This content is moved to a new MXNet install page. Redirecting... </a>
+</p>
diff --git a/versions/master/_sources/get_started/tx2_setup.txt b/versions/master/_sources/get_started/tx2_setup.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/get_started/tx2_setup.txt
@@ -0,0 +1,8 @@
+<!-- This page should be deleted after sometime (Allowing search engines
+to update links) -->
+<meta http-equiv="refresh" content="3; url=http://mxnet.io/get_started/install.html" />
+<!-- Just in case redirection does not work -->
+<p>
+ <a href="http://mxnet.io/get_started/install.html">
+ This content is moved to a new MXNet install page. Redirecting... </a>
+</p>
diff --git a/versions/master/_sources/get_started/ubuntu_setup.txt b/versions/master/_sources/get_started/ubuntu_setup.txt
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@@ -0,0 +1,266 @@
+# Installing MXNet on Ubuntu
+
+**NOTE:** For MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/get_started/install.html).
+
+MXNet currently supports Python, R, Julia, Scala, and Perl. For users of R on Ubuntu operating systems, MXNet provides a set of Git Bash scripts that installs all of the required MXNet dependencies and the MXNet library.
+
+The simple installation scripts set up MXNet for Python and R on computers running Ubuntu 12 or later. The scripts install MXNet in your home folder ```~/mxnet```.
+
+## Prepare environment for GPU Installation
+
+If you plan to build with GPU, you need to set up the environment for CUDA and CUDNN.
+
+First, download and install [CUDA 8 toolkit](https://developer.nvidia.com/cuda-toolkit).
+
+Then download [cudnn 6](https://developer.nvidia.com/cudnn).
+
+Unzip the file and change to the cudnn root directory. Move the header and libraries to your local CUDA Toolkit folder:
+
+```bash
+ tar xvzf cudnn-8.0-linux-x64-v6.0.tgz
+ sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
+ sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
+ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
+ sudo ldconfig
+```
+
+Finally, add configurations to config.mk file:
+
+```bash
+ cp make/config.mk .
+```
+
+## Quick Installation
+
+### Install MXNet for R
+
+MXNet requires R-version to be 3.2.0 and above. If you are running an earlier version of R, run below commands to update your R version, before running the installation script.
+
+```bash
+ sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9
+ sudo add-apt-repository ppa:marutter/rdev
+
+ sudo apt-get update
+ sudo apt-get upgrade
+ sudo apt-get install r-base r-base-dev
+```
+
+To install MXNet for R:
+
+```bash
+ # Clone mxnet repository. In terminal, run the commands WITHOUT "sudo"
+ git clone https://github.com/dmlc/mxnet.git ~/mxnet --recursive
+
+ cd ~/mxnet
+ cp make/config.mk .
+ # If building with GPU, add configurations to config.mk file:
+ echo "USE_CUDA=1" >>config.mk
+ echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk
+ echo "USE_CUDNN=1" >>config.mk
+
+ cd ~/mxnet/setup-utils
+ bash install-mxnet-ubuntu-r.sh
+```
+The installation script to install MXNet for R can be found [here](https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-ubuntu-r.sh).
+
+## Standard installation
+
+Installing MXNet is a two-step process:
+
+1. Build the shared library from the MXNet C++ source code.
+2. Install the supported language-specific packages for MXNet.
+
+**Note:** To change the compilation options for your build, edit the ```make/config.mk``` file and submit a build request with the ```make``` command.
+
+### Build the Shared Library
+
+On Ubuntu versions 13.10 or later, you need the following dependencies:
+
+**Step 1** Install build tools and git.
+```bash
+ sudo apt-get update
+ sudo apt-get install -y build-essential git
+```
+
+**Step 2** Install OpenBLAS.
+
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) library for accelerated numerical computations on CPU machine. There are several flavors of BLAS libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+
+```bash
+ sudo apt-get install -y libopenblas-dev
+```
+
+**Step 3** Install OpenCV.
+
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+
+```bash
+ sudo apt-get install -y libopencv-dev
+```
+
+**Step 4** Download MXNet sources and build MXNet core shared library.
+
+If building on CPU:
+
+```bash
+ git clone --recursive https://github.com/dmlc/mxnet
+ cd mxnet
+ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas
+```
+
+If building on GPU:
+
+```bash
+ git clone --recursive https://github.com/dmlc/mxnet
+ cd mxnet
+ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
+```
+
+*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in `make/config.mk`.
+
+Executing these commands creates a library called ```libmxnet.so```.
+
+Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/) used for running MXNet tutorials and examples.
+
+```bash
+ sudo apt-get install -y python-pip
+ sudo pip install graphviz
+ sudo pip install Jupyter
+```
+
+
+
+We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:
+- [R](#install-the-mxnet-package-for-r)
+- [Julia](#install-the-mxnet-package-for-julia)
+- [Scala](#install-the-mxnet-package-for-scala)
+- [Perl](#install-the-mxnet-package-for-perl)
+
+### Install the MXNet Package for R
+
+Run the following commands to install the MXNet dependencies and build the MXNet R package.
+
+```r
+ Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
+```
+```bash
+ cd R-package
+ Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
+ cd ..
+ make rpkg
+```
+
+**Note:** R-package is a folder in the MXNet source.
+
+These commands create the MXNet R package as a tar.gz file that you can install as an R package. To install the R package, run the following command, use your MXNet version number:
+
+```bash
+ R CMD INSTALL mxnet_current_r.tar.gz
+```
+
+### Install the MXNet Package for Julia
+
+The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:
+
+```bash
+ export MXNET_HOME=/<path to>/libmxnet
+```
+
+The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the ```libmxnet.so``` file at ```$MXNET_HOME/lib```. For example, if the root directory of libmxnet is ```~```, you would run the following command:
+
+```bash
+ export MXNET_HOME=/~/libmxnet
+```
+
+You might want to add this command to your ```~/.bashrc``` file. If you do, you can install the Julia package in the Julia console using the following command:
+
+```julia
+ Pkg.add("MXNet")
+```
+
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+
+### Install the MXNet Package for Scala
+There are two ways to install the MXNet package for Scala:
+
+* Use the prebuilt binary package
+
+* Build the library from source code
+
+#### Use the Prebuilt Binary Package
+For Linux users, MXNet provides prebuilt binary packages that support computers with either GPU or CPU processors. To download and build these packages using ```Maven```, change the ```artifactId``` in the following Maven dependency to match your architecture:
+
+```HTML
+<dependency>
+ <groupId>ml.dmlc.mxnet</groupId>
+ <artifactId>mxnet-full_<system architecture></artifactId>
+ <version>0.1.1</version>
+</dependency>
+```
+
+For example, to download and build the 64-bit CPU-only version for Linux, use:
+
+```HTML
+<dependency>
+ <groupId>ml.dmlc.mxnet</groupId>
+ <artifactId>mxnet-full_2.10-linux-x86_64-cpu</artifactId>
+ <version>0.1.1</version>
+</dependency>
+```
+
+If your native environment differs slightly from the assembly package, for example, if you use the openblas package instead of the atlas package, it's better to use the mxnet-core package and put the compiled Java native library in your load path:
+
+```HTML
+<dependency>
+ <groupId>ml.dmlc.mxnet</groupId>
+ <artifactId>mxnet-core_2.10</artifactId>
+ <version>0.1.1</version>
+</dependency>
+```
+
+#### Build the Library from Source Code
+Before you build MXNet for Scala from source code, you must complete [building the shared library](#build-the-shared-library). After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Scala package:
+
+```bash
+ make scalapkg
+```
+
+This command creates the JAR files for the assembly, core, and example modules. It also creates the native library in the ```native/{your-architecture}/target directory```, which you can use to cooperate with the core module.
+
+To install the MXNet Scala package into your local Maven repository, run the following command from the MXNet source root directory:
+
+```bash
+ make scalainstall
+```
+### Install the MXNet Package for Perl
+
+Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library). After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:
+
+```bash
+ sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
+ cpanm -q -L "${HOME}/perl5" Function::Parameters
+
+ MXNET_HOME=${PWD}
+ export LD_LIBRARY_PATH=${MXNET_HOME}/lib
+ export PERL5LIB=${HOME}/perl5/lib/perl5
+
+ cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
+ perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+ make install
+
+ cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
+ perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+ make install
+
+ cd ${MXNET_HOME}/perl-package/AI-MXNet/
+ perl Makefile.PL INSTALL_BASE=${HOME}/perl5
+ make install
+```
+
+**Note - ** You are more than welcome to contribute easy installation scripts for other operating systems and programming languages, see [community page](http://mxnet.io/community/index.html) for contributors guidelines.
+
+## Next Steps
+
+* [Tutorials](http://mxnet.io/tutorials/index.html)
+* [How To](http://mxnet.io/how_to/index.html)
+* [Architecture](http://mxnet.io/architecture/index.html)
diff --git a/versions/master/_sources/get_started/why_mxnet.txt b/versions/master/_sources/get_started/why_mxnet.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/get_started/why_mxnet.txt
@@ -0,0 +1,199 @@
+# Why MXNet?
+
+Probably, if you've stumbled upon this page, you've heard of _deep learning_.
+Deep learning denotes the modern incarnation of neural networks,
+and it's the technology behind recent breakthroughs
+in self-driving cars, machine translation, speech recognition and more.
+While widespread interest in deep learning took off in 2012,
+deep learning has become an indispensable tool for countless industries.
+
+
+
+It might not come as a surprise that researchers
+have investigated neural networks for decades.
+Warren McCulloch and Walter Pitts
+suggested the forerunner of today's artificial neurons back in 1943.
+Each neuron is connected to other neurons along _edges_, analogous to the synapses that connect real neurons.
+And associated with each edge is a _weight_ that indicates whether the connection is excitatory or inhibitatory and the strength of the connection.
+
+
+
+In the 1980s, the modern version of neural networks took shape.
+Researchers arranged artificial neurons into _layers_.
+Neurons in any layer get input from the neurons in the layers below them.
+And, in turn, their output feeds into the neurons in the layer above.
+Typically, the lowest layer represents the _input_ to a neural network.
+After computing the values of each layer, the _output_ values are read out from the topmost layer.
+The behavior of the network is determined by the setting of the weights.
+And the process of _learning_ in neural networks
+is precisely the process of searching for good settings of these _weights_.
+
+All that we need is an algorithm that tells us how to perform this search.
+And since David Rumelhart and colleagues
+introduced the _backpropagation_ learning algorithm to train neural networks,
+nearly all the major ideas have been in place.
+Still, for many years neural networks took a backseat
+to classical statistical methods like logistic regression and support vector machines (SVMs).
+So you might reasonably ask, what's changed to garner such interest?
+
+## Scale and Computation
+The two biggest factors driving innovation in deep learning now are data and computation.
+With distributed cloud computing and parallelism across GPU cores,
+we can train models millions of times faster than researchers could in the 1980s.
+The availability of large, high-quality datasets is another factor driving the field forward.
+In the 1990s, the best datasets in computer vision had thousands of low-resolution images and ground truth assignments to a small number of classes.
+Today, researchers cut their teeth on ImageNet, a massive dataset containing millions of high-resolution images from a thousand distinct classes.
+The falling price of storage and high network bandwidth
+make it affordable to work with big data at will.
+
+In this new world, with bigger datasets and abundant computation,
+neural networks dominate on most pattern recognition problems.
+Over the last five years, neural networks have come to dominate on nearly every problem in computer vision,
+replacing classical models and hand-engineered features.
+Similarly, nearly every production speech recognition system now relies on neural networks,
+where replacing the hidden Markov models that previously held sway.
+
+
+
+While GPUs and clusters present a huge opportunity for accelerating neural network training,
+adapting traditional machine learning code
+to take advantage of these resources can be challenging.
+The familiar scientific computing stacks (Matlab, R, or NumPy & SciPy)
+give no straight-forward way to exploit these distributed resources.
+
+Acceleration libraries like _MXNet_ offer powerful tools
+to help developers exploit the full capabilities of GPUs and cloud computing.
+While these tools are generally useful and applicable to any mathematical computation, _MXNet_ places a special emphasis on speeding up the development and deployment of large-scale deep neural networks. In particular, we offer the following capabilities:
+* __Device Placement:__ With _MXNet_, it's easy to specify where each data structures should live.
+* __Multi-GPU training__: _MXNet_ makes it easy to scale computation with number of available GPUs.
+* __Automatic differentiation__: _MXNet_ automates the derivative calculations that once bogged down neural network research.
+* __Optimized Predefined Layers__: While you can code up your own layers in _MXNet_, the predefined layers are optimized for speed, outperforming competing libraries.
+
+## Deep Nets on Fast Computers
+While MXNet can accelerate any numerical computation,
+we developed the library with neural networks in mind.
+However you plan to use MXNet, neural networks make for a powerful motivating example to display MXNet's capabilities.
+
+Neural networks are just functions for transforming input arrays `X` into output arrays `Y`.
+In the case of image classification, `X` might represent the pixel values of an image, and `Y` might represent the corresponding probabilities that the image belongs to each of `10` classes.
+For language translation, `X` and `Y` both might denote sequences of words. We'll revisit the way you might represent sequences in subsequent tutorials - so for now it's safe to think of `X` and `Y` as fixed length vectors.
+
+To perform this mapping, neural networks stack _layers_ of computation. Each layer consists of a linear function followed by a nonlinear transformation. In _MXNet_ we might express this as:
+~~~~
+hidden_linear = mx.sym.dot(X, W)
+hidden_activation = mx.sym.tanh(hidden_linear)
+~~~~
+The linear transformations consist of multiplication by parameter arrays (`W` above).
+When we talk about learning we mean finding the right set of values for `W`.
+With just one layer, we can implement the familiar family of linear models,
+including linear and logistic regression, linear support vector machines (SVMs), and the perceptron algorithm.
+With more layers and a few clever constraints, we can implement all of today's state-of-the-art deep learning techniques.
+
+Of course, tens or hundreds of matrix multiplications can be computationally taxing.
+Generally, these linear operations are the computational bottleneck.
+Fortunately, linear operators can be parallelized trivially across the thousands of cores on a GPU.
+But low-level GPU programming requires specialized skills that are not common even among leading researchers in the ML community. Moreover, even for CUDA experts, implementing a new neural network architecture shouldn't require weeks of programming to implement low-level linear algebra operations. That's where _MXNet_ comes in.
+* _MXNet_ provides optimized numerical computation for GPUs and distributed ecosystems, from the comfort of high-level environments like Python and R
+* _MXNet_ automates common workflows, so standard neural networks can be expressed concisely in just a few lines of code
+
+Now let's take a closer look at the computational demands of neural networks
+and give a sense of how _MXNet_ helps us to write better, faster, code.
+Say we have a neural network trained to recognize spam from the content of emails.
+The emails may be streaming from an online service (at inference time),
+or from a large offline dataset __D__ (at training time).
+In either case, the dataset typically must be managed by the CPU.
+
+
+
+To compute the transformation of a neural network quickly, we need both the parameters and data points to make it into GPU memory. For any example _X_, the parameters _W_ are the same. Moreover the size of the model tends to dwarf the size of an individual example. So we might arrive at the natural insight that parameters should always live on the GPU, even if the dataset itself must live on the CPU or stream in. This prevents IO from becoming the bottleneck during training or inference.
+
+Fortunately, _MXNet_ makes this kind of assignment easy.
+~~~~
+import mxnet.ndarray as nd
+
+X = nd.zeros((10000, 40000), mx.cpu(0)) #Allocate an array to store 1000 datapoints (of 40k dimensions) that lives on the CPU
+W1 = nd.zeros(shape=(40000, 1024), mx.gpu(0)) #Allocate a 40k x 1024 weight matrix on GPU for the 1st layer of the net
+W2 = nd.zeros(shape=(1024, 10), mx.gpu(0)) #Allocate a 1024 x 1024 weight matrix on GPU for the 2nd layer of the net
+~~~~
+
+<!-- * __Talk about how mxnet also makes it easy to assign a context (on which device the computation happens__ -->
+Similarly, _MXNet_ makes it easy to specify the computing device
+
+~~~~
+with mx.Context(mx.gpu()): # Absent this statement, by default, MXNet will execute on CPU
+ h = nd.tanh(nd.dot(X, W1))
+ y = nd.sigmoid(nd.dot(h1, W2))
+~~~~
+
+Thus, with only a high-level understanding of how our numerical computation maps onto an execution environment, _MXNet_ allows us to exert fine-grained control when needed.
+
+## Nuts and Bolts
+
+MXNet supports two styles of programming: _imperative programming_ (supported by the _NDArray_ API) and _symbolic programming_ (supported by the _Symbol_ API). In short, imperative programming is the style that you're likely to be most familiar with. Here if A and B are variables denoting matrices, then `C = A + B` is a piece of code that _when executed_ sums the values referenced by `A` and `B` and stores their sum `C` in a new variable. Symbolic programming, on the other hand, allows f [...]
+
+
+### Imperative Programming with _NDArray_
+If you're familiar with NumPy, then the mechanics of _NDArray_ should be old hat. Like the corresponding `numpy.ndarray`, `mxnet.ndarray` (`mxnet.nd` for short) allows us to represent and manipulate multi-dimensional, homogenous arrays of fixed-size components. Converting between the two is effortless:
+
+~~~~
+# Create a numpy array from an mxnet NDArray
+A_np = np.array([[0,1,2,3,4],[5,6,7,8,9]])
+A_nd = nd.array(A)
+
+# Convert back to a numpy array
+A2_np = A_nd.asnumpy()
+~~~~
+
+Other deep learning libraries tend to rely on NumPy exclusively for imperative programming and the syntax.
+So you might reasonably wonder, why do we need to bother with _NDArray_?
+Put simply, other libraries only reap the advantages of GPU computing when executing symbolic functions. By using _NDArray_, _MXNet_ users can specify device context and run on GPUs. In other words, _MXNet_ gives you access to the high-speed computation for imperative operations that Tensorflow and Theano only give for symbolic operations.
+
+
+~~~~
+X = mx.nd.array([[1,2],[3,4]])
+Y = mx.nd.array([[5,6],[7,8]])
+result = X + Y
+~~~~
+
+
+### Symbolic Programming in _MXNet_
+
+In addition to providing fast math operations through NDArray, _MXNet_ provides an interface for defining operations abstractly via a computation graph.
+With `mxnet.symbol`, we define operations abstractly in terms of place holders. For example, in the following code `a` and `b` stand in for real values that will be supplied at run time.
+When we call `c = a+b`, no numerical computation is performed. This operation simply builds a graph that defines the relationship between `a`, `b` and `c`. In order to perform a real calculation, we need to bind `c` to real values.
+
+~~~~
+a = mx.sym.Variable('a')
+b = mx.sym.Variable('b')
+c = a + b
+executor = c.bind(mx.cpu(), {'a': X, 'b': Y})
+result = executor.forward()
+~~~~
+
+
+Symbolic computation is useful for several reasons. First, because we define a full computation graph before executing it, _MXNet_ can perform sophisticated optimizations to eliminate unnecessary or repeated work. This tends to give better performance than imperative programming. Second, because we store the relationships between different variables in the computation graph, _MXNet_ can then perform efficient auto-differentiation.
+
+
+## Building Models with _MXNet_ Layers
+In addition to providing a general-purpose toolkit for optimizing mathematical operations,
+_MXNet_ provides predefined neural network layers.
+This higher-level interface has several benefits.
+
+* Predefined layers allow you to express large models concisely. Repetitive work, like allocating parameters and inferring their dimensions are eliminated.
+* Layers are written directly in C++, giving even faster performance than equivalent layers implemented manually in Python.
+
+
+~~~~
+data = mx.symbol.Variable('data')
+fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
+act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")
+fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
+act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu")
+fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=num_classes)
+mlp = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax')
+~~~~
+
+
+## Conclusions
+Given its combination of high performance, clean code, access to a high-level API, and low-level control, _MXNet_ stands out as a unique choice among deep learning frameworks.
+
diff --git a/versions/master/_sources/get_started/windows_setup.txt b/versions/master/_sources/get_started/windows_setup.txt
new file mode 100644
index 0000000..86104c6
--- /dev/null
+++ b/versions/master/_sources/get_started/windows_setup.txt
@@ -0,0 +1,291 @@
+# Installing MXNet in Windows
+
+On Windows, you can download and install the prebuilt MXNet package, or download, build, and install MXNet yourself.
+
+## Build the Shared Library
+You can either use a prebuilt binary package or build from source to build the MXNet shared library - ```libmxnet.dll```.
+
+### Installing the Prebuilt Package on Windows
+MXNet provides a prebuilt package for Windows. The prebuilt package includes the MXNet library, all of the dependent third-party libraries, a sample C++ solution for Visual Studio, and the Python installation script. To install the prebuilt package:
+
+1. Download the latest prebuilt package from the [Releases](https://github.com/dmlc/mxnet/releases) tab of MXNet.
+ There are two versions. One with GPU support (using CUDA and CUDNN v3), and one without GPU support. Choose the version that suits your hardware configuration. For more information on which version works on each hardware configuration, see [Requirements for GPU](http://mxnet.io/get_started/setup.html#requirements-for-using-gpus).
+2. Unpack the package into a folder, with an appropriate name, such as ```D:\MXNet```.
+3. Open the folder, and install the package by double-clicking ```setupenv.cmd```. This sets up all of the environment variables required by MXNet.
+4. Test the installation by opening the provided sample C++ Visual Studio solution and building it.
+
+
+
+This produces a library called ```libmxnet.dll```.
+
+### Building and Installing Packages on Windows
+
+To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
+
+1. If [Microsoft Visual Studio 2013](https://www.visualstudio.com/downloads/) is not already installed, download and install it. You can download and install the free community edition.
+2. Install [Visual C++ Compiler Nov 2013 CTP](https://www.microsoft.com/en-us/download/details.aspx?id=41151).
+3. Back up all of the files in the ```C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC``` folder to a different location.
+4. Copy all of the files in the ```C:\Program Files (x86)\Microsoft Visual C++ Compiler Nov 2013 CTP``` folder (or the folder where you extracted the zip archive) to the ```C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC``` folder, and overwrite all existing files.
+5. Download and install [OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download).
+6. Unzip the OpenCV package.
+7. Set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory```.
+8. If you don't have the Intel Math Kernel Library (MKL) installed, download and install [OpenBlas](http://sourceforge.net/projects/openblas/files/v0.2.14/).
+9. Set the environment variable ```OpenBLAS_HOME``` to point to the ```OpenBLAS``` directory that contains the ```include``` and ```lib``` directories. Typically, you can find the directory in ```C:\Program files (x86)\OpenBLAS\```.
+10. Download and install [CuDNN](https://developer.nvidia.com/cudnn). To get access to the download link, register as an NVIDIA community user.
+
+After you have installed all of the required dependencies, build the MXNet source code:
+
+1. Download the MXNet source code from [GitHub](https://github.com/dmlc/mxnet).
+2. Use [CMake](https://cmake.org/) to create a Visual Studio solution in ```./build```.
+3. In Visual Studio, open the solution file,```.sln```, and compile it.
+These commands produce a library called ```mxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.
+
+
+
+
+Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/) used for running MXNet tutorials and examples.
+- Install ```graphviz``` by downloading MSI installer from [Graphviz Download Page](http://www.graphviz.org/Download_windows.php).
+**Note** Make sure to add graphviz executable path to PATH environment variable. Refer [here for more details](http://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft)
+- Install ```Jupyter``` by installing [Anaconda for Python 2.7](https://www.continuum.io/downloads)
+**Note** Do not install Anaconda for Python 3.5. MXNet has few compatibility issue with Python 3.5.
+
+
+
+We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:
+- [Python](#install-the-mxnet-package-for-python)
+- [R](#install-mxnet-for-r)
+- [Julia](#install-the-mxnet-package-for-julia)
+- [Scala](#install-the-mxnet-package-for-scala)
+
+## Install MXNet for Python
+
+1. Install ```Python``` using windows installer available [here](https://www.python.org/downloads/release/python-2712/).
+2. Install ```Numpy``` using windows installer available [here](http://scipy.org/install.html).
+3. Next, we install Python package interface for MXNet. You can find the Python interface package for [MXNet on GitHub](https://github.com/dmlc/mxnet/tree/master/python/mxnet).
+
+```bash
+ # Assuming you are in root mxnet source code folder
+ cd python
+ sudo python setup.py install
+```
+Done! We have installed MXNet with Python interface. Run below commands to verify our installation is successful.
+```bash
+ # Open Python terminal
+ python
+
+ # You should be able to import mxnet library without any issues.
+ >>> import mxnet as mx;
+ >>> a = mx.nd.ones((2, 3));
+ >>> print ((a*2).asnumpy());
+ [[ 2. 2. 2.]
+ [ 2. 2. 2.]]
+```
+We actually did a small tensor computation using MXNet! You are all set with MXNet on your Windows machine.
+
+## Install MXNet for R
+MXNet for R is available for both CPUs and GPUs.
+
+### Installing MXNet on a Computer with a CPU Processor
+
+To install MXNet on a computer with a CPU processor, choose from two options:
+
+* Use the prebuilt binary package
+* Build the library from source code
+
+#### Installing MXNet with the Prebuilt Binary Package
+For Windows users, MXNet provides prebuilt binary packages.
+You can install the package directly in the R console.
+
+For CPU-only package:
+
+```r
+ cran <- getOption("repos")
+ cran["dmlc"] <- "https://s3-us-west-2.amazonaws.com/apache-mxnet/R/CRAN/"
+ options(repos = cran)
+ install.packages("mxnet")
+```
+
+For GPU-enabled package:
+
+```r
+ cran <- getOption("repos")
+ cran["dmlc"] <- "https://s3-us-west-2.amazonaws.com/apache-mxnet/R/CRAN/GPU"
+ options(repos = cran)
+ install.packages("mxnet")
+```
+
+#### Building MXNet from Source Code
+
+Run the following commands to install the MXNet dependencies and build the MXNet R package.
+
+```r
+ Rscript -e "install.packages('devtools', repo = 'https://cloud.r-project.org/')"
+```
+
+```bash
+ cd R-package
+ Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org/')); install_deps(dependencies = TRUE)"
+ cd ..
+ make rpkg
+```
+
+**Note:** R-package is a folder in the MXNet source.
+
+These commands create the MXNet R package as a tar.gz file that you can install as an R package. To install the R package, run the following command, use your MXNet version number:
+
+```bash
+ R CMD INSTALL mxnet_current_r.tar.gz
+```
+
+### Installing MXNet on a Computer with a GPU Processor
+
+To install MXNet R package on a computer with a GPU processor, you need the following:
+
+* Microsoft Visual Studio 2013
+
+* The NVidia CUDA Toolkit
+
+* The MXNet package
+
+* CuDNN (to provide a Deep Neural Network library)
+
+To install the required dependencies and install MXNet for R:
+
+1. Install the [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit). The CUDA Toolkit depends on Visual Studio. To check whether your GPU is compatible with the CUDA Toolkit and for information on installing it, see NVidia's [CUDA Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/).
+3. Clone the MXNet github repo.
+
+```sh
+git clone --recursive https://github.com/dmlc/mxnet
+```
+
+The `--recursive` is to clone all the submodules used by MXNet. You will be editing the ```"/mxnet/R-package"``` folder.
+4. Download prebuilt GPU-enabled MXNet libraries for Windows from https://github.com/yajiedesign/mxnet/releases. You will need `mxnet_x64_vc14_gpu.7z` and `prebuildbase_win10_x64_vc14.7z`.
+5. Download and install [CuDNN](https://developer.nvidia.com/cudnn).
+6. Create a folder called ```R-package/inst/libs/x64```. MXNet supports only 64-bit operating systems, so you need the x64 folder.
+7. Copy the following shared libraries (.dll files) into the ```R-package/inst/libs/x64``` folder:
+```
+cublas64_80.dll
+cudart64_80.dll
+cudnn64_5.dll
+curand64_80.dll
+libgcc_s_seh-1.dll
+libgfortran-3.dll
+libmxnet.dll
+libmxnet.lib
+libopenblas.dll
+libquadmath-0.dll
+nvrtc64_80.dll
+```
+These dlls can be found in `prebuildbase_win10_x64_vc14/3rdparty/cudart`, `prebuildbase_win10_x64_vc14/3rdparty/openblas/bin`, `mxnet_x64_vc14_gpu/build`, `mxnet_x64_vc14_gpu/lib` and the `cuDNN` downloaded from NVIDIA.
+8. Copy the header files from `dmlc`, `mxnet` and `nnvm` into `./R-package/inst/include`. It should look like:
+
+```
+./R-package/inst
+└── include
+ ├── dmlc
+ ├── mxnet
+ └── nnvm
+```
+9. Make sure that R is added to your ```PATH``` in the environment variables. Running the ```where R``` command at the command prompt should return the location.
+10. Now open the Windows CMD and change the directory to the `mxnet` folder. Then use the following commands
+to build R package:
+
+```bat
+echo import(Rcpp) > R-package\NAMESPACE
+echo import(methods) >> R-package\NAMESPACE
+Rscript -e "install.packages('devtools', repos = 'https://cloud.r-project.org')"
+cd R-package
+Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org')); install_deps(dependencies = TRUE)"
+cd ..
+
+R CMD INSTALL --no-multiarch R-package
+
+Rscript -e "require(mxnet); mxnet:::mxnet.export('R-package')"
+rm R-package/NAMESPACE
+Rscript -e "require(devtools); install_version('roxygen2', version = '5.0.1', repos = 'https://cloud.r-project.org/', quiet = TRUE)"
+Rscript -e "require(roxygen2); roxygen2::roxygenise('R-package')"
+
+R CMD INSTALL --build --no-multiarch R-package
+```
+
+**Note:** To maximize its portability, the MXNet library is built with the Rcpp end. Computers running Windows need [MSVC](https://en.wikipedia.org/wiki/Visual_C%2B%2B) (Microsoft Visual C++) to handle CUDA toolchain compatibilities.
+
+## Install the MXNet Package for Julia
+The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:
+
+```bash
+export MXNET_HOME=/<path to>/libmxnet
+```
+
+The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the ```libmxnet.so``` file at ```$MXNET_HOME/lib```. For example, if the root directory of libmxnet is ```~```, you would run the following command:
+
+```bash
+ export MXNET_HOME=/~/libmxnet
+```
+
+You might want to add this command to your ```~/.bashrc``` file. If you do, you can install the Julia package in the Julia console using the following command:
+
+```julia
+ Pkg.add("MXNet")
+```
+
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+
+## Installing the MXNet Package for Scala
+There are four ways to install the MXNet package for Scala:
+
+* Use the prebuilt binary package
+
+* Build the library from source code
+
+### Use the Prebuilt Binary Package
+For Linux and OS X (Mac) users, MXNet provides prebuilt binary packages that support computers with either GPU or CPU processors. To download and build these packages using ```Maven```, change the ```artifactId``` in the following Maven dependency to match your architecture:
+
+```HTML
+<dependency>
+ <groupId>ml.dmlc.mxnet</groupId>
+ <artifactId>mxnet-full_<system architecture></artifactId>
+ <version>0.1.1</version>
+</dependency>
+```
+
+For example, to download and build the 64-bit CPU-only version for Linux, use:
+
+```HTML
+<dependency>
+ <groupId>ml.dmlc.mxnet</groupId>
+ <artifactId>mxnet-full_2.10-linux-x86_64-cpu</artifactId>
+ <version>0.1.1</version>
+</dependency>
+```
+
+If your native environment differs slightly from the assembly package, for example, if you use the openblas package instead of the atlas package, it's better to use the mxnet-core package and put the compiled Java native library in your load path:
+
+```HTML
+<dependency>
+ <groupId>ml.dmlc.mxnet</groupId>
+ <artifactId>mxnet-core_2.10</artifactId>
+ <version>0.1.1</version>
+</dependency>
+```
+
+### Build the Library from Source Code
+Before you build MXNet for Scala from source code, you must complete [Step 1. Build the Shared Library](#build-the-shared-library). After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Scala package:
+
+```bash
+ make scalapkg
+```
+
+This command creates the JAR files for the assembly, core, and example modules. It also creates the native library in the ```native/{your-architecture}/target directory```, which you can use to cooperate with the core module.
+
+To install the MXNet Scala package into your local Maven repository, run the following command from the MXNet source root directory:
+
+```bash
+ make scalainstall
+```
+
+## Next Steps
+
+* [Tutorials](http://mxnet.io/tutorials/index.html)
+* [How To](http://mxnet.io/how_to/index.html)
+* [Architecture](http://mxnet.io/architecture/index.html)
diff --git a/versions/master/_sources/how_to/bucketing.txt b/versions/master/_sources/how_to/bucketing.txt
new file mode 100644
index 0000000..bc59c97
--- /dev/null
+++ b/versions/master/_sources/how_to/bucketing.txt
@@ -0,0 +1,96 @@
+# Bucketing in MXNet
+When we train recurrent neural networks (RNNs), we _unroll_ the network in time.
+For a single example of length T, we would unroll the network T steps.
+In the unrolled view, the network is acyclic and thus we can think of it
+as a feedforward neural network in which weights are tied across adjacent time steps.
+The unrolled view allows us to train the network via the traditional backpropagation algorithm -
+in this case, we call the algorithm _back-propagation through time_.
+
+Things get complicated when we work with datasets where the example sequences have varying lengths.
+In the _recurrent_ view, each example can pass through the same architecture.
+But in the _unrolled_ view, each example requires a different number of unrollings, and thus corresponds to a _slightly_ different feedforward network.
+If we train on one example at a time, we can simply unroll the desired amount on each training iteration.
+But things get more complicated when we try to perform mini-batch training.
+A naive approach might be to pad all the sequences so that they appear to have the length of the longest example.
+However, this could be wasteful because on shorter sequences, most of the computations are done on padded data.
+
+Borrowed from [TensorFlow's sequence training code](https://www.tensorflow.org/versions/r0.7/tutorials/seq2seq/index.html),
+_bucketing_ offers an effective solution to make minibatches out of varying-length sequences.
+Instead of unrolling the network to the maximum possible sequence length,
+we unroll multiple instances of different lengths (e.g., length 5, 10, 20, 30).
+During training, we use the most appropriate unrolled model
+for each mini-batch of data.
+Although the models *with different numbers of unrollings*
+have different feedforward architectures,
+their parameters are shared in time.
+_MXNet_ reuses the internal memory buffers among all executors.
+
+For simple RNNs, you can use a for loop to explicitly
+go over the input sequences and perform a backpropagation-through-time
+by maintaining the connection of the states and gradients through time.
+However, this implementation approach results in slow processing.
+This approach works with variable length sequences. For more complicated models (e.g., translation that uses a sequence-to-sequence model), explicitly unrolling is the easiest way. In this example, we introduce the MXNet APIs that allows us to implement bucketing.
+
+## Variable-length Sequence Training for PTB
+
+We use the [PennTreeBank language model example](https://github.com/dmlc/mxnet/tree/master/example/rnn) for this example. If you are not familiar with this example, see [this tutorial (in Julia)](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html) first.
+
+In this example, we use a simple architecture
+consisting of a word-embedding layer
+followed by two LSTM layers.
+In the original example,
+the model is unrolled explicitly in time for a fixed length of 32.
+ <!-- In this tutorial, we show how to use bucketing to implement variable-length sequence training. -->
+To enable bucketing, MXNet needs to know how to construct a new unrolled symbolic architecture for a different sequence length. To achieve this, instead of constructing a model with a fixed `Symbol`, we use a callback function that generates a new `Symbol` on a *bucket key*.
+
+
+```python
+model = mx.mod.BucketingModule(
+ sym_gen = sym_gen,
+ default_bucket_key = data_train.default_bucket_key,
+ context = contexts)
+```
+
+`sym_gen` must be a function that takes one argument, `bucket_key`, and returns a `Symbol` for this bucket. We'll use the sequence length as the bucket key. A bucket key could be anything. For example, in neural translation, because different combinations of input-output sequence lengths correspond to different unrolling, the bucket key could be a pair of lengths.
+
+```python
+def sym_gen(seq_len):
+ return lstm_unroll(num_lstm_layer, seq_len, len(vocab),
+ num_hidden=num_hidden, num_embed=num_embed,
+ num_label=len(vocab))
+```
+The data iterator needs to report the `default_bucket_key`, which allows MXNet to do some parameter initialization before reading the data. Now the model is capable of training with different buckets by sharing the parameters and intermediate computation buffers between bucket executors.
+
+To train, we still need to add some extra bits to our `DataIter`. Apart from reporting the `default_bucket_key` as mentioned previously, we also need to report the current `bucket_key` for each mini-batch. More specifically, the `DataBatch` object returned in each mini-batch by the `DataIter` should contain the following additional properties:
+
+* `bucket_key`: The bucket key that corresponds to this batch of data.
+In our example, it is the sequence length for this batch of data.
+If the executors corresponding to this bucket key have not yet been created,
+they will be constructed according to the symbolic model returned by `gen_sym` on this bucket key.
+The executors will be cached for future use.
+Note that generated `Symbol`s could be arbitrary,
+but they should all have the same trainable parameters and auxiliary states.
+* `provide_data`: The same information reported by the `DataIter` object.
+Because each bucket now corresponds to a different architecture,
+they could have different input data.
+Also, make sure that the `provide_data` information returned by the `DataIter` object
+is compatible with the architecture for `default_bucket_key`.
+* `provide_label`: The same as `provide_data`.
+
+The `DataIter` is responsible for grouping the data into different buckets.
+Assuming that randomization is enabled, at each iteration,
+`DataIter` chooses a random bucket (according to a distribution balanced by the bucket sizes),
+and then randomly chooses sequences from that bucket to form a mini-batch.
+It also applies padding for sequences of different length within the mini-batch as necessary.
+
+For a full, working implementation of a `DataIter`
+that reads text sequences by as described above, see [example/rnn/lstm_ptb_bucketing.py](https://github.com/dmlc/mxnet/blob/master/example/rnn/lstm_bucketing.py).
+In this example, you can use bucketing with a static configuration (e.g., `buckets = [10, 20, 30, 40, 50, 60]`), or let MXNet generate buckets automatically according to the characteristics of the dataset (`buckets = []`). The latter approach is implemented by adding a bucket as long as the number of sequences assigned to that bucket is exceeds some minimum count. For more information, see [default_gen_buckets()](https://github.com/dmlc/mxnet/blob/master/example/rnn/old/bucket_io.py#L43).
+
+## Beyond Sequence Training
+
+In this example, we briefly explained how the bucketing API works.
+However, the API is not limited to bucketing by sequence lengths.
+The bucket key can be an arbitrary object, as long
+as the architecture returned by `gen_sym`
+is compatible with (has the same set of parameters) as the object.
diff --git a/versions/master/_sources/how_to/caffe.txt b/versions/master/_sources/how_to/caffe.txt
new file mode 100644
index 0000000..a9bab3c
--- /dev/null
+++ b/versions/master/_sources/how_to/caffe.txt
@@ -0,0 +1,184 @@
+# How to | Convert from Caffe to MXNet
+
+Key topics covered include the following:
+
+- [Converting Caffe trained models to MXNet](#converting-caffe-trained-models-to-mxnet)
+- [Calling Caffe operators in MXNet](#calling-caffe-operators-in-mxnet)
+
+## Converting Caffe trained models to MXNet
+
+The converting tool is available at
+[tools/caffe_converter](https://github.com/dmlc/mxnet/tree/master/tools/caffe_converter). On
+the remaining of this section, we assume we are on the `tools/caffe_converter`
+directory.
+
+### How to build
+
+If Caffe's python package is installed, namely we can run `import caffe` in
+python, then we are ready to go.
+
+For example, we can used
+[AWS Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B06VSPXKDX) with
+both Caffe and MXNet installed.
+
+Otherwise we can install the
+[Google protobuf](https://developers.google.com/protocol-buffers/?hl=en)
+compiler and its python binding. It is easier to install, but may be slower
+during running.
+
+1. Install the compiler:
+ - Linux: install `protobuf-compiler` e.g. `sudo apt-get install
+ protobuf-compiler` for Ubuntu and `sudo yum install protobuf-compiler` for
+ Redhat/Fedora.
+ - Windows: Download the win32 build of
+ [protobuf](https://github.com/google/protobuf/releases). Make sure to
+ download the version that corresponds to the version of the python binding
+ on the next step. Extract to any location then add that location to your
+ `PATH`
+ - Mac OS X: `brew install protobuf`
+
+2. Install the python binding by either `conda install -c conda-forge protobuf`
+ or `pip install protobuf`.
+
+3. Compile Caffe proto definition. Run `make` in Linux or Mac OS X, or
+ `make_win32.bat` in Windows
+
+### How to use
+
+There are three tools:
+
+- `convert_symbol.py` : convert Caffe model definition in protobuf into MXNet's
+ Symbol in JSON format.
+- `convert_model.py` : convert Caffe model parameters into MXNet's NDArray format
+- `convert_mean.py` : convert Caffe input mean file into MXNet's NDArray format
+
+In addition, there are two tools:
+- `convert_caffe_modelzoo.py` : download and convert models from Caffe model
+ zoo.
+- `test_converter.py` : test the converted models by checking the prediction
+ accuracy.
+
+## Calling Caffe operators in MXNet
+
+Besides converting Caffe models, MXNet supports calling most Caffe operators,
+including network layer, data layer, and loss function, directly. It is
+particularly useful if there are customized operators implemented in Caffe, then
+we do not need to re-implement them in MXNet.
+
+### How to install
+
+This feature requires Caffe. In particular, we need to re-compile Caffe before
+[PR #4527](https://github.com/BVLC/caffe/pull/4527) is merged into Caffe. There
+are the steps of how to rebuild Caffe:
+
+1. Download [Caffe](https://github.com/BVLC/caffe). E.g. `git clone
+ https://github.com/BVLC/caffe`
+2. Download the
+ [patch for the MXNet interface](https://github.com/BVLC/caffe/pull/4527.patch)
+ and apply to Caffe. E.g.
+ ```bash
+ cd caffe && wget https://github.com/BVLC/caffe/pull/4527.patch && git apply 4527.patch
+ ```
+3. Build and install Caffe by following the
+ [official guide](http://caffe.berkeleyvision.org/installation.html).
+
+Next we need to compile MXNet with Caffe supports
+
+1. Copy `make/config.mk` (for Linux) or `make/osx.mk`
+ (for Mac) into the MXNet root folder as `config.mk` if you have not done it yet
+2. Open the copied `config.mk` and uncomment these two lines
+ ```bash
+ CAFFE_PATH = $(HOME)/caffe
+ MXNET_PLUGINS += plugin/caffe/caffe.mk
+ ```
+ Modify `CAFFE_PATH` to your Caffe installation, if necessary.
+3. Then build with 8 threads `make clean && make -j8`.
+
+### How to use
+
+This Caffe plugin adds three components into MXNet:
+
+- `sym.CaffeOp` : Caffe neural network layer
+- `sym.CaffeLoss` : Caffe loss functions
+- `io.CaffeDataIter` : Caffe data layer
+
+#### Use `sym.CaffeOp`
+The following example shows the definition of a 10 classes multi-layer perceptron:
+
+```Python
+data = mx.sym.Variable('data')
+fc1 = mx.sym.CaffeOp(data_0=data, num_weight=2, name='fc1', prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 128} }")
+act1 = mx.sym.CaffeOp(data_0=fc1, prototxt="layer{type:\"TanH\"}")
+fc2 = mx.sym.CaffeOp(data_0=act1, num_weight=2, name='fc2', prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 64} }")
+act2 = mx.sym.CaffeOp(data_0=fc2, prototxt="layer{type:\"TanH\"}")
+fc3 = mx.sym.CaffeOp(data_0=act2, num_weight=2, name='fc3', prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 10}}")
+mlp = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
+```
+
+Let's break it down. First, `data = mx.sym.Variable('data')` defines a variable
+as a placeholder for input. Then, it's fed through Caffe operators with `fc1 =
+mx.sym.CaffeOp(...)`. `CaffeOp` accepts several arguments:
+
+- The inputs to Caffe operators are named as `data_i` for *i=0, ..., num_data-1*
+- `num_data` is the number of inputs. In default it is 1, and therefore
+skipped in the above example.
+- `num_out` is the number of outputs. In default it is 1 and also skipped.
+- `num_weight` is the number of weights (`blobs_`). Its default value is 0. We
+need to explicitly specify it for a non-zero value.
+- `prototxt` is the protobuf configuration string.
+
+#### Use `sym.CaffeLoss`
+
+Using Caffe loss is similar.
+We can replace the MXNet loss with Caffe loss.
+We can replace
+
+Replacing the last line of the above example with the following two lines we can
+call Caffe loss instead of MXNet loss.
+
+```Python
+label = mx.sym.Variable('softmax_label')
+mlp = mx.sym.CaffeLoss(data=fc3, label=label, grad_scale=1, name='softmax', prototxt="layer{type:\"SoftmaxWithLoss\"}")
+```
+
+Similar to `CaffeOp`, `CaffeLoss` has arguments `num_data` (2 in default) and
+`num_out` (1 in default). But there are two differences
+
+1. Inputs are `data` and `label`. And we need to explicitly create a variable
+ placeholder for label, which is implicitly done in MXNet loss.
+2. `grad_scale` is the weight of this loss.
+
+#### Use `io.CaffeDataIter`
+
+We can also wrap a Caffe data layer into MXNet's data iterator. Below is an
+example for creating a data iterator for MNIST
+
+```python
+train = mx.io.CaffeDataIter(
+ prototxt =
+ 'layer { \
+ name: "mnist" \
+ type: "Data" \
+ top: "data" \
+ top: "label" \
+ include { \
+ phase: TEST \
+ } \
+ transform_param { \
+ scale: 0.00390625 \
+ } \
+ data_param { \
+ source: "caffe/examples/mnist/mnist_test_lmdb" \
+ batch_size: 100 \
+ backend: LMDB \
+ } \
+ }',
+ flat = flat,
+ num_examples = 60000,
+)
+```
+
+### Put it all together
+
+The complete example is available at
+[example/caffe](https://github.com/dmlc/mxnet/blob/master/example/caffe/)
diff --git a/versions/master/_sources/how_to/cloud.txt b/versions/master/_sources/how_to/cloud.txt
new file mode 100644
index 0000000..67b28f8
--- /dev/null
+++ b/versions/master/_sources/how_to/cloud.txt
@@ -0,0 +1,183 @@
+# MXNet on the Cloud
+
+Deep learning can require extremely powerful hardware, often for unpredictable durations of time.
+Moreover, _MXNet_ can benefit from both multiple GPUs and multiple machines.
+Accordingly, cloud computing, as offered by AWS and others,
+is especially well suited to training deep learning models.
+Using AWS, we can rapidly fire up multiple machines with multiple GPUs each at will
+and maintain the resources for precisely the amount of time needed.
+
+## Set Up an AWS GPU Cluster from Scratch
+
+In this document, we provide a step-by-step guide that will teach you
+how to set up an AWS cluster with _MXNet_. We show how to:
+
+- [Use Amazon S3 to host data](#use-amazon-s3-to-host-data)
+- [Set up an EC2 GPU instance with all dependencies installed](#set-up-an-ec2-gpu-instance)
+- [Build and run MXNet on a single computer](#build-and-run-mxnet-on-a-gpu-instance)
+- [Set up an EC2 GPU cluster for distributed training](#set-up-an-ec2-gpu-cluster-for-distributed-training)
+
+### Use Amazon S3 to Host Data
+
+Amazon S3 provides distributed data storage which proves especially convenient for hosting large datasets.
+To use S3, you need [AWS credentials](http://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html),
+including an `ACCESS_KEY_ID` and a `SECRET_ACCESS_KEY`.
+
+To use _MXNet_ with S3, set the environment variables `AWS_ACCESS_KEY_ID` and
+`AWS_SECRET_ACCESS_KEY` by adding the following two lines in
+`~/.bashrc` (replacing the strings with the correct ones):
+
+```bash
+export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
+export AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+```
+
+There are several ways to upload data to S3. One simple way is to use
+[s3cmd](http://s3tools.org/s3cmd). For example:
+
+```bash
+wget http://data.mxnet.io/mxnet/data/mnist.zip
+unzip mnist.zip && s3cmd put t*-ubyte s3://dmlc/mnist/
+```
+
+### Use Pre-installed EC2 GPU Instance
+The [Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B01M0AXXQB?qid=1475211685369&sr=0-1&ref_=srh_res_product_title) is an Amazon Linux image
+supported and maintained by Amazon Web Services for use on Amazon Elastic Compute Cloud (Amazon EC2).
+It contains [MXNet-v0.9.3 tag](https://github.com/dmlc/mxnet) and the necessary components to get going with deep learning,
+including Nvidia drivers, CUDA, cuDNN, Anaconda, Python2 and Python3.
+The AMI IDs are the following:
+
+* us-east-1: ami-e7c96af1
+* us-west-2: ami-dfb13ebf
+* eu-west-1: ami-6e5d6808
+
+Now you can launch _MXNet_ directly on an EC2 GPU instance.
+You can also use [Jupyter](http://jupyter.org) notebook on EC2 machine.
+Here is a [good tutorial](https://github.com/dmlc/mxnet-notebooks)
+on how to connect to a Jupyter notebook running on an EC2 instance.
+
+### Set Up an EC2 GPU Instance from Scratch
+
+_MXNet_ requires the following libraries:
+
+- C++ compiler with C++11 support, such as `gcc >= 4.8`
+- `CUDA` (`CUDNN` in optional) for GPU linear algebra
+- `BLAS` (cblas, open-blas, atblas, mkl, or others) for CPU linear algebra
+- `opencv` for image augmentations
+- `curl` and `openssl` for the ability to read/write to Amazon S3
+
+Installing `CUDA` on EC2 instances requires some effort. Caffe has a good
+[tutorial](https://github.com/BVLC/caffe/wiki/Install-Caffe-on-EC2-from-scratch-(Ubuntu,-CUDA-7,-cuDNN-3))
+on how to install CUDA 7.0 on Ubuntu 14.04.
+
+***Note:*** We tried CUDA 7.5 on Nov 7, 2015, but found it problematic.
+
+You can install the rest using the package manager. For example, on Ubuntu:
+
+```
+sudo apt-get update
+sudo apt-get install -y build-essential git libcurl4-openssl-dev libatlas-base-dev libopencv-dev python-numpy
+```
+
+The Amazon Machine Image (AMI) [ami-12fd8178](https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-12fd8178) has the packages listed above installed.
+
+
+### Build and Run MXNet on a GPU Instance
+
+The following commands build _MXNet_ with CUDA/CUDNN, Amazon S3, and distributed
+training.
+
+```bash
+git clone --recursive https://github.com/dmlc/mxnet
+cd mxnet; cp make/config.mk .
+echo "USE_CUDA=1" >>config.mk
+echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk
+echo "USE_CUDNN=1" >>config.mk
+echo "USE_BLAS=atlas" >> config.mk
+echo "USE_DIST_KVSTORE = 1" >>config.mk
+echo "USE_S3=1" >>config.mk
+make -j$(nproc)
+```
+
+To test whether everything is installed properly, we can try training a convolutional neural network (CNN) on the MNIST dataset using a GPU:
+
+```bash
+python example/image-classification/train_mnist.py
+```
+
+If you've placed the MNIST data on `s3://dmlc/mnist`, you can read the data stored on Amazon S3 directly with the following command:
+
+```bash
+sed -i.bak "s!data_dir = 'data'!data_dir = 's3://dmlc/mnist'!" example/image-classification/train_mnist.py
+```
+
+***Note:*** You can use `sudo ln /dev/null /dev/raw1394` to fix the opencv error `libdc1394 error: Failed to initialize libdc1394`.
+
+### Set Up an EC2 GPU Cluster for Distributed Training
+
+A cluster consists of multiple computers.
+You can use one computer with _MXNet_ installed as the root computer for submitting jobs,and then launch several
+slave computers to run the jobs. For example, launch multiple instances using an
+AMI, e.g.,
+[ami-12fd8178](https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-12fd8178),
+with dependencies installed. There are two options:
+
+- Make all slaves' ports accessible (same for the root) by setting type: All TCP,
+ Source: Anywhere in Configure Security Group.
+
+- Use the same `pem` as the root computer to access all slave computers, and
+ then copy the `pem` file into the root computer's `~/.ssh/id_rsa`. If you do this, all slave computers can be accessed with SSH from the root.
+
+Now, run the CNN on multiple computers. Assume that we are on a working
+directory of the root computer, such as `~/train`, and MXNet is built as `~/mxnet`.
+
+1. Pack the _MXNet_ Python library into this working directory for easy
+ synchronization:
+
+ ```bash
+ cp -r ~/mxnet/python/mxnet .
+ cp ~/mxnet/lib/libmxnet.so mxnet/
+ ```
+
+ And then copy the training program:
+
+ ```bash
+ cp ~/mxnet/example/image-classification/*.py .
+ cp -r ~/mxnet/example/image-classification/common .
+ ```
+
+2. Prepare a host file with all slaves private IPs. For example, `cat hosts`:
+
+ ```bash
+ 172.30.0.172
+ 172.30.0.171
+ ```
+
+3. Assuming that there are two computers, train the CNN using two workers:
+
+ ```bash
+ ../../tools/launch.py -n 2 -H hosts --sync-dir /tmp/mxnet python train_mnist.py --kv-store dist_sync
+ ```
+
+***Note:*** Sometimes the jobs linger at the slave computers even though you've pressed `Ctrl-c`
+at the root node. To terminate them, use the following command:
+
+```bash
+cat hosts | xargs -I{} ssh -o StrictHostKeyChecking=no {} 'uname -a; pgrep python | xargs kill -9'
+```
+
+***Note:*** The preceding example is very simple to train and therefore isn't a good
+benchmark for distributed training. Consider using other [examples](https://github.com/dmlc/mxnet/tree/master/example/image-classification).
+
+### More Options
+#### Use Multiple Data Shards
+It is common to pack a dataset into multiple files, especially when working in a distributed environment.
+_MXNet_ supports direct loading from multiple data shards.
+Put all of the record files into a folder, and point the data path to the folder.
+
+#### Use YARN and SGE
+Although using SSH can be simple when you don't have a cluster scheduling framework,
+_MXNet_ is designed to be portable to various platforms.
+We provide scripts available in [tracker](https://github.com/dmlc/dmlc-core/tree/master/tracker)
+to allow running on other cluster frameworks, including Hadoop (YARN) and SGE.
+We welcome contributions from the community of examples of running _MXNet_ on your favorite distributed platform.
diff --git a/versions/master/_sources/how_to/develop_and_hack.txt b/versions/master/_sources/how_to/develop_and_hack.txt
new file mode 100644
index 0000000..8b7dd67
--- /dev/null
+++ b/versions/master/_sources/how_to/develop_and_hack.txt
@@ -0,0 +1,8 @@
+# Develop and Hack MXNet
+- [Create new operators](new_op.md)
+- [Use Torch from MXNet](torch.md)
+- [Set environment variables of MXNet](env_var.md)
+
+# Other Resources
+- [MXNet System Architecture Overview](http://mxnet.io/architecture/overview.html)
+- [Contributor Guidelines](http://mxnet.io/community/contribute.html)
\ No newline at end of file
diff --git a/versions/master/_sources/how_to/env_var.txt b/versions/master/_sources/how_to/env_var.txt
new file mode 100644
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+++ b/versions/master/_sources/how_to/env_var.txt
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+Environment Variables
+=====================
+MXNet has several settings that you can change with environment variables.
+Typically, you wouldn't need to change these settings, but they are listed here for reference.
+
+For example, you can set these environment variables in Linux or macOS as follows:
+```
+export MXNET_GPU_WORKER_NTHREADS=3
+```
+
+## Set the Number of Threads
+
+* MXNET_GPU_WORKER_NTHREADS
+ - Values: Int ```(default=2)```
+ - The maximum number of threads to use on each GPU. This parameter is used to parallelize the computation within a single GPU card.
+* MXNET_GPU_COPY_NTHREADS
+ - Values: Int ```(default=1)```
+ - The maximum number of concurrent threads that do the memory copy job on each GPU.
+* MXNET_CPU_WORKER_NTHREADS
+ - Values: Int ```(default=1)```
+ - The maximum number of scheduling threads on CPU. It specifies how many operators can be run in parallel.
+* MXNET_CPU_PRIORITY_NTHREADS
+ - Values: Int ```(default=4)```
+ - The number of threads given to prioritized CPU jobs.
+* MXNET_CPU_NNPACK_NTHREADS
+ - Values: Int ```(default=4)```
+ - The number of threads used for NNPACK. NNPACK package aims to provide high-performance implementations of some layers for multi-core CPUs. Checkout [NNPACK](http://mxnet.io/how_to/nnpack.html) to know more about it.
+
+## Memory Options
+
+* MXNET_EXEC_ENABLE_INPLACE
+ - Values: true or false ```(default=true)```
+ - Whether to enable in-place optimization in symbolic execution. Checkout [in-place optimization](http://mxnet.io/architecture/note_memory.html#in-place-operations) to know more about it.
+* NNVM_EXEC_MATCH_RANGE
+ - Values: Int ```(default=16)```
+ - The approximate matching scale in the symbolic execution memory allocator.
+ - Set this to 0 if you don't want to enable memory sharing between graph nodes(for debugging purposes).
+ - This variable has impact on the result of memory planning. So, MXNet sweep between [1, NNVM_EXEC_MATCH_RANGE], and selects the best value.
+* MXNET_EXEC_NUM_TEMP
+ - Values: Int ```(default=1)```
+ - The maximum number of temporary workspaces to allocate to each device. This controls space replicas and in turn reduces the memory usage.
+ - Setting this to a small number can save GPU memory. It will also likely decrease the level of parallelism, which is usually acceptable.
+ - MXNet internally uses graph coloring algorithm to [optimize memory consumption](http://mxnet.io/architecture/note_memory.html).
+ - This parameter is also used to get number of matching colors in graph and in turn how much parallelism one can get in each GPU. Color based match usually costs more memory but also enables more parallelism.
+* MXNET_GPU_MEM_POOL_RESERVE
+ - Values: Int ```(default=5)```
+ - The percentage of GPU memory to reserve for things other than the GPU array, such as kernel launch or cudnn handle space.
+ - If you see a strange out-of-memory error from the kernel launch, after multiple iterations, try setting this to a larger value.
+
+## Engine Type
+
+* MXNET_ENGINE_TYPE
+ - Values: String ```(default=ThreadedEnginePerDevice)```
+ - The type of underlying execution engine of MXNet.
+ - Choices:
+ - NaiveEngine: A very simple engine that uses the master thread to do the computation synchronously. Setting this engine disables multi-threading. You can use this type for debugging in case of any error. Backtrace will give you the series of calls that lead to the error. Remember to set MXNET_ENGINE_TYPE back to empty after debugging.
+ - ThreadedEngine: A threaded engine that uses a global thread pool to schedule jobs.
+ - ThreadedEnginePerDevice: A threaded engine that allocates thread per GPU and executes jobs asynchronously.
+
+## Execution Options
+
+* MXNET_EXEC_BULK_EXEC_INFERENCE
+ - Values: 0(false) or 1(true) ```(default=1)```
+ - If set to `1`, during inference MXNet executes the entire computation graph in bulk mode, which reduces kernel launch gaps in between symbolic operators.
+* MXNET_EXEC_BULK_EXEC_TRAIN
+ - Values: 0(false) or 1(true) ```(default=1)```
+ - If set to `1`, during training MXNet executes the computation graph as several subgraphs in bulk mode.
+* MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN
+ - Values: Int ```(default=15)```
+ - The maximum number of nodes in the subgraph executed in bulk during training(not inference). Setting this to a larger number may reduce the degree of parallelism for multi-GPU training.
+
+## Control the Data Communication
+
+* MXNET_KVSTORE_REDUCTION_NTHREADS
+ - Values: Int ```(default=4)```
+ - The number of CPU threads used for summing big arrays.
+* MXNET_KVSTORE_BIGARRAY_BOUND
+ - Values: Int ```(default=1000000)```
+ - The minimum size of a "big array".
+ - When the array size is bigger than this threshold, MXNET_KVSTORE_REDUCTION_NTHREADS threads are used for reduction.
+ - This parameter is also used as a load balancer in kvstore. It controls when to partition a single weight to all the servers. If the size of a single weight is less than MXNET_KVSTORE_BIGARRAY_BOUND then, it is sent to a single randomly picked server otherwise it is partitioned to all the servers.
+* MXNET_ENABLE_GPU_P2P
+ - Values: 0(false) or 1(true) ```(default=1)```
+ - If true, MXNet tries to use GPU peer-to-peer communication, if available on your device,
+ when kvstore's type is `device`.
+
+## Memonger
+
+* MXNET_BACKWARD_DO_MIRROR
+ - Values: 0(false) or 1(true) ```(default=0)```
+ - MXNet uses mirroring concept to save memory. Normally backward pass needs some forward input and it is stored in memory but you can choose to release this saved input and recalculate it in backward pass when needed. This basically trades off the computation for memory consumption.
+ - This parameter decides whether to do `mirror` during training for saving device memory.
+ - When set to `1`, during forward propagation, graph executor will `mirror` some layer's feature map and drop others, but it will re-compute this dropped feature maps when needed.
+ - `MXNET_BACKWARD_DO_MIRROR=1` will save 30%~50% of device memory, but retains about 95% of running speed.
+ - One extension of `mirror` in MXNet is called [memonger technology](https://arxiv.org/abs/1604.06174), it will only use O(sqrt(N)) memory at 75% running speed. Checkout the code [here](https://github.com/dmlc/mxnet-memonger).
+
+## Control the profiler
+
+When USE_PROFILER is enabled in Makefile or CMake, the following environments can be used to profile the application without changing code. Execution options may affect the granularity of profiling result. If you need profiling result of every operator, please set MXNET_EXEC_BULK_EXEC_INFERENCE and MXNET_EXEC_BULK_EXEC_TRAIN to 0.
+
+* MXNET_PROFILER_AUTOSTART
+ - Values: 0(false) or 1(true) ```(default=0)```
+ - Set to 1, MXNet starts the profiler automatically. The profiling result is stored into profile.json in the working directory.
+
+* MXNET_PROFILER_MODE
+ - Values: 0(false) or 1(true) ```(default=0)```
+ - If set to '0', profiler records the events of the symbolic operators.
+ - If set to '1', profiler records the events of all operators.
+
+## Other Environment Variables
+
+* MXNET_CUDNN_AUTOTUNE_DEFAULT
+ - Values: 0(false) or 1(true) ```(default=0)```
+ - The default value of cudnn auto tunning for convolution layers.
+ - Auto tuning is turned off by default. For benchmarking, set this to 1 to turn it on by default.
+
+Settings for Minimum Memory Usage
+---------------------------------
+- Make sure ```min(MXNET_EXEC_NUM_TEMP, MXNET_GPU_WORKER_NTHREADS) = 1```
+ - The default setting satisfies this.
+
+Settings for More GPU Parallelism
+---------------------------------
+- Set ```MXNET_GPU_WORKER_NTHREADS``` to a larger number (e.g., 2)
+ - To reduce memory usage, consider setting ```MXNET_EXEC_NUM_TEMP```.
+ - This might not speed things up, especially for image applications, because GPU is usually fully utilized even with serialized jobs.
diff --git a/versions/master/_sources/how_to/faq.txt b/versions/master/_sources/how_to/faq.txt
new file mode 100644
index 0000000..0569963
--- /dev/null
+++ b/versions/master/_sources/how_to/faq.txt
@@ -0,0 +1,69 @@
+# Frequently Asked Questions
+
+This topic provides answers to the frequently asked questions on [mxnet/issues](https://github.com/dmlc/mxnet/issues). Before posting an issue, please check this page. If you would like to contribute to this page, please make the questions and answers simple. If your answer is extremely detailed, please post it elsewhere and link to it.
+
+## Building and Installation
+
+You can find answers to most questions the [build page](http://mxnet.io/get_started/setup.html).
+
+## Speed
+
+#### It took a long time to start running on a GPU
+
+Try to disable opencv to use a GPU: [build opencv from source with GPU module disabled](http://mxnet.io/get_started/setup.html#build-opencv-from-source-code).
+
+#### It's slow on a single GPU
+
+Check the following:
+
+1. Ensure that your CUDA/driver version is not too old.
+2. Build with `USE_CUDNN=1`. This often increases speed 50+%. Try to use the newest version.
+3. Set `export MXNET_CUDNN_AUTOTUNE_DEFAULT=1` before running. This often increases speed 10%-15%.
+4. If you are using Tesla GPUs by `nvidia-smi -e 0`, disable ECC. You might need root permission and have to reboot.
+5. For Tesla cards by `nvidia-smi -ac ??`, set to the maximal clock. For details, see [this blog](https://devblogs.nvidia.com/parallelforall/increase-performance-gpu-boost-k80-autoboost/).
+6. No throttle reason `nvidia-smi -q -d PERFORMANCE` is often caused by temperature.
+
+#### No increase in speed when using more than one GPU or computer
+
+Check the following:
+
+1. Does your neural network already run fast, such as >1000 example/sec or >10 batches/sec? If yes, it's unlikely to speed up any further by adding more resources because of the communication overhead.
+2. Are you using a small batch size? Try to increase it.
+3. Are you using more than 4 GPUs? Try using `--kv-store=device`.
+
+## Memory Usage
+
+#### Abnormal CPU memory usage
+
+This might be due to the data pre-fetch. See [issue 2111](https://github.com/dmlc/mxnet/issues/2111).
+
+## Pending Review
+The following topics need to be reviewed.
+
+#### How to Copy Part of Parameters to Another Model
+Most MXNet model consists two parts, the argument arrays and symbol. You can simply copy the argument array to the argument array of another model. For example, in the Python model API, you can do this:
+
+```python
+copied_model = mx.model.FeedForward(ctx=mx.gpu(), symbol=new_symbol,
+ arg_params=old_arg_params, aux_params=old_aux_params,
+ allow_extra_params=True);
+```
+For information about copying model parameters from an existing ```old_arg_params```, see this [notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb). More notebooks please refer to [dmlc/mxnet-notebooks](https://github.com/dmlc/mxnet-notebooks).
+
+#### How to Extract the Feature Map of a Certain Layer
+See this [notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb). More notebooks please refer to [dmlc/mxnet-notebooks](https://github.com/dmlc/mxnet-notebooks).
+
+
+#### What Is the Relationship Between MXNet and CXXNet, Minerva, and Purine2?
+MXNet is created in collaboration by authors from the three projects.
+MXNet reflects what we have learned from these projects.
+It combines the important aspects of the existing projects: general efficiency, flexibility, and memory efficiency.
+
+MXNet also contains new approaches that allow you to combine different
+ways of programming and write CPU/GPU applications that are more
+memory efficient than CXXNet and Purine, and more flexible than Minerva.
+
+
+#### What Is the Relationship Between MXNet and TensorFlow?
+Both MXNet and [TensorFlow](https://www.tensorflow.org/) use computation graph abstraction, which was initially used by Theano, then adopted by other packages, such as CGT, Caffe2, and Purine. Currently, TensorFlow adopts an optimized symbolic API. MXNet supports a [mixed approach](https://mxnet.io/architecture/program_model.html), with a dynamic dependency scheduler to combine symbolic and imperative programming.
+In short, MXNet is lightweight and *mixed* with flexibility from imperative programming, while using a computation graph to make it very fast and memory efficient.
diff --git a/versions/master/_sources/how_to/finetune.txt b/versions/master/_sources/how_to/finetune.txt
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+# Fine-tune with Pretrained Models
+
+Many of the exciting deep learning algorithms for computer vision require
+massive datasets for training. The most popular benchmark dataset,
+[ImageNet](http://www.image-net.org/), for example, contains one million images
+from one thousand categories. But for any practical problem, we typically have
+access to comparatively small datasets. In these cases, if we were to train a
+neural network's weights from scratch, starting from random initialized
+parameters, we would overfit the training set badly.
+
+One approach to get around this problem is to first pretrain a deep net on a
+large-scale dataset, like ImageNet. Then, given a new dataset, we can start
+with these pretrained weights when training on our new task. This process is
+commonly called _fine-tuning_. There are a number of variations of fine-tuning.
+Sometimes, the initial neural network is used only as a _feature extractor_.
+That means that we freeze every layer prior to the output layer and simply learn
+a new output layer. In [another document](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb), we explained how to
+do this kind of feature extraction. Another approach is to update all of
+the network's weights for the new task, and that's the approach we demonstrate in
+this document.
+
+To fine-tune a network, we must first replace the last fully-connected layer
+with a new one that outputs the desired number of classes. We initialize its
+weights randomly. Then we continue training as normal. Sometimes it's common to
+use a smaller learning rate based on the intuition that we may already be close
+to a good result.
+
+In this demonstration, we'll fine-tune a model pretrained on ImageNet to the
+smaller caltech-256 dataset. Following this example, you can fine-tune to other
+datasets, even for strikingly different applications such as face
+identification.
+
+We will show that, even with simple hyper-parameters setting, we can match and
+even outperform state-of-the-art results on caltech-256.
+
+| Network | Accuracy |
+| --- | --- |
+| Resnet-50 | 77.4% |
+| Resnet-152 | 86.4% |
+
+## Prepare data
+
+We follow the standard protocol to sample 60 images from each class as the
+training set, and the rest for the validation set. We resize images into 256x256
+size and pack them into the rec file. The scripts to prepare the data is as
+following.
+
+> In order to successfully run the following bash script on Windows please use https://cygwin.com/install.html .
+
+```sh
+wget http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tar
+tar -xf 256_ObjectCategories.tar
+
+mkdir -p caltech_256_train_60
+for i in 256_ObjectCategories/*; do
+ c=`basename $i`
+ mkdir -p caltech_256_train_60/$c
+ for j in `ls $i/*.jpg | shuf | head -n 60`; do
+ mv $j caltech_256_train_60/$c/
+ done
+done
+
+python ~/mxnet/tools/im2rec.py --list True --recursive True caltech-256-60-train caltech_256_train_60/
+python ~/mxnet/tools/im2rec.py --list True --recursive True caltech-256-60-val 256_ObjectCategories/
+python ~/mxnet/tools/im2rec.py --resize 256 --quality 90 --num-thread 16 caltech-256-60-val 256_ObjectCategories/
+python ~/mxnet/tools/im2rec.py --resize 256 --quality 90 --num-thread 16 caltech-256-60-train caltech_256_train_60/
+```
+
+The following code downloads the pregenerated rec files. It may take a few minutes.
+
+```python
+import os, urllib
+def download(url):
+ filename = url.split("/")[-1]
+ if not os.path.exists(filename):
+ urllib.urlretrieve(url, filename)
+download('http://data.mxnet.io/data/caltech-256/caltech-256-60-train.rec')
+download('http://data.mxnet.io/data/caltech-256/caltech-256-60-val.rec')
+```
+
+Next, we define the function which returns the data iterators:
+
+```python
+import mxnet as mx
+
+def get_iterators(batch_size, data_shape=(3, 224, 224)):
+ train = mx.io.ImageRecordIter(
+ path_imgrec = './caltech-256-60-train.rec',
+ data_name = 'data',
+ label_name = 'softmax_label',
+ batch_size = batch_size,
+ data_shape = data_shape,
+ shuffle = True,
+ rand_crop = True,
+ rand_mirror = True)
+ val = mx.io.ImageRecordIter(
+ path_imgrec = './caltech-256-60-val.rec',
+ data_name = 'data',
+ label_name = 'softmax_label',
+ batch_size = batch_size,
+ data_shape = data_shape,
+ rand_crop = False,
+ rand_mirror = False)
+ return (train, val)
+```
+
+We then download a pretrained 50-layer ResNet model and load it into memory. Note
+that if `load_checkpoint` reports an error, we can remove the downloaded files
+and try `get_model` again.
+
+```python
+def get_model(prefix, epoch):
+ download(prefix+'-symbol.json')
+ download(prefix+'-%04d.params' % (epoch,))
+
+get_model('http://data.mxnet.io/models/imagenet/resnet/50-layers/resnet-50', 0)
+sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-50', 0)
+```
+
+## Train
+
+We first define a function which replaces the last fully-connected layer for a given network.
+
+```python
+def get_fine_tune_model(symbol, arg_params, num_classes, layer_name='flatten0'):
+ """
+ symbol: the pretrained network symbol
+ arg_params: the argument parameters of the pretrained model
+ num_classes: the number of classes for the fine-tune datasets
+ layer_name: the layer name before the last fully-connected layer
+ """
+ all_layers = symbol.get_internals()
+ net = all_layers[layer_name+'_output']
+ net = mx.symbol.FullyConnected(data=net, num_hidden=num_classes, name='fc1')
+ net = mx.symbol.SoftmaxOutput(data=net, name='softmax')
+ new_args = dict({k:arg_params[k] for k in arg_params if 'fc1' not in k})
+ return (net, new_args)
+```
+
+Now we create a module. We first call `init_params` to randomly initialize parameters, next use `set_params` to replace all parameters except for the last fully-connected layer with pretrained model.
+
+
+```python
+import logging
+head = '%(asctime)-15s %(message)s'
+logging.basicConfig(level=logging.DEBUG, format=head)
+
+def fit(symbol, arg_params, aux_params, train, val, batch_size, num_gpus):
+ devs = [mx.gpu(i) for i in range(num_gpus)]
+ mod = mx.mod.Module(symbol=symbol, context=devs)
+ mod.fit(train, val,
+ num_epoch=8,
+ arg_params=arg_params,
+ aux_params=aux_params,
+ allow_missing=True,
+ batch_end_callback = mx.callback.Speedometer(batch_size, 10),
+ kvstore='device',
+ optimizer='sgd',
+ optimizer_params={'learning_rate':0.01},
+ initializer=mx.init.Xavier(rnd_type='gaussian', factor_type="in", magnitude=2),
+ eval_metric='acc')
+ metric = mx.metric.Accuracy()
+ return mod.score(val, metric)
+```
+
+Then we can start training. We use AWS EC2 g2.8xlarge, which has 8 GPUs.
+
+
+```python
+num_classes = 256
+batch_per_gpu = 16
+num_gpus = 8
+
+(new_sym, new_args) = get_fine_tune_model(sym, arg_params, num_classes)
+
+batch_size = batch_per_gpu * num_gpus
+(train, val) = get_iterators(batch_size)
+mod_score = fit(new_sym, new_args, aux_params, train, val, batch_size, num_gpus)
+assert mod_score > 0.77, "Low training accuracy."
+```
+
+You will see that, after only 8 epochs, we can get 78% validation accuracy. This
+matches the state-of-the-art results training on caltech-256 alone,
+e.g. [VGG](http://www.robots.ox.ac.uk/~vgg/research/deep_eval/).
+
+Next, we try to use another pretrained model. This model was trained on the
+complete Imagenet dataset, which is 10x larger than the Imagenet 1K classes
+version, and uses a 3x deeper Resnet architecture.
+
+
+```python
+get_model('http://data.mxnet.io/models/imagenet-11k/resnet-152/resnet-152', 0)
+sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-152', 0)
+(new_sym, new_args) = get_fine_tune_model(sym, arg_params, num_classes)
+mod_score = fit(new_sym, new_args, aux_params, train, val, batch_size, num_gpus)
+assert mod_score > 0.86, "Low training accuracy."
+```
+
+
+As can be seen, even for a single data epoch, it reaches 83% validation
+accuracy. After 8 epoches, the validation accuracy increases to 86.4%.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/how_to/index.txt b/versions/master/_sources/how_to/index.txt
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+# MXNet How To
+
+This section addresses common questions about how to use _MXNet_. These include performance issues, e.g., how to train with multiple GPUs.
+They also include workflow questions, e.g., how to visualize a neural network computation graph.
+These answers are fairly focused. For more didactic, self-contained introductions to neural networks
+and full working examples, visit the [tutorials section](../tutorials/index.md).
+
+
+## Modeling
+* [How do I fine-tune pre-trained models to a new dataset?](http://mxnet.io/how_to/finetune.html)
+
+* [How do I work with variable-length input in MXNet (bucketing)?](http://mxnet.io/how_to/bucketing.html)
+
+* [How do I visualize neural networks as computation graphs?](http://mxnet.io/how_to/visualize_graph.html)
+
+
+## Speed
+
+* [How can I train with multiple CPU/GPUs with data parallelism?](http://mxnet.io/how_to/multi_devices.html)
+
+* [How can I train with multiple GPUs with model parallelism?](http://mxnet.io/how_to/model_parallel_lstm.html)
+
+* [Can I use nnpack to improve the CPU performance of MXNet?](http://mxnet.io/how_to/nnpack.html)
+
+* [What are the best setup and data-handling tips and tricks for improving speed?](http://mxnet.io/how_to/perf.html)
+
+
+## Deployment Environments
+* [Can I run MXNet on smart or mobile devices?](http://mxnet.io/how_to/smart_device.html)
+
+* [How to use data from S3 for training?](s3_integration.md)
+
+* [How to setup MXNet on AWS?](http://docs.aws.amazon.com/mxnet/latest/dg/mxnet-on-ec2-instance.html)
+
+* [How to do distributed training using MXNet on AWS?](http://docs.aws.amazon.com/mxnet/latest/dg/mxnet-on-ec2-cluster.html)
+
+* [How do I run MXNet on a Raspberry Pi for computer vision?](http://mxnet.io/tutorials/embedded/wine_detector.html)
+
+* [How do I run Keras 1.2.2 with mxnet backend?](https://github.com/dmlc/keras/wiki/Installation)
+
+## Extend and Contribute to MXNet
+
+* [How do I join the MXNet development discussion?](http://mxnet.io/community/mxnet_channels.html)
+
+* [How do I contribute a patch to MXNet?](http://mxnet.io/community/contribute.html)
+
+* [How do I create new operators in MXNet?](http://mxnet.io/how_to/new_op.html)
+
+* [How do I set MXNet's environmental variables?](http://mxnet.io/how_to/env_var.html)
+
+* [How do I use MXNet as a front end for Torch?](http://mxnet.io/how_to/torch.html)
+
+## Questions about Using MXNet
+If you are not sure of how to use MXNet for something, or have questions about applying it to a particular kind of problem, please post a question at [Stackoverflow](http://stackoverflow.com/) with tag - ```mxnet```. You can view StackOverflow questions about mxnet [here](http://stackoverflow.com/questions/tagged/mxnet).
+
+## Issue Tracker
+We track bugs and new feature requests in the MXNet Github repo in the issues folder: [mxnet/issues](https://github.com/dmlc/mxnet/issues).
+
+## Roadmap
+MXNet is evolving fast. To see what's next and what we are working on internally, go to the [MXNet Roadmap](https://github.com/dmlc/mxnet/labels/Roadmap).
diff --git a/versions/master/_sources/how_to/model_parallel_lstm.txt b/versions/master/_sources/how_to/model_parallel_lstm.txt
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+# Training with Multiple GPUs Using Model Parallelism
+Training deep learning models can be resource intensive.
+Even with a powerful GPU, some models can take days or weeks to train.
+Large long short-term memory (LSTM) recurrent neural networks
+can be especially slow to train,
+with each layer, at each time step, requiring eight matrix multiplications.
+Fortunately, given cloud services like AWS,
+machine learning practitioners often have access
+to multiple machines and multiple GPUs.
+One key strength of _MXNet_ is its ability to leverage
+powerful heterogeneous hardware environments to achieve significant speedups.
+
+There are two primary ways that we can spread a workload across multiple devices.
+In a previous document, [we addressed data parallelism](./multi_devices.md),
+an approach in which examples within a batch are divvied among the available devices.
+With data parallelism, each device stores a complete copy of the model.
+Here, we explore _model parallelism_, a different approach.
+Instead of splitting the batch among the devices, we partition the model itself.
+Most commonly, we achieve model parallelism by assigning the parameters (and computation)
+of different layers of the network to different devices.
+
+In particular, we will focus on LSTM recurrent networks.
+LSTMS are powerful sequence models, that have proven especially useful
+for [natural language translation](https://arxiv.org/pdf/1409.0473.pdf), [speech recognition](https://arxiv.org/abs/1512.02595),
+and working with [time series data](https://arxiv.org/abs/1511.03677).
+For a general high-level introduction to LSTMs,
+see the excellent [tutorial](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) by Christopher Olah. For a working example of LSTM training with model parallelism,
+see [example/model-parallelism-lstm/](https://github.com/dmlc/mxnet/blob/master/example/model-parallel-lstm/lstm.py).
+
+
+## Model Parallelism: Using Multiple GPUs As a Pipeline
+Model parallelism in deep learning was first proposed
+for the _extraordinarily large_ convolutional layer in GoogleNet.
+From this implementation, we take the idea of placing each layer on a separate GPU.
+Using model parallelism in such a layer-wise fashion
+provides the benefit that no GPU has to maintain all of the model parameters in memory.
+
+<img width="517" alt="screen shot 2016-05-06 at 10 13 16 pm" src="https://cloud.githubusercontent.com/assets/5545640/15089697/d6f4fca0-13d7-11e6-9331-7f94fcc7b4c6.png">
+
+In the preceding figure, each LSTM layer is assigned to a different GPU.
+After GPU 1 finishes computing layer 1 for the first sentence, it passes its output to GPU 2.
+At the same time, GPU 1 fetches the next sentence and starts training.
+This differs significantly from data parallelism.
+Here, there is no contention to update the shared model at the end of each iteration,
+and most of the communication happens when passing intermediate results between GPUs.
+
+In the current implementation, the layers are defined in [lstm_unroll()](https://github.com/dmlc/mxnet/blob/master/example/model-parallel-lstm/lstm.py).
+
+## Workload Partitioning
+
+Implementing model parallelism requires knowledge of the training task.
+Here are some general heuristics that we find useful:
+
+- To minimize communication time, place neighboring layers on the same GPUs.
+- Be careful to balance the workload between GPUs.
+- Remember that different kinds of layers have different computation-memory properties.
+
+<img width="449" alt="screen shot 2016-05-07 at 1 51 02 am" src="https://cloud.githubusercontent.com/assets/5545640/15090455/37a30ab0-13f6-11e6-863b-efe2b10ec2e6.png">
+
+Let's take a quick look at the two pipelines in the preceding diagram.
+They both have eight layers with a decoder and an encoder layer.
+Based on our first principle, it's unwise to place all neighboring layers on separate GPUs.
+We also want to balance the workload across GPUs.
+Although the LSTM layers consume less memory than the decoder/encoder layers, they consume more computation time because of the dependency of the unrolled LSTM.
+Thus, the partition on the left will be faster than the one on the right
+because the workload is more evenly distributed.
+
+Currently, the layer partition is implemented in [lstm.py](https://github.com/eric-haibin-lin/mxnet/blob/master/example/model-parallel-lstm/lstm.py#L187) and configured in [lstm_ptb.py](https://github.com/eric-haibin-lin/mxnet/blob/master/example/model-parallel-lstm/lstm.py#L187) using the `group2ctx` option.
+
+## Apply Bucketing to Model Parallelism
+
+To achieve model parallelism while using bucketing,
+you need to unroll an LSTM model for each bucket
+to obtain an executor for each.
+For details about how the model is bound, see [lstm.py](https://github.com/eric-haibin-lin/mxnet/blob/master/example/model-parallel-lstm/lstm.py#L154).
+
+On the other hand, because model parallelism partitions the model/layers,
+the input data has to be transformed/transposed to the agreed shape.
+For more details, see [bucket_io](https://github.com/eric-haibin-lin/mxnet/blob/master/example/model-parallel-lstm/lstm.py#L154).
diff --git a/versions/master/_sources/how_to/multi_devices.txt b/versions/master/_sources/how_to/multi_devices.txt
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+# Run MXNet on Multiple CPU/GPUs with Data Parallelism
+
+_MXNet_ supports training with multiple CPUs and GPUs, which may be located on different physical machines.
+
+## Data Parallelism vs Model Parallelism
+
+By default, _MXNet_ uses data parallelism to partition the workload over multiple
+devices.
+Assume there are *n* devices.
+Then each one will receive a copy of the complete model
+and train it on *1/n* of the data.
+The results such as gradients and
+updated model are communicated across these devices.
+
+MXNet also supports model parallelism.
+In this approach, each device holds onto only part of the model.
+This proves useful when the model is too large to fit onto a single device.
+As an example, see the following [tutorial](./model_parallel_lstm.md)
+which shows how to use model parallelism for training a multi-layer LSTM model.
+In this tutorial, we'll focus on data parallelism.
+
+## Multiple GPUs within a Single Machine
+
+### Workload Partitioning
+
+By default, _MXNet_ partitions a data batch evenly among the available GPUs.
+Assume a batch size *b* and assume there are *k* GPUs, then in one iteration
+each GPU will perform forward and backward on *b/k* examples. The
+gradients are then summed over all GPUs before updating the model.
+
+### How to Use
+
+> To use GPUs, we need to compile MXNet with GPU support. For
+> example, set `USE_CUDA=1` in `config.mk` before `make`. (see
+> [MXNet installation guide](http://mxnet.io/get_started/install.html) for more options).
+
+If a machine has one or more GPU cards installed,
+then each card is labeled by a number starting from 0.
+To use a particular GPU, one can either
+specify the context `context` in code
+or pass `--gpus` at the command line.
+For example, to use GPU 0 and 2 in python,
+one can typically create a module with
+```python
+import mxnet as mx
+module = mx.module.Module(context=[mx.gpu(0), mx.gpu(2)], ...)
+```
+while if the program accepts a `--gpus` flag (as seen in
+[example/image-classification](https://github.com/dmlc/mxnet/tree/master/example/image-classification)),
+then we can try
+```bash
+python train_mnist.py --gpus 0,2 ...
+```
+
+### Advanced Usage
+If the available GPUs are not all equally powerful,
+we can partition the workload accordingly.
+For example, if GPU 0 is 3 times faster than GPU 2,
+then we might use the workload option `work_load_list=[3, 1]`,
+see [Module](../api/python/module.html#mxnet.module.Module)
+for more details.
+
+Training with multiple GPUs should yield the same results
+as training on a single GPU if all other hyper-parameters are the same.
+In practice, the results may exhibit small differences,
+owing to the randomness of I/O (random order or other augmentations),
+weight initialization with different seeds, and CUDNN.
+
+We can control on which devices the gradient is aggregated
+and on which device the model is updated via [`KVStore`](http://mxnet.io/api/python/kvstore.html),
+the _MXNet_ module that supports data communication.
+One can either use `mx.kvstore.create(type)` to get an instance
+or use the program flag `--kv-store type`.
+
+There are two commonly used types,
+
+- `local`: all gradients are copied to CPU memory and weights are updated there.
+- `device`: both gradient aggregation and weight updates are run on GPUs.
+With this setting, the `KVStore` also attempts to use GPU peer-to-peer communication,
+potentially accelerating the communication.
+Note that this option may result in higher GPU memory usage.
+
+When using a large number of GPUs, e.g. >=4, we suggest using `device` for better performance.
+
+## Distributed Training with Multiple Machines
+
+`KVStore` also supports a number of options for running on multiple machines.
+
+- `dist_sync` behaves similarly to `local` but exhibits one major difference.
+ With `dist_sync`, `batch-size` now means the batch size used on each machine.
+ So if there are *n* machines and we use batch size *b*,
+ then `dist_sync` behaves like `local` with batch size *n\*b*.
+- `dist_device_sync` is similar to `dist_sync`. The difference between them is that
+ `dist_device_sync` aggregates gradients and updates weight on GPUs
+ while `dist_sync` does so on CPU memory.
+- `dist_async` performs asynchronous updates.
+ The weight is updated whenever gradients are received from any machine.
+ The update is atomic, i.e., no two updates happen on the same weight at the same time.
+ However, the order is not guaranteed.
+
+### How to Launch a Job
+
+> To use distributed training, we need to compile with `USE_DIST_KVSTORE=1`
+> (see [MXNet installation guide](http://mxnet.io/get_started/install.html) for more options).
+
+Launching a distributed job is a bit different from running on a single
+machine. MXNet provides
+[tools/launch.py](https://github.com/dmlc/mxnet/blob/master/tools/launch.py) to
+start a job by using `ssh`, `mpi`, `sge`, or `yarn`.
+
+An easy way to set up a cluster of EC2 instances for distributed deep learning
+is using an [AWS CloudFormation template](https://github.com/awslabs/deeplearning-cfn).
+If you do not have a cluster, you can check the repository before you continue.
+
+Assume we are at the directory `mxnet/example/image-classification`
+and want to train LeNet to classify MNIST images, as demonstrated here:
+[train_mnist.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/train_mnist.py).
+
+On a single machine, we can run:
+
+```bash
+python train_mnist.py --network lenet
+```
+
+Now, say we are given two ssh-able machines and _MXNet_ is installed on both machines.
+We want to train LeNet on these two machines.
+First, we save the IPs (or hostname) of these two machines in file `hosts`, e.g.
+
+```bash
+$ cat hosts
+172.30.0.172
+172.30.0.171
+```
+
+Next, if the mxnet folder is accessible from both machines, e.g. on a
+[network filesystem](https://help.ubuntu.com/lts/serverguide/network-file-system.html),
+then we can run:
+
+```bash
+python ../../tools/launch.py -n 2 --launcher ssh -H hosts python train_mnist.py --network lenet --kv-store dist_sync
+```
+
+Note that here we
+
+- use `launch.py` to submit the job.
+- provide launcher, `ssh` if all machines are ssh-able, `mpi` if `mpirun` is
+ available, `sge` for Sun Grid Engine, and `yarn` for Apache Yarn.
+- `-n` number of worker nodes to run on
+- `-H` the host file which is required by `ssh` and `mpi`
+- `--kv-store` use either `dist_sync` or `dist_async`
+
+
+### Synchronize Directory
+
+Now consider if the mxnet folder is not accessible.
+We can first copy the `MXNet` library to this folder by
+```bash
+cp -r ../../python/mxnet .
+cp -r ../../lib/libmxnet.so mxnet
+```
+
+then ask `launch.py` to synchronize the current directory to all machines'
+ `/tmp/mxnet` directory with `--sync-dst-dir`
+
+```bash
+python ../../tools/launch.py -n 2 -H hosts --sync-dst-dir /tmp/mxnet \
+ python train_mnist.py --network lenet --kv-store dist_sync
+```
+
+### Use a Particular Network Interface
+
+_MXNet_ often chooses the first available network interface.
+But for machines that have multiple interfaces,
+we can specify which network interface to use for data
+communication by the environment variable `DMLC_INTERFACE`.
+For example, to use the interface `eth0`, we can
+
+```
+export DMLC_INTERFACE=eth0; python ../../tools/launch.py ...
+```
+
+### Debug Connection
+
+Set`PS_VERBOSE=1` to see the debug logging, e.g
+```
+export PS_VERBOSE=1; python ../../tools/launch.py ...
+```
+
+### More
+
+- See more launch options by `python ../../tools/launch.py -h`
+- See more options of [ps-lite](http://ps-lite.readthedocs.org/en/latest/how_to.html)
diff --git a/versions/master/_sources/how_to/new_op.txt b/versions/master/_sources/how_to/new_op.txt
new file mode 100644
index 0000000..55b7409
--- /dev/null
+++ b/versions/master/_sources/how_to/new_op.txt
@@ -0,0 +1,349 @@
+# How to Create New Operators (Layers)
+
+This tutorials walks you through the process of creating new MXNet operators (or layers).
+We've done our best to provide high-speed operators for most common use cases.
+However, if you're engaged in research,
+there's a good chance you'll want to define custom layers,
+like a novel loss function. In these cases, you have two options:
+
+* Use CustomOp to write new operators using a front-end language (e.g., Python) that run on CPUs or GPUs.
+Depending on your implementation, this can range from very fast (if you only use operators under mx.nd) to very slow (if you copy out the data, using `.asnumpy()`).
+
+* Use C++/mshadow (CUDA). This provides the best performance, but can be difficult
+if you're not familiar with MXNet, mshadow, or Cuda.
+
+## CustomOp
+Implementing an operator in Python is simple.
+As an example, let's create a softmax operator.
+Start by subclassing `mxnet.operator.CustomOp`,
+and then override a few methods:
+
+```python
+import os
+import mxnet as mx
+import numpy as np
+
+class Softmax(mx.operator.CustomOp):
+ def forward(self, is_train, req, in_data, out_data, aux):
+ x = in_data[0].asnumpy()
+ y = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
+ y /= y.sum(axis=1).reshape((x.shape[0], 1))
+ self.assign(out_data[0], req[0], mx.nd.array(y))
+```
+
+We defined the computation for the forward pass of our operator.
+The forward function takes a list of input and a list of output NDArrays.
+For convenience, we called `.asnumpy()` on the first NDArray in input
+and convert it to a CPU-based NumPy array.
+This can be very slow. If you want the best performance,
+keep data in the NDArray format and use operators under mx.nd to do the computation.
+
+At the end, we used CustomOp.assign to assign the resulting array y to out_data[0]. It handles assignment based on the value of req, which can be 'write', 'add', or 'null'.
+
+Then do the same for the backward pass:
+
+```python
+def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+ l = in_data[1].asnumpy().ravel().astype(np.int)
+ y = out_data[0].asnumpy()
+ y[np.arange(l.shape[0]), l] -= 1.0
+ self.assign(in_grad[0], req[0], mx.nd.array(y))
+```
+
+Softmax defines the computation of our custom operator,
+but you still need to define its input/output format
+by subclassing mx.operator.CustomOpProp.
+First, register the new operator with the name 'softmax':
+
+```python
+@mx.operator.register("softmax")
+class SoftmaxProp(mx.operator.CustomOpProp):
+```
+
+Then, call the base constructor with `need_top_grad=False`
+because softmax is a loss layer and you don't need gradient input from preceding layers:
+
+```python
+def __init__(self):
+ super(SoftmaxProp, self).__init__(need_top_grad=False)
+```
+
+Then declare the input and output:
+
+```python
+def list_arguments(self):
+ return ['data', 'label']
+
+def list_outputs(self):
+ return ['output']
+```
+
+Note that list_arguments declares both input and parameter.
+We recommend ordering them as follows: `['input1', 'input2', ... , 'weight1', 'weight2', ...]`
+
+Next, provide `infer_shape` to declare the shape of the output/weight
+and check the consistency of the input shapes:
+
+```python
+def infer_shape(self, in_shape):
+ data_shape = in_shape[0]
+ label_shape = (in_shape[0][0],)
+ output_shape = in_shape[0]
+ return [data_shape, label_shape], [output_shape], []
+```
+The first axis of an input/output tensor corresponds to different examples within the batch.
+The label is a set of integers, one for each data entry,
+and the output has the same shape as the input.
+The `infer_shape` function should always return three lists in this order:
+inputs, outputs, and auxiliary states (which we don't have here),
+even if one of them is empty.
+
+Optionally, you can also define `infer_type` to declare the input and output data type of your operator. Supported types are `np.float32`, `np.float64`, `np.float16`, `np.uint8`, and `np.int32`.
+
+```python
+def infer_type(self, in_type):
+ dtype = in_type[0]
+ return [dtype, dtype], [dtype], []
+```
+
+Finally, define a create_operator function that will be called by the back end to create an instance of softmax:
+
+```python
+def create_operator(self, ctx, shapes, dtypes):
+ return Softmax()
+```
+
+To use the custom operator, create a mx.sym.Custom symbol with op_type as the registered name:
+
+```python
+mlp = mx.symbol.Custom(data=fc3, name='softmax', op_type='softmax')
+```
+
+Please see the full code for this example [here](https://github.com/dmlc/mxnet/blob/master/example/numpy-ops/custom_softmax.py).
+
+## C++
+With MXNet v0.9 (the NNVM refactor) or later, creating new operators has become easier.
+Operators are now registered with NNVM.
+The following code is an example on how to register an operator (checkout [src/operator/tensor](https://github.com/dmlc/mxnet/tree/master/src/operator/tensor) for more examples):
+
+```c++
+NNVM_REGISTER_OP(abs)
+.MXNET_DESCRIBE("Take absolute value of the src")
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1,1>);
+```
+
+The syntax is quite simple, we register the operator with a name,
+then set number of inputs and outputs.
+You can register attributes with any key (`FInferShape` for example) to any operator,
+without having to modify a central class interface definition.
+
+### Operator Attribute System
+
+One of the biggest improvements brought by NNVM is the operator attribute system.
+This is like traits for types in common languages like C++.
+We can register any attribute to any operator, with the syntax
+
+``` c++
+NNVM_REGISTER_OP(op-name)
+.set_attr<AttributeType>("AttributeKey", CorrespondingAttributeObject);
+```
+
+These attributes can be retrieved later for various purposes.
+For example, `FInferShape` is used for shape inference, `FCompute<cpu>` is used for carrying out actual computation on CPU.
+
+As long as all attributes registered with the same key have the same type,
+we can register any attributes to operators.
+The more attribute an operator provides,
+the more information the system can use for optimization.
+
+### List of basic attributes
+
+In this section, we will go through the basic attributes MXNet expect for all operators.
+You can find the definition for them in the following two files:
+
+- [nnvm/op_attr_types.h](https://github.com/dmlc/nnvm/blob/master/include/nnvm/op_attr_types.h)
+- [mxnet/op_attr_types.h](https://github.com/dmlc/mxnet/blob/master/include/mxnet/op_attr_types.h)
+
+#### Descriptions (Optional)
+
+`.describe(comment)` adds a comment to the operator. Use `.MXNET_DESCRIBE(comment)` to add the current file name and line number to comment.
+
+#### Attribute Parser (Optional)
+
+Set attribute parser with `.set_attr_parser(PARSER)` where PARSER is a function with prototype `void(nnvm::NodeAttr* attrs)`. This function should parse the key-word arguments in `attrs->dict` and store the result in `attrs->parsed`.
+
+Simple arguments can be parsed like
+```c++
+NNVM_REGISTER_OP(scalar_op)
+.set_attr_parser(
+ [](NodeAttrs* attrs) {
+ attrs->parsed = std::stod(attrs->dict["scalar"]);
+ })
+```
+
+The parsed arguments can then be accessed in other attribute functions with
+```
+double alpha = nnvm::get<double>(attrs.parsed);
+```
+
+More complex ops can use `dmlc::Parameters` and `ParamParser` (defined in operator_common.h) for parsing:
+
+``` c++
+#include <dmlc/parameter.h>
+#include <operator_common.h>
+struct ActivationParam : public dmlc::Parameter<ActivationParam> {
+ // use int for enumeration
+ int act_type;
+ DMLC_DECLARE_PARAMETER(ActivationParam) {
+ DMLC_DECLARE_FIELD(act_type)
+ .add_enum("relu", activation::kReLU)
+ .add_enum("sigmoid", activation::kSigmoid)
+ .add_enum("tanh", activation::kTanh)
+ .add_enum("softrelu", activation::kSoftReLU)
+ .describe("Activation function to be applied.");
+ }
+};
+NNVM_REGISTER_OP(Activation)
+.set_attr_parser(ParamParser<ActivationParam>);
+// access with:
+// const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
+```
+
+#### Inputs & Outputs
+
+Number of inputs/outputs can be set with `.set_num_inputs(n_in)` and `.set_num_outputs(n_out)`
+where n_in and n_out are integers.
+
+Alternatively, if the number of inputs/outputs is variable and depends on arguments,
+you can set `n_in`/`n_out` to functions with prototype `uint32_t(const nnvm::NodeAttrs& attrs)`
+that return the number of inputs/outputs based on parsed arguments.
+
+Outputs can be made invisible to other operators by registering `FNumVisibleOutputs`
+and returning an integer smaller than `n_out`.
+
+Inputs/outputs can be named by registering `FListInputNames` and `FListOutputNames` with prototype `std::vector<std::string>(const NodeAttrs& attrs)`.
+
+
+#### Argument Descriptions
+
+Set argument descriptions with `.add_argument(name, type, comment)`.
+This is necessary for operators to be properly called imperatively.
+
+First, add NDArray arguments `num_inputs` times with type "NDArray"
+or one time with type "NDArray[]" for ops with variable length inputs.
+
+Then add key-word arguments with proper type (float, string, etc).
+Operators that parse key-word arguments with `dmlc::Parameter`
+can add argument descriptions in bulk with `.add_arguments(ActivationParam::__FIELDS__())`
+(NDArray arguments still need to be manually added with type "NDArray").
+
+#### FInferShape or TIsBackward (for Backward Only Ops)
+
+Normally operators need to have `FInferShape` with prototype `bool(const nnvm::NodeAttrs& attrs, std::vector<TShape> *in_attrs, std::vector<TShape> *out_attrs)`. `FInferShape` fills unknown shapes (`shape.ndim() == 0`) in in_attrs/out_attrs based on known shapes in in_attrs/out_attrs. Use `ElemwiseShape<n_in, n_out>` for simple operators with uniform shapes.
+
+Operators that are only used for a backward pass can instead register `.set_attr<nnvm::TIsBackward>("TIsBackward", true)`
+and their shapes with be copied from the corresponding forward operators.
+
+#### FInferType
+
+Similar to `FInferShape`, `FInferType` fills unknown types (-1) based on known types. Use `ElemwiseType<n_in, n_out>` for simple operators with uniform types. Operators that registered `TIsBackward` don't need to register this.
+
+
+#### FInplaceOption (Optional)
+
+`FInplaceOption` with prototype `std::vector<std::pair<int, int> >(const NodeAttrs& attrs)`
+specifies which input/output pairs can be computed in-place
+and share memory with each other.
+Each pair (i, j) in the returned list means
+that the i-th input can share memory with the j-th output.
+
+
+#### FGradient (Optional for imperative use, required for symbolic use)
+
+If an operator has gradient, it can be described with `FGradient` with prototype
+
+``` c++
+std::vector<nnvm::NodeEntry>(const nnvm::NodePtr& n,
+ const std::vector<nnvm::NodeEntry>& ograds)
+```
+
+Use utility functions `ElemwiseGradUseIn{op_name}`, `ElemwiseGradUseOut{op_name}`, `ElemwiseGradUseNone{op_name}` for ops that need corresponding forward op's input,
+output or nothing to calculating gradient.
+
+For more complicated patterns, use `MakeGradNode(op_name, n, heads, dict)` to create gradient entries,
+where heads are input entries to the backward op, composed from ograds and n->inputs.
+
+#### FCompute\<xpu\>
+
+Simple operators can register FCompute<xpu> with `.set_attr<FCompute>("FCompute<cpu>", ...)` and `.set_attr<FCompute>("FCompute<gpu>", ...)` for both CPU and (optionally) GPU computation.
+
+FCompute has prototype
+
+```c++
+void(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs)
+```
+
+`req` has the same length as `outputs`.
+Each entry of `req` specifies
+how the corresponding `output` should be written to.
+`OpReqType` is defined as:
+
+```c++
+enum OpReqType {
+ kNullOp,
+ kWriteTo,
+ kWriteInplace,
+ kAddTo
+};
+```
+
+Normally, the `req` of all `outputs` should be `kWriteTo`,
+meaning that the provided `outputs` tensor is a *raw* memory block,
+so the operator should write results directly into it.
+In some cases, for example, when calculating the gradient tensor,
+it would be great if we could accumulate the result,
+rather than directly overwrite the tensor contents
+so that no extra space needs to be created each time.
+In such cases, the corresponding `req` is set to `kAddTo`,
+indicating that a `+=` should be used.
+
+### Example: abs operator
+
+```c++
+NNVM_REGISTER_OP(abs)
+.MXNET_DESCRIBE("Take absolute value of the src")
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+[](const NodeAttrs& attrs){
+ return std::vector<std::pair<int, int> >{{0, 0}};
+})
+.set_attr<FCompute>("FCompute<cpu>", UnaryCompute<cpu, mshadow_op::abs>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_abs"});
+.add_argument("data", "NDArray", "Source input")
+
+NNVM_REGISTER_OP(_backward_abs)
+.set_num_inputs(2)
+.set_num_outputs(1)
+.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<2, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+[](const NodeAttrs& attrs){
+ return std::vector<std::pair<int, int> >{{0, 0}, {1, 0}};
+})
+.set_attr<FCompute>("FCompute<cpu>", BinaryCompute<cpu, unary_bwd<mshadow_op::sign> >);
+```
+
+### Legacy Operators
+
+For the legacy (pre 0.9) way of defining operators with C++, please see:
+- [Developer Guide - Operators](http://mxnet.io/architecture/overview.html#operators-in-mxnet)
+- [Developer Guide - SimpleOp](http://mxnet.io/architecture/overview.html#simpleop-the-unified-operator-api)
diff --git a/versions/master/_sources/how_to/nnpack.txt b/versions/master/_sources/how_to/nnpack.txt
new file mode 100644
index 0000000..b17c6ee
--- /dev/null
+++ b/versions/master/_sources/how_to/nnpack.txt
@@ -0,0 +1,138 @@
+### NNPACK for Multi-Core CPU Support in MXNet
+[NNPACK](https://github.com/Maratyszcza/NNPACK) is an acceleration package
+for neural network computations, which can run on x86-64, ARMv7, or ARM64 architecture CPUs.
+Using NNPACK, higher-level libraries like _MXNet_ can speed up
+the execution on multi-core CPU computers, including laptops and mobile devices.
+
+_MXNet_ supports NNPACK for forward propagation (inference only) in convolution, max-pooling, and fully-connected layers.
+In this document, we give a high level overview of how to use NNPACK with _MXNet_.
+
+
+### Conditions
+The underlying implementation of NNPACK utilizes several acceleration methods,
+including [fft](https://arxiv.org/abs/1312.5851) and [winograd](https://arxiv.org/abs/1509.09308).
+These algorithms work better on some special `batch size`, `kernel size`, and `stride` settings than on other,
+so depending on the context, not all convolution, max-pooling, or fully-connected layers can be powered by NNPACK.
+When favorable conditions for running NNPACKS are not met,
+_MXNet_ will fall back to the default implementation automatically.
+
+NNPACK only supports Linux and OS X systems. Windows is not supported at present.
+The following table explains under which conditions NNPACK will work.
+
+| operation | conditions |
+|:--------- |:---------- |
+|convolution |2d convolution `and` no-bias=False `and` dilate=(1,1) `and` num_group=1 `and` batch-size = 1 or batch-size > 1 && stride = (1,1);|
+|pooling | max-pooling `and` kernel=(2,2) `and` stride=(2,2) `and` pooling_convention=full |
+|fully-connected| without any restrictions |
+
+### Build/Install NNPACK with MXNet
+
+If the trained model meets some conditions of using NNPACK,
+you can build MXNet with NNPACK support.
+Follow these simple steps:
+* Build NNPACK shared library with the following commands. _MXNet_ will link NNPACK dynamically.
+
+Note: The following NNPACK installation instructions have been tested on Ubuntu 14.04 and 16.04.
+
+```bash
+
+# Install Pip
+$ sudo apt-get update
+$ sudo apt-get install -y python-pip
+$ sudo pip install --upgrade pip
+
+# Install Peach
+$ git clone https://github.com/Maratyszcza/PeachPy.git
+$ cd PeachPy
+$ sudo pip install --upgrade -r requirements.txt
+$ python setup.py generate
+$ sudo pip install --upgrade .
+
+# Install Ninja Build System
+$ sudo apt-get install ninja-build
+$ pip install ninja-syntax
+
+# Build NNPack shared library
+$ cd ~
+$ git clone --recursive https://github.com/Maratyszcza/NNPACK.git
+$ cd NNPACK
+# Latest NNPACK do not support building NNPACK as shared library using --enable-shared flag
+# Reset to commit that supports it.
+$ git reset --hard 9c6747d7b80051b40e6f92d6828e2ed997529cd2
+$ git submodule init && git submodule update --recursive
+$ python ./configure.py --enable-shared
+$ ninja
+$ cd ~
+
+```
+
+* Set lib path of NNPACK as the environment variable, e.g. `export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$YOUR_NNPACK_INSTALL_PATH/lib`
+* Add the include file of NNPACK and its third-party to `ADD_CFLAGS` in config.mk, e.g. `ADD_CFLAGS = -I$(YOUR_NNPACK_INSTALL_PATH)/include/ -I$(YOUR_NNPACK_INSTALL_PATH)/third-party/pthreadpool/include/`
+* Set `USE_NNPACK = 1` in config.mk.
+* Build MXNet from source following the [install guide](http://mxnet.io/get_started/install.html).
+
+### NNPACK Performance
+
+Though not all convolutional, pooling, and fully-connected layers can make full use of NNPACK,
+for some popular models it provides significant speedups. These include the most popular image recognition networks: Alexnet, VGG, and Inception-bn.
+
+To benchmark NNPACK, we use `example/image-classification/benchmark_score.py`(changed with more range of batch-size). We use CPU e5-2670, MXNET_CPU_NNPACK_NTHREADS=4.
+
+build MXNet without NNPACK, the log is:
+```
+INFO:root:network: alexnet
+INFO:root:device: cpu(0)
+INFO:root:batch size 1, image/sec: 6.389429
+INFO:root:batch size 2, image/sec: 7.961457
+INFO:root:batch size 4, image/sec: 8.950112
+INFO:root:batch size 8, image/sec: 9.578176
+INFO:root:batch size 16, image/sec: 9.701248
+INFO:root:batch size 32, image/sec: 9.839940
+INFO:root:batch size 64, image/sec: 10.075369
+INFO:root:batch size 128, image/sec: 10.053556
+INFO:root:batch size 256, image/sec: 9.972228
+INFO:root:network: vgg
+INFO:root:device: cpu(0)
+INFO:root:batch size 1, image/sec: 1.223822
+INFO:root:batch size 2, image/sec: 1.322814
+INFO:root:batch size 4, image/sec: 1.383586
+INFO:root:batch size 8, image/sec: 1.402376
+INFO:root:batch size 16, image/sec: 1.415972
+INFO:root:batch size 32, image/sec: 1.428377
+INFO:root:batch size 64, image/sec: 1.443987
+INFO:root:batch size 128, image/sec: 1.427531
+INFO:root:batch size 256, image/sec: 1.435279
+```
+
+build MXNet with NNPACK, log is:
+
+```
+INFO:root:network: alexnet
+INFO:root:device: cpu(0)
+INFO:root:batch size 1, image/sec: 19.027215
+INFO:root:batch size 2, image/sec: 12.879975
+INFO:root:batch size 4, image/sec: 17.424076
+INFO:root:batch size 8, image/sec: 21.283966
+INFO:root:batch size 16, image/sec: 24.469325
+INFO:root:batch size 32, image/sec: 25.910348
+INFO:root:batch size 64, image/sec: 27.441672
+INFO:root:batch size 128, image/sec: 28.009156
+INFO:root:batch size 256, image/sec: 28.918950
+INFO:root:network: vgg
+INFO:root:device: cpu(0)
+INFO:root:batch size 1, image/sec: 3.980907
+INFO:root:batch size 2, image/sec: 2.392069
+INFO:root:batch size 4, image/sec: 3.610553
+INFO:root:batch size 8, image/sec: 4.994450
+INFO:root:batch size 16, image/sec: 6.396612
+INFO:root:batch size 32, image/sec: 7.614288
+INFO:root:batch size 64, image/sec: 8.826084
+INFO:root:batch size 128, image/sec: 9.193653
+INFO:root:batch size 256, image/sec: 9.991472
+```
+
+The results show that NNPACK can confer a speedup of about 2X~7X as compared to the original _MXNet_ CPU implementation.
+
+### Tips
+
+NNPACK aims to provide high-performance implementations of some layers for multi-core CPUs, so you can easily set the thread number by changing the environmental variable `MXNET_CPU_NNPACK_NTHREADS`. However, we found that the performance is not proportional to the number of threads, and suggest using 4~8 threads when using NNPACK.
diff --git a/versions/master/_sources/how_to/perf.txt b/versions/master/_sources/how_to/perf.txt
new file mode 100644
index 0000000..8899ecc
--- /dev/null
+++ b/versions/master/_sources/how_to/perf.txt
@@ -0,0 +1,251 @@
+# Some Tips for Improving MXNet Performance
+Even after fixing the training or deployment environment and parallelization scheme,
+a number of configuration settings and data-handling choices can impact the _MXNet_ performance.
+In this document, we address some tips for improving _MXNet_ performance.
+
+Performance is mainly affected by the following 4 factors:
+
+1. Implementation of operators (Convolution, Pooling, ..)
+ - [Intel CPU](#intel-cpu)
+ - [Nvidia GPU](#nvidia-gpu)
+2. Input data loading and augmentation
+ - [Input Data](#input-data)
+3. Workloads (computation graph) optimization and scheduling
+ - [Profiler](#profiler)
+4. Communication for multi-devices training
+ - [Multiple Devices](#multiple-devices)
+
+## Intel CPU
+
+For using Intel Xeon CPUs for training and inference, we suggest enabling
+both `USE_MKL2017 = 1` and `USE_MKL2017_EXPERIMENTAL = 1` in
+`config.mk`. Check
+[MKL_README.md](https://github.com/dmlc/mxnet/blob/master/MKL_README.md) for
+details.
+
+We also find that setting the following two environment variables can help:
+- `export KMP_AFFINITY=granularity=fine,compact,1,0` if there are two physical CPUs
+- `export OMP_NUM_THREADS=vCPUs / 2` in which `vCPUs` is the number of virtual CPUs.
+ Whe using Linux, we can access this information by running `cat /proc/cpuinfo | grep processor | wc -l`
+
+Note that _MXNet_ treats all CPUs on a single machine as a single device.
+So whether you specify `cpu(0)` or `cpu()`, _MXNet_ will use all CPU cores on the machine.
+
+### Scoring results
+The following table shows performance,
+namely number of images that can be predicted per second.
+We used [example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
+to measure the performance on different AWS EC2 machines.
+
+AWS EC2 C4.8xlarge:
+
+| Batch | Alexnet | VGG | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 1 | 119.57 | 34.23 | 111.36 | 54.42 | 42.83 | 19.51 |
+| 2 | 210.58 | 51.63 | 137.10 | 67.30 | 57.54 | 23.56 |
+| 4 | 318.54 | 70.00 | 187.21 | 76.53 | 63.64 | 25.80 |
+| 8 | 389.34 | 77.39 | 211.90 | 84.26 | 63.89 | 28.11 |
+| 16 | 489.12 | 85.26 | 220.52 | 82.00 | 63.93 | 27.08 |
+| 32 | 564.04 | 87.15 | 208.21 | 83.05 | 62.19 | 25.76 |
+
+AWS EC2 C4.4xlarge:
+
+| Batch | Alexnet | VGG | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 1 | 109.96 | 23.00 | 71.82 | 28.10 | 30.66 | 11.81 |
+| 2 | 124.56 | 24.86 | 81.61 | 31.32 | 32.73 | 12.82 |
+| 4 | 157.01 | 26.60 | 86.77 | 32.94 | 33.32 | 13.16 |
+| 8 | 178.40 | 30.67 | 88.58 | 33.52 | 33.32 | 13.32 |
+| 16 | 189.52 | 35.61 | 90.36 | 33.63 | 32.94 | 13.18 |
+| 32 | 196.61 | 38.98 | 105.27 | 33.77 | 32.65 | 13.00 |
+
+AWS EC2 C4.2xlarge:
+
+| Batch | Alexnet | VGG | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 1 | 70.75 | 12.87 | 42.86 | 16.53 | 18.14 | 7.01 |
+| 2 | 71.53 | 13.08 | 45.66 | 17.38 | 18.53 | 7.18 |
+| 4 | 84.72 | 15.38 | 47.50 | 17.80 | 18.96 | 7.35 |
+| 8 | 93.44 | 18.33 | 48.08 | 17.93 | 18.99 | 7.40 |
+| 16 | 97.03 | 20.12 | 55.73 | 18.00 | 18.91 | 7.36 |
+| 32 | 113.90 | 21.10 | 62.54 | 17.98 | 18.80 | 7.33 |
+
+AWS EC2 C4.xlarge:
+
+| Batch | Alexnet | VGG | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 1 | 37.92 | 6.57 | 23.09 | 8.79 | 9.65 | 3.73 |
+| 2 | 36.77 | 7.31 | 24.00 | 9.00 | 9.84 | 3.78 |
+| 4 | 43.18 | 8.94 | 24.42 | 9.12 | 9.91 | 3.83 |
+| 8 | 47.05 | 10.01 | 28.32 | 9.13 | 9.88 | 3.83 |
+| 16 | 55.74 | 10.61 | 31.96 | 9.14 | 9.86 | 3.80 |
+| 32 | 65.05 | 10.91 | 33.86 | 9.34 | 10.31 | 3.86 |
+
+AWS EC2 C4.large:
+
+| Batch | Alexnet | VGG | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 1 | 19.86 | 3.67 | 12.20 | 4.59 | 5.11 | 1.97 |
+| 2 | 19.37 | 4.24 | 12.41 | 4.64 | 5.15 | 1.98 |
+| 4 | 22.64 | 4.89 | 14.34 | 4.66 | 5.16 | 2.00 |
+| 8 | 27.19 | 5.25 | 16.17 | 4.66 | 5.16 | 1.99 |
+| 16 | 31.82 | 5.46 | 17.24 | 4.76 | 5.35 | OOM |
+| 32 | 34.67 | 5.55 | 17.64 | 4.88 | OOM | OOM |
+
+## Other CPU
+
+If using CPUs (not just Intel CPUs -- ARMs also), NNPACK can improve the running performance with 2x~7x, please check [nnpack.md](./nnpack.md) for details.
+
+## Nvidia GPU
+
+`cuDNN` typically accelerates _MXNet_ performance on NVIDIA GPUs significantly,
+especially for convolution layers.
+We suggest always checking to make sure that a recent cuDNN version is used.
+
+Setting the environment `export MXNET_CUDNN_AUTOTUNE_DEFAULT=1` sometimes also helps.
+
+We show results when using various GPUs including K80 (EC2 p2.2xlarge), M40,
+and P100 (DGX-1).
+
+### Scoring results
+
+Based on
+[example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
+and MXNet commit `0a03417`, with cuDNN 5.1
+
+- K80 (single GPU)
+
+ | Batch | Alexnet | VGG | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+ | --- | --- | --- | --- | --- | --- | --- |
+ | 1 | 202.66 | 70.76 | 74.91 | 42.61 | 70.94 | 24.87 |
+ | 2 | 233.76 | 63.53 | 119.60 | 60.09 | 92.28 | 34.23 |
+ | 4 | 367.91 | 78.16 | 164.41 | 72.30 | 116.68 | 44.76 |
+ | 8 | 624.14 | 119.06 | 195.24 | 79.62 | 129.37 | 50.96 |
+ | 16 | 1071.19 | 195.83 | 256.06 | 99.38 | 160.40 | 66.51 |
+ | 32 | 1443.90 | 228.96 | 287.93 | 106.43 | 167.12 | 69.73 |
+
+- M40
+
+ | Batch | Alexnet | VGG | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+ | --- | --- | --- | --- | --- | --- | --- |
+ | 1 | 412.09 | 142.10 | 115.89 | 64.40 | 126.90 | 46.15 |
+ | 2 | 743.49 | 212.21 | 205.31 | 108.06 | 202.17 | 75.05 |
+ | 4 | 1155.43 | 280.92 | 335.69 | 161.59 | 266.53 | 106.83 |
+ | 8 | 1606.87 | 332.76 | 491.12 | 224.22 | 317.20 | 128.67 |
+ | 16 | 2070.97 | 400.10 | 618.25 | 251.87 | 335.62 | 134.60 |
+ | 32 | 2694.91 | 466.95 | 624.27 | 258.59 | 373.35 | 152.71 |
+
+- P100
+
+ | Batch | Alexnet | VGG | Inception-BN | Inception-v3 | Resnet 50 | Resnet 152 |
+ | --- | --- | --- | --- | --- | --- | --- |
+ | 1 | 624.84 | 294.6 | 139.82 | 80.17 | 162.27 | 58.99 |
+ | 2 | 1226.85 | 282.3 | 267.41 | 142.63 | 278.02 | 102.95 |
+ | 4 | 1934.97 | 399.3 | 463.38 | 225.56 | 423.63 | 168.91 |
+ | 8 | 2900.54 | 522.9 | 709.30 | 319.52 | 529.34 | 210.10 |
+ | 16 | 4063.70 | 755.3 | 949.22 | 444.65 | 647.43 | 270.07 |
+ | 32 | 4883.77 | 854.4 | 1197.74 | 493.72 | 713.17 | 294.17 |
+
+### Training results
+
+Based on
+[example/image-classification/train_imagenet.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/train_imagenet.py)
+and MXNet commit `0a03417`, with CUDNN 5.1. The benchmark script is available at
+[here](https://github.com/mli/mxnet-benchmark/blob/master/run_vary_batch.sh),
+where the batch size for Alexnet is increased by 8x.
+
+- K80 (single GPU)
+
+ | Batch | Alexnet(\*8) | Inception-v3 | Resnet 50 |
+ | --- | --- | --- | --- |
+ | 1 | 230.69 | 9.81 | 13.83 |
+ | 2 | 348.10 | 15.31 | 21.85 |
+ | 4 | 457.28 | 20.48 | 29.58 |
+ | 8 | 533.51 | 24.47 | 36.83 |
+ | 16 | 582.36 | 28.46 | 43.60 |
+ | 32 | 483.37 | 29.62 | 45.52 |
+
+- M40
+
+ | Batch | Alexnet(\*8) | Inception-v3 | Resnet 50 |
+ | --- | --- | --- | --- |
+ | 1 | 405.17 | 14.35 | 21.56 |
+ | 2 | 606.32 | 23.96 | 36.48 |
+ | 4 | 792.66 | 37.38 | 52.96 |
+ | 8 | 1016.51 | 52.69 | 70.21 |
+ | 16 | 1105.18 | 62.35 | 83.13 |
+ | 32 | 1046.23 | 68.87 | 90.74 |
+
+- P100
+
+ | Batch | Alexnet(\*8) | Inception-v3 | Resnet 50 |
+ | --- | --- | --- | --- |
+ | 1 | 809.94 | 15.14 | 27.20 |
+ | 2 | 1202.93 | 30.34 | 49.55 |
+ | 4 | 1631.37 | 50.59 | 78.31 |
+ | 8 | 1882.74 | 77.75 | 122.45 |
+ | 16 | 2012.04 | 111.11 | 156.79 |
+ | 32 | 1869.69 | 129.98 | 181.53 |
+
+## Multiple Devices
+
+If more than one GPU or machine are used, MXNet uses `kvstore` to communicate data.
+It's critical to use the proper type of `kvstore` to get the best performance.
+Refer to [multi_device.md](http://mxnet.io/how_to/multi_devices.html) for more
+details.
+
+Besides, we can use [tools/bandwidth](https://github.com/dmlc/mxnet/tree/master/tools/bandwidth)
+to find the communication cost per batch.
+Ideally, the communication cost should be less than the time to compute a batch.
+To reduce the communication cost, we can consider:
+
+- Exploring different `--kv-store` options.
+- Increasing the batch size to improve the computation to communication ratio.
+
+## Input Data
+
+To make sure you're handling input data in a reasonable way consider the following:
+
+* Data format: If you are using the `rec` format, then everything should be fine.
+* Decoding: By default, _MXNet_ uses 4 CPU threads for decoding images.
+This is often sufficient to decode more than 1K images per second.
+If you are using a low-end CPU or your GPUs are very powerful, you can increase the number of threads.
+* Storage location. Any local or distributed file system (HDFS, Amazon S3) should be fine.
+If multiple devices read the data from the shared network file system (NFS) at the same time, problems might occur.
+* Use a large batch size. We often choose the largest one that fits into GPU memory.
+A value that's too large can slow down convergence.
+For example, the safe batch size for CIFAR 10 is approximately 200, while for ImageNet 1K, the batch size can exceed 1K.
+
+## Profiler
+
+As of v0.9.1 (with the NNVM merge), _MXNet_ has a built-in profiler
+that gives detailed information about execution time at the symbol level.
+This feature complements general profiling tools like _nvprof_ and _gprof_
+by summarizing at the operator level, instead of a function, kernel, or instruction level.
+
+In order to be able to use the profiler, you must compile _MXNet_ with the `USE_PROFILER=1` flag in `config.mk`.
+
+The profiler can then be turned on with an [environment variable](http://mxnet.io/how_to/env_var.html#control-the-profiler)
+for an entire program run, or programmatically for just part of a run.
+See [example/profiler](https://github.com/dmlc/mxnet/tree/master/example/profiler)
+for complete examples of how to use the profiler in code, but briefly, the Python code looks like:
+
+```
+ mx.profiler.profiler_set_config(mode='all', filename='profile_output.json')
+ mx.profiler.profiler_set_state('run')
+
+ # Code to be profiled goes here...
+
+ mx.profiler.profiler_set_state('stop')
+```
+
+The `mode` parameter can be set to
+
+* `symbolic` to only include symbolic operations
+* `all` to include all operations
+
+After the program finishes, navigate to your browser's tracing (Example - chrome://tracing in a Chrome browser) and load the `profile_output.json` file output by the profiler to inspect the results.
+
+
+
+Note that the output file can grow extremely large, so this approach is not recommended for general use.
diff --git a/versions/master/_sources/how_to/recordio.txt b/versions/master/_sources/how_to/recordio.txt
new file mode 100644
index 0000000..10ab6c7
--- /dev/null
+++ b/versions/master/_sources/how_to/recordio.txt
@@ -0,0 +1,73 @@
+## Create a Dataset Using RecordIO
+
+RecordIO implements a file format for a sequence of records. We recommend storing images as records and packing them together. The benefits include:
+
+* Storing images in a compact format--e.g., JPEG, for records--greatly reduces the size of the dataset on the disk.
+* Packing data together allows continuous reading on the disk.
+* RecordIO has a simple way to partition, simplifying distributed setting. We provide an example later.
+
+We provide the [im2rec tool](https://github.com/dmlc/mxnet/blob/master/tools/im2rec.cc) so you can create an Image RecordIO dataset by yourself. The following walkthrough shows you how.
+
+### Prerequisites
+Download the data. You don't need to resize the images manually. You can use ```im2rec``` to resize them automatically. For details, see the "Extension: Using Multiple Labels for a Single Image," later in this topic.
+
+### Step 1. Make an Image List File
+After you download the data, you need to make an image list file. The format is:
+
+```
+integer_image_index \t label_index \t path_to_image
+```
+Typically, the program takes the list of names of all of the images, shuffles them, then separates them into two lists: a training filename list and a testing filename list. Write the list in the right format.
+
+This is an example file:
+
+```bash
+95099 464 n04467665_17283.JPEG
+10025081 412 ILSVRC2010_val_00025082.JPEG
+74181 789 n01915811_2739.JPEG
+10035553 859 ILSVRC2010_val_00035554.JPEG
+10048727 929 ILSVRC2010_val_00048728.JPEG
+94028 924 n01980166_4956.JPEG
+1080682 650 n11807979_571.JPEG
+972457 633 n07723039_1627.JPEG
+7534 11 n01630670_4486.JPEG
+1191261 249 n12407079_5106.JPEG
+```
+
+### Step 2. Create the Binary File
+To generate a binary image, use `im2rec` in the tool folder. `im2rec` takes the path of the `_image list file_` you generated, the `_root path_` of the images, and the `_output file path_` as input. This process usually takes several hours, so be patient.
+
+Sample command:
+
+```bash
+./bin/im2rec image.lst image_root_dir output.bin resize=256
+```
+For more details, run ```./bin/im2rec```.
+
+### Extension: Multiple Labels for a Single Image
+
+The `im2rec` tool and `mx.io.ImageRecordIter` have multi-label support for a single image.
+For example, if you have four labels for a single image, you can use the following procedure to use the RecordIO tools.
+
+1. Write the image list files as follows:
+
+```
+integer_image_index \t label_1 \t label_2 \t label_3 \t label_4 \t path_to_image
+```
+
+2. Run `im2rec`, adding a 'label_width=4' to the command argument, for example:
+
+```bash
+./bin/im2rec image.lst image_root_dir output.bin resize=256 label_width=4
+```
+
+3. In the iterator generation code, set `label_width=4` and `path_imglist=<<The PATH TO YOUR image.lst>>`, for example:
+
+```python
+dataiter = mx.io.ImageRecordIter(
+ path_imgrec="data/cifar/train.rec",
+ data_shape=(3,28,28),
+ path_imglist="data/cifar/image.lst",
+ label_width=4
+)
+```
diff --git a/versions/master/_sources/how_to/s3_integration.txt b/versions/master/_sources/how_to/s3_integration.txt
new file mode 100644
index 0000000..4e6e965
--- /dev/null
+++ b/versions/master/_sources/how_to/s3_integration.txt
@@ -0,0 +1,99 @@
+# Use data from S3 for training
+
+AWS S3 is a cloud-based object storage service that allows storage and retrieval of large amounts of data at a very low cost. This makes it an attractive option to store large training datasets. MXNet is deeply integrated with S3 for this purpose.
+
+An S3 protocol URL (like `s3://bucket-name/training-data`) can be provided as a parameter for any data iterator that takes a file path as input. For example,
+
+```
+data_iter = mx.io.ImageRecordIter(
+ path_imgrec="s3://bucket-name/training-data/caltech_train.rec",
+ data_shape=(3, 227, 227),
+ batch_size=4,
+ resize=256)
+```
+Following are detailed instructions on how to use data from S3 for training.
+
+## Step 1: Build MXNet with S3 integration enabled
+
+Follow instructions [here](http://mxnet.io/get_started/install.html) to install MXNet from source with the following additional steps to enable S3 integration.
+
+1. Install `libcurl4-openssl-dev` and `libssl-dev` before building MXNet. These packages are required to read/write from AWS S3.
+2. Append `USE_S3=1` to `config.mk` before building MXNet.
+ ```
+ echo "USE_S3=1" >> config.mk
+ ```
+
+## Step 2: Configure S3 authentication tokens
+
+MXNet requires the S3 environment variables `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` to be set. [Here](https://aws.amazon.com/blogs/security/wheres-my-secret-access-key/) are instructions to get the access keys from AWS console.
+
+```
+export AWS_ACCESS_KEY_ID=<your-access-key-id>
+AWS_SECRET_ACCESS_KEY=<your-secret-access-key>
+```
+
+## Step 3: Upload data to S3
+
+There are several ways to upload data to S3. One easy way is to use the AWS command line utility. For example, the following `sync` command will recursively copy contents from a local directory to a directory in S3.
+
+```
+aws s3 sync ./training-data s3://bucket-name/training-data
+```
+
+## Step 4: Train with data from S3
+
+Once the data is in S3, it is very straightforward to use it from MXNet. Any data iterator that can read/write data from a local drive can also read/write data from S3.
+
+Let's modify an existing example code in MXNet repository to read data from S3 instead of local disk. [`mxnet/tests/python/train/test_conv.py`](https://github.com/dmlc/mxnet/blob/master/tests/python/train/test_conv.py) trains a convolutional network using MNIST data from local disk. We'll do the following change to read the data from S3 instead.
+
+```
+~/mxnet$ sed -i -- 's/data\//s3:\/\/bucket-name\/training-data\//g' ./tests/python/train/test_conv.py
+
+~/mxnet$ git diff ./tests/python/train/test_conv.py
+diff --git a/tests/python/train/test_conv.py b/tests/python/train/test_conv.py
+index 039790e..66a60ce 100644
+--- a/tests/python/train/test_conv.py
++++ b/tests/python/train/test_conv.py
+@@ -39,14 +39,14 @@ def get_iters():
+
+ batch_size = 100
+ train_dataiter = mx.io.MNISTIter(
+- image="data/train-images-idx3-ubyte",
+- label="data/train-labels-idx1-ubyte",
++ image="s3://bucket-name/training-data/train-images-idx3-ubyte",
++ label="s3://bucket-name/training-data/train-labels-idx1-ubyte",
+ data_shape=(1, 28, 28),
+ label_name='sm_label',
+ batch_size=batch_size, shuffle=True, flat=False, silent=False, seed=10)
+ val_dataiter = mx.io.MNISTIter(
+- image="data/t10k-images-idx3-ubyte",
+- label="data/t10k-labels-idx1-ubyte",
++ image="s3://bucket-name/training-data/t10k-images-idx3-ubyte",
++ label="s3://bucket-name/training-data/t10k-labels-idx1-ubyte",
+ data_shape=(1, 28, 28),
+ label_name='sm_label',
+ batch_size=batch_size, shuffle=True, flat=False, silent=False)
+```
+
+After the above change `test_conv.py` will fetch data from S3 instead of the local disk.
+
+```
+python ./tests/python/train/test_conv.py
+[21:59:19] src/io/s3_filesys.cc:878: No AWS Region set, using default region us-east-1
+[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 60000 images, shuffle=1, shape=(100,1,28,28)
+[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 10000 images, shuffle=1, shape=(100,1,28,28)
+INFO:root:Start training with [cpu(0)]
+Start training with [cpu(0)]
+INFO:root:Epoch[0] Resetting Data Iterator
+Epoch[0] Resetting Data Iterator
+INFO:root:Epoch[0] Time cost=11.277
+Epoch[0] Time cost=11.277
+INFO:root:Epoch[0] Validation-accuracy=0.955100
+Epoch[0] Validation-accuracy=0.955100
+INFO:root:Finish fit...
+Finish fit...
+INFO:root:Finish predict...
+Finish predict...
+INFO:root:final accuracy = 0.955100
+final accuracy = 0.955100
+```
diff --git a/versions/master/_sources/how_to/smart_device.txt b/versions/master/_sources/how_to/smart_device.txt
new file mode 100644
index 0000000..2584b4c
--- /dev/null
+++ b/versions/master/_sources/how_to/smart_device.txt
@@ -0,0 +1,95 @@
+# Deep Learning in a Single File for Smart Devices
+
+Deep learning (DL) systems are complex and often depend on a number of libraries.
+Porting a DL library to different platforms can be painful, especially for smart devices.
+One simple solution to this problem is to provide a light interface to the library, complete with all required code in a single file with minimal dependencies.
+In this document, we explain how to amalgamate all necessary code into a single file,
+and demonstrate the approach with an example in which we run image recognition on a mobile device.
+
+## Amalgamation: Making the Whole System a Single File
+
+We come to the idea of of amalgamation following the example of SQLite,
+which pack all the code needed to run a simple database into a single source file.
+All that's necessary to create the library is to compile that single file.
+This simplifies the problem of porting to various platforms.
+
+Thanks to [Jack Deng](https://github.com/jdeng),
+MXNet provides an [amalgamation](https://github.com/dmlc/mxnet/tree/master/amalgamation) script
+that compiles all code needed for prediction based on trained DL models into a single `.cc` file,
+containing approximately 30K lines of code. This code only depends on the BLAS library.
+Moreover, we've also created an even more minimal version,
+with the BLAS dependency removed.
+You can compile the single file into JavaScript by using [emscripten](https://github.com/kripken/emscripten).
+
+The compiled library can be used by any other programming language.
+The `.h` file contains a light prediction API.
+Porting to another language with a C foreign function interface requires little effort.
+
+For examples, see the following examples on GitHub:
+
+- Go: [https://github.com/jdeng/gomxnet](https://github.com/jdeng/gomxnet)
+- Java: [https://github.com/dmlc/mxnet/tree/master/amalgamation/jni](https://github.com/dmlc/mxnet/tree/master/amalgamation/jni)
+- Python: [https://github.com/dmlc/mxnet/tree/master/amalgamation/python](https://github.com/dmlc/mxnet/tree/master/amalgamation/python)
+
+
+If you plan to amalgamate your system, there are a few guidelines you ought to observe when building the project:
+
+- Minimize dependence on other libraries.
+- Use namespace to encapsulate the types and operators.
+- Avoid running commands such as ```using namespace xyz``` on the global scope.
+- Avoid cyclic include dependencies.
+
+
+## Image Recognition Demo on Mobile Devices
+
+With amalgamation, deploying the system on smart devices (such as Android or iOS) is simple. But there are two additional considerations:
+
+- The model should be small enough to fit into the device's memory.
+- The model shouldn't be too expensive to run given the relatively low computational power of these devices.
+
+Let's use image recognition as an example.
+We start with the state-of-the-art inception model.
+We train it on an ImageNet dataset,
+using multiple servers with GTX 980 cards.
+The resulting model fits into memory,
+but it's too expensive to run.
+We remove some layers, but now the results are poor.
+
+Finally, we show an Android example, thanks to Leliana, [https://github.com/Leliana/WhatsThis](https://github.com/Leliana/WhatsThis) to demonstrate how to run on Android.
+
+<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/subinception.png" height="488" width="274">
+
+
+By using amalgamation, we can easily port the prediction library to mobile devices, with nearly no dependency.
+After compiling the library for smart platforms, the last thing we must do is to call C-API in the target language (Java/Swift).
+
+<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/poolnet.png" height="488" width="274">
+
+Besides this pre-trained Inception-BatchNorm network, we've provided two pre-trained models.
+
+We tested our model on a Nexus 5:
+
+
+| | Top-1 Validation on ILSVRC2012 | Time | App Size | Runtime Temp Memory Req |
+| ---------------- | ----------------------------------- | ----- | --- | ------------ |
+| FastPoorNet | around 52%, similar to 2011 winner | 1s | <10MB | <5MB |
+| Sub InceptionBN | around 64%, similar to 2013 winner | 2.7s | <40MB | <10MB |
+| InceptionBN | around 70% | 4s-5s | <60MB | 10MB |
+
+These models are for demonstration only.
+They aren't fine-tuned for mobile devices,
+and there is definitely room for improvement.
+We believe that making a lightweight, portable,
+and fast deep learning library is fun and interesting,
+and hope you enjoy using the library.
+
+## Source Code
+[https://github.com/Leliana/WhatsThis](https://github.com/Leliana/WhatsThis)
+
+
+## Demo APK Download
+
+- [FastPoorNet](https://github.com/dmlc/web-data/blob/master/mxnet/apk/fastpoornet.apk?raw=true)
+
+
+- [SubInception](https://github.com/dmlc/web-data/blob/master/mxnet/apk/subinception.apk?raw=true)
diff --git a/versions/master/_sources/how_to/torch.txt b/versions/master/_sources/how_to/torch.txt
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--- /dev/null
+++ b/versions/master/_sources/how_to/torch.txt
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+# How to Use MXNet As an (Almost) Full-function Torch Front End
+
+This topic demonstrates how to use MXNet as a front end to two of Torch's major functionalities:
+
+* Call Torch's tensor mathematical functions with MXNet.NDArray
+
+* Embed Torch's neural network modules (layers) into MXNet's symbolic graph
+## Compile with Torch
+
+
+* Install Torch using the [official guide](http://torch.ch/docs/getting-started.html).
+ * If you haven't already done so, copy `make/config.mk` (Linux) or `make/osx.mk` (Mac) into the MXNet root folder as `config.mk`. In `config.mk` uncomment the lines `TORCH_PATH = $(HOME)/torch` and `MXNET_PLUGINS += plugin/torch/torch.mk`.
+ * By default, Torch should be installed in your home folder (so `TORCH_PATH = $(HOME)/torch`). Modify TORCH_PATH to point to your torch installation, if necessary.
+* Run `make clean && make` to build MXNet with Torch support.
+
+## Tensor Mathematics
+The mxnet.th module supports calling Torch's tensor mathematical functions with mxnet.nd.NDArray. See [complete code](https://github.com/dmlc/mxnet/blob/master/example/torch/torch_function.py):
+
+ ```Python
+ import mxnet as mx
+ x = mx.th.randn(2, 2, ctx=mx.cpu(0))
+ print x.asnumpy()
+ y = mx.th.abs(x)
+ print y.asnumpy()
+
+ x = mx.th.randn(2, 2, ctx=mx.cpu(0))
+ print x.asnumpy()
+ mx.th.abs(x, x) # in-place
+ print x.asnumpy()
+ ```
+For help, use the `help(mx.th)` command.
+
+We've added support for most common functions listed on [Torch's documentation page](https://github.com/torch/torch7/blob/master/doc/maths.md).
+If you find that the function you need is not supported, you can easily register it in `mxnet_root/plugin/torch/torch_function.cc` by using the existing registrations as examples.
+
+## Torch Modules (Layers)
+MXNet supports Torch's neural network modules through the`mxnet.symbol.TorchModule` symbol.
+For example, the following code defines a three-layer DNN for classifying MNIST digits ([full code](https://github.com/dmlc/mxnet/blob/master/example/torch/torch_module.py)):
+
+ ```Python
+ data = mx.symbol.Variable('data')
+ fc1 = mx.symbol.TorchModule(data_0=data, lua_string='nn.Linear(784, 128)', num_data=1, num_params=2, num_outputs=1, name='fc1')
+ act1 = mx.symbol.TorchModule(data_0=fc1, lua_string='nn.ReLU(false)', num_data=1, num_params=0, num_outputs=1, name='relu1')
+ fc2 = mx.symbol.TorchModule(data_0=act1, lua_string='nn.Linear(128, 64)', num_data=1, num_params=2, num_outputs=1, name='fc2')
+ act2 = mx.symbol.TorchModule(data_0=fc2, lua_string='nn.ReLU(false)', num_data=1, num_params=0, num_outputs=1, name='relu2')
+ fc3 = mx.symbol.TorchModule(data_0=act2, lua_string='nn.Linear(64, 10)', num_data=1, num_params=2, num_outputs=1, name='fc3')
+ mlp = mx.symbol.SoftmaxOutput(data=fc3, name='softmax')
+ ```
+Let's break it down. First `data = mx.symbol.Variable('data')` defines a Variable as a placeholder for input.
+Then, it's fed through Torch's nn modules with:
+ `fc1 = mx.symbol.TorchModule(data_0=data, lua_string='nn.Linear(784, 128)', num_data=1, num_params=2, num_outputs=1, name='fc1')`.
+To use Torch's criterion as loss functions, you can replace the last line with:
+ ```Python
+ logsoftmax = mx.symbol.TorchModule(data_0=fc3, lua_string='nn.LogSoftMax()', num_data=1, num_params=0, num_outputs=1, name='logsoftmax')
+ # Torch's label starts from 1
+ label = mx.symbol.Variable('softmax_label') + 1
+ mlp = mx.symbol.TorchCriterion(data=logsoftmax, label=label, lua_string='nn.ClassNLLCriterion()', name='softmax')
+ ```
+The input to the nn module is named data_i for i = 0 ... num_data-1. `lua_string` is a single Lua statement that creates the module object.
+For Torch's built-in module, this is simply `nn.module_name(arguments)`.
+If you are using a custom module, place it in a .lua script file and load it with `require 'module_file.lua'` if your script returns a torch.nn object, or `(require 'module_file.lua')()` if your script returns a torch.nn class.
+
diff --git a/versions/master/_sources/how_to/visualize_graph.txt b/versions/master/_sources/how_to/visualize_graph.txt
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--- /dev/null
+++ b/versions/master/_sources/how_to/visualize_graph.txt
@@ -0,0 +1,64 @@
+# How to visualize Neural Networks as computation graph
+
+Here, we'll demonstrate how to use ```mx.viz.plot_network```
+for visualizing your neural networks. ```mx.viz.plot_network```
+represents the neural network as a computation graph consisting of nodes and edges.
+The visualizations make clear which nodes correspond to inputs,
+where the computation starts,
+and which correspond to output nodes,
+from which the result can be read.
+
+## Prerequisites
+You need the [Jupyter Notebook](http://jupyter.readthedocs.io/en/latest/)
+and [Graphviz](http://www.graphviz.org/) libraries to visualize the network.
+Please make sure you have followed [installation instructions](http://mxnet.io/get_started/install.html)
+in setting up above dependencies along with setting up MXNet.
+
+## Visualize the sample Neural Network
+
+```mx.viz.plot_network``` takes [Symbol](http://mxnet.io/api/python/symbol.html), with your Network definition, and optional node_attrs, parameters for the shape of the node in the graph, as input and generates a computation graph.
+
+We will now try to visualize a sample Neural Network for linear matrix factorization:
+- Start Jupyter notebook server
+```bash
+ $ jupyter notebook
+```
+- Access Jupyter notebook in your browser - http://localhost:8888/.
+- Create a new notebook - "File -> New Notebook -> Python 2"
+- Copy and run below code to visualize a simple network.
+
+```python
+import mxnet as mx
+user = mx.symbol.Variable('user')
+item = mx.symbol.Variable('item')
+score = mx.symbol.Variable('score')
+
+# Set dummy dimensions
+k = 64
+max_user = 100
+max_item = 50
+
+# user feature lookup
+user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)
+
+# item feature lookup
+item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)
+
+# predict by the inner product, which is elementwise product and then sum
+net = user * item
+net = mx.symbol.sum_axis(data = net, axis = 1)
+net = mx.symbol.Flatten(data = net)
+
+# loss layer
+net = mx.symbol.LinearRegressionOutput(data = net, label = score)
+
+# Visualize your network
+mx.viz.plot_network(net)
+```
+You should see computation graph something like the following image:
+<img src=https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/SampleNetworkVisualization.png
+width=400/>
+
+# References
+* [Example MXNet Matrix Factorization](https://github.com/dmlc/mxnet/blob/master/example/recommenders/demo1-MF.ipynb)
+* [Visualizing CNN Architecture of MXNet Tutorials](http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/)
diff --git a/versions/master/_sources/index.txt b/versions/master/_sources/index.txt
new file mode 100644
index 0000000..37d5887
--- /dev/null
+++ b/versions/master/_sources/index.txt
@@ -0,0 +1,14 @@
+Contents
+--------
+These are used to generate the indexes for search functionality.
+
+- [Python Documents](api/python/index.md)
+- [R Documents](api/r/index.md)
+- [Julia Documents](api/julia/index.md)
+- [C++ Documents](api/c++/index.md)
+- [Scala Documents](api/scala/index.md)
+- [Perl Documents](api/perl/index.md)
+- [HowTo Documents](how_to/index.md)
+- [Get Started Documents](get_started/index.md)
+- [System Documents](architecture/index.md)
+- [Tutorials](tutorials/index.md)
diff --git a/versions/master/_sources/model_zoo/index.txt b/versions/master/_sources/model_zoo/index.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/model_zoo/index.txt
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+# MXNet Model Zoo
+
+MXNet features fast implementations of many state-of-the-art models reported in the academic literature. This Model Zoo is an
+ongoing project to collect complete models, with python scripts, pre-trained weights as well as instructions on how to build and fine tune these models.
+
+## How to Contribute a Pre-Trained Model (and what to include)
+
+The Model Zoo has good entries for CNNs but is seeking content in other areas.
+
+Issue a Pull Request containing the following:
+* Gist Log
+* .json model definition
+* Model parameter file
+* Readme file (details below)
+
+Readme file should contain:
+* Model Location, access instructions (wget)
+* Confirmation the trained model meets published accuracy from original paper
+* Step by step instructions on how to use the trained model
+* References to any other applicable docs or arxiv papers the model is based on
+
+## Convolutional Neural Networks (CNNs)
+
+Convolutional neural networks are the state-of-art architecture for many image and video processing problems. Some available datasets include:
+
+* [ImageNet](http://image-net.org/): a large corpus of 1 million natural images, divided into 1000 categories.
+* [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html): 60,000 natural images (32 x 32 pixels) from 10 categories.
+* [PASCAL_VOC](http://host.robots.ox.ac.uk/pascal/VOC/): A subset of ImageNet images with object bounding boxes.
+* [UCF101](http://crcv.ucf.edu/data/UCF101.php): 13,320 videos from 101 action categories.
+* [Mini-Places2](http://6.869.csail.mit.edu/fa15/project.html): Subset of the Places2 dataset. Includes 100,000 images from 100 scene categories.
+* ImageNet 11k
+* [Places2](http://places2.csail.mit.edu/download.html): There are 1.6 million train images from 365 scene categories in the Places365-Standard, which are used to train the Places365 CNNs. There are 50 images per category in the validation set and 900 images per category in the testing set. Compared to the train set of Places365-Standard, the train set of Places365-Challenge has 6.2 million extra images, leading to totally 8 million train images for the Places365 challenge 2016. The vali [...]
+* [Multimedia Commons](https://aws.amazon.com/public-datasets/multimedia-commons/): YFCC100M (99.2 million images and 0.8 million videos from Flickr) and supplemental material (pre-extracted features, additional annotations).
+
+For instructions on using these models, see [the python tutorial on using pre-trained ImageNet models](http://mxnet.io/tutorials/python/predict_imagenet.html).
+
+| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
+| --- | --- | --- | --- | --- |
+| [CaffeNet](http://data.dmlc.ml/mxnet/models/imagenet/caffenet/caffenet-symbol.json) | ImageNet | [Param File](http://data.dmlc.ml/models/imagenet/caffenet/caffenet-0000.params) | [Krizhevsky, 2012](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) | @jspisak |
+| [Network in Network (NiN)](http://data.dmlc.ml/models/imagenet/nin/nin-symbol.json) | ImageNet | [Param File](http://data.dmlc.ml/models/imagenet/nin/nin-0000.params) | [Lin et al.., 2014](https://arxiv.org/pdf/1312.4400v3.pdf) | @jspisak |
+| [SqueezeNet v1.1](http://data.dmlc.ml/models/imagenet/squeezenet/squeezenet_v1.1-symbol.json) | ImageNet | [Param File](http://data.dmlc.ml/models/imagenet/squeezenet/squeezenet_v1.1-0000.params) | [Iandola et al.., 2016](https://arxiv.org/pdf/1602.07360v4.pdf) | @jspisak |
+| [VGG16](http://data.dmlc.ml/models/imagenet/vgg/vgg16-symbol.json) | ImageNet | [Param File](http://data.dmlc.ml/models/imagenet/vgg/vgg16-0000.params)| [Simonyan et al.., 2015](https://arxiv.org/pdf/1409.1556v6.pdf) | @jspisak |
+| [VGG19](http://data.dmlc.ml/models/imagenet/vgg/vgg19-symbol.json) | ImageNet | [Param File](http://data.dmlc.ml/models/imagenet/vgg/vgg19-0000.params) | [Simonyan et al.., 2015](https://arxiv.org/pdf/1409.1556v6.pdf) | @jspisak |
+| [Inception v3 w/BatchNorm](http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-symbol.json) | ImageNet | [Param File](http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-0126.params) | [Szegedy et al.., 2015](https://arxiv.org/pdf/1512.00567.pdf) | @jspisak |
+| [ResidualNet152](http://data.dmlc.ml/models/imagenet/resnet/152-layers/resnet-152-symbol.json) | ImageNet | [Param File](http://data.dmlc.ml/models/imagenet/resnet/152-layers/resnet-152-0000.params) | [He et al.., 2015](https://arxiv.org/pdf/1512.03385v1.pdf) | @jspisak |
+| [ResNext101-64x4d](http://data.dmlc.ml/models/imagenet/resnext/101-layers/resnext-101-64x4d-symbol.json) | ImageNet | [Param File](http://data.dmlc.ml/models/imagenet/resnext/101-layers/resnext-101-64x4d-0000.params) | [Xie et al.., 2016](https://arxiv.org/pdf/1611.05431.pdf) | @Jerryzcn |
+| Fast-RCNN | PASCAL VOC | [Param File] | [Girshick, 2015](https://arxiv.org/pdf/1504.08083v2.pdf) | |
+| Faster-RCNN | PASCAL VOC | [Param File] | [Ren et al..,2016](https://arxiv.org/pdf/1506.01497v3.pdf) | |
+| Single Shot Detection (SSD) | PASCAL VOC | [Param File] | [Liu et al.., 2016](https://arxiv.org/pdf/1512.02325v4.pdf) | |
+| [LocationNet](https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-symbol.json) | [MultimediaCommons](https://aws.amazon.com/public-datasets/multimedia-commons/) | [Param File](https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-0012.params) | [Weyand et al.., 2016](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45488.pdf) | @jychoi84 @kevinli7 |
+
+
+
+## Recurrent Neural Networks (RNNs) including LSTMs
+
+MXNet supports many types of recurrent neural networks (RNNs), including Long Short-Term Memory ([LSTM](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf))
+and Gated Recurrent Units (GRU) networks. Some available datasets include:
+
+* [Penn Treebank (PTB)](https://www.cis.upenn.edu/~treebank/): Text corpus with ~1 million words. Vocabulary is limited to 10,000 words. The task is predicting downstream words/characters.
+* [Shakespeare](http://cs.stanford.edu/people/karpathy/char-rnn/): Complete text from Shakespeare's works.
+* [IMDB reviews](https://s3.amazonaws.com/text-datasets): 25,000 movie reviews, labeled as positive or negative
+* [Facebook bAbI](https://research.facebook.com/researchers/1543934539189348): As a set of 20 question & answer tasks, each with 1,000 training examples.
+* [Flickr8k, COCO](http://mscoco.org/): Images with associated caption (sentences). Flickr8k consists of 8,092 images captioned by AmazonTurkers with ~40,000 captions. COCO has 328,000 images, each with 5 captions. The COCO images also come with labeled objects using segmentation algorithms.
+
+
+| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
+| --- | --- | --- | --- | --- |
+| LSTM - Image Captioning | Flickr8k, MS COCO | | [Vinyals et al.., 2015](https://arxiv.org/pdf/ 1411.4555v2.pdf) | @... |
+| LSTM - Q&A System| bAbl | | [Weston et al.., 2015](https://arxiv.org/pdf/1502.05698v10.pdf) | |
+| LSTM - Sentiment Analysis| IMDB | | [Li et al.., 2015](http://arxiv.org/pdf/1503.00185v5.pdf) | |
+
+
+## Generative Adversarial Networks (GANs)
+
+[Generative Adversarial Networks](http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf) train a competing pair of
+neural networks: a generator network which transforms a latent vector into content like an image, and a discriminator
+network that tries to distinguish between generated content and supplied "real" training content. When properly
+trained the two achieve a [Nash equilibrium](https://en.wikipedia.org/wiki/Nash_equilibrium).
+
+| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
+| --- | --- | --- | --- | --- |
+| DCGANs | ImageNet | | [Radford et al..,2016](https://arxiv.org/pdf/1511.06434v2.pdf) | @... |
+| Text to Image Synthesis |MS COCO| | [Reed et al.., 2016](https://arxiv.org/pdf/1605.05396v2.pdf) | |
+| Deep Jazz | | | [Deepjazz.io](https://deepjazz.io) | |
+
+
+
+## Other Models
+
+MXNet Supports a variety of model types beyond the canonical CNN and LSTM model types. These include deep reinforcement learning, linear models, etc.. Some available datasets and sources include:
+
+* [Google News](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit): A text corpus with a vocabulary of 3 million words architected for word2vec.
+* [MovieLens 20M Dataset](http://grouplens.org/datasets/movielens/): 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users. Includes tag genome data with 12 million relevance scores across 1,100 tags.
+* [Atari Video Game Emulator](http://stella.sourceforge.net/): Stella is a multi-platform Atari 2600 VCS emulator released under the GNU General Public License (GPL).
+
+
+| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
+| --- | --- | --- | --- | --- |
+| Word2Vec | Google News | | [Mikolov et al.., 2013](https://arxiv.org/pdf/1310.4546v1.pdf) | @... |
+| Matrix Factorization | MovieLens 20M | | [Huang et al.., 2013](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) | |
+| Deep Q-Network | Atari video games | | [Minh et al.., 2015](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html) | |
+| Asynchronous advantage actor-critic (A3C) | Atari video games | | [Minh et al.., 2016](https://arxiv.org/pdf/1602.01783.pdf) | |
diff --git a/versions/master/_sources/tutorials/basic/data.txt b/versions/master/_sources/tutorials/basic/data.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/tutorials/basic/data.txt
@@ -0,0 +1,421 @@
+# Iterators - Loading data
+In this tutorial, we focus on how to feed data into a training or inference program.
+Most training and inference modules in MXNet accept data iterators,
+which simplifies this procedure, especially when reading large datasets.
+Here we discuss the API conventions and several provided iterators.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html).
+
+- [OpenCV Python library](http://opencv.org/opencv-3-2.html), [Python Requests](http://docs.python-requests.org/en/master/), [Matplotlib](https://matplotlib.org/) and [Jupyter Notebook](http://jupyter.org/index.html).
+
+```
+$ pip install opencv-python requests matplotlib jupyter
+```
+- Set the environment variable `MXNET_HOME` to the root of the MXNet source folder.
+
+```
+$ git clone https://github.com/dmlc/mxnet ~/mxnet
+$ MXNET_HOME = '~/mxnet'
+```
+
+## MXNet Data Iterator
+Data Iterators in *MXNet* are similar to Python iterator objects.
+In Python, the function `iter` allows fetching items sequentially by calling `next()` on
+ iterable objects such as a Python `list`.
+Iterators provide an abstract interface for traversing various types of iterable collections
+ without needing to expose details about the underlying data source.
+
+In MXNet, data iterators return a batch of data as `DataBatch` on each call to `next`.
+A `DataBatch` often contains *n* training examples and their corresponding labels. Here *n* is the `batch_size` of the iterator. At the end of the data stream when there is no more data to read, the iterator raises ``StopIteration`` exception like Python `iter`.
+The structure of `DataBatch` is defined [here](http://mxnet.io/api/python/io.html#mxnet.io.DataBatch).
+
+Information such as name, shape, type and layout on each training example and their corresponding label can be provided as `DataDesc` data descriptor objects via the `provide_data` and `provide_label` properties in `DataBatch`.
+The structure of `DataDesc` is defined [here](http://mxnet.io/api/python/io.html#mxnet.io.DataDesc).
+
+All IO in MXNet is handled via `mx.io.DataIter` and its subclasses. In this tutorial, we'll discuss a few commonly used iterators provided by MXNet.
+
+Before diving into the details let's setup the environment by importing some required packages:
+
+```python
+import mxnet as mx
+%matplotlib inline
+import os
+import subprocess
+import numpy as np
+import matplotlib.pyplot as plt
+import tarfile
+
+import warnings
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+```
+
+## Reading data in memory
+When data is stored in memory, backed by either an `NDArray` or ``numpy`` `ndarray`,
+we can use the [__`NDArrayIter`__](http://mxnet.io/api/python/io.html#mxnet.io.NDArrayIter) to read data as below:
+
+
+```python
+import numpy as np
+data = np.random.rand(100,3)
+label = np.random.randint(0, 10, (100,))
+data_iter = mx.io.NDArrayIter(data=data, label=label, batch_size=30)
+for batch in data_iter:
+ print([batch.data, batch.label, batch.pad])
+```
+
+## Reading data from CSV files
+MXNet provides [`CSVIter`](http://mxnet.io/api/python/io.html#mxnet.io.CSVIter)
+to read from CSV files and can be used as below:
+
+```python
+#lets save `data` into a csv file first and try reading it back
+np.savetxt('data.csv', data, delimiter=',')
+data_iter = mx.io.CSVIter(data_csv='data.csv', data_shape=(3,), batch_size=30)
+for batch in data_iter:
+ print([batch.data, batch.pad])
+```
+
+## Custom Iterator
+When the built-in iterators do not suit your application needs,
+you can create your own custom data iterator.
+
+An iterator in _MXNet_ should
+1. Implement `next()` in ``Python2`` or `__next()__` in ``Python3``,
+ returning a `DataBatch` or raising a `StopIteration` exception if at the end of the data stream.
+2. Implement the `reset()` method to restart reading from the beginning.
+3. Have a `provide_data` attribute, consisting of a list of `DataDesc` objects that store the name, shape, type and layout information of the data (more info [here](http://mxnet.io/api/python/io.html#mxnet.io.DataBatch)).
+4. Have a `provide_label` attribute consisting of a list of `DataDesc` objects that store the name, shape, type and layout information of the label.
+
+When creating a new iterator, you can either start from scratch and define an iterator or reuse one of the existing iterators.
+For example, in the image captioning application, the input example is an image while the label is a sentence.
+Thus we can create a new iterator by:
+- creating a `image_iter` by using `ImageRecordIter` which provides multithreaded pre-fetch and augmentation.
+- creating a `caption_iter` by using `NDArrayIter` or the bucketing iterator provided in the *rnn* package.
+- `next()` returns the combined result of `image_iter.next()` and `caption_iter.next()`
+
+The example below shows how to create a Simple iterator.
+
+```python
+
+class SimpleIter(mx.io.DataIter):
+ def __init__(self, data_names, data_shapes, data_gen,
+ label_names, label_shapes, label_gen, num_batches=10):
+ self._provide_data = zip(data_names, data_shapes)
+ self._provide_label = zip(label_names, label_shapes)
+ self.num_batches = num_batches
+ self.data_gen = data_gen
+ self.label_gen = label_gen
+ self.cur_batch = 0
+
+ def __iter__(self):
+ return self
+
+ def reset(self):
+ self.cur_batch = 0
+
+ def __next__(self):
+ return self.next()
+
+ @property
+ def provide_data(self):
+ return self._provide_data
+
+ @property
+ def provide_label(self):
+ return self._provide_label
+
+ def next(self):
+ if self.cur_batch < self.num_batches:
+ self.cur_batch += 1
+ data = [mx.nd.array(g(d[1])) for d,g in zip(self._provide_data, self.data_gen)]
+ label = [mx.nd.array(g(d[1])) for d,g in zip(self._provide_label, self.label_gen)]
+ return mx.io.DataBatch(data, label)
+ else:
+ raise StopIteration
+```
+
+We can use the above defined `SimpleIter` to train a simple MLP program below:
+
+
+```python
+import mxnet as mx
+num_classes = 10
+net = mx.sym.Variable('data')
+net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=64)
+net = mx.sym.Activation(data=net, name='relu1', act_type="relu")
+net = mx.sym.FullyConnected(data=net, name='fc2', num_hidden=num_classes)
+net = mx.sym.SoftmaxOutput(data=net, name='softmax')
+print(net.list_arguments())
+print(net.list_outputs())
+```
+
+Here, there are four variables that are learnable parameters:
+the *weights* and *biases* of FullyConnected layers *fc1* and *fc2*,
+two variables for input data: *data* for the training examples
+and *softmax_label* contains the respective labels and the *softmax_output*.
+
+The *data* variables are called free variables in MXNet's Symbol API.
+To execute a Symbol, they need to be bound with data.
+[Click here learn more about Symbol](http://mxnet.io/tutorials/basic/symbol.html).
+
+We use the data iterator to feed examples to a neural network via MXNet's `module` API.
+[Click here to learn more about Module](http://mxnet.io/tutorials/basic/module.html).
+
+
+```python
+import logging
+logging.basicConfig(level=logging.INFO)
+
+n = 32
+data_iter = SimpleIter(['data'], [(n, 100)],
+ [lambda s: np.random.uniform(-1, 1, s)],
+ ['softmax_label'], [(n,)],
+ [lambda s: np.random.randint(0, num_classes, s)])
+
+mod = mx.mod.Module(symbol=net)
+mod.fit(data_iter, num_epoch=5)
+```
+
+## Record IO
+Record IO is a file format used by MXNet for data IO.
+It compactly packs the data for efficient read and writes from distributed file system like Hadoop HDFS and AWS S3.
+You can learn more about the design of `RecordIO` [here](http://mxnet.io/architecture/note_data_loading.html).
+
+MXNet provides [__`MXRecordIO`__](http://mxnet.io/api/python/io.html#mxnet.recordio.MXRecordIO)
+and [__`MXIndexedRecordIO`__](http://mxnet.io/api/python/io.html#mxnet.recordio.MXIndexedRecordIO)
+for sequential access of data and random access of the data.
+
+### MXRecordIO
+First, let's look at an example on how to read and write sequentially
+using `MXRecordIO`. The files are named with a `.rec` extension.
+
+
+```python
+record = mx.recordio.MXRecordIO('tmp.rec', 'w')
+for i in range(5):
+ record.write('record_%d'%i)
+record.close()
+```
+
+We can read the data back by opening the file with an option `r` as below:
+
+```python
+record = mx.recordio.MXRecordIO('tmp.rec', 'r')
+while True:
+ item = record.read()
+ if not item:
+ break
+ print (item)
+record.close()
+```
+
+### MXIndexedRecordIO
+`MXIndexedRecordIO` supports random or indexed access to the data.
+We will create an indexed record file and a corresponding index file as below:
+
+
+```python
+record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
+for i in range(5):
+ record.write_idx(i, 'record_%d'%i)
+record.close()
+```
+
+Now, we can access the individual records using the keys
+
+
+```python
+record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'r')
+record.read_idx(3)
+```
+
+You can also list all the keys in the file.
+
+
+```python
+record.keys
+```
+
+### Packing and Unpacking data
+
+Each record in a .rec file can contain arbitrary binary data. However, most deep learning tasks require data to be input in label/data format.
+The `mx.recordio` package provides a few utility functions for such operations, namely: `pack`, `unpack`, `pack_img`, and `unpack_img`.
+
+#### Packing/Unpacking Binary Data
+
+[__`pack`__](http://mxnet.io/api/python/io.html#mxnet.recordio.pack) and [__`unpack`__](http://mxnet.io/api/python/io.html#mxnet.recordio.unpack) are used for storing float (or 1d array of float) label and binary data. The data is packed along with a header. The header structure is defined [here](http://mxnet.io/api/python/io.html#mxnet.recordio.IRHeader).
+
+
+```python
+# pack
+data = 'data'
+label1 = 1.0
+header1 = mx.recordio.IRHeader(flag=0, label=label1, id=1, id2=0)
+s1 = mx.recordio.pack(header1, data)
+
+label2 = [1.0, 2.0, 3.0]
+header2 = mx.recordio.IRHeader(flag=3, label=label2, id=2, id2=0)
+s2 = mx.recordio.pack(header2, data)
+```
+
+```python
+# unpack
+print(mx.recordio.unpack(s1))
+print(mx.recordio.unpack(s2))
+```
+
+#### Packing/Unpacking Image Data
+
+MXNet provides [__`pack_img`__](http://mxnet.io/api/python/io.html#mxnet.recordio.pack_img) and [__`unpack_img`__](http://mxnet.io/api/python/io.html#mxnet.recordio.unpack_img) to pack/unpack image data.
+Records packed by `pack_img` can be loaded by `mx.io.ImageRecordIter`.
+
+
+```python
+data = np.ones((3,3,1), dtype=np.uint8)
+label = 1.0
+header = mx.recordio.IRHeader(flag=0, label=label, id=0, id2=0)
+s = mx.recordio.pack_img(header, data, quality=100, img_fmt='.jpg')
+```
+
+```python
+# unpack_img
+print(mx.recordio.unpack_img(s))
+```
+
+#### Using tools/im2rec.py
+You can also convert raw images into *RecordIO* format using the ``im2rec.py`` utility script that is provided in the MXNet [src/tools](https://github.com/dmlc/mxnet/tree/master/tools) folder.
+An example of how to use the script for converting to *RecordIO* format is shown in the `Image IO` section below.
+
+## Image IO
+
+In this section, we will learn how to preprocess and load image data in MXNet.
+
+There are 4 ways of loading image data in MXNet.
+ 1. Using [__mx.image.imdecode__](http://mxnet.io/api/python/io.html#mxnet.image.imdecode) to load raw image files.
+ 2. Using [__`mx.img.ImageIter`__](http://mxnet.io/api/python/io.html#mxnet.image.ImageIter) implemented in Python which is very flexible to customization. It can read from .rec(`RecordIO`) files and raw image files.
+ 3. Using [__`mx.io.ImageRecordIter`__](http://mxnet.io/api/python/io.html#mxnet.io.ImageRecordIter) implemented on the MXNet backend in C++. This is less flexible to customization but provides various language bindings.
+ 4. Creating a Custom iterator inheriting `mx.io.DataIter`
+
+
+### Preprocessing Images
+Images can be preprocessed in different ways. We list some of them below:
+- Using `mx.io.ImageRecordIter` which is fast but not very flexible. It is great for simple tasks like image recognition but won't work for more complex tasks like detection and segmentation.
+- Using `mx.recordio.unpack_img` (or `cv2.imread`, `skimage`, etc) + `numpy` is flexible but slow due to Python Global Interpreter Lock (GIL).
+- Using MXNet provided `mx.image` package. It stores images in [__`NDArray`__](http://mxnet.io/tutorials/basic/ndarray.html) format and leverages MXNet's [dependency engine](http://mxnet.io/architecture/note_engine.html) to automatically parallelize processing and circumvent GIL.
+
+Below, we demonstrate some of the frequently used preprocessing routines provided by the `mx.image` package.
+
+Let's download sample images that we can work with.
+
+
+```python
+fname = mx.test_utils.download(url='http://data.mxnet.io/data/test_images.tar.gz', dirname='data', overwrite=False)
+tar = tarfile.open(fname)
+tar.extractall(path='./data')
+tar.close()
+```
+
+#### Loading raw images
+`mx.image.imdecode` lets us load the images. `imdecode` provides a similar interface to ``OpenCV``.
+
+**Note:** You will still need ``OpenCV``(not the CV2 Python library) installed to use `mx.image.imdecode`.
+
+```python
+img = mx.image.imdecode(open('data/test_images/ILSVRC2012_val_00000001.JPEG').read())
+plt.imshow(img.asnumpy()); plt.show()
+```
+
+#### Image Transformations
+
+
+```python
+# resize to w x h
+tmp = mx.image.imresize(img, 100, 70)
+plt.imshow(tmp.asnumpy()); plt.show()
+```
+
+```python
+# crop a random w x h region from image
+tmp, coord = mx.image.random_crop(img, (150, 200))
+print(coord)
+plt.imshow(tmp.asnumpy()); plt.show()
+```
+
+### Loading Data using Image Iterators
+
+Before we see how to read data using the two built-in Image iterators,
+ lets get a sample __Caltech 101__ dataset
+ that contains 101 classes of objects and converts them into record io format.
+Download and unzip
+
+
+```python
+fname = mx.test_utils.download(url='http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz', dirname='data', overwrite=False)
+tar = tarfile.open(fname)
+tar.extractall(path='./data')
+tar.close()
+```
+
+Let's take a look at the data. As you can see, under the root folder (./data/101_ObjectCategories) every category has a subfolder(./data/101_ObjectCategories/yin_yang).
+
+Now let's convert them into record io format using the `im2rec.py` utility script.
+First, we need to make a list that contains all the image files and their categories:
+
+```python
+os.system('python %s/tools/im2rec.py --list=1 --recursive=1 --shuffle=1 --test-ratio=0.2 data/caltech data/101_ObjectCategories'%MXNET_HOME)
+```
+
+The resulting list file (./data/caltech_train.lst) is in the format `index\t(one or more label)\tpath`. In this case, there is only one label for each image but you can modify the list to add in more for multi-label training.
+
+Then we can use this list to create our record io file:
+
+
+```python
+os.system("python %s/tools/im2rec.py --num-thread=4 --pass-through=1 data/caltech data/101_ObjectCategories"%MXNET_HOME)
+```
+
+The record io files are now saved at here (./data)
+
+#### Using ImageRecordIter
+[__`ImageRecordIter`__](http://mxnet.io/api/python/io.html#mxnet.io.ImageRecordIter) can be used for loading image data saved in record io format. To use ImageRecordIter, simply create an instance by loading your record file:
+
+
+```python
+data_iter = mx.io.ImageRecordIter(
+ path_imgrec="./data/caltech.rec", # the target record file
+ data_shape=(3, 227, 227), # output data shape. An 227x227 region will be cropped from the original image.
+ batch_size=4, # number of samples per batch
+ resize=256 # resize the shorter edge to 256 before cropping
+ # ... you can add more augumentation options as defined in ImageRecordIter.
+ )
+data_iter.reset()
+batch = data_iter.next()
+data = batch.data[0]
+for i in range(4):
+ plt.subplot(1,4,i+1)
+ plt.imshow(data[i].asnumpy().astype(np.uint8).transpose((1,2,0)))
+plt.show()
+```
+
+#### Using ImageIter
+[__ImageIter__](http://mxnet.io/api/python/io.html#mxnet.io.ImageIter) is a flexible interface that supports loading of images in both RecordIO and Raw format.
+
+
+```python
+data_iter = mx.image.ImageIter(batch_size=4, data_shape=(3, 227, 227),
+ path_imgrec="./data/caltech.rec",
+ path_imgidx="./data/caltech.idx" )
+data_iter.reset()
+batch = data_iter.next()
+data = batch.data[0]
+for i in range(4):
+ plt.subplot(1,4,i+1)
+ plt.imshow(data[i].asnumpy().astype(np.uint8).transpose((1,2,0)))
+plt.show()
+```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/basic/image_io.txt b/versions/master/_sources/tutorials/basic/image_io.txt
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+# Image IO - Loading and pre-processing images
+
+This tutorial explains how to prepare, load and train with image data in
+MXNet. All IO in MXNet is handled via `mx.io.DataIter` and its subclasses. In
+this tutorial we focus on how to use pre-built data iterators as while as custom
+iterators to process image data.
+
+There are mainly three ways of loading image data in MXNet:
+
+- [NEW] `mx.img.ImageIter`: implemented in python, easily customizable, can load
+ from both .rec files and raw image files.
+- [OLD] `mx.io.ImageRecordIter`: implemented in backend (C++), less customizable
+ but can be used in all language bindings, load from .rec files
+- Custom iterator by inheriting mx.io.DataIter
+
+First, we explain the record io file format used by mxnet:
+
+## RecordIO
+
+Record IO is the main file format used by MXNet for data IO. It supports reading
+and writing on various file systems including distributed file systems like
+Hadoop HDFS and AWS S3. First, we download the Caltech 101 dataset that
+contains 101 classes of objects and convert them into record io format:
+
+```python
+%matplotlib inline
+import os
+import subprocess
+import mxnet as mx
+import numpy as np
+import matplotlib.pyplot as plt
+
+# change this to your mxnet location
+MXNET_HOME = '/scratch/mxnet'
+```
+
+Download and unzip:
+
+```python
+os.system('wget http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz -P data/')
+os.chdir('data')
+os.system('tar -xf 101_ObjectCategories.tar.gz')
+os.chdir('../')
+```
+
+Let's take a look at the data. As you can see, under the
+[root folder](./data/101_ObjectCategories) every category has a
+[subfolder](./data/101_ObjectCategories/yin_yang).
+
+Now let's convert them into record io format. First we need to make a list that
+contains all the image files and their categories:
+
+
+```python
+os.system('python %s/tools/im2rec.py --list=1 --recursive=1 --shuffle=1 --test-ratio=0.2 data/caltech data/101_ObjectCategories'%MXNET_HOME)
+```
+
+The resulting [list file](./data/caltech_train.lst) is in the format
+`index\t(one or more label)\tpath`. In this case there is only one label for
+each image but you can modify the list to add in more for multi label training.
+
+Then we can use this list to create our record io file:
+
+
+```python
+os.system("python %s/tools/im2rec.py --num-thread=4 --pass-through=1 data/caltech data/101_ObjectCategories"%MXNET_HOME)
+```
+
+The record io files are now saved at [here](./data)
+
+## ImageRecordIter
+
+`mx.io.ImageRecordIter` can be used for loading image data saved in record io
+format. It is available in all frontend languages, but as it's implemented in
+C++, it is less flexible.
+
+To use ImageRecordIter, simply create an instance by loading your record file:
+
+```python
+data_iter = mx.io.ImageRecordIter(
+ path_imgrec="./data/caltech_train.rec", # the target record file
+ data_shape=(3, 227, 227), # output data shape. An 227x227 region will be cropped from the original image.
+ batch_size=4, # number of samples per batch
+ resize=256 # resize the shorter edge to 256 before cropping
+ # ... you can add more augumentation options here. use help(mx.io.ImageRecordIter) to see all possible choices
+ )
+data_iter.reset()
+batch = data_iter.next()
+data = batch.data[0]
+for i in range(4):
+ plt.subplot(1,4,i+1)
+ plt.imshow(data[i].asnumpy().astype(np.uint8).transpose((1,2,0)))
+plt.show()
+```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/basic/module.txt b/versions/master/_sources/tutorials/basic/module.txt
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+# Module - Neural network training and inference
+
+Training a neural network involves quite a few steps. One need to specify how
+to feed input training data, initialize model parameters, perform forward and
+backward passes through the network, update weights based on computed gradients, do
+model checkpoints, etc. During prediction, one ends up repeating most of these
+steps. All this can be quite daunting to both newcomers as well as experienced
+developers.
+
+Luckily, MXNet modularizes commonly used code for training and inference in
+the `module` (`mod` for short) package. `Module` provides both high-level and
+intermediate-level interfaces for executing predefined networks. One can use
+both interfaces interchangeably. We will show the usage of both interfaces in
+this tutorial.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html).
+
+- [Jupyter Notebook](http://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages.
+```
+pip install jupyter requests
+```
+
+## Preliminary
+
+In this tutorial we will demonstrate `module` usage by training a
+[Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron) (MLP)
+on the [UCI letter recognition](https://archive.ics.uci.edu/ml/datasets/letter+recognition)
+dataset.
+
+The following code downloads the dataset and creates an 80:20 train:test
+split. It also initializes a training data iterator to return a batch of 32
+training examples each time. A separate iterator is also created for test data.
+
+```python
+import logging
+logging.getLogger().setLevel(logging.INFO)
+import mxnet as mx
+import numpy as np
+
+fname = mx.test_utils.download('http://archive.ics.uci.edu/ml/machine-learning-databases/letter-recognition/letter-recognition.data')
+data = np.genfromtxt(fname, delimiter=',')[:,1:]
+label = np.array([ord(l.split(',')[0])-ord('A') for l in open(fname, 'r')])
+
+batch_size = 32
+ntrain = int(data.shape[0]*0.8)
+train_iter = mx.io.NDArrayIter(data[:ntrain, :], label[:ntrain], batch_size, shuffle=True)
+val_iter = mx.io.NDArrayIter(data[ntrain:, :], label[ntrain:], batch_size)
+```
+
+Next, we define the network.
+
+```python
+net = mx.sym.Variable('data')
+net = mx.sym.FullyConnected(net, name='fc1', num_hidden=64)
+net = mx.sym.Activation(net, name='relu1', act_type="relu")
+net = mx.sym.FullyConnected(net, name='fc2', num_hidden=26)
+net = mx.sym.SoftmaxOutput(net, name='softmax')
+mx.viz.plot_network(net)
+```
+
+## Creating a Module
+
+Now we are ready to introduce module. The commonly used module class is
+`Module`. We can construct a module by specifying the following parameters:
+
+- `symbol`: the network definition
+- `context`: the device (or a list of devices) to use for execution
+- `data_names` : the list of input data variable names
+- `label_names` : the list of input label variable names
+
+For `net`, we have only one data named `data`, and one label named `softmax_label`,
+which is automatically named for us following the name `softmax` we specified for the `SoftmaxOutput` operator.
+
+```python
+mod = mx.mod.Module(symbol=net,
+ context=mx.cpu(),
+ data_names=['data'],
+ label_names=['softmax_label'])
+```
+
+## Intermediate-level Interface
+
+We have created module. Now let us see how to run training and inference using module's intermediate-level APIs. These APIs give developers flexibility to do step-by-step
+computation by running `forward` and `backward` passes. It's also useful for debugging.
+
+To train a module, we need to perform following steps:
+
+- `bind` : Prepares environment for the computation by allocating memory.
+- `init_params` : Assigns and initializes parameters.
+- `init_optimizer` : Initializes optimizers. Defaults to `sgd`.
+- `metric.create` : Creates evaluation metric from input metric name.
+- `forward` : Forward computation.
+- `update_metric` : Evaluates and accumulates evaluation metric on outputs of the last forward computation.
+- `backward` : Backward computation.
+- `update` : Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch.
+
+This can be used as follows:
+
+```python
+# allocate memory given the input data and label shapes
+mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)
+# initialize parameters by uniform random numbers
+mod.init_params(initializer=mx.init.Uniform(scale=.1))
+# use SGD with learning rate 0.1 to train
+mod.init_optimizer(optimizer='sgd', optimizer_params=(('learning_rate', 0.1), ))
+# use accuracy as the metric
+metric = mx.metric.create('acc')
+# train 5 epochs, i.e. going over the data iter one pass
+for epoch in range(5):
+ train_iter.reset()
+ metric.reset()
+ for batch in train_iter:
+ mod.forward(batch, is_train=True) # compute predictions
+ mod.update_metric(metric, batch.label) # accumulate prediction accuracy
+ mod.backward() # compute gradients
+ mod.update() # update parameters
+ print('Epoch %d, Training %s' % (epoch, metric.get()))
+```
+
+To learn more about these APIs, visit [Module API](http://mxnet.io/api/python/module.html).
+
+## High-level Interface
+
+### Train
+
+Module also provides high-level APIs for training, predicting and evaluating for
+user convenience. Instead of doing all the steps mentioned in the above section,
+one can simply call [fit API](http://mxnet.io/api/python/module.html#mxnet.module.BaseModule.fit)
+and it internally executes the same steps.
+
+To fit a module, call the `fit` function as follows:
+
+```python
+# reset train_iter to the beginning
+train_iter.reset()
+
+# create a module
+mod = mx.mod.Module(symbol=net,
+ context=mx.cpu(),
+ data_names=['data'],
+ label_names=['softmax_label'])
+
+# fit the module
+mod.fit(train_iter,
+ eval_data=val_iter,
+ optimizer='sgd',
+ optimizer_params={'learning_rate':0.1},
+ eval_metric='acc',
+ num_epoch=8)
+```
+
+By default, `fit` function has `eval_metric` set to `accuracy`, `optimizer` to `sgd`
+and optimizer_params to `(('learning_rate', 0.01),)`.
+
+### Predict and Evaluate
+
+To predict with module, we can call `predict()`. It will collect and
+return all the prediction results.
+
+```python
+y = mod.predict(val_iter)
+assert y.shape == (4000, 26)
+```
+
+If we do not need the prediction outputs, but just need to evaluate on a test
+set, we can call the `score()` function. It runs prediction in the input validation
+dataset and evaluates the performance according to the given input metric.
+
+It can be used as follows:
+
+```python
+score = mod.score(val_iter, ['acc'])
+print("Accuracy score is %f" % (score[0][1]))
+```
+
+Some of the other metrics which can be used are `top_k_acc`(top-k-accuracy),
+`F1`, `RMSE`, `MSE`, `MAE`, `ce`(CrossEntropy). To learn more about the metrics,
+visit [Evaluation metric](http://mxnet.io/api/python/metric.html).
+
+One can vary number of epochs, learning_rate, optimizer parameters to change the score
+and tune these parameters to get best score.
+
+### Save and Load
+
+We can save the module parameters after each training epoch by using a checkpoint callback.
+
+```python
+# construct a callback function to save checkpoints
+model_prefix = 'mx_mlp'
+checkpoint = mx.callback.do_checkpoint(model_prefix)
+
+mod = mx.mod.Module(symbol=net)
+mod.fit(train_iter, num_epoch=5, epoch_end_callback=checkpoint)
+```
+
+To load the saved module parameters, call the `load_checkpoint` function. It
+loads the Symbol and the associated parameters. We can then set the loaded
+parameters into the module.
+
+```python
+sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, 3)
+assert sym.tojson() == net.tojson()
+
+# assign the loaded parameters to the module
+mod.set_params(arg_params, aux_params)
+```
+
+Or if we just want to resume training from a saved checkpoint, instead of
+calling `set_params()`, we can directly call `fit()`, passing the loaded
+parameters, so that `fit()` knows to start from those parameters instead of
+initializing randomly from scratch. We also set the `begin_epoch` parameter so that
+`fit()` knows we are resuming from a previously saved epoch.
+
+```python
+mod = mx.mod.Module(symbol=sym)
+mod.fit(train_iter,
+ num_epoch=8,
+ arg_params=arg_params,
+ aux_params=aux_params,
+ begin_epoch=3)
+```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/basic/ndarray.txt b/versions/master/_sources/tutorials/basic/ndarray.txt
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+# NDArray - Imperative tensor operations on CPU/GPU
+
+In _MXNet_, `NDArray` is the core data structure for all mathematical
+computations. An `NDArray` represents a multidimensional, fixed-size homogenous
+array. If you're familiar with the scientific computing python package
+[NumPy](http://www.numpy.org/), you might notice that `mxnet.ndarray` is similar
+to `numpy.ndarray`. Like the corresponding NumPy data structure, MXNet's
+`NDArray` enables imperative computation.
+
+So you might wonder, why not just use NumPy? MXNet offers two compelling
+advantages. First, MXNet's `NDArray` supports fast execution on a wide range of
+hardware configurations, including CPU, GPU, and multi-GPU machines. _MXNet_
+also scales to distributed systems in the cloud. Second, MXNet's `NDArray`
+executes code lazily, allowing it to automatically parallelize multiple
+operations across the available hardware.
+
+An `NDArray` is a multidimensional array of numbers with the same type. We
+could represent the coordinates of a point in 3D space, e.g. `[2, 1, 6]` as a 1D
+array with shape (3). Similarly, we could represent a 2D array. Below, we
+present an array with length 2 along the first axis and length 3 along the
+second axis.
+```
+[[0, 1, 2]
+ [3, 4, 5]]
+```
+Note that here the use of "dimension" is overloaded. When we say a 2D array, we
+mean an array with 2 axes, not an array with two components.
+
+Each NDArray supports some important attributes that you'll often want to query:
+
+- **ndarray.shape**: The dimensions of the array. It is a tuple of integers
+ indicating the length of the array along each axis. For a matrix with `n` rows
+ and `m` columns, its `shape` will be `(n, m)`.
+- **ndarray.dtype**: A `numpy` _type_ object describing the type of its
+ elements.
+- **ndarray.size**: the total number of components in the array - equal to the
+ product of the components of its `shape`
+- **ndarray.context**: The device on which this array is stored, e.g. `cpu()` or
+ `gpu(1)`.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html)
+- [Jupyter](http://jupyter.org/)
+ ```
+ pip install jupyter
+ ```
+- GPUs - A section of this tutorial uses GPUs. If you don't have GPUs on your
+machine, simply set the variable gpu_device (set in the GPUs section of this
+tutorial) to mx.cpu().
+
+## Array Creation
+
+There are a few different ways to create an `NDArray`.
+
+* We can create an NDArray from a regular Python list or tuple by using the `array` function:
+
+```python
+import mxnet as mx
+# create a 1-dimensional array with a python list
+a = mx.nd.array([1,2,3])
+# create a 2-dimensional array with a nested python list
+b = mx.nd.array([[1,2,3], [2,3,4]])
+{'a.shape':a.shape, 'b.shape':b.shape}
+```
+
+* We can also create an MXNet NDArray from a `numpy.ndarray` object:
+
+```python
+import numpy as np
+import math
+c = np.arange(15).reshape(3,5)
+# create a 2-dimensional array from a numpy.ndarray object
+a = mx.nd.array(c)
+{'a.shape':a.shape}
+```
+
+We can specify the element type with the option `dtype`, which accepts a numpy
+type. By default, `float32` is used:
+
+```python
+# float32 is used in default
+a = mx.nd.array([1,2,3])
+# create an int32 array
+b = mx.nd.array([1,2,3], dtype=np.int32)
+# create a 16-bit float array
+c = mx.nd.array([1.2, 2.3], dtype=np.float16)
+(a.dtype, b.dtype, c.dtype)
+```
+
+If we know the size of the desired NDArray, but not the element values, MXNet
+offers several functions to create arrays with placeholder content:
+
+```python
+# create a 2-dimensional array full of zeros with shape (2,3)
+a = mx.nd.zeros((2,3))
+# create a same shape array full of ones
+b = mx.nd.ones((2,3))
+# create a same shape array with all elements set to 7
+c = mx.nd.full((2,3), 7)
+# create a same shape whose initial content is random and
+# depends on the state of the memory
+d = mx.nd.empty((2,3))
+```
+
+## Printing Arrays
+
+When inspecting the contents of an `NDArray`, it's often convenient to first
+extract its contents as a `numpy.ndarray` using the `asnumpy` function. Numpy
+uses the following layout:
+
+- The last axis is printed from left to right,
+- The second-to-last is printed from top to bottom,
+- The rest are also printed from top to bottom, with each slice separated from
+ the next by an empty line.
+
+```python
+b = mx.nd.arange(18).reshape((3,2,3))
+b.asnumpy()
+```
+
+## Basic Operations
+
+When applied to NDArrays, the standard arithmetic operators apply *elementwise*
+calculations. The returned value is a new array whose content contains the
+result.
+
+```python
+a = mx.nd.ones((2,3))
+b = mx.nd.ones((2,3))
+# elementwise plus
+c = a + b
+# elementwise minus
+d = - c
+# elementwise pow and sin, and then transpose
+e = mx.nd.sin(c**2).T
+# elementwise max
+f = mx.nd.maximum(a, c)
+f.asnumpy()
+```
+
+As in `NumPy`, `*` represents element-wise multiplication. For matrix-matrix
+multiplication, use `dot`.
+
+```python
+a = mx.nd.arange(4).reshape((2,2))
+b = a * a
+c = mx.nd.dot(a,a)
+print("b: %s, \n c: %s" % (b.asnumpy(), c.asnumpy()))
+```
+
+The assignment operators such as `+=` and `*=` modify arrays in place, and thus
+don't allocate new memory to create a new array.
+
+```python
+a = mx.nd.ones((2,2))
+b = mx.nd.ones(a.shape)
+b += a
+b.asnumpy()
+```
+
+## Indexing and Slicing
+
+The slice operator `[]` applies on axis 0.
+
+```python
+a = mx.nd.array(np.arange(6).reshape(3,2))
+a[1:2] = 1
+a[:].asnumpy()
+```
+
+We can also slice a particular axis with the method `slice_axis`
+
+```python
+d = mx.nd.slice_axis(a, axis=1, begin=1, end=2)
+d.asnumpy()
+```
+
+## Shape Manipulation
+
+Using `reshape`, we can manipulate any arrays shape as long as the size remains
+unchanged.
+
+```python
+a = mx.nd.array(np.arange(24))
+b = a.reshape((2,3,4))
+b.asnumpy()
+```
+
+The `concat` method stacks multiple arrays along the first axis. Their
+shapes must be the same along the other axes.
+
+```python
+a = mx.nd.ones((2,3))
+b = mx.nd.ones((2,3))*2
+c = mx.nd.concat(a,b)
+c.asnumpy()
+```
+
+## Reduce
+
+Some functions, like `sum` and `mean` reduce arrays to scalars.
+
+```python
+a = mx.nd.ones((2,3))
+b = mx.nd.sum(a)
+b.asnumpy()
+```
+
+We can also reduce an array along a particular axis:
+
+```python
+c = mx.nd.sum_axis(a, axis=1)
+c.asnumpy()
+```
+
+## Broadcast
+
+We can also broadcast an array. Broadcasting operations, duplicate an array's
+value along an axis with length 1. The following code broadcasts along axis 1:
+
+```python
+a = mx.nd.array(np.arange(6).reshape(6,1))
+b = a.broadcast_to((6,4)) #
+b.asnumpy()
+```
+
+It's possible to simultaneously broadcast along multiple axes. In the following example, we broadcast along axes 1 and 2:
+
+```python
+c = a.reshape((2,1,1,3))
+d = c.broadcast_to((2,2,2,3))
+d.asnumpy()
+```
+
+Broadcasting can be applied automatically when executing some operations,
+e.g. `*` and `+` on arrays of different shapes.
+
+```python
+a = mx.nd.ones((3,2))
+b = mx.nd.ones((1,2))
+c = a + b
+c.asnumpy()
+```
+
+## Copies
+
+When assigning an NDArray to another Python variable, we copy a reference to the
+*same* NDArray. However, we often need to make a copy of the data, so that we
+can manipulate the new array without overwriting the original values.
+
+```python
+a = mx.nd.ones((2,2))
+b = a
+b is a # will be True
+```
+
+The `copy` method makes a deep copy of the array and its data:
+
+```python
+b = a.copy()
+b is a # will be False
+```
+
+The above code allocates a new NDArray and then assigns to *b*. When we do not
+want to allocate additional memory, we can use the `copyto` method or the slice
+operator `[]` instead.
+
+```python
+b = mx.nd.ones(a.shape)
+c = b
+c[:] = a
+d = b
+a.copyto(d)
+(c is b, d is b) # Both will be True
+```
+
+## Advanced Topics
+
+MXNet's NDArray offers some advanced features that differentiate it from the
+offerings you'll find in most other libraries.
+
+### GPU Support
+
+By default, NDArray operators are executed on CPU. But with MXNet, it's easy to
+switch to another computation resource, such as GPU, when available. Each
+NDArray's device information is stored in `ndarray.context`. When MXNet is
+compiled with flag `USE_CUDA=1` and the machine has at least one NVIDIA GPU, we
+can cause all computations to run on GPU 0 by using context `mx.gpu(0)`, or
+simply `mx.gpu()`. When we have access to two or more GPUs, the 2nd GPU is
+represented by `mx.gpu(1)`, etc.
+
+**Note** In order to execute the following section on a cpu set gpu_device to mx.cpu().
+```python
+gpu_device=mx.gpu() # Change this to mx.cpu() in absence of GPUs.
+
+
+def f():
+ a = mx.nd.ones((100,100))
+ b = mx.nd.ones((100,100))
+ c = a + b
+ print(c)
+# in default mx.cpu() is used
+f()
+# change the default context to the first GPU
+with mx.Context(gpu_device):
+ f()
+```
+
+We can also explicitly specify the context when creating an array:
+
+```python
+a = mx.nd.ones((100, 100), gpu_device)
+a
+```
+
+Currently, MXNet requires two arrays to sit on the same device for
+computation. There are several methods for copying data between devices.
+
+```python
+a = mx.nd.ones((100,100), mx.cpu())
+b = mx.nd.ones((100,100), gpu_device)
+c = mx.nd.ones((100,100), gpu_device)
+a.copyto(c) # copy from CPU to GPU
+d = b + c
+e = b.as_in_context(c.context) + c # same to above
+{'d':d, 'e':e}
+```
+
+### Serialize From/To (Distributed) Filesystems
+
+MXNet offers two simple ways to save (load) data to (from) disk. The first way
+is to use `pickle`, as you might with any other Python objects. `NDArray` is
+pickle-compatible.
+
+```python
+import pickle as pkl
+a = mx.nd.ones((2, 3))
+# pack and then dump into disk
+data = pkl.dumps(a)
+pkl.dump(data, open('tmp.pickle', 'wb'))
+# load from disk and then unpack
+data = pkl.load(open('tmp.pickle', 'rb'))
+b = pkl.loads(data)
+b.asnumpy()
+```
+
+The second way is to directly dump to disk in binary format by using the `save`
+and `load` methods. We can save/load a single NDArray, or a list of NDArrays:
+
+```python
+a = mx.nd.ones((2,3))
+b = mx.nd.ones((5,6))
+mx.nd.save("temp.ndarray", [a,b])
+c = mx.nd.load("temp.ndarray")
+c
+```
+
+It's also possible to save or load a dict of NDArrays in this fashion:
+
+```python
+d = {'a':a, 'b':b}
+mx.nd.save("temp.ndarray", d)
+c = mx.nd.load("temp.ndarray")
+c
+```
+
+The `load` and `save` methods are preferable to pickle in two respects
+
+1. When using these methods, you can save data from within the Python interface
+ and then use it later from another language's binding. For example, if we save
+ the data in Python:
+
+```python
+a = mx.nd.ones((2, 3))
+mx.nd.save("temp.ndarray", [a,])
+```
+
+we can later load it from R:
+```
+a <- mx.nd.load("temp.ndarray")
+as.array(a[[1]])
+## [,1] [,2] [,3]
+## [1,] 1 1 1
+## [2,] 1 1 1
+```
+
+2. When a distributed filesystem such as Amazon S3 or Hadoop HDFS is set up, we
+ can directly save to and load from it.
+
+```
+mx.nd.save('s3://mybucket/mydata.ndarray', [a,]) # if compiled with USE_S3=1
+mx.nd.save('hdfs///users/myname/mydata.bin', [a,]) # if compiled with USE_HDFS=1
+```
+
+### Lazy Evaluation and Automatic Parallelization
+
+MXNet uses lazy evaluation to achieve superior performance. When we run `a=b+1`
+in Python, the Python thread just pushes this operation into the backend engine
+and then returns. There are two benefits to this approach:
+
+1. The main Python thread can continue to execute other computations once the
+ previous one is pushed. It is useful for frontend languages with heavy
+ overheads.
+2. It is easier for the backend engine to explore further optimization, such as
+ auto parallelization.
+
+The backend engine can resolve data dependencies and schedule the computations
+correctly. It is transparent to frontend users. We can explicitly call the
+method `wait_to_read` on the result array to wait until the computation
+finishes. Operations that copy data from an array to other packages, such as
+`asnumpy`, will implicitly call `wait_to_read`.
+
+
+```python
+import time
+def do(x, n):
+ """push computation into the backend engine"""
+ return [mx.nd.dot(x,x) for i in range(n)]
+def wait(x):
+ """wait until all results are available"""
+ for y in x:
+ y.wait_to_read()
+
+tic = time.time()
+a = mx.nd.ones((1000,1000))
+b = do(a, 50)
+print('time for all computations are pushed into the backend engine:\n %f sec' % (time.time() - tic))
+wait(b)
+print('time for all computations are finished:\n %f sec' % (time.time() - tic))
+```
+
+Besides analyzing data read and write dependencies, the backend engine is able
+to schedule computations with no dependency in parallel. For example, in the
+following code:
+
+```python
+a = mx.nd.ones((2,3))
+b = a + 1
+c = a + 2
+d = b * c
+```
+
+the second and third lines can be executed in parallel. The following example
+first runs on CPU and then on GPU:
+
+```python
+n = 10
+a = mx.nd.ones((1000,1000))
+b = mx.nd.ones((6000,6000), gpu_device)
+tic = time.time()
+c = do(a, n)
+wait(c)
+print('Time to finish the CPU workload: %f sec' % (time.time() - tic))
+d = do(b, n)
+wait(d)
+print('Time to finish both CPU/GPU workloads: %f sec' % (time.time() - tic))
+```
+
+Now we issue all workloads at the same time. The backend engine will try to
+parallel the CPU and GPU computations.
+
+```python
+tic = time.time()
+c = do(a, n)
+d = do(b, n)
+wait(c)
+wait(d)
+print('Both as finished in: %f sec' % (time.time() - tic))
+```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/basic/record_io.txt b/versions/master/_sources/tutorials/basic/record_io.txt
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+# Record IO - Pack free-format data in binary files
+
+This tutorial will walk through the python interface for reading and writing
+record io files. It can be useful when you need more more control over the
+details of data pipeline. For example, when you need to augument image and label
+together for detection and segmentation, or when you need a custom data iterator
+for triplet sampling and negative sampling.
+
+Setup environment first:
+
+```python
+%matplotlib inline
+from __future__ import print_function
+import mxnet as mx
+import numpy as np
+import matplotlib.pyplot as plt
+```
+
+The relevent code is under `mx.recordio`. There are two classes: `MXRecordIO`,
+which supports sequential read and write, and `MXIndexedRecordIO`, which
+supports random read and sequential write.
+
+## MXRecordIO
+
+First let's take a look at `MXRecordIO`. We open a file `tmp.rec` and write 5
+strings to it:
+
+```python
+record = mx.recordio.MXRecordIO('tmp.rec', 'w')
+for i in range(5):
+ record.write('record_%d'%i)
+record.close()
+```
+
+Then we can read it back by opening the same file with 'r':
+
+```python
+record = mx.recordio.MXRecordIO('tmp.rec', 'r')
+while True:
+ item = record.read()
+ if not item:
+ break
+ print item
+record.close()
+```
+
+## MXIndexedRecordIO
+
+Some times you need random access for more complex tasks. `MXIndexedRecordIO` is
+designed for this. Here we create a indexed record `tmp.rec` and a corresponding
+index file `tmp.idx`:
+
+
+```python
+record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
+for i in range(5):
+ record.write_idx(i, 'record_%d'%i)
+record.close()
+```
+
+We can then access records with keys:
+
+```python
+record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'r')
+record.read_idx(3)
+```
+
+You can list all keys with:
+
+```python
+record.keys
+```
+
+## Packing and Unpacking Data
+
+Each record in a .rec file can contain arbitrary binary data, but machine
+learning data typically has a label/data structure. `mx.recordio` also contains
+a few utility functions for packing such data, namely: `pack`, `unpack`,
+`pack_img`, and `unpack_img`.
+
+### Binary Data
+
+`pack` and `unpack` are used for storing float (or 1d array of float) label and
+binary data:
+
+- pack:
+
+```python
+# pack
+data = 'data'
+label1 = 1.0
+header1 = mx.recordio.IRHeader(flag=0, label=label1, id=1, id2=0)
+s1 = mx.recordio.pack(header1, data)
+print('float label:', repr(s1))
+label2 = [1.0, 2.0, 3.0]
+header2 = mx.recordio.IRHeader(flag=0, label=label2, id=2, id2=0)
+s2 = mx.recordio.pack(header2, data)
+print('array label:', repr(s2))
+```
+
+- unpack:
+
+```python
+print(*mx.recordio.unpack(s1))
+print(*mx.recordio.unpack(s2))
+```
+
+### Image Data
+
+`pack_img` and `unpack_img` are used for packing image data. Records packed by
+`pack_img` can be loaded by `mx.io.ImageRecordIter`.
+
+- pack images
+
+```python
+data = np.ones((3,3,1), dtype=np.uint8)
+label = 1.0
+header = mx.recordio.IRHeader(flag=0, label=label, id=0, id2=0)
+s = mx.recordio.pack_img(header, data, quality=100, img_fmt='.jpg')
+print(repr(s))
+```
+
+- unpack images
+
+```python
+print(*mx.recordio.unpack_img(s))
+```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/basic/symbol.txt b/versions/master/_sources/tutorials/basic/symbol.txt
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+# Symbol - Neural network graphs and auto-differentiation
+
+In a [previous tutorial](http://mxnet.io/tutorials/basic/ndarray.html), we introduced `NDArray`,
+the basic data structure for manipulating data in MXNet.
+And just using NDArray by itself, we can execute a wide range of mathematical operations.
+In fact, we could define and update a full neural network just by using `NDArray`.
+`NDArray` allows you to write programs for scientific computation
+in an imperative fashion, making full use of the native control of any front-end language.
+So you might wonder, why don't we just use `NDArray` for all computation?
+
+MXNet provides the Symbol API, an interface for symbolic programming.
+With symbolic programming, rather than executing operations step by step,
+we first define a *computation graph*.
+This graph contains placeholders for inputs and designated outputs.
+We can then compile the graph, yielding a function
+that can be bound to `NDArray`s and run.
+MXNet's Symbol API is similar to the network configurations
+used by [Caffe](http://caffe.berkeleyvision.org/)
+and the symbolic programming in [Theano](http://deeplearning.net/software/theano/).
+
+Another advantage conferred by symbolic approach is that
+we can optimize our functions before using them.
+For example, when we execute mathematical computations in imperative fashion,
+we don't know at the time that we run each operation,
+which values will be needed later on.
+But with symbolic programming, we declare the required outputs in advance.
+This means that we can recycle memory allocated in intermediate steps,
+as by performing operations in place. Symbolic API also uses less memory for the
+same network. Refer to [How To](http://mxnet.io/how_to/index.html) and
+[Architecture](http://mxnet.io/architecture/index.html) section to know more.
+
+In our design notes, we present [a more thorough discussion on the comparative strengths
+of imperative and symbolic programing](http://mxnet.io/architecture/program_model.html).
+But in this document, we'll focus on teaching you how to use MXNet's Symbol API.
+In MXNet, we can compose Symbols from other Symbols, using operators,
+such as simple matrix operations (e.g. "+"),
+or whole neural network layers (e.g. convolution layer).
+Operator can take multiple input variables,
+can produce multiple output symbols
+and can maintain internal state symbols.
+
+For a visual explanation of these concepts, see
+[Symbolic Configuration and Execution in Pictures](http://mxnet.io/api/python/symbol_in_pictures.html).
+
+To make things concrete, let's take a hands-on look at the Symbol API.
+There are a few different ways to compose a `Symbol`.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html)
+- [Jupyter](http://jupyter.org/)
+ ```
+ pip install jupyter
+ ```
+- GPUs - A section of this tutorial uses GPUs. If you don't have GPUs on your machine, simply
+set the variable gpu_device to mx.cpu().
+
+## Basic Symbol Composition
+
+### Basic Operators
+
+The following example builds a simple expression: `a + b`.
+First, we create two placeholders with `mx.sym.Variable`,
+giving them the names `a` and `b`.
+We then construct the desired symbol by using the operator `+`.
+We don't need to name our variables while creating them,
+MXNet will automatically generate a unique name for each.
+In the example below, `c` is assigned a unique name automatically.
+
+```python
+import mxnet as mx
+a = mx.sym.Variable('a')
+b = mx.sym.Variable('b')
+c = a + b
+(a, b, c)
+```
+
+Most operators supported by `NDArray` are also supported by `Symbol`, for example:
+
+```python
+# elemental wise multiplication
+d = a * b
+# matrix multiplication
+e = mx.sym.dot(a, b)
+# reshape
+f = mx.sym.reshape(d+e, shape=(1,4))
+# broadcast
+g = mx.sym.broadcast_to(f, shape=(2,4))
+# plot
+mx.viz.plot_network(symbol=g)
+```
+
+The computations declared in the above examples can be bound to the input data
+for evaluation by using `bind` method. We discuss this further in the
+[symbol manipulation](#Symbol Manipulation) section.
+
+### Basic Neural Networks
+
+Besides the basic operators, `Symbol` also supports a rich set of neural network layers.
+The following example constructs a two layer fully connected neural network
+and then visualizes the structure of that network given the input data shape.
+
+```python
+net = mx.sym.Variable('data')
+net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=128)
+net = mx.sym.Activation(data=net, name='relu1', act_type="relu")
+net = mx.sym.FullyConnected(data=net, name='fc2', num_hidden=10)
+net = mx.sym.SoftmaxOutput(data=net, name='out')
+mx.viz.plot_network(net, shape={'data':(100,200)})
+```
+
+Each symbol takes a (unique) string name. NDArray and Symbol both represent
+a single tensor. *Operators* represent the computation between tensors.
+Operators take symbol (or NDArray) as inputs and might also additionally accept
+other hyperparameters such as the number of hidden neurons (*num_hidden*) or the
+activation type (*act_type*) and produce the output.
+
+We can view a symbol simply as a function taking several arguments.
+And we can retrieve those arguments with the following method call:
+
+```python
+net.list_arguments()
+```
+
+These arguments are the parameters and inputs needed by each symbol:
+
+- *data*: Input data needed by the variable *data*.
+- *fc1_weight* and *fc1_bias*: The weight and bias for the first fully connected layer *fc1*.
+- *fc2_weight* and *fc2_bias*: The weight and bias for the second fully connected layer *fc2*.
+- *out_label*: The label needed by the loss.
+
+We can also specify the names explicitly:
+
+```python
+net = mx.symbol.Variable('data')
+w = mx.symbol.Variable('myweight')
+net = mx.symbol.FullyConnected(data=net, weight=w, name='fc1', num_hidden=128)
+net.list_arguments()
+```
+
+In the above example, `FullyConnected` layer has 3 inputs: data, weight, bias.
+When any input is not specified, a variable will be automatically generated for it.
+
+## More Complicated Composition
+
+MXNet provides well-optimized symbols for layers commonly used in deep learning
+(see [src/operator](https://github.com/dmlc/mxnet/tree/master/src/operator)).
+We can also define new operators in Python. The following example first
+performs an element-wise add between two symbols, then feeds them to the fully
+connected operator:
+
+```python
+lhs = mx.symbol.Variable('data1')
+rhs = mx.symbol.Variable('data2')
+net = mx.symbol.FullyConnected(data=lhs + rhs, name='fc1', num_hidden=128)
+net.list_arguments()
+```
+
+We can also construct a symbol in a more flexible way than the single forward
+composition depicted in the preceding example:
+
+```python
+data = mx.symbol.Variable('data')
+net1 = mx.symbol.FullyConnected(data=data, name='fc1', num_hidden=10)
+net1.list_arguments()
+net2 = mx.symbol.Variable('data2')
+net2 = mx.symbol.FullyConnected(data=net2, name='fc2', num_hidden=10)
+composed = net2(data2=net1, name='composed')
+composed.list_arguments()
+```
+
+In this example, *net2* is used as a function to apply to an existing symbol *net1*,
+and the resulting *composed* symbol will have all the attributes of *net1* and *net2*.
+
+Once you start building some bigger networks, you might want to name some
+symbols with a common prefix to outline the structure of your network.
+You can use the
+[Prefix](https://github.com/dmlc/mxnet/blob/master/python/mxnet/name.py)
+NameManager as follows:
+
+```python
+data = mx.sym.Variable("data")
+net = data
+n_layer = 2
+for i in range(n_layer):
+ with mx.name.Prefix("layer%d_" % (i + 1)):
+ net = mx.sym.FullyConnected(data=net, name="fc", num_hidden=100)
+net.list_arguments()
+```
+
+### Modularized Construction for Deep Networks
+
+Constructing a *deep* network layer by layer, (like the Google Inception network),
+can be tedious owing to the large number of layers.
+So, for such networks, we often modularize the construction.
+
+For example, in Google Inception network,
+we can first define a factory function which chains the convolution,
+batch normalization and rectified linear unit (ReLU) activation layers together.
+
+```python
+def ConvFactory(data, num_filter, kernel, stride=(1,1), pad=(0, 0),name=None, suffix=''):
+ conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel,
+ stride=stride, pad=pad, name='conv_%s%s' %(name, suffix))
+ bn = mx.sym.BatchNorm(data=conv, name='bn_%s%s' %(name, suffix))
+ act = mx.sym.Activation(data=bn, act_type='relu', name='relu_%s%s'
+ %(name, suffix))
+ return act
+prev = mx.sym.Variable(name="Previous Output")
+conv_comp = ConvFactory(data=prev, num_filter=64, kernel=(7,7), stride=(2, 2))
+shape = {"Previous Output" : (128, 3, 28, 28)}
+mx.viz.plot_network(symbol=conv_comp, shape=shape)
+```
+
+Then we can define a function that constructs an inception module based on
+factory function `ConvFactory`.
+
+```python
+def InceptionFactoryA(data, num_1x1, num_3x3red, num_3x3, num_d3x3red, num_d3x3,
+ pool, proj, name):
+ # 1x1
+ c1x1 = ConvFactory(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_1x1' % name))
+ # 3x3 reduce + 3x3
+ c3x3r = ConvFactory(data=data, num_filter=num_3x3red, kernel=(1, 1), name=('%s_3x3' % name), suffix='_reduce')
+ c3x3 = ConvFactory(data=c3x3r, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1), name=('%s_3x3' % name))
+ # double 3x3 reduce + double 3x3
+ cd3x3r = ConvFactory(data=data, num_filter=num_d3x3red, kernel=(1, 1), name=('%s_double_3x3' % name), suffix='_reduce')
+ cd3x3 = ConvFactory(data=cd3x3r, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), name=('%s_double_3x3_0' % name))
+ cd3x3 = ConvFactory(data=cd3x3, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), name=('%s_double_3x3_1' % name))
+ # pool + proj
+ pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
+ cproj = ConvFactory(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_proj' % name))
+ # concat
+ concat = mx.sym.Concat(*[c1x1, c3x3, cd3x3, cproj], name='ch_concat_%s_chconcat' % name)
+ return concat
+prev = mx.sym.Variable(name="Previous Output")
+in3a = InceptionFactoryA(prev, 64, 64, 64, 64, 96, "avg", 32, name="in3a")
+mx.viz.plot_network(symbol=in3a, shape=shape)
+```
+
+Finally, we can obtain the whole network by chaining multiple inception
+modules. See a complete example
+[here](https://github.com/dmlc/mxnet/blob/master/example/image-classification/symbols/inception-bn.py).
+
+### Group Multiple Symbols
+
+To construct neural networks with multiple loss layers, we can use
+`mxnet.sym.Group` to group multiple symbols together. The following example
+groups two outputs:
+
+```python
+net = mx.sym.Variable('data')
+fc1 = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=128)
+net = mx.sym.Activation(data=fc1, name='relu1', act_type="relu")
+out1 = mx.sym.SoftmaxOutput(data=net, name='softmax')
+out2 = mx.sym.LinearRegressionOutput(data=net, name='regression')
+group = mx.sym.Group([out1, out2])
+group.list_outputs()
+```
+
+## Relations to NDArray
+
+As you can see now, both `Symbol` and `NDArray` provide multi-dimensional array
+operations, such as `c = a + b` in MXNet. We briefly clarify the differences here.
+
+The `NDArray` provides an imperative programming alike interface, in which the
+computations are evaluated sentence by sentence. While `Symbol` is closer to
+declarative programming, in which we first declare the computation and then
+evaluate with data. Examples in this category include regular expressions and
+SQL.
+
+The pros for `NDArray`:
+
+- Straightforward.
+- Easy to work with native language features (for loop, if-else condition, ..)
+ and libraries (numpy, ..).
+- Easy step-by-step code debugging.
+
+The pros for `Symbol`:
+
+- Provides almost all functionalities of NDArray, such as `+`, `*`, `sin`,
+ `reshape` etc.
+- Easy to save, load and visualize.
+- Easy for the backend to optimize the computation and memory usage.
+
+## Symbol Manipulation
+
+One important difference of `Symbol` compared to `NDArray` is that we first
+declare the computation and then bind the computation with data to run.
+
+In this section, we introduce the functions to manipulate a symbol directly. But
+note that, most of them are wrapped by the `module` package.
+
+### Shape and Type Inference
+
+For each symbol, we can query its arguments, auxiliary states and outputs.
+We can also infer the output shape and type of the symbol given the known input
+shape or type of some arguments, which facilitates memory allocation.
+
+```python
+arg_name = c.list_arguments() # get the names of the inputs
+out_name = c.list_outputs() # get the names of the outputs
+# infers output shape given the shape of input arguments
+arg_shape, out_shape, _ = c.infer_shape(a=(2,3), b=(2,3))
+# infers output type given the type of input arguments
+arg_type, out_type, _ = c.infer_type(a='float32', b='float32')
+{'input' : dict(zip(arg_name, arg_shape)),
+ 'output' : dict(zip(out_name, out_shape))}
+{'input' : dict(zip(arg_name, arg_type)),
+ 'output' : dict(zip(out_name, out_type))}
+```
+
+### Bind with Data and Evaluate
+
+The symbol `c` constructed above declares what computation should be run. To
+evaluate it, we first need to feed the arguments, namely free variables, with data.
+
+We can do it by using the `bind` method, which accepts device context and
+a `dict` mapping free variable names to `NDArray`s as arguments and returns an
+executor. The executor provides `forward` method for evaluation and an attribute
+`outputs` to get all the results.
+
+```python
+ex = c.bind(ctx=mx.cpu(), args={'a' : mx.nd.ones([2,3]),
+ 'b' : mx.nd.ones([2,3])})
+ex.forward()
+print('number of outputs = %d\nthe first output = \n%s' % (
+ len(ex.outputs), ex.outputs[0].asnumpy()))
+```
+
+We can evaluate the same symbol on GPU with different data.
+
+**Note** In order to execute the following section on a cpu set gpu_device to mx.cpu().
+```python
+gpu_device=mx.gpu() # Change this to mx.cpu() in absence of GPUs.
+
+ex_gpu = c.bind(ctx=gpu_device, args={'a' : mx.nd.ones([3,4], gpu_device)*2,
+ 'b' : mx.nd.ones([3,4], gpu_device)*3})
+ex_gpu.forward()
+ex_gpu.outputs[0].asnumpy()
+```
+
+We can also use `eval` method to evaluate the symbol. It combines calls to `bind`
+and `forward` methods.
+
+```python
+ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3]))
+print('number of outputs = %d\nthe first output = \n%s' % (
+ len(ex), ex[0].asnumpy()))
+```
+
+For neural nets, a more commonly used pattern is ```simple_bind```, which
+creates all of the argument arrays for you. Then you can call ```forward```,
+and ```backward``` (if the gradient is needed) to get the gradient.
+
+### Load and Save
+
+Logically symbols correspond to ndarrays. They both represent a tensor. They both
+are inputs/outputs of operators. We can either serialize a `Symbol` object by
+using `pickle`, or by using `save` and `load` methods directly as we discussed in
+[NDArray tutorial](http://mxnet.io/tutorials/basic/ndarray.html#serialize-from-to-distributed-filesystems).
+
+When serializing `NDArray`, we serialize the tensor data in it and directly dump to
+disk in binary format.
+But symbol uses a concept of graph. Graphs are composed by chaining operators. They are
+implicitly represented by output symbols. So, when serializing a `Symbol`, we
+serialize the graph of which the symbol is an output. While serialization, Symbol
+uses more readable `json` format for serialization. To convert symbol to `json`
+string, use `tojson` method.
+
+```python
+print(c.tojson())
+c.save('symbol-c.json')
+c2 = mx.sym.load('symbol-c.json')
+c.tojson() == c2.tojson()
+```
+
+## Customized Symbol
+
+Most operators such as `mx.sym.Convolution` and `mx.sym.Reshape` are implemented
+in C++ for better performance. MXNet also allows users to write new operators
+using any front-end language such as Python. It often makes the developing and
+debugging much easier. To implement an operator in Python, refer to
+[How to create new operators](http://mxnet.io/how_to/new_op.html).
+
+## Advanced Usages
+
+### Type Cast
+
+By default, MXNet uses 32-bit floats.
+But for better accuracy-performance,
+we can also use a lower precision data type.
+For example, The Nvidia Tesla Pascal GPUs
+(e.g. P100) have improved 16-bit float performance,
+while GTX Pascal GPUs (e.g. GTX 1080) are fast on 8-bit integers.
+
+To convert the data type as per the requirements,
+we can use `mx.sym.cast` operator as follows:
+
+```python
+a = mx.sym.Variable('data')
+b = mx.sym.cast(data=a, dtype='float16')
+arg, out, _ = b.infer_type(data='float32')
+print({'input':arg, 'output':out})
+
+c = mx.sym.cast(data=a, dtype='uint8')
+arg, out, _ = c.infer_type(data='int32')
+print({'input':arg, 'output':out})
+```
+
+### Variable Sharing
+
+To share the contents between several symbols,
+we can bind these symbols with the same array as follows:
+
+```python
+a = mx.sym.Variable('a')
+b = mx.sym.Variable('b')
+b = a + a * a
+
+data = mx.nd.ones((2,3))*2
+ex = b.bind(ctx=mx.cpu(), args={'a':data, 'b':data})
+ex.forward()
+ex.outputs[0].asnumpy()
+```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/c++/basics.txt b/versions/master/_sources/tutorials/c++/basics.txt
new file mode 100644
index 0000000..cdf1a28
--- /dev/null
+++ b/versions/master/_sources/tutorials/c++/basics.txt
@@ -0,0 +1,189 @@
+Basics
+======
+
+This tutorial provides basic usages of the C++ package through the classical handwritten digits
+identification database--[MNIST](http://yann.lecun.com/exdb/mnist/).
+
+The following contents assume that the working directory is `/path/to/mxnet/cpp-package/example`.
+
+Load Data
+--------
+Before going into codes, we need to fetch MNIST data. You can either use the script `get_mnist.sh`,
+or download mnist data by yourself from Lecun's [website](http://yann.lecun.com/exdb/mnist/)
+and decompress them into `mnist_data` folder.
+
+Except linking the MXNet shared library, the C++ package itself is a header-only package,
+which means all you need to do is to include the header files. Among the header files,
+`op.h` is special since it is generated dynamically. The generation should be done when
+[building the C++ package](http://mxnet.io/get_started/build_from_source.html#build-the-c++-package).
+After that, you also need to copy the shared library (`libmxnet.so` in linux,
+`libmxnet.dll` in windows) from `/path/to/mxnet/lib` to the working directory.
+We do not recommend you to use pre-built binaries because MXNet is under heavy development,
+the operator definitions in `op.h` may be incompatible with the pre-built version.
+
+In order to use functionalities provides by the C++ package, first we include the general
+header file `MxNetCpp.h` and specify the namespaces.
+
+```cpp
+#include "mxnet-cpp/MxNetCpp.h"
+
+using namespace std;
+using namespace mxnet::cpp;
+```
+
+Next we can use the data iter to load MNIST data (separated to training sets and validation sets).
+The digits in MNIST are 2-dimension arrays, so we should set `flat` to true to flatten the data.
+
+```cpp
+auto train_iter = MXDataIter("MNISTIter")
+ .SetParam("image", "./mnist_data/train-images-idx3-ubyte")
+ .SetParam("label", "./mnist_data/train-labels-idx1-ubyte")
+ .SetParam("batch_size", batch_size)
+ .SetParam("flat", 1)
+ .CreateDataIter();
+auto val_iter = MXDataIter("MNISTIter")
+ .SetParam("image", "./mnist_data/t10k-images-idx3-ubyte")
+ .SetParam("label", "./mnist_data/t10k-labels-idx1-ubyte")
+ .SetParam("batch_size", batch_size)
+ .SetParam("flat", 1)
+ .CreateDataIter();
+```
+
+The data have been successfully loaded, we can now easily construct various models to identify
+the digits with the help of C++ package.
+
+
+Multilayer Perceptron
+---------------------
+If you are not familiar with multilayer perceptron, you can get some basic information
+[here](http://mxnet.io/tutorials/python/mnist.html#multilayer-perceptron). We only focus on
+the implementation in this tutorial.
+
+Constructing multilayer perceptron model is straightforward, assume we store the hidden size
+for each layer in `layers`, and each layer uses
+[ReLu](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) function as activation.
+
+```cpp
+Symbol mlp(const vector<int> &layers) {
+ auto x = Symbol::Variable("X");
+ auto label = Symbol::Variable("label");
+
+ vector<Symbol> weights(layers.size());
+ vector<Symbol> biases(layers.size());
+ vector<Symbol> outputs(layers.size());
+
+ for (int i=0; i<layers.size(); ++i) {
+ weights[i] = Symbol::Variable("w" + to_string(i));
+ biases[i] = Symbol::Variable("b" + to_string(i));
+ Symbol fc = FullyConnected(
+ i == 0? x : outputs[i-1]
+ weights[i],
+ biases[i],
+ layers[i]
+ );
+ outputs[i] = i == layers.size()-1 ? fc : Activation(fc, ActivationActType::relu);
+ }
+
+ return SoftmaxOutput(outputs.back(), label);
+}
+```
+
+The above function defines a multilayer perceptron model where hidden sizes are specified
+by `layers`.
+
+We now create and initialize the parameters after the model is constructed. MXNet can help
+ you to infer shapes of most of the parameters. Basically only the shape of data and label
+ is needed.
+
+```cpp
+std::map<string, NDArray> args;
+args["X"] = NDArray(Shape(batch_size, image_size*image_size), ctx);
+args["label"] = NDArray(Shape(batch_size), ctx);
+// Let MXNet infer shapes other parameters such as weights
+net.InferArgsMap(ctx, &args, args);
+
+// Initialize all parameters with uniform distribution U(-0.01, 0.01)
+auto initializer = Uniform(0.01);
+for (auto& arg : args) {
+ // arg.first is parameter name, and arg.second is the value
+ initializer(arg.first, &arg.second);
+}
+```
+
+The rest is to train the model with an optimizer.
+```cpp
+// Create sgd optimizer
+Optimizer* opt = OptimizerRegistry::Find("sgd");
+opt->SetParam("rescale_grad", 1.0/batch_size);
+
+// Start training
+for (int iter = 0; iter < max_epoch; ++iter) {
+ train_iter.Reset();
+
+ while (train_iter.Next()) {
+ auto data_batch = train_iter.GetDataBatch();
+ // Set data and label
+ args["X"] = data_batch.data;
+ args["label"] = data_batch.label;
+
+ // Create executor by binding parameters to the model
+ auto *exec = net.SimpleBind(ctx, args);
+ // Compute gradients
+ exec->Forward(true);
+ exec->Backward();
+ // Update parameters
+ exec->UpdateAll(opt, learning_rate, weight_decay);
+ // Remember to free the memory
+ delete exec;
+ }
+}
+```
+
+We also want to see how our model performs. The C++ package provides convenient APIs for
+evaluating. Here we use accuracy as metric. The inference is almost the same as training,
+ except that we don't need gradients.
+
+```cpp
+Accuracy acc;
+val_iter.Reset();
+while (val_iter.Next()) {
+ auto data_batch = val_iter.GetDataBatch();
+ args["X"] = data_batch.data;
+ args["label"] = data_batch.label;
+ auto *exec = net.SimpleBind(ctx, args);
+ // Forward pass is enough as no gradient is needed when evaluating
+ exec->Forward(false);
+ acc.Update(data_batch.label, exec->outputs[0]);
+ delete exec;
+}
+```
+
+You can find the complete code in `mlp_cpu.cpp`. Use `make mlp_cpu` to compile it,
+ and `./mlp_cpu` to run it.
+
+GPU Support
+-----------
+It's worth noting that changing context from `Context::cpu()` to `Context::gpu()` is not enough,
+because the data read by data iter are stored in memory, we cannot assign it directly to the
+parameters. To bridge this gap, NDArray provides data synchronization functionalities between
+GPU and CPU. We will illustrate it by making the mlp code run on GPU.
+
+In the previous code, data are used like
+
+```cpp
+args["X"] = data_batch.data;
+args["label"] = data_batch.label;
+```
+
+It will be problematic if other parameters are created in the context of GPU. We can use
+`NDArray::CopyTo` to solve this problem.
+
+```cpp
+// Data provided by DataIter are stored in memory, should be copied to GPU first.
+data_batch.data.CopyTo(&args["X"]);
+data_batch.label.CopyTo(&args["label"]);
+// CopyTo is imperative, need to wait for it to complete.
+NDArray::WaitAll();
+```
+
+By replacing the former code to the latter one, we successfully port the code to GPU. You can find the complete code in `mlp_gpu.cpp`. Compilation is similar to the cpu version. (Note: The shared library should be built with GPU support on)
diff --git a/versions/master/_sources/tutorials/embedded/wine_detector.txt b/versions/master/_sources/tutorials/embedded/wine_detector.txt
new file mode 100644
index 0000000..f2f7a4e
--- /dev/null
+++ b/versions/master/_sources/tutorials/embedded/wine_detector.txt
@@ -0,0 +1,375 @@
+# Real-time Object Detection with MXNet On The Raspberry Pi
+
+This tutorial shows developers who work with the Raspberry Pi or similar embedded ARM-based devices how to compile MXNet for those devices and run a pretrained deep network model. It also shows how to use AWS IoT to manage and monitor MXNet models running on your devices.
+
+## What's In This Tutorial?
+
+This tutorial shows how to:
+
+1. Use MXNet to set up a real-time object classifier on a Raspberry Pi 3 device.
+
+2. Connect the local Raspberry Pi model to the AWS Cloud with AWS IoT to get real-time updates from the device.
+
+### Who's This Tutorial For?
+
+It assumes that you are familiar with the Raspbian operating system and the [Raspberry Pi ecosystem](https://www.raspberrypi.org/) and are somewhat familiar with machine learning, MXNet, and [AWS IoT](https://aws.amazon.com/iot/). All code is written in Python 2.7.
+
+### How to Use This Tutorial
+
+To follow this tutorial, you must set up your Pi as instructed (preferably from a fresh Raspbian install), and then create the files and run the bash commands described below. All instructions described are can be executed on the Raspberry Pi directly or via SSH.
+
+You will accomplish the following:
+
+- Build and Install MXNet with Python bindings on your Raspbian Based Raspberry Pi
+- Fetch and run a pre-trained MXNet model on your Pi
+- Create a real-time video analysis application for the Pi
+- Connect the application to the AWS IoT service
+
+## Prerequisites
+
+To complete this tutorial, you need:
+
+* Raspbian Wheezy or later, which can be downloaded [here](https://www.raspberrypi.org/downloads/raspbian/), loaded onto a 8GB+ micro SD card (with at least 4GB+ free)
+* A [Raspberry Pi 3](https://www.raspberrypi.org/blog/raspberry-pi-3-on-sale/) or equivalent Raspberry Pi with 1GB+ of RAM
+* A [Raspberry Pi Camera Module](https://www.raspberrypi.org/products/camera-module/) [activated and running with the corresponding Python module](http://www.pyimagesearch.com/2015/02/23/install-opencv-and-python-on-your-raspberry-pi-2-and-b/) (for the real-time video analysis with the deep network model)
+* An AWS account With AWS IoT enabled and the [AWS IoT Python SDK](https://github.com/aws/aws-iot-device-sdk-python) (for remote, real-time managing and monitoring of the model running on the Pi)
+* The [cv2 Python library](http://www.pyimagesearch.com/2015/02/23/install-opencv-and-python-on-your-raspberry-pi-2-and-b/) for the Pi
+
+## Building MXNet for The Pi
+
+The first step will be to get MXNet with the Python bindings running on your Raspberry Pi 3. There is a tutorial for that provided on [here](http://mxnet.io/get_started/raspbian_setup.html). In short you will have to download the dependencies, and build the full MXNet library for the Pi with the ARM specific compile flags. Be sure to build the library with open CV as we will be using a model that requires it to process images. Then you will finally the Python bindings. Once this is done [...]
+
+The first step is to get MXNet with the Python bindings running on your Raspberry Pi 3. There is a tutorial for that provided [here](http://mxnet.io/get_started/raspbian_setup.html). The linked tutorial walks you through downloading the dependencies, and building the full MXNet library for the Pi with the ARM specific compile flags. Be sure to build the library with open CV as we will be using a model that requires it to process images. Then you will register the Python bindings to MXNet [...]
+
+
+```bash
+python
+>>> import mxnet as mx
+```
+
+*Note: If you are getting memory allocation failed errors at this point (or at any point in this tutorial) it is likely because the full MXNet library takes up a large amount of RAM when loaded. You might want to kill the GUI and other processes that are occupying memory.*
+
+
+## Running A Pre-Trained Inception Model on The Pi
+
+We are now ready to load a pre-trained model and run inference on the Pi. We will be using a simple object recognition model trained on the ImageNet data set. The model is called batch normalized Inception network (or Inception_BN for short) and it is found in the MXNet model zoo.
+
+### Getting the Model
+
+The first step is to download, unzip, and set up the pre-trained deep network model files that we will be using to classify images. To do this run the following commands in your home directory:
+
+```bash
+curl --header 'Host: data.mxnet.io' --header 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:45.0) Gecko/20100101 Firefox/45.0' --header 'Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8' --header 'Accept-Language: en-US,en;q=0.5' --header 'Referer: http://data.mxnet.io/models/imagenet/' --header 'Connection: keep-alive' 'http://data.mxnet.io/models/imagenet/inception-bn.tar.gz' -o 'inception-bn.tar.gz' -L
+
+tar -xvzf inception-bn.tar.gz
+
+mv Inception_BN-0039.params Inception_BN-0000.params
+```
+
+### Running the Model
+
+The next step is to create a python script to load the model, and run inference on local image files. To do this create a new file in your home directory called inception_predict.py and add the following code to it:
+
+
+```python
+# inception_predict.py
+
+import mxnet as mx
+import numpy as np
+import cv2, os, urllib
+from collections import namedtuple
+Batch = namedtuple('Batch', ['data'])
+
+# Load the symbols for the networks
+with open('synset.txt', 'r') as f:
+ synsets = [l.rstrip() for l in f]
+
+# Load the network parameters
+sym, arg_params, aux_params = mx.model.load_checkpoint('Inception_BN', 0)
+
+# Load the network into an MXNet module and bind the corresponding parameters
+mod = mx.mod.Module(symbol=sym, context=mx.cpu())
+mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])
+mod.set_params(arg_params, aux_params)
+
+'''
+Function to predict objects by giving the model a pointer to an image file and running a forward pass through the model.
+
+inputs:
+filename = jpeg file of image to classify objects in
+mod = the module object representing the loaded model
+synsets = the list of symbols representing the model
+N = Optional parameter denoting how many predictions to return (default is top 5)
+
+outputs:
+python list of top N predicted objects and corresponding probabilities
+'''
+def predict(filename, mod, synsets, N=5):
+ tic = time.time()
+ img = cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB)
+ if img is None:
+ return None
+ img = cv2.resize(img, (224, 224))
+ img = np.swapaxes(img, 0, 2)
+ img = np.swapaxes(img, 1, 2)
+ img = img[np.newaxis, :]
+ print "pre-processed image in "+str(time.time()-tic)
+
+ toc = time.time()
+ mod.forward(Batch([mx.nd.array(img)]))
+ prob = mod.get_outputs()[0].asnumpy()
+ prob = np.squeeze(prob)
+ print "forward pass in "+str(time.time()-toc)
+
+
+ topN = []
+ a = np.argsort(prob)[::-1]
+ for i in a[0:N]:
+ print('probability=%f, class=%s' %(prob[i], synsets[i]))
+ topN.append((prob[i], synsets[i]))
+ return topN
+
+
+# Code to download an image from the internet and run a prediction on it
+def predict_from_url(url, N=5):
+ filename = url.split("/")[-1]
+ urllib.urlretrieve(url, filename)
+ img = cv2.imread(filename)
+ if img is None:
+ print "Failed to download"
+ else:
+ return predict(filename, mod, synsets, N)
+
+# Code to predict on a local file
+def predict_from_local_file(filename, N=5):
+ return predict(filename, mod, synsets, N)
+```
+
+Now that we have defined inception_predict.py we can test that the model is running correctly. Open a Python REPL in your home directory and enter the following:
+
+```bash
+python
+>>> import inception_predict
+>>> predict_from_url("http://imgur.com/HzafyBA")
+```
+
+This should give a reasonable prediction for the fluffy cow in this [image](http://imgur.com/HzafyBA).
+
+
+## Running an Inception on Real-Time Video From PiCamera
+
+We can now move on to using this network for object detection in real-time video from the PiCamera.
+
+Doing this requires sending the images that the camera is capturing to the prediction code that we created in the previous step. To do this, create a new file in your home directory called camera_test.py and add the following code to it:
+
+
+```python
+# camera_test.py
+
+import picamera
+import inception_predict
+
+# Create camera interface
+camera = picamera.PiCamera()
+while True:
+ # Take the jpg image from camera
+ print "Capturing"
+ filename = '/home/pi/cap.jpg'
+ # Show quick preview of what's being captured
+ camera.start_preview()
+ camera.capture(filename)
+ camera.stop_preview()
+
+ # Run inception prediction on image
+ print "Predicting"
+ topn = inception_predict.predict_from_local_file(filename, N=5)
+
+ # Print the top N most likely objects in image (default set to 5, change this in the function call above)
+ print topn
+```
+
+You can then run this file by entering the following command:
+
+```bash
+python camera_test.py
+```
+
+If camera_test.py is working you should see a preview every few seconds of the image that is being captured and fed to the model, as well as predicted classes for objects in the image being written to the terminal.
+
+Try pointing the PiCamera at a few different objects and see what predictions the network comes out with.
+
+## Connecting Our Model To The AWS Cloud
+
+We can now move on to adding the code to send the predictions that this real-time model is making locally to the AWS cloud if certain conditions are met.
+
+The first step is to set up an AWS account if you don't have one yet. Then go to the [AWS IoT dashboard](https://us-west-2.console.aws.amazon.com/iotv2/home?region=us-west-2#/thinghub) and register a new device.
+
+After the device is registered, download and copy the corresponding rootCA, Certificate, and Private key to your home directory. Note the unique endpoint of your device shadow on the AWS IoT Dashboard.
+
+We will now build an application, based off the code in camera_test.py, which will send a message to the cloud whenever a wine bottle is detected in a frame by the PiCamera.
+
+To do this create a new file in your home directory called wine_alerter.py and add the following code to it:
+
+
+```python
+# wine_alerter.py
+
+import AWSIoTPythonSDK
+from AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTClient
+import sys
+import logging
+import time
+import getopt
+import picamera
+import inception_predict
+
+# Custom MQTT message callback
+def customCallback(client, userdata, message):
+ print("Received a new message: ")
+ print(message.payload)
+ print("from topic: ")
+ print(message.topic)
+ print("--------------\n\n")
+
+# Usage
+usageInfo = """Usage:
+
+Use certificate based mutual authentication:
+python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -c <certFilePath> -k <privateKeyFilePath>
+
+Use MQTT over WebSocket:
+python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -w
+
+Type "python wine_alerter.py -h" for available options.
+"""
+
+# Help info
+helpInfo = """-e, --endpoint
+ Your AWS IoT custom endpoint
+-r, --rootCA
+ Root CA file path
+-c, --cert
+ Certificate file path
+-k, --key
+ Private key file path
+-w, --websocket
+ Use MQTT over WebSocket
+-h, --help
+ Help information
+"""
+
+# Read in command-line parameters
+useWebsocket = False
+host = ""
+rootCAPath = ""
+certificatePath = ""
+privateKeyPath = ""
+try:
+ opts, args = getopt.getopt(sys.argv[1:], "hwe:k:c:r:", ["help", "endpoint=", "key=","cert=","rootCA=", "websocket"])
+ if len(opts) == 0:
+ raise getopt.GetoptError("No input parameters!")
+ for opt, arg in opts:
+ if opt in ("-h", "--help"):
+ print(helpInfo)
+ exit(0)
+ if opt in ("-e", "--endpoint"):
+ host = arg
+ if opt in ("-r", "--rootCA"):
+ rootCAPath = arg
+ if opt in ("-c", "--cert"):
+ certificatePath = arg
+ if opt in ("-k", "--key"):
+ privateKeyPath = arg
+ if opt in ("-w", "--websocket"):
+ useWebsocket = True
+except getopt.GetoptError:
+ print(usageInfo)
+ exit(1)
+
+# Missing configuration notification
+missingConfiguration = False
+if not host:
+ print("Missing '-e' or '--endpoint'")
+ missingConfiguration = True
+if not rootCAPath:
+ print("Missing '-r' or '--rootCA'")
+ missingConfiguration = True
+if not useWebsocket:
+ if not certificatePath:
+ print("Missing '-c' or '--cert'")
+ missingConfiguration = True
+ if not privateKeyPath:
+ print("Missing '-k' or '--key'")
+ missingConfiguration = True
+if missingConfiguration:
+ exit(2)
+
+
+# Configure logging
+logger = logging.getLogger("AWSIoTPythonSDK.core")
+logger.setLevel(logging.DEBUG)
+streamHandler = logging.StreamHandler()
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+streamHandler.setFormatter(formatter)
+logger.addHandler(streamHandler)
+
+
+# Init AWSIoTMQTTClient For Publish/Subscribe Communication With Server
+myAWSIoTMQTTClient = None
+if useWebsocket:
+ myAWSIoTMQTTClient = AWSIoTMQTTClient("basicPubSub", useWebsocket=True)
+ myAWSIoTMQTTClient.configureEndpoint(host, 443)
+ myAWSIoTMQTTClient.configureCredentials(rootCAPath)
+else:
+ myAWSIoTMQTTClient = AWSIoTMQTTClient("basicPubSub")
+ myAWSIoTMQTTClient.configureEndpoint(host, 8883)
+ myAWSIoTMQTTClient.configureCredentials(rootCAPath, privateKeyPath, certificatePath)
+
+
+# AWSIoTMQTTClient connection configuration
+myAWSIoTMQTTClient.configureAutoReconnectBackoffTime(1, 32, 20)
+myAWSIoTMQTTClient.configureOfflinePublishQueueing(-1) # Infinite offline Publish queueing
+myAWSIoTMQTTClient.configureDrainingFrequency(2) # Draining: 2 Hz
+myAWSIoTMQTTClient.configureConnectDisconnectTimeout(10) # 10 sec
+myAWSIoTMQTTClient.configureMQTTOperationTimeout(5) # 5 sec
+
+
+# Connect and subscribe to AWS IoT
+myAWSIoTMQTTClient.connect()
+myAWSIoTMQTTClient.subscribe("sdk/test/Python", 1, customCallback)
+time.sleep(2)
+
+
+# Start the Camera and tell the Server we are alive
+print "Running camera"
+myAWSIoTMQTTClient.publish("sdk/test/Python", "New Message: Starting Camera", 0)
+camera = picamera.PiCamera()
+
+# Capture forever (this is a modified version of camera_test.py)
+while True:
+ filename = '/home/pi/cap.jpg'
+ camera.start_preview()
+ camera.capture(filename)
+ camera.stop_preview()
+ topn = inception_predict.predict_from_local_file(filename, N=5)
+
+ # Check if either of the top two predictions are wine related and publish a message if it is
+ # you can change 'wine' here to anything you want to alert the server about detecting
+ if 'wine' in topn[0][1] or 'wine' in topn[1][1]:
+ myAWSIoTMQTTClient.publish("sdk/test/Python", "New Message: WINE DETECTED!", 0)
+```
+
+You can then run this file by entering the following command
+
+```bash
+python wine_alerter.py -e <endpointURL> -r <rootCAFilePath> -c <certFilePath> -k <privateKeyFilePath>
+```
+
+If this is working you should see the same kind of image preview you did with camera_test.py every few seconds, however the console will only print a message now when a wine bottle is detected in the shot (you can edit the bottom lines in the wine_alerter.py code to make this alert for any object label from the [ImageNet-11k dataset](http://image-net.org/index) that you specify).
+
+You can open up the activity tab for the thing that you registered on the AWS IoT Dashboard and see the corresponding messages pushed to the server whenever a wine bottle is detected in a camera shot. Even if network connectivity periodically fails, AWS IoT will push updates out to the server when possible, allowing this system to reliably let you know when there is wine around.
+
+## Summary
+You now have a Raspberry Pi running a pre-trained MXNet model fully locally. This model is also linked to the cloud via AWS IoT and can reliably alert AWS whenever it sees a wine bottle.
+
+You can now extend this demo to create more interesting applications, such as using AWS IoT to push model updates to your Pi, loading different pre-trained models from the MXNet Model Zoo onto the Pi, or even training full end-to-end models on the Pi.
diff --git a/versions/master/_sources/tutorials/general_ml/recommendation_systems.txt b/versions/master/_sources/tutorials/general_ml/recommendation_systems.txt
new file mode 100644
index 0000000..81dc587
--- /dev/null
+++ b/versions/master/_sources/tutorials/general_ml/recommendation_systems.txt
@@ -0,0 +1,6 @@
+# Recommendation Systems
+
+Get the source code for an example of a recommendation system based on MXNet on [GitHub](https://github.com/dmlc/mxnet-notebooks/tree/master/python/recommendation_systems).
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
\ No newline at end of file
diff --git a/versions/master/_sources/tutorials/gluon/autograd.txt b/versions/master/_sources/tutorials/gluon/autograd.txt
new file mode 100644
index 0000000..4b296dd
--- /dev/null
+++ b/versions/master/_sources/tutorials/gluon/autograd.txt
@@ -0,0 +1,55 @@
+# Automatic differentiation
+
+MXNet supports automatic differentiation with the `autograd` package.
+`autograd` allows you to differentiate a graph of NDArray operations
+with the chain rule.
+This is called define-by-run, i.e., the network is defined on-the-fly by
+running forward computation. You can define exotic network structures
+and differentiate them, and each iteration can have a totally different
+network structure.
+
+```python
+import mxnet as mx
+from mxnet import autograd
+```
+
+To use `autograd`, we must first mark variables that require gradient and
+attach gradient buffers to them:
+
+```python
+x = mx.nd.array([[1, 2], [3, 4]])
+x.attach_grad()
+```
+
+Now we can define the network while running forward computation by wrapping
+it inside a `record` (operations out of `record` does not define
+a graph and cannot be differentiated):
+
+```python
+with autograd.record():
+ y = x * 2
+ z = y * x
+```
+
+Let's backprop with `z.backward()`, which is equivalent to
+`z.backward(mx.nd.ones_like(z))`. When z has more than one entry, `z.backward()`
+is equivalent to `mx.nd.sum(z).backward()`:
+
+```python
+z.backward()
+print(x.grad)
+```
+
+Now, let's see if this is the expected output.
+
+Here, y = f(x), z = f(y) = f(g(x))
+which means y = 2 * x and z = 2 * x * x.
+
+After, doing backprop with `z.backward()`, we will get gradient dz/dx as follows:
+
+dy/dx = 2,
+dz/dx = 4 * x
+
+So, we should get x.grad as an array of [[4, 8],[12, 16]].
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/gluon/customop.txt b/versions/master/_sources/tutorials/gluon/customop.txt
new file mode 100644
index 0000000..dbb1907
--- /dev/null
+++ b/versions/master/_sources/tutorials/gluon/customop.txt
@@ -0,0 +1,198 @@
+
+# Creating custom operators with numpy
+
+In this tutorial, we will learn how to build custom operators with numpy in python. We will go through two examples:
+- Custom operator without any `Parameter`s
+- Custom operator with `Parameter`s
+
+Custom operator in python is easy to develop and good for prototyping, but may hurt performance. If you find it to be a bottleneck, please consider moving to a C++ based implementation in the backend.
+
+
+
+```python
+import numpy as np
+import mxnet as mx
+from mxnet import gluon, autograd
+```
+
+## Parameter-less operators
+
+This operator implements the standard sigmoid activation function. This is only for illustration purposes, in real life you would use the built-in operator `mx.nd.relu`.
+
+### Forward & backward implementation
+
+First we implement the forward and backward computation by sub-classing `mx.operator.CustomOp`:
+
+
+```python
+class Sigmoid(mx.operator.CustomOp):
+ def forward(self, is_train, req, in_data, out_data, aux):
+ """Implements forward computation.
+
+ is_train : bool, whether forwarding for training or testing.
+ req : list of {'null', 'write', 'inplace', 'add'}, how to assign to out_data. 'null' means skip assignment, etc.
+ in_data : list of NDArray, input data.
+ out_data : list of NDArray, pre-allocated output buffers.
+ aux : list of NDArray, mutable auxiliary states. Usually not used.
+ """
+ x = in_data[0].asnumpy()
+ y = 1.0 / (1.0 + np.exp(-x))
+ self.assign(out_data[0], req[0], mx.nd.array(y))
+
+ def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+ """Implements backward computation
+
+ req : list of {'null', 'write', 'inplace', 'add'}, how to assign to in_grad
+ out_grad : list of NDArray, gradient w.r.t. output data.
+ in_grad : list of NDArray, gradient w.r.t. input data. This is the output buffer.
+ """
+ y = out_data[0].asnumpy()
+ dy = out_grad[0].asnumpy()
+ dx = dy*(1.0 - y)*y
+ self.assign(in_grad[0], req[0], mx.nd.array(dx))
+```
+
+### Register custom operator
+
+Then we need to register the custom op and describe it's properties like input and output shapes so that mxnet can recognize it. This is done by sub-classing `mx.operator.CustomOpProp`:
+
+
+```python
+@mx.operator.register("sigmoid") # register with name "sigmoid"
+class SigmoidProp(mx.operator.CustomOpProp):
+ def __init__(self):
+ super(SigmoidProp, self).__init__(True)
+
+ def list_arguments(self):
+ # this can be omitted if you only have 1 input.
+ return ['data']
+
+ def list_outputs(self):
+ # this can be omitted if you only have 1 output.
+ return ['output']
+
+ def infer_shape(self, in_shapes):
+ """Calculate output shapes from input shapes. This can be
+ omited if all your inputs and outputs have the same shape.
+
+ in_shapes : list of shape. Shape is described by a tuple of int.
+ """
+ data_shape = in_shapes[0]
+ output_shape = data_shape
+ # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
+ return (data_shape,), (output_shape,), ()
+
+ def create_operator(self, ctx, in_shapes, in_dtypes):
+ # create and return the CustomOp class.
+ return Sigmoid()
+```
+
+### Example Usage
+
+We can now use this operator by calling `mx.nd.Custom`:
+
+
+```python
+x = mx.nd.array([0, 1, 2, 3])
+# attach gradient buffer to x for autograd
+x.attach_grad()
+# forward in a record() section to save computation graph for backward
+# see autograd tutorial to learn more.
+with autograd.record():
+ y = mx.nd.Custom(x, op_type='sigmoid')
+print(y)
+```
+
+```python
+# call backward computation
+y.backward()
+# gradient is now saved to the grad buffer we attached previously
+print(x.grad)
+```
+
+## Parametrized Operator
+
+In the second use case we implement an operator with learnable weights. We implement the dense (or fully connected) layer that has one input, one output, and two learnable parameters: weight and bias.
+
+The dense operator performs a dot product between data and weight, then add bias to it.
+
+### Forward & backward implementation
+
+
+```python
+class Dense(mx.operator.CustomOp):
+ def __init__(self, bias):
+ self._bias = bias
+
+ def forward(self, is_train, req, in_data, out_data, aux):
+ x = in_data[0].asnumpy()
+ weight = in_data[1].asnumpy()
+ y = x.dot(weight.T) + self._bias
+ self.assign(out_data[0], req[0], mx.nd.array(y))
+
+ def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+ x = in_data[0].asnumpy()
+ dy = out_grad[0].asnumpy()
+ dx = dy.T.dot(x)
+ self.assign(in_grad[0], req[0], mx.nd.array(dx))
+```
+
+### Registration
+
+
+```python
+@mx.operator.register("dense") # register with name "sigmoid"
+class DenseProp(mx.operator.CustomOpProp):
+ def __init__(self, bias):
+ super(DenseProp, self).__init__(True)
+ # we use constant bias here to illustrate how to pass arguments
+ # to operators. All arguments are in string format so you need
+ # to convert them back to the type you want.
+ self._bias = float(bias)
+
+ def list_arguments(self):
+ return ['data', 'weight']
+
+ def list_outputs(self):
+ # this can be omitted if you only have 1 output.
+ return ['output']
+
+ def infer_shape(self, in_shapes):
+ data_shape = in_shapes[0]
+ weight_shape = in_shapes[1]
+ output_shape = (data_shape[0], weight_shape[0])
+ # return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.
+ return (data_shape, weight_shape), (output_shape,), ()
+
+ def create_operator(self, ctx, in_shapes, in_dtypes):
+ # create and return the CustomOp class.
+ return Dense(self._bias)
+```
+
+### Use CustomOp together with Block
+
+Parameterized CustomOp are ususally used together with Blocks, which holds the parameter.
+
+
+```python
+class DenseBlock(mx.gluon.Block):
+ def __init__(self, in_channels, channels, bias, **kwargs):
+ super(DenseBlock, self).__init__(**kwargs)
+ self._bias = bias
+ self.weight = self.params.get('weight', shape=(channels, in_channels))
+
+ def forward(self, x):
+ ctx = x.context
+ return mx.nd.Custom(x, self.weight.data(ctx), bias=self._bias, op_type='dense')
+```
+
+### Example usage
+
+
+```python
+dense = DenseBlock(3, 5, 0.1)
+dense.initialize()
+x = mx.nd.uniform(shape=(4, 3))
+y = dense(x)
+print(y)
+```
diff --git a/versions/master/_sources/tutorials/gluon/gluon.txt b/versions/master/_sources/tutorials/gluon/gluon.txt
new file mode 100644
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--- /dev/null
+++ b/versions/master/_sources/tutorials/gluon/gluon.txt
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+# Gluon - Neural network building blocks
+
+Gluon package is a high-level interface for MXNet designed to be easy to use while
+keeping most of the flexibility of low level API. Gluon supports both imperative
+and symbolic programming, making it easy to train complex models imperatively
+in Python and then deploy with symbolic graph in C++ and Scala.
+
+
+```python
+# import dependencies
+from __future__ import print_function
+import numpy as np
+import mxnet as mx
+import mxnet.ndarray as F
+import mxnet.gluon as gluon
+from mxnet.gluon import nn
+from mxnet import autograd
+```
+
+Neural networks (and other machine learning models) can be defined and trained
+with `gluon.nn` and `gluon.rnn` package. A typical training script has the following
+steps:
+
+- Define network
+- Initialize parameters
+- Loop over inputs
+- Forward input through network to get output
+- Compute loss with output and label
+- Backprop gradient
+- Update parameters with gradient descent.
+
+
+## Define Network
+
+`gluon.Block` is the basic building block of models. You can define networks by
+composing and inheriting `Block`:
+
+```python
+class Net(gluon.Block):
+ def __init__(self, **kwargs):
+ super(Net, self).__init__(**kwargs)
+ with self.name_scope():
+ # layers created in name_scope will inherit name space
+ # from parent layer.
+ self.conv1 = nn.Conv2D(6, kernel_size=5)
+ self.pool1 = nn.MaxPool2D(pool_size=(2,2))
+ self.conv2 = nn.Conv2D(16, kernel_size=5)
+ self.pool2 = nn.MaxPool2D(pool_size=(2,2))
+ self.fc1 = nn.Dense(120)
+ self.fc2 = nn.Dense(84)
+ self.fc3 = nn.Dense(10)
+
+ def forward(self, x):
+ x = self.pool1(F.relu(self.conv1(x)))
+ x = self.pool2(F.relu(self.conv2(x)))
+ # 0 means copy over size from corresponding dimension.
+ # -1 means infer size from the rest of dimensions.
+ x = x.reshape((0, -1))
+ x = F.relu(self.fc1(x))
+ x = F.relu(self.fc2(x))
+ x = self.fc3(x)
+ return x
+```
+
+## Initialize Parameters
+
+A network must be created and initialized before it can be used:
+
+```python
+net = Net()
+# Initialize on CPU. Replace with `mx.gpu(0)`, or `[mx.gpu(0), mx.gpu(1)]`,
+# etc to use one or more GPUs.
+net.collect_params().initialize(mx.init.Xavier(), ctx=mx.cpu())
+```
+
+Note that because we didn't specify input size to layers in Net's constructor,
+the shape of parameters cannot be determined at this point. Actual initialization
+is deferred to the first forward pass, i.e. if you access `net.fc1.weight.data()`
+now an exception will be raised.
+
+You can actually initialize the weights by running a forward pass:
+
+```python
+data = mx.nd.random_normal(shape=(10, 1, 32, 32)) # dummy data
+output = net(data)
+```
+
+Or you can specify input size when creating layers, i.e. `nn.Dense(84, in_units=120)`
+instead of `nn.Dense(84)`.
+
+## Loss Functions
+
+Loss functions take (output, label) pairs and compute a scalar loss for each sample
+in the mini-batch. The scalars measure how far each output is from the label.
+
+There are many predefined loss functions in `gluon.loss`. Here we use
+`softmax_cross_entropy_loss` for digit classification.
+
+To compute loss and backprop for one iteration, we do:
+
+```python
+label = mx.nd.arange(10) # dummy label
+with autograd.record():
+ output = net(data)
+ loss = gluon.loss.softmax_cross_entropy_loss(output, label)
+ loss.backward()
+print('loss:', loss)
+print('grad:', net.fc1.weight.grad())
+```
+
+## Updating the weights
+
+Now that gradient is computed, we just need to update the weights. This is usually
+done with formulas like `weight = weight - learning_rate * grad / batch_size`.
+Note we divide gradient by batch_size because gradient is aggregated over the
+entire batch. For example,
+
+```python
+lr = 0.01
+for p in net.collect_params().values():
+ p.data()[:] -= lr / data.shape[0] * p.grad()
+```
+
+But sometimes you want more fancy updating rules like momentum and Adam, and since
+this is a commonly used functionality, gluon provide a `Trainer` class for it:
+
+```python
+trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})
+
+with record():
+ output = net(data)
+ loss = gluon.loss.softmax_cross_entropy_loss(output, label)
+ loss.backward()
+
+# do the update. Trainer needs to know the batch size of data to normalize
+# the gradient by 1/batch_size.
+trainer.step(data.shape[0])
+```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/gluon/hybrid.txt b/versions/master/_sources/tutorials/gluon/hybrid.txt
new file mode 100644
index 0000000..7e043c8
--- /dev/null
+++ b/versions/master/_sources/tutorials/gluon/hybrid.txt
@@ -0,0 +1,125 @@
+# Hybrid - Faster training and easy deployment
+
+*Note: a newer version is available [here](http://gluon.mxnet.io/P14-C05-hybridize.html).*
+
+Deep learning frameworks can be roughly divided into two categories: declarative
+and imperative. With declarative frameworks (including Tensorflow, Theano, etc)
+users first declare a fixed computation graph and then execute it end-to-end.
+The benefit of fixed computation graph is it's portable and runs more
+efficiently. However, it's less flexible because any logic must be encoded
+into the graph as special operators like `scan`, `while_loop` and `cond`.
+It's also hard to debug.
+
+Imperative frameworks (including PyTorch, Chainer, etc) are just the opposite:
+they execute commands one-by-one just like old fashioned Matlab and Numpy.
+This style is more flexible, easier to debug, but less efficient.
+
+`HybridBlock` seamlessly combines declarative programming and imperative programming
+to offer the benefit of both. Users can quickly develop and debug models with
+imperative programming and switch to efficient declarative execution by simply
+calling: `HybridBlock.hybridize()`.
+
+## HybridBlock
+
+`HybridBlock` is very similar to `Block` but has a few restrictions:
+
+- All children layers of `HybridBlock` must also be `HybridBlock`.
+- Only methods that are implemented for both `NDArray` and `Symbol` can be used.
+ For example you cannot use `.asnumpy()`, `.shape`, etc.
+- Operations cannot change from run to run. For example, you cannot do `if x:`
+ if `x` is different for each iteration.
+
+To use hybrid support, we subclass the `HybridBlock`:
+
+```python
+import mxnet as mx
+from mxnet import gluon
+from mxnet.gluon import nn
+
+class Net(gluon.HybridBlock):
+ def __init__(self, **kwargs):
+ super(Net, self).__init__(**kwargs)
+ with self.name_scope:
+ # layers created in name_scope will inherit name space
+ # from parent layer.
+ self.conv1 = nn.Conv2D(6, kernel_size=5)
+ self.pool1 = nn.Pool2D(kernel_size=2)
+ self.conv2 = nn.Conv2D(16, kernel_size=5)
+ self.pool2 = nn.Pool2D(kernel_size=2)
+ self.fc1 = nn.Dense(120)
+ self.fc2 = nn.Dense(84)
+ # You can use a Dense layer for fc3 but we do dot product manually
+ # here for illustration purposes.
+ self.fc3_weight = self.params.get('fc3_weight', shape=(10, 84))
+
+ def hybrid_forward(self, F, x, fc3_weight):
+ # Here `F` can be either mx.nd or mx.sym, x is the input data,
+ # and fc3_weight is either self.fc3_weight.data() or
+ # self.fc3_weight.var() depending on whether x is Symbol or NDArray
+ print(x)
+ x = self.pool1(F.relu(self.conv1(x)))
+ x = self.pool2(F.relu(self.conv2(x)))
+ # 0 means copy over size from corresponding dimension.
+ # -1 means infer size from the rest of dimensions.
+ x = x.reshape((0, -1))
+ x = F.relu(self.fc1(x))
+ x = F.relu(self.fc2(x))
+ x = F.dot(x, fc3_weight, transpose_b=True)
+ return x
+```
+
+## Hybridize
+
+By default, `HybridBlock` runs just like a standard `Block`. Each time a layer
+is called, its `hybrid_forward` will be run:
+
+```python
+net = Net()
+net.collect_params().initialize()
+x = mx.nd.random_normal(shape=(16, 1, 28, 28))
+net(x)
+x = mx.nd.random_normal(shape=(16, 1, 28, 28))
+net(x)
+```
+
+Hybrid execution can be activated by simply calling `.hybridize()` on the top
+level layer. The first forward call after activation will try to build a
+computation graph from `hybrid_forward` and cache it. On subsequent forward
+calls the cached graph instead of `hybrid_forward` will be invoked:
+
+```python
+net.hybridize()
+x = mx.nd.random_normal(shape=(16, 1, 28, 28))
+net(x)
+x = mx.nd.random_normal(shape=(16, 1, 28, 28))
+net(x)
+```
+
+Note that before hybridize, `print(x)` printed out one NDArray for forward,
+but after hybridize, only the first forward printed out a Symbol. On subsequent
+forward `hybrid_forward` is not called so nothing was printed.
+
+Hybridize will speed up execution and save memory. If the top level layer is
+not a `HybridBlock`, you can still call `.hybridize()` on it and Gluon will try
+to hybridize its children layers instead.
+
+## Serializing trained model for deployment
+
+Models implemented as `HybridBlock` can be easily serialized for deployment
+using other language front-ends like C, C++ and Scala. To this end, we simply
+forward the model with symbolic variables instead of NDArrays and save the
+output Symbol(s):
+
+```python
+x = mx.sym.var('data')
+y = net(x)
+print(y)
+y.save('model.json')
+net.collect_params().save('model.params')
+```
+
+If your network outputs more than one value, you can use `mx.sym.Group` to
+combine them into a grouped Symbol and then save. The saved json and params
+files can then be loaded with C, C++ and Scala interface for prediction.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/gluon/mnist.txt b/versions/master/_sources/tutorials/gluon/mnist.txt
new file mode 100644
index 0000000..0abb8ea
--- /dev/null
+++ b/versions/master/_sources/tutorials/gluon/mnist.txt
@@ -0,0 +1,325 @@
+# Handwritten Digit Recognition
+
+In this tutorial, we'll give you a step by step walk-through of how to build a hand-written digit classifier using the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset.
+
+MNIST is a widely used dataset for the hand-written digit classification task. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). The task at hand is to train a model using the 60,000 training images and subsequently test its classification accuracy on the 10,000 test images.
+
+
+
+**Figure 1:** Sample images from the MNIST dataset.
+
+This tutorial uses MXNet's new high-level interface, gluon package to implement MLP using
+imperative fashion.
+
+This is based on the Mnist tutorial with symbolic approach. You can find it [here](http://mxnet.io/tutorials/python/mnist.html).
+
+## Prerequisites
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html).
+
+- [Python Requests](http://docs.python-requests.org/en/master/) and [Jupyter Notebook](http://jupyter.org/index.html).
+
+```
+$ pip install requests jupyter
+```
+
+## Loading Data
+
+Before we define the model, let's first fetch the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset.
+
+The following source code downloads and loads the images and the corresponding labels into memory.
+
+```python
+import mxnet as mx
+mnist = mx.test_utils.get_mnist()
+```
+
+After running the above source code, the entire MNIST dataset should be fully loaded into memory. Note that for large datasets it is not feasible to pre-load the entire dataset first like we did here. What is needed is a mechanism by which we can quickly and efficiently stream data directly from the source. MXNet Data iterators come to the rescue here by providing exactly that. Data iterator is the mechanism by which we feed input data into an MXNet training algorithm and they are very s [...]
+
+Image batches are commonly represented by a 4-D array with shape `(batch_size, num_channels, width, height)`. For the MNIST dataset, since the images are grayscale, there is only one color channel. Also, the images are 28x28 pixels, and so each image has width and height equal to 28. Therefore, the shape of input is `(batch_size, 1, 28, 28)`. Another important consideration is the order of input samples. When feeding training examples, it is critical that we don't feed samples with the s [...]
+Data iterators take care of this by randomly shuffling the inputs. Note that we only need to shuffle the training data. The order does not matter for test data.
+
+The following source code initializes the data iterators for the MNIST dataset. Note that we initialize two iterators: one for train data and one for test data.
+
+```python
+batch_size = 100
+train_data = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
+val_data = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+```
+
+## Approaches
+
+We will cover a couple of approaches for performing the hand written digit recognition task. The first approach makes use of a traditional deep neural network architecture called Multilayer Percepton (MLP). We'll discuss its drawbacks and use that as a motivation to introduce a second more advanced approach called Convolution Neural Network (CNN) that has proven to work very well for image classification tasks.
+
+Now, let's import required nn modules
+
+```python
+from __future__ import print_function
+import mxnet as mx
+from mxnet import gluon
+from mxnet.gluon import nn
+from mxnet import autograd as ag
+```
+
+### Define a network: Multilayer Perceptron
+
+The first approach makes use of a [Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron) to solve this problem. We'll define the MLP using MXNet's imperative approach.
+
+MLPs contains several fully connected layers. A fully connected layer or FC layer for short, is one where each neuron in the layer is connected to every neuron in its preceding layer. From a linear algebra perspective, an FC layer applies an [affine transform](https://en.wikipedia.org/wiki/Affine_transformation) to the *n x m* input matrix *X* and outputs a matrix *Y* of size *n x k*, where *k* is the number of neurons in the FC layer. *k* is also referred to as the hidden size. The outp [...]
+
+In an MLP, the outputs of most FC layers are fed into an activation function, which applies an element-wise non-linearity. This step is critical and it gives neural networks the ability to classify inputs that are not linearly separable. Common choices for activation functions are sigmoid, tanh, and [rectified linear unit](https://en.wikipedia.org/wiki/Rectifier_%28neural_networks%29) (ReLU). In this example, we'll use the ReLU activation function which has several desirable properties a [...]
+
+The following code declares three fully connected layers with 128, 64 and 10 neurons each.
+The last fully connected layer often has its hidden size equal to the number of output classes in the dataset. Furthermore, these FC layers uses ReLU activation for performing an element-wise ReLU transformation on the FC layer output.
+
+To do this, we will use [Sequential layer](http://mxnet.io/api/python/gluon.html#mxnet.gluon.nn.Sequential) type. This is simply a linear stack of neural network layers. `nn.Dense` layers are nothing but the fully connected layers we discussed above.
+
+```python
+# define network
+net = nn.Sequential()
+with net.name_scope():
+ net.add(nn.Dense(128, activation='relu'))
+ net.add(nn.Dense(64, activation='relu'))
+ net.add(nn.Dense(10))
+```
+
+#### Initialize parameters and optimizer
+
+The following source code initializes all parameters received from parameter dict using [Xavier](http://mxnet.io/api/python/optimization.html#mxnet.initializer.Xavier) initializer
+to train the MLP network we defined above.
+
+For our training, we will make use of the stochastic gradient descent (SGD) optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes train data one example at a time. In practice, this is very slow and one can speed up the process by processing examples in small batches. In this case, our batch size will be 100, which is a reasonable choice. Another parameter we select here is the learning rate, which controls the step size the optimizer takes in search of a soluti [...]
+
+We will use [Trainer](http://mxnet.io/api/python/gluon.html#trainer) class to apply the
+[SGD optimizer](http://mxnet.io/api/python/optimization.html#mxnet.optimizer.SGD) on the
+initialized parameters.
+
+```python
+ctx = [mx.cpu(0), mx.cpu(1)]
+net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
+trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
+```
+
+#### Train the network
+
+Typically, one runs the training until convergence, which means that we have learned a good set of model parameters (weights + biases) from the train data. For the purpose of this tutorial, we'll run training for 10 epochs and stop. An epoch is one full pass over the entire train data.
+
+We will take following steps for training:
+
+- Define [Accuracy evaluation metric](http://mxnet.io/api/python/metric.html#mxnet.metric.Accuracy) over training data.
+- Loop over inputs for every epoch.
+- Forward input through network to get output.
+- Compute loss with output and label inside record scope.
+- Backprop gradient inside record scope.
+- Update evaluation metric and parameters with gradient descent.
+
+Loss function takes (output, label) pairs and computes a scalar loss for each sample in the mini-batch. The scalars measure how far each output is from the label.
+There are many predefined loss functions in gluon.loss. Here we use
+[softmax_cross_entropy_loss](http://mxnet.io/api/python/gluon.html#mxnet.gluon.loss.softmax_cross_entropy_loss) for digit classification. We will compute loss and do backward propagation inside
+training scope which is defined by `autograd.record()`.
+
+```python
+epoch = 10
+# Use Accuracy as the evaluation metric.
+metric = mx.metric.Accuracy()
+
+for i in range(epoch):
+ # Reset the train data iterator.
+ train_data.reset()
+ # Loop over the train data iterator.
+ for batch in train_data:
+ # Splits train data into multiple slices along batch_axis
+ # and copy each slice into a context.
+ data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
+ # Splits train labels into multiple slices along batch_axis
+ # and copy each slice into a context.
+ label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
+ outputs = []
+ # Inside training scope
+ with ag.record():
+ for x, y in zip(data, label):
+ z = net(x)
+ # Computes softmax cross entropy loss.
+ loss = gluon.loss.softmax_cross_entropy_loss(z, y)
+ # Backpropogate the error for one iteration.
+ ag.backward([loss])
+ outputs.append(z)
+ # Updates internal evaluation
+ metric.update(label, outputs)
+ # Make one step of parameter update. Trainer needs to know the
+ # batch size of data to normalize the gradient by 1/batch_size.
+ trainer.step(batch.data[0].shape[0])
+ # Gets the evaluation result.
+ name, acc = metric.get()
+ # Reset evaluation result to initial state.
+ metric.reset()
+ print('training acc at epoch %d: %s=%f'%(i, name, acc))
+```
+
+#### Prediction
+
+After the above training completes, we can evaluate the trained model by running predictions on validation dataset. Since the dataset also has labels for all test images, we can compute the accuracy metric over validation data as follows:
+
+```python
+# Use Accuracy as the evaluation metric.
+metric = mx.metric.Accuracy()
+# Reset the validation data iterator.
+val_data.reset()
+# Loop over the validation data iterator.
+for batch in val_data:
+ # Splits validation data into multiple slices along batch_axis
+ # and copy each slice into a context.
+ data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
+ # Splits validation label into multiple slices along batch_axis
+ # and copy each slice into a context.
+ label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
+ outputs = []
+ for x in data:
+ outputs.append(net(x))
+ # Updates internal evaluation
+ metric.update(label, outputs)
+print('validation acc: %s=%f'%metric.get())
+assert metric.get()[1] > 0.96
+```
+
+If everything went well, we should see an accuracy value that is around 0.96, which means that we are able to accurately predict the digit in 96% of test images. This is a pretty good result. But as we will see in the next part of this tutorial, we can do a lot better than that.
+
+### Convolutional Neural Network
+
+Earlier, we briefly touched on a drawback of MLP when we said we need to discard the input image's original shape and flatten it as a vector before we can feed it as input to the MLP's first fully connected layer. Turns out this is an important issue because we don't take advantage of the fact that pixels in the image have natural spatial correlation along the horizontal and vertical axes. A convolutional neural network (CNN) aims to address this problem by using a more structured weight [...]
+
+A single convolution layer consists of one or more filters that each play the role of a feature detector. During training, a CNN learns appropriate representations (parameters) for these filters. Similar to MLP, the output from the convolutional layer is transformed by applying a non-linearity. Besides the convolutional layer, another key aspect of a CNN is the pooling layer. A pooling layer serves to make the CNN translation invariant: a digit remains the same even when it is shifted le [...]
+
+The following source code defines a convolutional neural network architecture called LeNet. LeNet is a popular network known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with tanh activations for the neurons.
+
+A typical way to write your network is creating a new class inherited from `gluon.Block`
+class. We can define the network by composing and inheriting Block class as follows:
+
+```python
+import mxnet.ndarray as F
+
+class Net(gluon.Block):
+ def __init__(self, **kwargs):
+ super(Net, self).__init__(**kwargs)
+ with self.name_scope():
+ # layers created in name_scope will inherit name space
+ # from parent layer.
+ self.conv1 = nn.Conv2D(20, kernel_size=(5,5))
+ self.pool1 = nn.MaxPool2D(pool_size=(2,2), strides = (2,2))
+ self.conv2 = nn.Conv2D(50, kernel_size=(5,5))
+ self.pool2 = nn.MaxPool2D(pool_size=(2,2), strides = (2,2))
+ self.fc1 = nn.Dense(500)
+ self.fc2 = nn.Dense(10)
+
+ def forward(self, x):
+ x = self.pool1(F.tanh(self.conv1(x)))
+ x = self.pool2(F.tanh(self.conv2(x)))
+ # 0 means copy over size from corresponding dimension.
+ # -1 means infer size from the rest of dimensions.
+ x = x.reshape((0, -1))
+ x = F.tanh(self.fc1(x))
+ x = F.tanh(self.fc2(x))
+ return x
+```
+
+We just defined the forward function here, and the backward function to compute gradients
+is automatically defined for you using autograd.
+We also imported `mxnet.ndarray` package to use activation functions from `ndarray` API.
+
+Now, We will create the network as follows:
+
+```python
+net = Net()
+```
+
+
+
+**Figure 3:** First conv + pooling layer in LeNet.
+
+Now we train LeNet with the same hyper-parameters as before. Note that, if a GPU is available, we recommend using it. This greatly speeds up computation given that LeNet is more complex and compute-intensive than the previous multilayer perceptron. To do so, we only need to change `mx.cpu()` to `mx.gpu()` and MXNet takes care of the rest. Just like before, we'll stop training after 10 epochs.
+
+Training and prediction can be done in the similar way as we did for MLP.
+
+#### Initialize parameters and optimizer
+
+We will initialize the network parameters as follows:
+
+```python
+ctx = [mx.cpu(0), mx.cpu(1)]
+net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
+trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
+```
+
+#### Training
+
+```python
+# Use Accuracy as the evaluation metric.
+metric = mx.metric.Accuracy()
+
+for i in range(epoch):
+ # Reset the train data iterator.
+ train_data.reset()
+ # Loop over the train data iterator.
+ for batch in train_data:
+ # Splits train data into multiple slices along batch_axis
+ # and copy each slice into a context.
+ data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
+ # Splits train labels into multiple slices along batch_axis
+ # and copy each slice into a context.
+ label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
+ outputs = []
+ # Inside training scope
+ with ag.record():
+ for x, y in zip(data, label):
+ z = net(x)
+ # Computes softmax cross entropy loss.
+ loss = gluon.loss.softmax_cross_entropy_loss(z, y)
+ # Backpropogate the error for one iteration.
+ ag.backward([loss])
+ outputs.append(z)
+ # Updates internal evaluation
+ metric.update(label, outputs)
+ # Make one step of parameter update. Trainer needs to know the
+ # batch size of data to normalize the gradient by 1/batch_size.
+ trainer.step(batch.data[0].shape[0])
+ # Gets the evaluation result.
+ name, acc = metric.get()
+ # Reset evaluation result to initial state.
+ metric.reset()
+ print('training acc at epoch %d: %s=%f'%(i, name, acc))
+```
+
+#### Prediction
+
+Finally, we'll use the trained LeNet model to generate predictions for the test data.
+
+```python
+# Use Accuracy as the evaluation metric.
+metric = mx.metric.Accuracy()
+# Reset the validation data iterator.
+val_data.reset()
+# Loop over the validation data iterator.
+for batch in val_data:
+ # Splits validation data into multiple slices along batch_axis
+ # and copy each slice into a context.
+ data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
+ # Splits validation label into multiple slices along batch_axis
+ # and copy each slice into a context.
+ label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
+ outputs = []
+ for x in data:
+ outputs.append(net(x))
+ # Updates internal evaluation
+ metric.update(label, outputs)
+print('validation acc: %s=%f'%metric.get())
+assert metric.get()[1] > 0.98
+```
+
+If all went well, we should see a higher accuracy metric for predictions made using LeNet. With CNN we should be able to correctly predict around 98% of all test images.
+
+## Summary
+
+In this tutorial, we have learned how to use MXNet to solve a standard computer vision problem: classifying images of hand written digits. You have seen how to quickly and easily build, train and evaluate models such as MLP and CNN with MXNet Gluon package.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/gluon/ndarray.txt b/versions/master/_sources/tutorials/gluon/ndarray.txt
new file mode 100644
index 0000000..7cf08a8
--- /dev/null
+++ b/versions/master/_sources/tutorials/gluon/ndarray.txt
@@ -0,0 +1,145 @@
+# NDArray - Scientific computing on CPU and GPU
+
+NDArray is a tensor data structure similar to numpy's multi-dimensional array.
+In addition, it supports asynchronous computation on CPU and GPU.
+
+First, let's import MXNet:
+
+```python
+from __future__ import print_function
+import numpy as np
+import mxnet as mx
+```
+
+## Creating NDArray
+
+There are many ways to create NDArray.
+
+Construct from (nested) list:
+```python
+x = mx.nd.array([[1, 2, 3], [4, 5, 6]])
+print(x)
+```
+
+Construct from numpy array:
+```python
+x_numpy = np.ones((2, 3))
+x = mx.nd.array(x_numpy)
+print(x)
+```
+
+Array construction routines:
+```python
+# create an 2x3 array of ones
+x = mx.nd.ones((2, 3))
+print(x)
+# create an 2x3 array of zeros
+x = mx.nd.zeros((2, 3))
+print(x)
+# create an 1d-array of 0 to 5 and reshape to 2x3
+x = mx.nd.arange(6).reshape((2, 3))
+print(x)
+```
+
+You can convert an NDArray to numpy array to retrieve its data with `.asnumpy()`:
+```python
+z = x.asnumpy()
+print(z)
+```
+
+## Basic attributes
+
+NDArray has some basic attributes that you often want to query:
+
+**NDArray.shape**: The dimensions of the array. It is a tuple of integers
+indicating the length of the array along each axis. For a matrix with `n` rows
+and `m` columns, its `shape` will be `(n, m)`.
+
+```python
+print('x.shape:', x.shape)
+```
+
+**NDArray.dtype**: A `numpy` _type_ object describing the type of array
+elements.
+
+```python
+print('x.dtype:', x.dtype)
+```
+
+**NDArray.size**: the total number of components in the array - equals to the
+product of the components of its `shape`
+
+```python
+print('x.size:', x.size)
+```
+
+**NDArray.context**: The device on which this array is stored, e.g. `mx.cpu()`
+or `mx.gpu(1)`.
+
+```python
+print('x.context:', x.context)
+```
+
+## NDArray Operations
+
+NDArray supports a wide range of operations. Simple operations can be called
+with python syntax:
+
+```python
+x = mx.nd.array([[1, 2], [3, 4]])
+y = mx.nd.array([[4, 3], [2, 1]])
+print(x + y)
+```
+
+You can also call operators from the `mxnet.ndarray` (or `mx.nd` for short) name space:
+
+```python
+z = mx.nd.add(x, y)
+print(z)
+```
+
+You can also pass additional flags to operators:
+
+```python
+z = mx.nd.sum(x, axis=0)
+print('axis=0:', z)
+z = mx.nd.sum(x, axis=1)
+print('axis=1:', z)
+```
+
+## Using GPU
+
+Each NDArray lives on a `Context`. MXNet supports `mx.cpu()` for CPU and `mx.gpu(0)`,
+`mx.gpu(1)`, etc for GPU. You can specify context when creating NDArray:
+
+```python
+# creates on CPU (the default).
+# Replace mx.cpu() with mx.gpu(0) if you have a GPU.
+x = mx.nd.zeros((2, 2), ctx=mx.cpu())
+print(x)
+```
+
+```python
+x = mx.nd.array([[1, 2], [3, 4]], ctx=mx.cpu())
+print(x)
+```
+
+You can copy arrays between devices with `.copyto()`:
+
+```python
+# Copy x to cpu. Replace with mx.gpu(0) if you have GPU.
+y = x.copyto(mx.cpu())
+print(y)
+```
+
+```python
+# Copy x to another NDArray, possibly on another Context.
+y = mx.nd.zeros_like(x)
+x.copyto(y)
+print(y)
+```
+
+See the [Advanced NDArray tutorial](../basic/ndarray.md) for a more detailed
+introduction to NDArray API.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/index.txt b/versions/master/_sources/tutorials/index.txt
new file mode 100644
index 0000000..32d8bd8
--- /dev/null
+++ b/versions/master/_sources/tutorials/index.txt
@@ -0,0 +1,37 @@
+# Tutorials
+
+These tutorials introduce a few fundamental concepts in deep learning and how to implement them in _MXNet_. The _Basics_ section contains tutorials on manipulating arrays, building networks, loading/preprocessing data, etc. The _Training and Inference_ section talks about implementing Linear Regression, training a Handwritten digit classifier using MLP and CNN, running inferences using a pre-trained model, and lastly, efficiently training a large scale image classifier.
+
+**Note:** We are working on a set of tutorials for the new imperative interface called Gluon. A preview version is hosted at [thestraightdope.mxnet.io](http://thestraightdope.mxnet.io).
+
+## Python
+
+### Basic
+
+```eval_rst
+.. toctree::
+ :maxdepth: 1
+
+ basic/ndarray
+ basic/symbol
+ basic/module
+ basic/data
+```
+
+### Training and Inference
+
+```eval_rst
+.. toctree::
+ :maxdepth: 1
+
+ python/linear-regression
+ python/mnist
+ python/predict_image
+ vision/large_scale_classification
+```
+<br>
+More tutorials and examples are available in the GitHub [repository](https://github.com/dmlc/mxnet/tree/master/example).
+
+## Contributing Tutorials
+
+Want to contribute an MXNet tutorial? To get started, download the [tutorial template](https://github.com/dmlc/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb).
diff --git a/versions/master/_sources/tutorials/nlp/cnn.txt b/versions/master/_sources/tutorials/nlp/cnn.txt
new file mode 100644
index 0000000..23f74c4
--- /dev/null
+++ b/versions/master/_sources/tutorials/nlp/cnn.txt
@@ -0,0 +1,420 @@
+# Text Classification Using a Convolutional Neural Network on MXNet
+
+This tutorial is based of Yoon Kim's [paper](https://arxiv.org/abs/1408.5882) on using convolutional neural networks for sentence sentiment classification.
+
+For this tutorial, we will train a convolutional deep network model on movie review sentences from Rotten Tomatoes labeled with their sentiment. The result will be a model that can classify a sentence based on its sentiment (with 1 being a purely positive sentiment, 0 being a purely negative sentiment and 0.5 being neutral).
+
+Our first step will be to fetch the labeled training data of positive and negative sentiment sentences and process it into sets of vectors that are then randomly split into train and test sets.
+
+
+```python
+import urllib2
+import numpy as np
+import re
+import itertools
+from collections import Counter
+
+def clean_str(string):
+ """
+ Tokenization/string cleaning for all datasets except for SST.
+ Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
+ """
+ string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
+ string = re.sub(r"\'s", " \'s", string)
+ string = re.sub(r"\'ve", " \'ve", string)
+ string = re.sub(r"n\'t", " n\'t", string)
+ string = re.sub(r"\'re", " \'re", string)
+ string = re.sub(r"\'d", " \'d", string)
+ string = re.sub(r"\'ll", " \'ll", string)
+ string = re.sub(r",", " , ", string)
+ string = re.sub(r"!", " ! ", string)
+ string = re.sub(r"\(", " \( ", string)
+ string = re.sub(r"\)", " \) ", string)
+ string = re.sub(r"\?", " \? ", string)
+ string = re.sub(r"\s{2,}", " ", string)
+ return string.strip().lower()
+
+def load_data_and_labels():
+ """
+ Loads MR polarity data from files, splits the data into words and generates labels.
+ Returns split sentences and labels.
+ """
+ # Pull sentences with positive sentiment
+ pos_file = urllib2.urlopen('https://raw.githubusercontent.com/yoonkim/CNN_sentence/master/rt-polarity.pos')
+
+ # Pull sentences with negative sentiment
+ neg_file = urllib2.urlopen('https://raw.githubusercontent.com/yoonkim/CNN_sentence/master/rt-polarity.neg')
+
+ # Load data from files
+ positive_examples = list(pos_file.readlines())
+ positive_examples = [s.strip() for s in positive_examples]
+ negative_examples = list(neg_file.readlines())
+ negative_examples = [s.strip() for s in negative_examples]
+ # Split by words
+ x_text = positive_examples + negative_examples
+ x_text = [clean_str(sent) for sent in x_text]
+ x_text = [s.split(" ") for s in x_text]
+ # Generate labels
+ positive_labels = [1 for _ in positive_examples]
+ negative_labels = [0 for _ in negative_examples]
+ y = np.concatenate([positive_labels, negative_labels], 0)
+ return [x_text, y]
+
+
+def pad_sentences(sentences, padding_word="</s>"):
+ """
+ Pads all sentences to the same length. The length is defined by the longest sentence.
+ Returns padded sentences.
+ """
+ sequence_length = max(len(x) for x in sentences)
+ padded_sentences = []
+ for i in range(len(sentences)):
+ sentence = sentences[i]
+ num_padding = sequence_length - len(sentence)
+ new_sentence = sentence + [padding_word] * num_padding
+ padded_sentences.append(new_sentence)
+ return padded_sentences
+
+
+def build_vocab(sentences):
+ """
+ Builds a vocabulary mapping from word to index based on the sentences.
+ Returns vocabulary mapping and inverse vocabulary mapping.
+ """
+ # Build vocabulary
+ word_counts = Counter(itertools.chain(*sentences))
+ # Mapping from index to word
+ vocabulary_inv = [x[0] for x in word_counts.most_common()]
+ # Mapping from word to index
+ vocabulary = {x: i for i, x in enumerate(vocabulary_inv)}
+ return [vocabulary, vocabulary_inv]
+
+
+def build_input_data(sentences, labels, vocabulary):
+ """
+ Maps sentences and labels to vectors based on a vocabulary.
+ """
+ x = np.array([[vocabulary[word] for word in sentence] for sentence in sentences])
+ y = np.array(labels)
+ return [x, y]
+
+"""
+Loads and preprocessed data for the MR dataset.
+Returns input vectors, labels, vocabulary, and inverse vocabulary.
+"""
+# Load and preprocess data
+sentences, labels = load_data_and_labels()
+sentences_padded = pad_sentences(sentences)
+vocabulary, vocabulary_inv = build_vocab(sentences_padded)
+x, y = build_input_data(sentences_padded, labels, vocabulary)
+
+vocab_size = len(vocabulary)
+
+# randomly shuffle data
+np.random.seed(10)
+shuffle_indices = np.random.permutation(np.arange(len(y)))
+x_shuffled = x[shuffle_indices]
+y_shuffled = y[shuffle_indices]
+
+# split train/dev set
+# there are a total of 10662 labeled examples to train on
+x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
+y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
+
+sentence_size = x_train.shape[1]
+
+print 'Train/Dev split: %d/%d' % (len(y_train), len(y_dev))
+print 'train shape:', x_train.shape
+print 'dev shape:', x_dev.shape
+print 'vocab_size', vocab_size
+print 'sentence max words', sentence_size
+```
+
+ Train/Dev split: 9662/1000
+ train shape: (9662, 56)
+ dev shape: (1000, 56)
+ vocab_size 18766
+ sentence max words 56
+
+
+Now that we prepared the training and test data by loading, vectorizing and shuffling it we can go on to defining the network architecture we want to train with the data.
+
+We will first set up some placeholders for the input and output of the network then define the first layer, an embedding layer, which learns to map word vectors into a lower dimensional vector space where distances between words correspond to how related they are (with respect to sentiment they convey).
+
+
+```python
+import mxnet as mx
+import sys,os
+
+'''
+Define batch size and the place holders for network inputs and outputs
+'''
+
+batch_size = 50 # the size of batches to train network with
+print 'batch size', batch_size
+
+input_x = mx.sym.Variable('data') # placeholder for input data
+input_y = mx.sym.Variable('softmax_label') # placeholder for output label
+
+
+'''
+Define the first network layer (embedding)
+'''
+
+# create embedding layer to learn representation of words in a lower dimensional subspace (much like word2vec)
+num_embed = 300 # dimensions to embed words into
+print 'embedding dimensions', num_embed
+
+embed_layer = mx.sym.Embedding(data=input_x, input_dim=vocab_size, output_dim=num_embed, name='vocab_embed')
+
+# reshape embedded data for next layer
+conv_input = mx.sym.Reshape(data=embed_layer, target_shape=(batch_size, 1, sentence_size, num_embed))
+```
+
+ batch size 50
+ embedding dimensions 300
+
+
+The next layer in the network performs convolutions over the ordered embedded word vectors in a sentence using multiple filter sizes, sliding over 3, 4 or 5 words at a time. This is the equivalent of looking at all 3-grams, 4-grams and 5-grams in a sentence and will allow us to understand how words contribute to sentiment in the context of those around them.
+
+After each convolution, we add a max-pool layer to extract the most significant elements in each convolution and turn them into a feature vector.
+
+Because each convolution+pool filter produces tensors of different shapes we need to create a layer for each of them, and then concatenate the results of these layers into one big feature vector.
+
+
+```python
+# create convolution + (max) pooling layer for each filter operation
+filter_list=[3, 4, 5] # the size of filters to use
+print 'convolution filters', filter_list
+
+num_filter=100
+pooled_outputs = []
+for i, filter_size in enumerate(filter_list):
+ convi = mx.sym.Convolution(data=conv_input, kernel=(filter_size, num_embed), num_filter=num_filter)
+ relui = mx.sym.Activation(data=convi, act_type='relu')
+ pooli = mx.sym.Pooling(data=relui, pool_type='max', kernel=(sentence_size - filter_size + 1, 1), stride=(1,1))
+ pooled_outputs.append(pooli)
+
+# combine all pooled outputs
+total_filters = num_filter * len(filter_list)
+concat = mx.sym.Concat(*pooled_outputs, dim=1)
+
+# reshape for next layer
+h_pool = mx.sym.Reshape(data=concat, target_shape=(batch_size, total_filters))
+```
+
+ convolution filters [3, 4, 5]
+
+
+Next, we add dropout regularization, which will randomly disable a fraction of neurons in the layer (set to 50% here) to ensure that that model does not overfit. This works by preventing neurons from co-adapting and forcing them to learn individually useful features.
+
+This is necessary for our model because the dataset has a vocabulary of size around 20k and only around 10k examples so since this data set is pretty small we're likely to overfit with a powerful model (like this neural net).
+
+
+```python
+# dropout layer
+dropout=0.5
+print 'dropout probability', dropout
+
+if dropout > 0.0:
+ h_drop = mx.sym.Dropout(data=h_pool, p=dropout)
+else:
+ h_drop = h_pool
+
+```
+
+ dropout probability 0.5
+
+
+Finally, we add a fully connected layer to add non-linearity to the model. We then classify the resulting output of this layer using a softmax function, yielding a result between 0 (negative sentiment) and 1 (positive).
+
+
+```python
+# fully connected layer
+num_label=2
+
+cls_weight = mx.sym.Variable('cls_weight')
+cls_bias = mx.sym.Variable('cls_bias')
+
+fc = mx.sym.FullyConnected(data=h_drop, weight=cls_weight, bias=cls_bias, num_hidden=num_label)
+
+# softmax output
+sm = mx.sym.SoftmaxOutput(data=fc, label=input_y, name='softmax')
+
+# set CNN pointer to the "back" of the network
+cnn = sm
+```
+
+Now that we have defined our CNN model we will define the device on our machine that we will train and execute this model on, as well as the datasets to train and test this model with.
+
+*If you are running this code be sure that you have a GPU on your machine if your ctx is set to mx.gpu(0) otherwise you can set your ctx to mx.cpu(0) which will run the training much slower*
+
+
+```python
+from collections import namedtuple
+import time
+import math
+
+# Define the structure of our CNN Model (as a named tuple)
+CNNModel = namedtuple("CNNModel", ['cnn_exec', 'symbol', 'data', 'label', 'param_blocks'])
+
+# Define what device to train/test on
+ctx=mx.gpu(0)
+# If you have no GPU on your machine change this to
+# ctx=mx.cpu(0)
+
+arg_names = cnn.list_arguments()
+
+input_shapes = {}
+input_shapes['data'] = (batch_size, sentence_size)
+
+arg_shape, out_shape, aux_shape = cnn.infer_shape(**input_shapes)
+arg_arrays = [mx.nd.zeros(s, ctx) for s in arg_shape]
+args_grad = {}
+for shape, name in zip(arg_shape, arg_names):
+ if name in ['softmax_label', 'data']: # input, output
+ continue
+ args_grad[name] = mx.nd.zeros(shape, ctx)
+
+cnn_exec = cnn.bind(ctx=ctx, args=arg_arrays, args_grad=args_grad, grad_req='add')
+
+param_blocks = []
+arg_dict = dict(zip(arg_names, cnn_exec.arg_arrays))
+initializer=mx.initializer.Uniform(0.1)
+for i, name in enumerate(arg_names):
+ if name in ['softmax_label', 'data']: # input, output
+ continue
+ initializer(name, arg_dict[name])
+
+ param_blocks.append( (i, arg_dict[name], args_grad[name], name) )
+
+out_dict = dict(zip(cnn.list_outputs(), cnn_exec.outputs))
+
+data = cnn_exec.arg_dict['data']
+label = cnn_exec.arg_dict['softmax_label']
+
+cnn_model= CNNModel(cnn_exec=cnn_exec, symbol=cnn, data=data, label=label, param_blocks=param_blocks)
+```
+
+We can now execute the training and testing of our network, which in-part mxnet automatically does for us with its forward and backward propagation methods, along with its automatic gradient calculations.
+
+
+```python
+'''
+Train the cnn_model using back prop
+'''
+
+optimizer='rmsprop'
+max_grad_norm=5.0
+learning_rate=0.0005
+epoch=50
+
+print 'optimizer', optimizer
+print 'maximum gradient', max_grad_norm
+print 'learning rate (step size)', learning_rate
+print 'epochs to train for', epoch
+
+# create optimizer
+opt = mx.optimizer.create(optimizer)
+opt.lr = learning_rate
+
+updater = mx.optimizer.get_updater(opt)
+
+# create logging output
+logs = sys.stderr
+
+# For each training epoch
+for iteration in range(epoch):
+ tic = time.time()
+ num_correct = 0
+ num_total = 0
+
+ # Over each batch of training data
+ for begin in range(0, x_train.shape[0], batch_size):
+ batchX = x_train[begin:begin+batch_size]
+ batchY = y_train[begin:begin+batch_size]
+ if batchX.shape[0] != batch_size:
+ continue
+
+ cnn_model.data[:] = batchX
+ cnn_model.label[:] = batchY
+
+ # forward
+ cnn_model.cnn_exec.forward(is_train=True)
+
+ # backward
+ cnn_model.cnn_exec.backward()
+
+ # eval on training data
+ num_correct += sum(batchY == np.argmax(cnn_model.cnn_exec.outputs[0].asnumpy(), axis=1))
+ num_total += len(batchY)
+
+ # update weights
+ norm = 0
+ for idx, weight, grad, name in cnn_model.param_blocks:
+ grad /= batch_size
+ l2_norm = mx.nd.norm(grad).asscalar()
+ norm += l2_norm * l2_norm
+
+ norm = math.sqrt(norm)
+ for idx, weight, grad, name in cnn_model.param_blocks:
+ if norm > max_grad_norm:
+ grad *= (max_grad_norm / norm)
+
+ updater(idx, grad, weight)
+
+ # reset gradient to zero
+ grad[:] = 0.0
+
+ # Decay learning rate for this epoch to ensure we are not "overshooting" optima
+ if iteration % 50 == 0 and iteration > 0:
+ opt.lr *= 0.5
+ print >> logs, 'reset learning rate to %g' % opt.lr
+
+ # End of training loop for this epoch
+ toc = time.time()
+ train_time = toc - tic
+ train_acc = num_correct * 100 / float(num_total)
+
+ # Saving checkpoint to disk
+ if (iteration + 1) % 10 == 0:
+ prefix = 'cnn'
+ cnn_model.symbol.save('./%s-symbol.json' % prefix)
+ save_dict = {('arg:%s' % k) :v for k, v in cnn_model.cnn_exec.arg_dict.items()}
+ save_dict.update({('aux:%s' % k) : v for k, v in cnn_model.cnn_exec.aux_dict.items()})
+ param_name = './%s-%04d.params' % (prefix, iteration)
+ mx.nd.save(param_name, save_dict)
+ print >> logs, 'Saved checkpoint to %s' % param_name
+
+
+ # Evaluate model after this epoch on dev (test) set
+ num_correct = 0
+ num_total = 0
+
+ # For each test batch
+ for begin in range(0, x_dev.shape[0], batch_size):
+ batchX = x_dev[begin:begin+batch_size]
+ batchY = y_dev[begin:begin+batch_size]
+
+ if batchX.shape[0] != batch_size:
+ continue
+
+ cnn_model.data[:] = batchX
+ cnn_model.cnn_exec.forward(is_train=False)
+
+ num_correct += sum(batchY == np.argmax(cnn_model.cnn_exec.outputs[0].asnumpy(), axis=1))
+ num_total += len(batchY)
+
+ dev_acc = num_correct * 100 / float(num_total)
+ print >> logs, 'Iter [%d] Train: Time: %.3fs, Training Accuracy: %.3f \
+ --- Dev Accuracy thus far: %.3f' % (iteration, train_time, train_acc, dev_acc)
+```
+
+Now that we have gone through the trouble of training the model, we have stored the learned parameters in the .params file in our local directory. We can now load this file whenever we want and predict the sentiment of new sentences by running them through a forward pass of the trained model.
+
+## References
+- ["Implementing a CNN for Text Classification in TensorFlow" blog post](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)
+- [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882)
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/versions/master/_sources/tutorials/nlp/nce_loss.txt b/versions/master/_sources/tutorials/nlp/nce_loss.txt
new file mode 100644
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+# NCE Loss
+This tutorial shows how to use nce-loss to speed up multi-class classification when the number of classes is huge.
+
+You can get the source code for this example on [GitHub](https://github.com/dmlc/mxnet/tree/master/example/nce-loss).
+
+## Toy Examples
+
+* toy_softmax.py. A multi class example using softmax output
+* toy_nce.py. A multi-class example using nce loss
+
+### Word2Vec
+
+* word2vec.py. A CBOW word2vec example using nce loss
+
+Run word2vec.py with the following command:
+
+```
+ ./get_text8.sh
+ python word2vec.py
+```
+
+### LSTM
+
+* lstm_word.py. An LSTM example using nce loss
+
+Run lstm_word.py with the following command:
+
+```
+ ./get_text8.sh
+ python lstm_word.py
+```
+
+## References
+
+For more details, see [http://www.jianshu.com/p/e439b43ea464](http://www.jianshu.com/p/e439b43ea464) (in Chinese).
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
\ No newline at end of file
diff --git a/versions/master/_sources/tutorials/nlp/rnn.txt b/versions/master/_sources/tutorials/nlp/rnn.txt
new file mode 100644
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+# Recurrent Neural Networks
+This folder contains RNN examples using a low-level symbol interface. You can get the source code for this example on [GitHub](https://github.com/dmlc/mxnet/tree/master/example/rnn).
+
+## Python
+
+- [https://github.com/dmlc/mxnet/blob/master/example/rnn/lstm_bucketing.py](lstm_bucketing.py). A PennTreeBank language model using LSTM
+- [https://github.com/dmlc/mxnet/blob/master/example/rnn/cudnn_lstm_bucketing.py](cudnn_lstm_bucketing.py). A PennTreeBank language model using LSTM and CUDNN
+
+Performance Note:
+
+Using more ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For information on setting ```MXNET_GPU_WORKER_NTHREADS```, refer to [Environment Variables](http://mxnet.io/how_to/env_var.html).
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/versions/master/_sources/tutorials/python/kvstore.txt b/versions/master/_sources/tutorials/python/kvstore.txt
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+# Distributed Key-Value Store
+
+KVStore is a place for data sharing. Think of it as a single object shared
+across different devices (GPUs and computers), where each device can push data in
+and pull data out.
+
+## Initialization
+
+Let's consider a simple example: initializing
+a (`int`, `NDArray`) pair into the store, and then pulling the value out:
+
+```python
+ >>> kv = mx.kv.create('local') # create a local kv store.
+ >>> shape = (2,3)
+ >>> kv.init(3, mx.nd.ones(shape)*2)
+ >>> a = mx.nd.zeros(shape)
+ >>> kv.pull(3, out = a)
+ >>> print a.asnumpy()
+ [[ 2. 2. 2.]
+ [ 2. 2. 2.]]
+```
+
+## Push, Aggregate, and Update
+
+For any key that has been initialized, you can push a new value with the same shape to the key:
+
+```python
+ >>> kv.push(3, mx.nd.ones(shape)*8)
+ >>> kv.pull(3, out = a) # pull out the value
+ >>> print a.asnumpy()
+ [[ 8. 8. 8.]
+ [ 8. 8. 8.]]
+```
+
+The data for pushing can be stored on any device. Furthermore, you can push multiple
+values into the same key, where KVStore will first sum all of these
+values and then push the aggregated value:
+
+```python
+ >>> gpus = [mx.gpu(i) for i in range(4)]
+ >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
+ >>> kv.push(3, b)
+ >>> kv.pull(3, out = a)
+ >>> print a.asnumpy()
+ [[ 4. 4. 4.]
+ [ 4. 4. 4.]]
+```
+
+For each push, KVStore combines the pushed value with the value stored using an
+`updater`. The default updater is `ASSIGN`. You can replace the default to
+control how data is merged:
+
+```python
+ >>> def update(key, input, stored):
+ >>> print "update on key: %d" % key
+ >>> stored += input * 2
+ >>> kv._set_updater(update)
+ >>> kv.pull(3, out=a)
+ >>> print a.asnumpy()
+ [[ 4. 4. 4.]
+ [ 4. 4. 4.]]
+ >>> kv.push(3, mx.nd.ones(shape))
+ update on key: 3
+ >>> kv.pull(3, out=a)
+ >>> print a.asnumpy()
+ [[ 6. 6. 6.]
+ [ 6. 6. 6.]]
+```
+
+## Pull
+
+You've already seen how to pull a single key-value pair. Similarly, to push, you can
+pull the value onto several devices with a single call:
+
+```python
+ >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
+ >>> kv.pull(3, out = b)
+ >>> print b[1].asnumpy()
+ [[ 6. 6. 6.]
+ [ 6. 6. 6.]]
+```
+
+## Handle a List of Key-Value Pairs
+
+All operations introduced so far involve a single key. KVStore also provides
+an interface for a list of key-value pairs.
+
+For a single device:
+
+```python
+ >>> keys = [5, 7, 9]
+ >>> kv.init(keys, [mx.nd.ones(shape)]*len(keys))
+ >>> kv.push(keys, [mx.nd.ones(shape)]*len(keys))
+ update on key: 5
+ update on key: 7
+ update on key: 9
+ >>> b = [mx.nd.zeros(shape)]*len(keys)
+ >>> kv.pull(keys, out = b)
+ >>> print b[1].asnumpy()
+ [[ 3. 3. 3.]
+ [ 3. 3. 3.]]
+```
+
+For multiple devices:
+
+```python
+ >>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
+ >>> kv.push(keys, b)
+ update on key: 5
+ update on key: 7
+ update on key: 9
+ >>> kv.pull(keys, out = b)
+ >>> print b[1][1].asnumpy()
+ [[ 11. 11. 11.]
+ [ 11. 11. 11.]]
+```
+
+## Run on Multiple Machines
+Based on parameter server, the `updater` runs on the server nodes.
+When the distributed version is ready, we will update this section.
+
+
+<!-- ## How to Choose Between APIs -->
+
+<!-- You can mix APIs as much as you like. Here are some guidelines -->
+<!-- * Use the Symbolic API and a coarse-grained operator to create an established structure. -->
+<!-- * Use a fine-grained operator to extend parts of a more flexible symbolic graph. -->
+<!-- * Do some dynamic NDArray tricks, which are even more flexible, between the calls of forward and backward executors. -->
+
+<!-- Different approaches offer you different levels of flexibility and -->
+<!-- efficiency. Normally, you do not need to be flexible in all parts of the -->
+<!-- network, so use the parts optimized for speed, and compose it -->
+<!-- flexibly with a fine-grained operator or a dynamic NDArray. Such a -->
+<!-- mixture allows you to build the deep learning architecture both efficiently and -->
+<!-- flexibly as your choice. -->
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
\ No newline at end of file
diff --git a/versions/master/_sources/tutorials/python/linear-regression.txt b/versions/master/_sources/tutorials/python/linear-regression.txt
new file mode 100644
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+# Linear Regression
+
+In this tutorial we'll walk through how one can implement *linear regression* using MXNet APIs.
+
+The function we are trying to learn is: *y = x<sub>1</sub> + 2x<sub>2</sub>*, where *(x<sub>1</sub>,x<sub>2</sub>)* are input features and *y* is the corresponding label.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html).
+
+- [Jupyter Notebook](http://jupyter.org/index.html).
+
+```
+$ pip install jupyter
+```
+
+To begin, the following code imports the necessary packages we'll need for this exercise.
+
+```python
+import mxnet as mx
+import numpy as np
+
+import logging
+logging.getLogger().setLevel(logging.DEBUG)
+```
+
+## Preparing the Data
+
+In MXNet, data is input via **Data Iterators**. Here we will illustrate
+how to encode a dataset into an iterator that MXNet can use. The data used in the example is made up of 2D data points with corresponding integer labels.
+
+```python
+#Training data
+train_data = np.random.uniform(0, 1, [100, 2])
+train_label = np.array([train_data[i][0] + 2 * train_data[i][1] for i in range(100)])
+batch_size = 1
+
+#Evaluation Data
+eval_data = np.array([[7,2],[6,10],[12,2]])
+eval_label = np.array([11,26,16])
+```
+
+Once we have the data ready, we need to put it into an iterator and specify
+parameters such as `batch_size` and `shuffle`. `batch_size` specifies the number
+of examples shown to the model each time we update its parameters and `shuffle`
+tells the iterator to randomize the order in which examples are shown to the model.
+
+
+```python
+train_iter = mx.io.NDArrayIter(train_data,train_label, batch_size, shuffle=True,label_name='lin_reg_label')
+eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)
+```
+
+In the above example, we have made use of `NDArrayIter`, which is useful for iterating
+over both numpy ndarrays and MXNet NDArrays. In general, there are different types of iterators in
+MXNet and you can use one based on the type of data you are processing.
+Documentation for iterators can be found [here](http://mxnet.io/api/python/io.html).
+
+## MXNet Classes
+
+1. **IO:** The IO class as we already saw works on the data and carries out
+ operations such as feeding data in batches and shuffling.
+
+2. **Symbol:** The actual MXNet neural network is composed using symbols. MXNet has
+ different types of symbols, including variable placeholders for input data,
+ neural network layers, and operators that manipulate NDArrays.
+
+3. **Module:** The module class in MXNet is used to define the overall computation.
+ It is initialized with the model we want to train, the training inputs (data and labels)
+ and some additional parameters such as learning rate and the optimization
+ algorithm to use.
+
+## Defining the Model
+
+MXNet uses **Symbols** for defining a model. Symbols are the building blocks
+and make up various components of the model. Symbols are used to define:
+
+1. **Variables:** A variable is a placeholder for future data. This symbol is used
+ to define a spot which will be filled with training data/labels in the future
+ when we commence training.
+2. **Neural Network Layers:** The layers of a network or any other type of model are
+ also defined by Symbols. Such a symbol takes one or more previous symbols as
+ inputs, performs some transformations on them, and creates one or more outputs.
+ One such example is the `FullyConnected` symbol which specifies a fully connected
+ layer of a neural network.
+3. **Outputs:** Output symbols are MXNet's way of defining a loss. They are
+ suffixed with the word "Output" (eg. the `SoftmaxOutput` layer). You can also
+ [create your own loss function](https://github.com/dmlc/mxnet/blob/master/docs/tutorials/r/CustomLossFunction.md#how-to-use-your-own-loss-function).
+ Some examples of existing losses are: `LinearRegressionOutput`, which computes
+ the l2-loss between it's input symbol and the labels provided to it;
+ `SoftmaxOutput`, which computes the categorical cross-entropy.
+
+The ones described above and other symbols are chained together with the output of
+one symbol serving as input to the next to build the network topology. More information
+about the different types of symbols can be found [here](http://mxnet.io/api/python/symbol.html).
+
+```python
+X = mx.sym.Variable('data')
+Y = mx.symbol.Variable('lin_reg_label')
+fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden = 1)
+lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro")
+```
+
+The above network uses the following layers:
+
+1. `FullyConnected`: The fully connected symbol represents a fully connected layer
+ of a neural network (without any activation being applied), which in essence,
+ is just a linear regression on the input attributes. It takes the following
+ parameters:
+
+ - `data`: Input to the layer (specifies the symbol whose output should be fed here)
+ - `num_hidden`: Number of hidden neurons in the layer, which is same as the dimensionality
+ of the layer's output
+
+2. `LinearRegressionOutput`: Output layers in MXNet compute training loss, which is
+ the measure of inaccuracy in the model's predictions. The goal of training is to minimize the
+ training loss. In our example, the `LinearRegressionOutput` layer computes the *l2* loss against
+ its input and the labels provided to it. The parameters to this layer are:
+
+ - `data`: Input to this layer (specifies the symbol whose output should be fed here)
+ - `label`: The training labels against which we will compare the input to the layer for calculation of l2 loss
+
+**Note on naming convention:** the label variable's name should be the same as the
+`label_name` parameter passed to your training data iterator. The default value of
+this is `softmax_label`, but we have updated it to `lin_reg_label` in this
+tutorial as you can see in `Y = mx.symbol.Variable('lin_reg_label')` and
+`train_iter = mx.io.NDArrayIter(..., label_name='lin_reg_label')`.
+
+Finally, the network is input to a *Module*, where we specify the symbol
+whose output needs to be minimized (in our case, `lro` or the `lin_reg_output`), the
+learning rate to be used while optimization and the number of epochs we want to
+train our model for.
+
+```python
+model = mx.mod.Module(
+ symbol = lro ,
+ data_names=['data'],
+ label_names = ['lin_reg_label']# network structure
+)
+```
+
+We can visualize the network we created by plotting it:
+
+```python
+mx.viz.plot_network(symbol=lro)
+```
+
+## Training the model
+
+Once we have defined the model structure, the next step is to train the
+parameters of the model to fit the training data. This is accomplished using the
+`fit()` function of the `Module` class.
+
+```python
+model.fit(train_iter, eval_iter,
+ optimizer_params={'learning_rate':0.005, 'momentum': 0.9},
+ num_epoch=50,
+ eval_metric='mse',
+ batch_end_callback = mx.callback.Speedometer(batch_size, 2))
+```
+
+## Using a trained model: (Testing and Inference)
+
+Once we have a trained model, we can do a couple of things with it - we can either
+use it for inference or we can evaluate the trained model on test data. The latter is shown below:
+
+```python
+model.predict(eval_iter).asnumpy()
+```
+
+We can also evaluate our model according to some metric. In this example, we are
+evaluating our model's mean squared error (MSE) on the evaluation data.
+
+```python
+metric = mx.metric.MSE()
+model.score(eval_iter, metric)
+```
+
+Let us try and add some noise to the evaluation data and see how the MSE changes:
+
+```python
+eval_data = np.array([[7,2],[6,10],[12,2]])
+eval_label = np.array([11.1,26.1,16.1]) #Adding 0.1 to each of the values
+eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)
+model.score(eval_iter, metric)
+```
+
+We can also create a custom metric and use it to evaluate a model. More
+information on metrics can be found in the [API documentation](http://mxnet.io/api/python/model.html#evaluation-metric-api-reference).
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/python/matrix_factorization.txt b/versions/master/_sources/tutorials/python/matrix_factorization.txt
new file mode 100644
index 0000000..d75da6a
--- /dev/null
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@@ -0,0 +1,261 @@
+# Matrix Factorization
+
+In a recommendation system, there is a group of users and a set of items. Given
+that each users have rated some items in the system, we would like to predict
+how the users would rate the items that they have not yet rated, such that we
+can make recommendations to the users.
+
+Matrix factorization is one of the mainly used algorithm in recommendation
+systems. It can be used to discover latent features underlying the interactions
+between two different kinds of entities.
+
+Assume we assign a k-dimensional vector to each user and a k-dimensional vector
+to each item such that the dot product of these two vectors gives the user's
+rating of that item. We can learn the user and item vectors directly, which is
+essentially performing SVD on the user-item matrix. We can also try to learn the
+latent features using multi-layer neural networks.
+
+In this tutorial, we will work though the steps to implement these ideas in
+MXNet.
+
+## Prepare Data
+
+We use the [MovieLens](http://grouplens.org/datasets/movielens/) data here, but
+it can apply to other datasets as well. Each row of this dataset contains a
+tuple of user id, movie id, rating, and time stamp, we will only use the first
+three items. We first define the a batch which contains n tuples. It also
+provides name and shape information to MXNet about the data and label.
+
+
+```python
+class Batch(object):
+ def __init__(self, data_names, data, label_names, label):
+ self.data = data
+ self.label = label
+ self.data_names = data_names
+ self.label_names = label_names
+
+ @property
+ def provide_data(self):
+ return [(n, x.shape) for n, x in zip(self.data_names, self.data)]
+
+ @property
+ def provide_label(self):
+ return [(n, x.shape) for n, x in zip(self.label_names, self.label)]
+```
+
+Then we define a data iterator, which returns a batch of tuples each time.
+
+
+```python
+import mxnet as mx
+import random
+
+class Batch(object):
+ def __init__(self, data_names, data, label_names, label):
+ self.data = data
+ self.label = label
+ self.data_names = data_names
+ self.label_names = label_names
+
+ @property
+ def provide_data(self):
+ return [(n, x.shape) for n, x in zip(self.data_names, self.data)]
+
+ @property
+ def provide_label(self):
+ return [(n, x.shape) for n, x in zip(self.label_names, self.label)]
+
+class DataIter(mx.io.DataIter):
+ def __init__(self, fname, batch_size):
+ super(DataIter, self).__init__()
+ self.batch_size = batch_size
+ self.data = []
+ for line in file(fname):
+ tks = line.strip().split('\t')
+ if len(tks) != 4:
+ continue
+ self.data.append((int(tks[0]), int(tks[1]), float(tks[2])))
+ self.provide_data = [('user', (batch_size, )), ('item', (batch_size, ))]
+ self.provide_label = [('score', (self.batch_size, ))]
+
+ def __iter__(self):
+ for k in range(len(self.data) / self.batch_size):
+ users = []
+ items = []
+ scores = []
+ for i in range(self.batch_size):
+ j = k * self.batch_size + i
+ user, item, score = self.data[j]
+ users.append(user)
+ items.append(item)
+ scores.append(score)
+
+ data_all = [mx.nd.array(users), mx.nd.array(items)]
+ label_all = [mx.nd.array(scores)]
+ data_names = ['user', 'item']
+ label_names = ['score']
+
+ data_batch = Batch(data_names, data_all, label_names, label_all)
+ yield data_batch
+
+ def reset(self):
+ random.shuffle(self.data)
+```
+
+Now we download the data and provide a function to obtain the data iterator:
+
+
+```python
+import os
+import urllib
+import zipfile
+if not os.path.exists('ml-100k.zip'):
+ urllib.urlretrieve('http://files.grouplens.org/datasets/movielens/ml-100k.zip', 'ml-100k.zip')
+with zipfile.ZipFile("ml-100k.zip","r") as f:
+ f.extractall("./")
+def get_data(batch_size):
+ return (DataIter('./ml-100k/u1.base', batch_size), DataIter('./ml-100k/u1.test', batch_size))
+```
+
+Finally we calculate the numbers of users and items for later use.
+
+```python
+def max_id(fname):
+ mu = 0
+ mi = 0
+ for line in file(fname):
+ tks = line.strip().split('\t')
+ if len(tks) != 4:
+ continue
+ mu = max(mu, int(tks[0]))
+ mi = max(mi, int(tks[1]))
+ return mu + 1, mi + 1
+max_user, max_item = max_id('./ml-100k/u.data')
+(max_user, max_item)
+```
+
+## Optimization
+
+We first implement the RMSE (root-mean-square error) measurement, which is
+commonly used by matrix factorization.
+
+```python
+import math
+def RMSE(label, pred):
+ ret = 0.0
+ n = 0.0
+ pred = pred.flatten()
+ for i in range(len(label)):
+ ret += (label[i] - pred[i]) * (label[i] - pred[i])
+ n += 1.0
+ return math.sqrt(ret / n)
+```
+
+Then we define a general training module, which is borrowed from the image
+classification application.
+
+```python
+def train(network, batch_size, num_epoch, learning_rate):
+ model = mx.model.FeedForward(
+ ctx = mx.gpu(0),
+ symbol = network,
+ num_epoch = num_epoch,
+ learning_rate = learning_rate,
+ wd = 0.0001,
+ momentum = 0.9)
+
+ batch_size = 64
+ train, test = get_data(batch_size)
+
+ import logging
+ head = '%(asctime)-15s %(message)s'
+ logging.basicConfig(level=logging.DEBUG)
+
+ model.fit(X = train,
+ eval_data = test,
+ eval_metric = RMSE,
+ batch_end_callback=mx.callback.Speedometer(batch_size, 20000/batch_size),)
+```
+
+## Networks
+
+Now we try various networks. We first learn the latent vectors directly.
+
+```python
+def plain_net(k):
+ # input
+ user = mx.symbol.Variable('user')
+ item = mx.symbol.Variable('item')
+ score = mx.symbol.Variable('score')
+ # user feature lookup
+ user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)
+ # item feature lookup
+ item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)
+ # predict by the inner product, which is elementwise product and then sum
+ pred = user * item
+ pred = mx.symbol.sum_axis(data = pred, axis = 1)
+ pred = mx.symbol.Flatten(data = pred)
+ # loss layer
+ pred = mx.symbol.LinearRegressionOutput(data = pred, label = score)
+ return pred
+
+train(plain_net(64), batch_size=64, num_epoch=10, learning_rate=.05)
+```
+
+Next we try to use 2 layers neural network to learn the latent variables, which stack a fully connected layer above the embedding layers:
+
+```python
+def get_one_layer_mlp(hidden, k):
+ # input
+ user = mx.symbol.Variable('user')
+ item = mx.symbol.Variable('item')
+ score = mx.symbol.Variable('score')
+ # user latent features
+ user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)
+ user = mx.symbol.Activation(data = user, act_type="relu")
+ user = mx.symbol.FullyConnected(data = user, num_hidden = hidden)
+ # item latent features
+ item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)
+ item = mx.symbol.Activation(data = item, act_type="relu")
+ item = mx.symbol.FullyConnected(data = item, num_hidden = hidden)
+ # predict by the inner product
+ pred = user * item
+ pred = mx.symbol.sum_axis(data = pred, axis = 1)
+ pred = mx.symbol.Flatten(data = pred)
+ # loss layer
+ pred = mx.symbol.LinearRegressionOutput(data = pred, label = score)
+ return pred
+
+train(get_one_layer_mlp(64, 64), batch_size=64, num_epoch=10, learning_rate=.05)
+```
+
+Adding dropout layers to relief the over-fitting.
+
+```python
+def get_one_layer_dropout_mlp(hidden, k):
+ # input
+ user = mx.symbol.Variable('user')
+ item = mx.symbol.Variable('item')
+ score = mx.symbol.Variable('score')
+ # user latent features
+ user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)
+ user = mx.symbol.Activation(data = user, act_type="relu")
+ user = mx.symbol.FullyConnected(data = user, num_hidden = hidden)
+ user = mx.symbol.Dropout(data=user, p=0.5)
+ # item latent features
+ item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)
+ item = mx.symbol.Activation(data = item, act_type="relu")
+ item = mx.symbol.FullyConnected(data = item, num_hidden = hidden)
+ item = mx.symbol.Dropout(data=item, p=0.5)
+ # predict by the inner product
+ pred = user * item
+ pred = mx.symbol.sum_axis(data = pred, axis = 1)
+ pred = mx.symbol.Flatten(data = pred)
+ # loss layer
+ pred = mx.symbol.LinearRegressionOutput(data = pred, label = score)
+ return pred
+train(get_one_layer_mlp(256, 512), batch_size=64, num_epoch=10, learning_rate=.05)
+```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/python/mnist.txt b/versions/master/_sources/tutorials/python/mnist.txt
new file mode 100644
index 0000000..4fdf372
--- /dev/null
+++ b/versions/master/_sources/tutorials/python/mnist.txt
@@ -0,0 +1,199 @@
+# Handwritten Digit Recognition
+
+In this tutorial, we'll give you a step by step walk-through of how to build a hand-written digit classifier using the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset. For someone new to deep learning, this exercise is arguably the "Hello World" equivalent.
+
+MNIST is a widely used dataset for the hand-written digit classification task. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). The task at hand is to train a model using the 60,000 training images and subsequently test its classification accuracy on the 10,000 test images.
+
+
+
+**Figure 1:** Sample images from the MNIST dataset.
+
+## Prerequisites
+To complete this tutorial, we need:
+
+- MXNet version 0.10 or later. See the installation instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html).
+
+- [Python Requests](http://docs.python-requests.org/en/master/) and [Jupyter Notebook](http://jupyter.org/index.html).
+
+```
+$ pip install requests jupyter
+```
+
+## Loading Data
+
+Before we define the model, let's first fetch the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset.
+
+The following source code downloads and loads the images and the corresponding labels into memory.
+
+```python
+import mxnet as mx
+mnist = mx.test_utils.get_mnist()
+```
+
+After running the above source code, the entire MNIST dataset should be fully loaded into memory. Note that for large datasets it is not feasible to pre-load the entire dataset first like we did here. What is needed is a mechanism by which we can quickly and efficiently stream data directly from the source. MXNet Data iterators come to the rescue here by providing exactly that. Data iterator is the mechanism by which we feed input data into an MXNet training algorithm and they are very s [...]
+
+Image batches are commonly represented by a 4-D array with shape `(batch_size, num_channels, width, height)`. For the MNIST dataset, since the images are grayscale, there is only one color channel. Also, the images are 28x28 pixels, and so each image has width and height equal to 28. Therefore, the shape of input is `(batch_size, 1, 28, 28)`. Another important consideration is the order of input samples. When feeding training examples, it is critical that we don't feed samples with the s [...]
+Data iterators take care of this by randomly shuffling the inputs. Note that we only need to shuffle the training data. The order does not matter for test data.
+
+The following source code initializes the data iterators for the MNIST dataset. Note that we initialize two iterators: one for train data and one for test data.
+
+```python
+batch_size = 100
+train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
+val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+```
+
+## Training
+We will cover a couple of approaches for performing the hand written digit recognition task. The first approach makes use of a traditional deep neural network architecture called Multilayer Percepton (MLP). We'll discuss its drawbacks and use that as a motivation to introduce a second more advanced approach called Convolution Neural Network (CNN) that has proven to work very well for image classification tasks.
+
+### Multilayer Perceptron
+
+The first approach makes use of a [Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron) to solve this problem. We'll define the MLP using MXNet's symbolic interface. We begin by creating a place holder variable for the input data. When working with an MLP, we need to flatten our 28x28 images into a flat 1-D structure of 784 (28 * 28) raw pixel values. The order of pixel values in the flattened vector does not matter as long as we are being consistent about how we d [...]
+
+```python
+data = mx.sym.var('data')
+# Flatten the data from 4-D shape into 2-D (batch_size, num_channel*width*height)
+data = mx.sym.flatten(data=data)
+```
+One might wonder if we are discarding valuable information by flattening. That is indeed true and we'll cover this more when we talk about convolutional neural networks where we preserve the input shape. For now, we'll go ahead and work with flattened images.
+
+MLPs contains several fully connected layers. A fully connected layer or FC layer for short, is one where each neuron in the layer is connected to every neuron in its preceding layer. From a linear algebra perspective, an FC layer applies an [affine transform](https://en.wikipedia.org/wiki/Affine_transformation) to the *n x m* input matrix *X* and outputs a matrix *Y* of size *n x k*, where *k* is the number of neurons in the FC layer. *k* is also referred to as the hidden size. The outp [...]
+
+
+In an MLP, the outputs of most FC layers are fed into an activation function, which applies an element-wise non-linearity. This step is critical and it gives neural networks the ability to classify inputs that are not linearly separable. Common choices for activation functions are sigmoid, tanh, and [rectified linear unit](https://en.wikipedia.org/wiki/Rectifier_%28neural_networks%29) (ReLU). In this example, we'll use the ReLU activation function which has several desirable properties a [...]
+
+The following code declares two fully connected layers with 128 and 64 neurons each. Furthermore, these FC layers are sandwiched between ReLU activation layers each one responsible for performing an element-wise ReLU transformation on the FC layer output.
+
+```python
+# The first fully-connected layer and the corresponding activation function
+fc1 = mx.sym.FullyConnected(data=data, num_hidden=128)
+act1 = mx.sym.Activation(data=fc1, act_type="relu")
+
+# The second fully-connected layer and the corresponding activation function
+fc2 = mx.sym.FullyConnected(data=act1, num_hidden = 64)
+act2 = mx.sym.Activation(data=fc2, act_type="relu")
+```
+
+The last fully connected layer often has its hidden size equal to the number of output classes in the dataset. The activation function for this layer will be the softmax function. The Softmax layer maps its input to a probability score for each class of output. During the training stage, a loss function computes the [cross entropy](https://en.wikipedia.org/wiki/Cross_entropy) between the probability distribution (softmax output) predicted by the network and the true probability distribut [...]
+
+The following source code declares the final fully connected layer of size 10. 10 incidentally is the total number of digits. The output from this layer is fed into a `SoftMaxOutput` layer that performs softmax and cross-entropy loss computation in one go. Note that loss computation only happens during training.
+
+```python
+# MNIST has 10 classes
+fc3 = mx.sym.FullyConnected(data=act2, num_hidden=10)
+# Softmax with cross entropy loss
+mlp = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
+```
+
+
+**Figure 2:** MLP network architecture for MNIST.
+
+Now that both the data iterator and neural network are defined, we can commence training. Here we'll employ the `module` feature in MXNet which provides a high-level abstraction for running training and inference on predefined networks. The module API allows the user to specify appropriate parameters that control how the training proceeds.
+
+The following source code initializes a module to train the MLP network we defined above. For our training, we will make use of the stochastic gradient descent (SGD) optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes train data one example at a time. In practice, this is very slow and one can speed up the process by processing examples in small batches. In this case, our batch size will be 100, which is a reasonable choice. Another parameter we select here is [...]
+
+Typically, one runs the training until convergence, which means that we have learned a good set of model parameters (weights + biases) from the train data. For the purpose of this tutorial, we'll run training for 10 epochs and stop. An epoch is one full pass over the entire train data.
+
+```python
+import logging
+logging.getLogger().setLevel(logging.DEBUG) # logging to stdout
+# create a trainable module on CPU
+mlp_model = mx.mod.Module(symbol=mlp, context=mx.cpu())
+mlp_model.fit(train_iter, # train data
+ eval_data=val_iter, # validation data
+ optimizer='sgd', # use SGD to train
+ optimizer_params={'learning_rate':0.1}, # use fixed learning rate
+ eval_metric='acc', # report accuracy during training
+ batch_end_callback = mx.callback.Speedometer(batch_size, 100), # output progress for each 100 data batches
+ num_epoch=10) # train for at most 10 dataset passes
+```
+
+### Prediction
+
+After the above training completes, we can evaluate the trained model by running predictions on test data. The following source code computes the prediction probability scores for each test image. *prob[i][j]* is the probability that the *i*-th test image contains the *j*-th output class.
+
+```python
+test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
+prob = mlp_model.predict(test_iter)
+assert prob.shape == (10000, 10)
+```
+
+Since the dataset also has labels for all test images, we can compute the accuracy metric as follows:
+
+```python
+test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+# predict accuracy of mlp
+acc = mx.metric.Accuracy()
+mlp_model.score(test_iter, acc)
+print(acc)
+assert acc.get()[1] > 0.96
+```
+If everything went well, we should see an accuracy value that is around 0.96, which means that we are able to accurately predict the digit in 96% of test images. This is a pretty good result. But as we will see in the next part of this tutorial, we can do a lot better than that.
+
+### Convolutional Neural Network
+
+Earlier, we briefly touched on a drawback of MLP when we said we need to discard the input image's original shape and flatten it as a vector before we can feed it as input to the MLP's first fully connected layer. Turns out this is an important issue because we don't take advantage of the fact that pixels in the image have natural spatial correlation along the horizontal and vertical axes. A convolutional neural network (CNN) aims to address this problem by using a more structured weight [...]
+
+A single convolution layer consists of one or more filters that each play the role of a feature detector. During training, a CNN learns appropriate representations (parameters) for these filters. Similar to MLP, the output from the convolutional layer is transformed by applying a non-linearity. Besides the convolutional layer, another key aspect of a CNN is the pooling layer. A pooling layer serves to make the CNN translation invariant: a digit remains the same even when it is shifted le [...]
+
+The following source code defines a convolutional neural network architecture called LeNet. LeNet is a popular network known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with tanh activations for the neurons
+
+```python
+data = mx.sym.var('data')
+# first conv layer
+conv1 = mx.sym.Convolution(data=data, kernel=(5,5), num_filter=20)
+tanh1 = mx.sym.Activation(data=conv1, act_type="tanh")
+pool1 = mx.sym.Pooling(data=tanh1, pool_type="max", kernel=(2,2), stride=(2,2))
+# second conv layer
+conv2 = mx.sym.Convolution(data=pool1, kernel=(5,5), num_filter=50)
+tanh2 = mx.sym.Activation(data=conv2, act_type="tanh")
+pool2 = mx.sym.Pooling(data=tanh2, pool_type="max", kernel=(2,2), stride=(2,2))
+# first fullc layer
+flatten = mx.sym.flatten(data=pool2)
+fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500)
+tanh3 = mx.sym.Activation(data=fc1, act_type="tanh")
+# second fullc
+fc2 = mx.sym.FullyConnected(data=tanh3, num_hidden=10)
+# softmax loss
+lenet = mx.sym.SoftmaxOutput(data=fc2, name='softmax')
+```
+
+
+**Figure 3:** First conv + pooling layer in LeNet.
+
+Now we train LeNet with the same hyper-parameters as before. Note that, if a GPU is available, we recommend using it. This greatly speeds up computation given that LeNet is more complex and compute-intensive than the previous multilayer perceptron. To do so, we only need to change `mx.cpu()` to `mx.gpu()` and MXNet takes care of the rest. Just like before, we'll stop training after 10 epochs.
+
+```python
+# create a trainable module on GPU 0
+lenet_model = mx.mod.Module(symbol=lenet, context=mx.cpu())
+# train with the same
+lenet_model.fit(train_iter,
+ eval_data=val_iter,
+ optimizer='sgd',
+ optimizer_params={'learning_rate':0.1},
+ eval_metric='acc',
+ batch_end_callback = mx.callback.Speedometer(batch_size, 100),
+ num_epoch=10)
+```
+
+### Prediction
+
+Finally, we'll use the trained LeNet model to generate predictions for the test data.
+
+```python
+test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
+prob = lenet_model.predict(test_iter)
+test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+# predict accuracy for lenet
+acc = mx.metric.Accuracy()
+lenet_model.score(test_iter, acc)
+print(acc)
+assert acc.get()[1] > 0.98
+```
+If all went well, we should see a higher accuracy metric for predictions made using LeNet. With CNN we should be able to correctly predict around 98% of all test images.
+
+## Summary
+
+In this tutorial, we have learned how to use MXNet to solve a standard computer vision problem: classifying images of hand written digits. You have seen how to quickly and easily build, train and evaluate models such as MLP and CNN with MXNet.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/python/predict_image.txt b/versions/master/_sources/tutorials/python/predict_image.txt
new file mode 100644
index 0000000..1c6cfa8
--- /dev/null
+++ b/versions/master/_sources/tutorials/python/predict_image.txt
@@ -0,0 +1,138 @@
+# Predict with pre-trained models
+
+This tutorial explains how to recognize objects in an image with a
+pre-trained model, and how to perform feature extraction.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html)
+
+- [Python Requests](http://docs.python-requests.org/en/master/), [Matplotlib](https://matplotlib.org/) and [Jupyter Notebook](http://jupyter.org/index.html).
+
+```
+$ pip install requests matplotlib jupyter
+```
+
+## Loading
+
+We first download a pre-trained ResNet 152 layer that is trained on the full
+ImageNet dataset with over 10 million images and 10 thousand classes. A
+pre-trained model contains two parts, a json file containing the model
+definition and a binary file containing the parameters. In addition, there may be
+a text file for the labels.
+
+```python
+import mxnet as mx
+path='http://data.mxnet.io/models/imagenet-11k/'
+[mx.test_utils.download(path+'resnet-152/resnet-152-symbol.json'),
+ mx.test_utils.download(path+'resnet-152/resnet-152-0000.params'),
+ mx.test_utils.download(path+'synset.txt')]
+```
+
+Next, we load the downloaded model. *Note:* If GPU is available, we can replace all
+occurrences of `mx.cpu()` with `mx.gpu()` to accelerate the computation.
+
+```python
+sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-152', 0)
+mod = mx.mod.Module(symbol=sym, context=mx.cpu(), label_names=None)
+mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))],
+ label_shapes=mod._label_shapes)
+mod.set_params(arg_params, aux_params, allow_missing=True)
+with open('synset.txt', 'r') as f:
+ labels = [l.rstrip() for l in f]
+```
+
+## Predicting
+
+We first define helper functions for downloading an image and performing the
+prediction:
+
+```python
+%matplotlib inline
+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+# define a simple data batch
+from collections import namedtuple
+Batch = namedtuple('Batch', ['data'])
+
+def get_image(url, show=False):
+ # download and show the image
+ fname = mx.test_utils.download(url)
+ img = cv2.cvtColor(cv2.imread(fname), cv2.COLOR_BGR2RGB)
+ if img is None:
+ return None
+ if show:
+ plt.imshow(img)
+ plt.axis('off')
+ # convert into format (batch, RGB, width, height)
+ img = cv2.resize(img, (224, 224))
+ img = np.swapaxes(img, 0, 2)
+ img = np.swapaxes(img, 1, 2)
+ img = img[np.newaxis, :]
+ return img
+
+def predict(url):
+ img = get_image(url, show=True)
+ # compute the predict probabilities
+ mod.forward(Batch([mx.nd.array(img)]))
+ prob = mod.get_outputs()[0].asnumpy()
+ # print the top-5
+ prob = np.squeeze(prob)
+ a = np.argsort(prob)[::-1]
+ for i in a[0:5]:
+ print('probability=%f, class=%s' %(prob[i], labels[i]))
+```
+
+Now, we can perform prediction with any downloadable URL:
+
+```python
+predict('http://writm.com/wp-content/uploads/2016/08/Cat-hd-wallpapers.jpg')
+```
+
+```python
+predict('http://thenotoriouspug.com/wp-content/uploads/2015/01/Pug-Cookie-1920x1080-1024x576.jpg')
+```
+
+## Feature extraction
+
+By feature extraction, we mean presenting the input images by the output of an
+internal layer rather than the last softmax layer. These outputs, which can be
+viewed as the feature of the raw input image, can then be used by other
+applications such as object detection.
+
+We can use the ``get_internals`` method to get all internal layers from a
+Symbol.
+
+```python
+# list the last 10 layers
+all_layers = sym.get_internals()
+all_layers.list_outputs()[-10:]
+```
+
+An often used layer for feature extraction is the one before the last fully
+connected layer. For ResNet, and also Inception, it is the flattened layer with
+name `flatten0` which reshapes the 4-D convolutional layer output into 2-D for
+the fully connected layer. The following source code extracts a new Symbol which
+outputs the flattened layer and creates a model.
+
+```python
+fe_sym = all_layers['flatten0_output']
+fe_mod = mx.mod.Module(symbol=fe_sym, context=mx.cpu(), label_names=None)
+fe_mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])
+fe_mod.set_params(arg_params, aux_params)
+```
+
+We can now invoke `forward` to obtain the features:
+
+```python
+img = get_image('http://writm.com/wp-content/uploads/2016/08/Cat-hd-wallpapers.jpg')
+fe_mod.forward(Batch([mx.nd.array(img)]))
+features = fe_mod.get_outputs()[0].asnumpy()
+print(features)
+assert features.shape == (1, 2048)
+```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/versions/master/_sources/tutorials/r/CallbackFunctionTutorial.txt b/versions/master/_sources/tutorials/r/CallbackFunctionTutorial.txt
new file mode 100644
index 0000000..103352d
--- /dev/null
+++ b/versions/master/_sources/tutorials/r/CallbackFunctionTutorial.txt
@@ -0,0 +1,254 @@
+Callback Function
+======================================
+
+This tutorial provides guidelines for using and writing callback functions,
+which can very useful in model training.
+
+Model Training Example
+----------
+
+Let's begin with a small example. We can build and train a model with the following code:
+
+
+ ```r
+ library(mxnet)
+ data(BostonHousing, package="mlbench")
+ train.ind = seq(1, 506, 3)
+ train.x = data.matrix(BostonHousing[train.ind, -14])
+ train.y = BostonHousing[train.ind, 14]
+ test.x = data.matrix(BostonHousing[-train.ind, -14])
+ test.y = BostonHousing[-train.ind, 14]
+ data <- mx.symbol.Variable("data")
+ fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)
+ lro <- mx.symbol.LinearRegressionOutput(fc1)
+ mx.set.seed(0)
+ model <- mx.model.FeedForward.create(
+ lro, X=train.x, y=train.y,
+ eval.data=list(data=test.x, label=test.y),
+ ctx=mx.cpu(), num.round=10, array.batch.size=20,
+ learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse)
+ ```
+
+ ```
+ ## Auto detect layout of input matrix, use row major..
+ ## Start training with 1 devices
+ ## [1] Train-rmse=16.063282524034
+ ## [1] Validation-rmse=10.1766446093622
+ ## [2] Train-rmse=12.2792375712573
+ ## [2] Validation-rmse=12.4331776190813
+ ## [3] Train-rmse=11.1984634005885
+ ## [3] Validation-rmse=10.3303041888193
+ ## [4] Train-rmse=10.2645236892904
+ ## [4] Validation-rmse=8.42760407903415
+ ## [5] Train-rmse=9.49711005504284
+ ## [5] Validation-rmse=8.44557808483234
+ ## [6] Train-rmse=9.07733734175182
+ ## [6] Validation-rmse=8.33225500266177
+ ## [7] Train-rmse=9.07884450847991
+ ## [7] Validation-rmse=8.38827833418459
+ ## [8] Train-rmse=9.10463850277417
+ ## [8] Validation-rmse=8.37394452365264
+ ## [9] Train-rmse=9.03977049028532
+ ## [9] Validation-rmse=8.25927979725672
+ ## [10] Train-rmse=8.96870685004475
+ ## [10] Validation-rmse=8.19509291481822
+ ```
+
+We also provide two optional parameters, `batch.end.callback` and `epoch.end.callback`, which can provide great flexibility in model training.
+
+How to Use Callback Functions
+---------
+
+This package provides two callback functions:
+
+- `mx.callback.save.checkpoint` saves a checkpoint to files during each period iteration.
+
+```r
+ model <- mx.model.FeedForward.create(
+ lro, X=train.x, y=train.y,
+ eval.data=list(data=test.x, label=test.y),
+ ctx=mx.cpu(), num.round=10, array.batch.size=20,
+ learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
+ epoch.end.callback = mx.callback.save.checkpoint("boston"))
+```
+
+```
+ ## Auto detect layout of input matrix, use row major..
+ ## Start training with 1 devices
+ ## [1] Train-rmse=19.1621424021617
+ ## [1] Validation-rmse=20.721515592165
+ ## Model checkpoint saved to boston-0001.params
+ ## [2] Train-rmse=13.5127391952367
+ ## [2] Validation-rmse=14.1822123675007
+ ## Model checkpoint saved to boston-0002.params
+```
+
+
+- `mx.callback.log.train.metric` logs a training metric each period. You can use it either as a `batch.end.callback` or an
+`epoch.end.callback`.
+
+
+```r
+ model <- mx.model.FeedForward.create(
+ lro, X=train.x, y=train.y,
+ eval.data=list(data=test.x, label=test.y),
+ ctx=mx.cpu(), num.round=10, array.batch.size=20,
+ learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
+ batch.end.callback = mx.callback.log.train.metric(5))
+ ```
+
+```
+ ## Auto detect layout of input matrix, use row major..
+ ## Start training with 1 devices
+ ## Batch [5] Train-rmse=17.6514558545416
+ ## [1] Train-rmse=15.2879610219001
+ ## [1] Validation-rmse=12.3332062820921
+ ## Batch [5] Train-rmse=11.939392828565
+ ## [2] Train-rmse=11.4382242547217
+ ## [2] Validation-rmse=9.91176550103181
+ ............
+```
+
+You also can save the training and evaluation errors for later use by passing a reference class:
+
+
+ ```r
+ logger <- mx.metric.logger$new()
+ model <- mx.model.FeedForward.create(
+ lro, X=train.x, y=train.y,
+ eval.data=list(data=test.x, label=test.y),
+ ctx=mx.cpu(), num.round=10, array.batch.size=20,
+ learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
+ epoch.end.callback = mx.callback.log.train.metric(5, logger))
+ ```
+
+ ```
+ ## Auto detect layout of input matrix, use row major..
+ ## Start training with 1 devices
+ ## [1] Train-rmse=19.1083228733256
+ ## [1] Validation-rmse=12.7150687428974
+ ## [2] Train-rmse=15.7684378116157
+ ## [2] Validation-rmse=14.8105319420491
+ ............
+ ```
+
+ ```r
+ head(logger$train)
+ ```
+
+ ```
+ ## [1] 19.108323 15.768438 13.531470 11.386050 9.555477 9.351324
+ ```
+
+ ```r
+ head(logger$eval)
+ ```
+
+ ```
+ ## [1] 12.715069 14.810532 15.840361 10.898733 9.349706 9.363087
+ ```
+
+How to Write Your Own Callback Functions
+----------
+
+You can find the source code for the two callback functions on [GitHub](https://github.com/dmlc/mxnet/blob/master/R-package/R/callback.R) and use it as a template:
+
+Basically, all callback functions follow the following structure:
+
+
+ ```r
+ mx.callback.fun <- function() {
+ function(iteration, nbatch, env) {
+ }
+ }
+ ```
+
+The following `mx.callback.save.checkpoint` function is stateless. It gets the model from the environment and saves it:.
+
+
+ ```r
+ mx.callback.save.checkpoint <- function(prefix, period=1) {
+ function(iteration, nbatch, env) {
+ if (iteration %% period == 0) {
+ mx.model.save(env$model, prefix, iteration)
+ cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration))
+ }
+ return(TRUE)
+ }
+ }
+ ```
+
+The `mx.callback.log.train.metric` is a little more complex. It holds a reference class and updates it during the training
+process:
+
+
+ ```r
+ mx.callback.log.train.metric <- function(period, logger=NULL) {
+ function(iteration, nbatch, env) {
+ if (nbatch %% period == 0 && !is.null(env$metric)) {
+ result <- env$metric$get(env$train.metric)
+ if (nbatch != 0)
+ cat(paste0("Batch [", nbatch, "] Train-", result$name, "=", result$value, "\n"))
+ if (!is.null(logger)) {
+ if (class(logger) != "mx.metric.logger") {
+ stop("Invalid mx.metric.logger.")
+ }
+ logger$train <- c(logger$train, result$value)
+ if (!is.null(env$eval.metric)) {
+ result <- env$metric$get(env$eval.metric)
+ if (nbatch != 0)
+ cat(paste0("Batch [", nbatch, "] Validation-", result$name, "=", result$value, "\n"))
+ logger$eval <- c(logger$eval, result$value)
+ }
+ }
+ }
+ return(TRUE)
+ }
+ }
+ ```
+
+Now you might be curious why both callback functions `return(TRUE)`.
+
+Can we `return(FALSE)`?
+
+Yes! You can stop the training early with `return(FALSE)`. See the following examples.
+
+
+ ```r
+ mx.callback.early.stop <- function(eval.metric) {
+ function(iteration, nbatch, env) {
+ if (!is.null(env$metric)) {
+ if (!is.null(eval.metric)) {
+ result <- env$metric$get(env$eval.metric)
+ if (result$value < eval.metric) {
+ return(FALSE)
+ }
+ }
+ }
+ return(TRUE)
+ }
+ }
+ model <- mx.model.FeedForward.create(
+ lro, X=train.x, y=train.y,
+ eval.data=list(data=test.x, label=test.y),
+ ctx=mx.cpu(), num.round=10, array.batch.size=20,
+ learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse,
+ epoch.end.callback = mx.callback.early.stop(10))
+ ```
+
+ ```
+ ## Auto detect layout of input matrix, use row major..
+ ## Start training with 1 devices
+ ## [1] Train-rmse=18.5897984387033
+ ## [1] Validation-rmse=13.5555213820571
+ ## [2] Train-rmse=12.5867564040256
+ ## [2] Validation-rmse=9.76304967080928
+ ```
+
+When the validation metric dips below the threshold we set, the training process stops.
+
+## Next Steps
+* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with a Pretrained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model Using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/versions/master/_sources/tutorials/r/CustomIteratorTutorial.txt b/versions/master/_sources/tutorials/r/CustomIteratorTutorial.txt
new file mode 100644
index 0000000..1ad634b
--- /dev/null
+++ b/versions/master/_sources/tutorials/r/CustomIteratorTutorial.txt
@@ -0,0 +1,202 @@
+Custom Iterator Tutorial
+======================================
+
+This tutorial provides a guideline on how to use and write custom iterators, which can very useful when having a dataset that does not fit into memory.
+
+Getting the data
+----------
+The data we are going to use is the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) in CSV format, the data can be found in this [web](http://pjreddie.com/projects/mnist-in-csv/).
+
+To download the data:
+
+```bash
+wget http://pjreddie.com/media/files/mnist_train.csv
+wget http://pjreddie.com/media/files/mnist_test.csv
+```
+
+You'll get two files, `mnist_train.csv` that contains 60.000 examples of hand written numbers and `mxnist_test.csv` that contains 10.000 examples. The first element of each line in the CSV is the label, which is a number between 0 and 9. The rest of the line are 784 numbers between 0 and 255, corresponding to the levels of grey of a matrix of 28x28. Therefore, each line contains an image of 28x28 pixels of a hand written number and its true label.
+
+Custom CSV Iterator
+----------
+Next we are going to create a custom CSV Iterator based on the [C++ CSVIterator class](https://github.com/dmlc/mxnet/blob/master/src/io/iter_csv.cc).
+
+For that we are going to use the R function `mx.io.CSVIter` as a base class. This class has as parameters `data.csv, data.shape, batch.size` and two main functions, `iter.next()` that calls the iterator in the next batch of data and `value()` that returns the train data and the label.
+
+The R Custom Iterator needs to inherit from the C++ data iterator class, for that we used the class `Rcpp_MXArrayDataIter` extracted with RCPP. Also, it needs to have the same parameters: `data.csv, data.shape, batch.size`. Apart from that, we can also add the field `iter`, which is the CSV Iterator that we are going to expand.
+
+```r
+CustomCSVIter <- setRefClass("CustomCSVIter",
+ fields=c("iter", "data.csv", "data.shape", "batch.size"),
+ contains = "Rcpp_MXArrayDataIter",
+ #...
+ )
+```
+
+The next step is to initialize the class. For that we call the base `mx.io.CSVIter` and fill the rest of the fields.
+
+```r
+CustomCSVIter <- setRefClass("CustomCSVIter",
+ fields=c("iter", "data.csv", "data.shape", "batch.size"),
+ contains = "Rcpp_MXArrayDataIter",
+ methods=list(
+ initialize=function(iter, data.csv, data.shape, batch.size){
+ feature_len <- data.shape*data.shape + 1
+ csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
+ .self$iter <- csv_iter
+ .self$data.csv <- data.csv
+ .self$data.shape <- data.shape
+ .self$batch.size <- batch.size
+ .self
+ },
+ #...
+ )
+ )
+```
+
+So far there is no difference between the original class and the custom class. Let's implement the function `value()`. In this case what we are going to do is transform the data that comes from the original class as an array of 785 numbers into a matrix of 28x28 and a label. We will also normalize the training data to be between 0 and 1.
+
+```r
+CustomCSVIter <- setRefClass("CustomCSVIter",
+ fields=c("iter", "data.csv", "data.shape", "batch.size"),
+ contains = "Rcpp_MXArrayDataIter",
+ methods=list(
+ initialize=function(iter, data.csv, data.shape, batch.size){
+ feature_len <- data.shape*data.shape + 1
+ csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
+ .self$iter <- csv_iter
+ .self$data.csv <- data.csv
+ .self$data.shape <- data.shape
+ .self$batch.size <- batch.size
+ .self
+ },
+ value=function(){
+ val <- as.array(.self$iter$value()$data)
+ val.x <- val[-1,]
+ val.y <- val[1,]
+ val.x <- val.x/255
+ dim(val.x) <- c(data.shape, data.shape, 1, ncol(val.x))
+ val.x <- mx.nd.array(val.x)
+ val.y <- mx.nd.array(val.y)
+ list(data=val.x, label=val.y)
+ },
+ #...
+ )
+ )
+```
+Finally we are going to add the rest of the functions needed for the training to work correctly. The final `CustomCSVIter` looks like this:
+
+```r
+CustomCSVIter <- setRefClass("CustomCSVIter",
+ fields=c("iter", "data.csv", "data.shape", "batch.size"),
+ contains = "Rcpp_MXArrayDataIter",
+ methods=list(
+ initialize=function(iter, data.csv, data.shape, batch.size){
+ feature_len <- data.shape*data.shape + 1
+ csv_iter <- mx.io.CSVIter(data.csv=data.csv, data.shape=c(feature_len), batch.size=batch.size)
+ .self$iter <- csv_iter
+ .self$data.csv <- data.csv
+ .self$data.shape <- data.shape
+ .self$batch.size <- batch.size
+ .self
+ },
+ value=function(){
+ val <- as.array(.self$iter$value()$data)
+ val.x <- val[-1,]
+ val.y <- val[1,]
+ val.x <- val.x/255
+ dim(val.x) <- c(data.shape, data.shape, 1, ncol(val.x))
+ val.x <- mx.nd.array(val.x)
+ val.y <- mx.nd.array(val.y)
+ list(data=val.x, label=val.y)
+ },
+ iter.next=function(){
+ .self$iter$iter.next()
+ },
+ reset=function(){
+ .self$iter$reset()
+ },
+ num.pad=function(){
+ .self$iter$num.pad()
+ },
+ finalize=function(){
+ .self$iter$finalize()
+ }
+ )
+ )
+```
+
+To call the class we can just do:
+
+```r
+batch.size <- 100
+train.iter <- CustomCSVIter$new(iter = NULL, data.csv = "mnist_train.csv", data.shape = 28, batch.size = batch.size)
+```
+
+CNN Model
+----------
+
+For this tutorial we are going to use the known LeNet architecture:
+
+```r
+lenet.model <- function(){
+ data <- mx.symbol.Variable('data')
+ conv1 <- mx.symbol.Convolution(data=data, kernel=c(5,5), num_filter=20) #first conv
+ tanh1 <- mx.symbol.Activation(data=conv1, act_type="tanh")
+ pool1 <- mx.symbol.Pooling(data=tanh1, pool_type="max", kernel=c(2,2), stride=c(2,2))
+ conv2 <- mx.symbol.Convolution(data=pool1, kernel=c(5,5), num_filter=50)# second conv
+ tanh2 <- mx.symbol.Activation(data=conv2, act_type="tanh")
+ pool2 <- mx.symbol.Pooling(data=tanh2, pool_type="max", kernel=c(2,2), stride=c(2,2))
+ flatten <- mx.symbol.Flatten(data=pool2)
+ fc1 <- mx.symbol.FullyConnected(data=flatten, num_hidden=100) # first fullc
+ tanh3 <- mx.symbol.Activation(data=fc1, act_type="tanh")
+ fc2 <- mx.symbol.FullyConnected(data=tanh3, num_hidden=10) # second fullc
+ network <- mx.symbol.SoftmaxOutput(data=fc2) # loss
+ network
+}
+network <- lenet.model()
+```
+
+Training with the Custom Iterator
+----------
+Finally, we can directly add the custom iterator as the training data source.
+
+```r
+model <- mx.model.FeedForward.create(symbol=network,
+ X=train.iter,
+ ctx=mx.gpu(0),
+ num.round=10,
+ array.batch.size=batch.size,
+ learning.rate=0.1,
+ momentum=0.9,
+ eval.metric=mx.metric.accuracy,
+ wd=0.00001,
+ batch.end.callback=mx.callback.log.speedometer(batch.size, frequency = 100)
+ )
+```
+
+The last 2 iterations with a K80 GPU looks like this:
+
+```bash
+[8] Train-accuracy=0.998866666666667
+Batch [100] Speed: 15413.0104454713 samples/sec Train-accuracy=0.999
+Batch [200] Speed: 16629.3412459049 samples/sec Train-accuracy=0.99935
+Batch [300] Speed: 18412.6900509319 samples/sec Train-accuracy=0.9995
+Batch [400] Speed: 16757.2882328335 samples/sec Train-accuracy=0.999425
+Batch [500] Speed: 17116.6529207406 samples/sec Train-accuracy=0.99946
+Batch [600] Speed: 19627.589505195 samples/sec Train-accuracy=0.99945
+[9] Train-accuracy=0.9991
+Batch [100] Speed: 18971.5745536982 samples/sec Train-accuracy=0.9992
+Batch [200] Speed: 15554.8822435383 samples/sec Train-accuracy=0.99955
+Batch [300] Speed: 18327.6950115053 samples/sec Train-accuracy=0.9997
+Batch [400] Speed: 17103.0705411788 samples/sec Train-accuracy=0.9997
+Batch [500] Speed: 15104.8656902394 samples/sec Train-accuracy=0.99974
+Batch [600] Speed: 13818.7899518255 samples/sec Train-accuracy=0.99975
+[10] Train-accuracy=0.99975
+```
+
+Conclusion
+----------
+
+We have shown how to create a custom CSV Iterator by extending the class `mx.io.CSVIter`. In our class, we iteratively read from a CSV file a batch of data that will be transformed and then processed in the stochastic gradient descent optimization. That way, we are able to manage CSV files that are bigger than the memory of the machine we are using.
+
+Based of this custom iterator, we can also create data loaders that internally transform or expand the data, allowing to manage files of any size.
diff --git a/versions/master/_sources/tutorials/r/CustomLossFunction.txt b/versions/master/_sources/tutorials/r/CustomLossFunction.txt
new file mode 100644
index 0000000..afb9951
--- /dev/null
+++ b/versions/master/_sources/tutorials/r/CustomLossFunction.txt
@@ -0,0 +1,206 @@
+Customized loss function
+======================================
+
+This tutorial provides guidelines for using customized loss function in network construction.
+
+Model Training Example
+----------------------
+
+Let's begin with a small regression example. We can build and train a regression model with the following code:
+
+``` r
+data(BostonHousing, package = "mlbench")
+BostonHousing[, sapply(BostonHousing, is.factor)] <-
+ as.numeric(as.character(BostonHousing[, sapply(BostonHousing, is.factor)]))
+BostonHousing <- data.frame(scale(BostonHousing))
+
+test.ind = seq(1, 506, 5) # 1 pt in 5 used for testing
+train.x = data.matrix(BostonHousing[-test.ind,-14])
+train.y = BostonHousing[-test.ind, 14]
+test.x = data.matrix(BostonHousing[--test.ind,-14])
+test.y = BostonHousing[--test.ind, 14]
+
+require(mxnet)
+```
+
+ ## Loading required package: mxnet
+
+``` r
+data <- mx.symbol.Variable("data")
+label <- mx.symbol.Variable("label")
+fc1 <- mx.symbol.FullyConnected(data, num_hidden = 14, name = "fc1")
+tanh1 <- mx.symbol.Activation(fc1, act_type = "tanh", name = "tanh1")
+fc2 <- mx.symbol.FullyConnected(tanh1, num_hidden = 1, name = "fc2")
+lro <- mx.symbol.LinearRegressionOutput(fc2, name = "lro")
+
+mx.set.seed(0)
+model <- mx.model.FeedForward.create(lro, X = train.x, y = train.y,
+ ctx = mx.cpu(),
+ num.round = 5,
+ array.batch.size = 60,
+ optimizer = "rmsprop",
+ verbose = TRUE,
+ array.layout = "rowmajor",
+ batch.end.callback = NULL,
+ epoch.end.callback = NULL)
+```
+
+ ## Start training with 1 devices
+
+``` r
+pred <- predict(model, test.x)
+```
+
+ ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum((test.y - pred[1,])^2) / length(test.y)
+```
+
+ ## [1] 0.2485236
+
+Besides the `LinearRegressionOutput`, we also provide `LogisticRegressionOutput` and `MAERegressionOutput`. However, this might not be enough for real-world models. You can provide your own loss function by using `mx.symbol.MakeLoss` when constructing the network.
+
+How to Use Your Own Loss Function
+---------------------------------
+
+We still use our previous example, but this time we use `mx.symbol.MakeLoss` to minimize the `(pred-label)^2`
+
+``` r
+data <- mx.symbol.Variable("data")
+label <- mx.symbol.Variable("label")
+fc1 <- mx.symbol.FullyConnected(data, num_hidden = 14, name = "fc1")
+tanh1 <- mx.symbol.Activation(fc1, act_type = "tanh", name = "tanh1")
+fc2 <- mx.symbol.FullyConnected(tanh1, num_hidden = 1, name = "fc2")
+lro2 <- mx.symbol.MakeLoss(mx.symbol.square(mx.symbol.Reshape(fc2, shape = 0) - label), name="lro2")
+```
+
+Then we can train the network just as usual.
+
+``` r
+mx.set.seed(0)
+model2 <- mx.model.FeedForward.create(lro2, X = train.x, y = train.y,
+ ctx = mx.cpu(),
+ num.round = 5,
+ array.batch.size = 60,
+ optimizer = "rmsprop",
+ verbose = TRUE,
+ array.layout = "rowmajor",
+ batch.end.callback = NULL,
+ epoch.end.callback = NULL)
+```
+
+ ## Start training with 1 devices
+
+We should get very similar results because we are actually minimizing the same loss function. However, the result is quite different.
+
+``` r
+pred2 <- predict(model2, test.x)
+```
+
+ ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum((test.y - pred2)^2) / length(test.y)
+```
+
+ ## [1] 1.234584
+
+This is because output of `mx.symbol.MakeLoss` is the gradient of loss with respect to the input data. We can get the real prediction as below.
+
+``` r
+internals = internals(model2$symbol)
+fc_symbol = internals[[match("fc2_output", outputs(internals))]]
+
+model3 <- list(symbol = fc_symbol,
+ arg.params = model2$arg.params,
+ aux.params = model2$aux.params)
+
+class(model3) <- "MXFeedForwardModel"
+
+pred3 <- predict(model3, test.x)
+```
+
+ ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum((test.y - pred3[1,])^2) / length(test.y)
+```
+
+ ## [1] 0.248294
+
+We have provided many operations on the symbols. An example of `|pred-label|` can be found below.
+
+``` r
+lro_abs <- mx.symbol.MakeLoss(mx.symbol.abs(mx.symbol.Reshape(fc2, shape = 0) - label))
+mx.set.seed(0)
+model4 <- mx.model.FeedForward.create(lro_abs, X = train.x, y = train.y,
+ ctx = mx.cpu(),
+ num.round = 20,
+ array.batch.size = 60,
+ optimizer = "sgd",
+ learning.rate = 0.001,
+ verbose = TRUE,
+ array.layout = "rowmajor",
+ batch.end.callback = NULL,
+ epoch.end.callback = NULL)
+```
+
+ ## Start training with 1 devices
+
+``` r
+internals = internals(model4$symbol)
+fc_symbol = internals[[match("fc2_output", outputs(internals))]]
+
+model5 <- list(symbol = fc_symbol,
+ arg.params = model4$arg.params,
+ aux.params = model4$aux.params)
+
+class(model5) <- "MXFeedForwardModel"
+
+pred5 <- predict(model5, test.x)
+```
+
+ ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum(abs(test.y - pred5[1,])) / length(test.y)
+```
+
+ ## [1] 0.7056902
+
+``` r
+lro_mae <- mx.symbol.MAERegressionOutput(fc2, name = "lro")
+mx.set.seed(0)
+model6 <- mx.model.FeedForward.create(lro_mae, X = train.x, y = train.y,
+ ctx = mx.cpu(),
+ num.round = 20,
+ array.batch.size = 60,
+ optimizer = "sgd",
+ learning.rate = 0.001,
+ verbose = TRUE,
+ array.layout = "rowmajor",
+ batch.end.callback = NULL,
+ epoch.end.callback = NULL)
+```
+
+ ## Start training with 1 devices
+
+``` r
+pred6 <- predict(model6, test.x)
+```
+
+ ## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..
+
+``` r
+sum(abs(test.y - pred6[1,])) / length(test.y)
+```
+
+ ## [1] 0.7056902
+
+
+## Next Steps
+* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with a PreTrained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model Using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/versions/master/_sources/tutorials/r/charRnnModel.txt b/versions/master/_sources/tutorials/r/charRnnModel.txt
new file mode 100644
index 0000000..82e10a1
--- /dev/null
+++ b/versions/master/_sources/tutorials/r/charRnnModel.txt
@@ -0,0 +1,314 @@
+Char RNN Example
+=============================================
+
+This tutorial shows how to use an LSTM model to build a char-level language model, and generate text from it. For demonstration purposes, we use a Shakespearean text. You can find the data on [GitHub](https://github.com/dmlc/web-data/tree/master/mxnet/tinyshakespeare).
+
+Load the Data
+---------
+Load in the data and preprocess it:
+
+ ```r
+ require(mxnet)
+ ```
+
+ ```
+ ## Loading required package: mxnet
+ ```
+
+ ```
+ ## Loading required package: methods
+ ```
+Set the basic network parameters:
+
+ ```r
+ batch.size = 32
+ seq.len = 32
+ num.hidden = 16
+ num.embed = 16
+ num.lstm.layer = 1
+ num.round = 1
+ learning.rate= 0.1
+ wd=0.00001
+ clip_gradient=1
+ update.period = 1
+ ```
+Download the data:
+
+ ```r
+ download.data <- function(data_dir) {
+ dir.create(data_dir, showWarnings = FALSE)
+ if (!file.exists(paste0(data_dir,'input.txt'))) {
+ download.file(url='https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tinyshakespeare/input.txt',
+ destfile=paste0(data_dir,'input.txt'), method='wget')
+ }
+ }
+ ```
+Make a dictionary from the text:
+
+ ```r
+ make.dict <- function(text, max.vocab=10000) {
+ text <- strsplit(text, '')
+ dic <- list()
+ idx <- 1
+ for (c in text[[1]]) {
+ if (!(c %in% names(dic))) {
+ dic[[c]] <- idx
+ idx <- idx + 1
+ }
+ }
+ if (length(dic) == max.vocab - 1)
+ dic[["UNKNOWN"]] <- idx
+ cat(paste0("Total unique char: ", length(dic), "\n"))
+ return (dic)
+ }
+ ```
+Transfer the text into a data feature:
+
+ ```r
+ make.data <- function(file.path, seq.len=32, max.vocab=10000, dic=NULL) {
+ fi <- file(file.path, "r")
+ text <- paste(readLines(fi), collapse="\n")
+ close(fi)
+
+ if (is.null(dic))
+ dic <- make.dict(text, max.vocab)
+ lookup.table <- list()
+ for (c in names(dic)) {
+ idx <- dic[[c]]
+ lookup.table[[idx]] <- c
+ }
+
+ char.lst <- strsplit(text, '')[[1]]
+ num.seq <- as.integer(length(char.lst) / seq.len)
+ char.lst <- char.lst[1:(num.seq * seq.len)]
+ data <- array(0, dim=c(seq.len, num.seq))
+ idx <- 1
+ for (i in 1:num.seq) {
+ for (j in 1:seq.len) {
+ if (char.lst[idx] %in% names(dic))
+ data[j, i] <- dic[[ char.lst[idx] ]]-1
+ else {
+ data[j, i] <- dic[["UNKNOWN"]]-1
+ }
+ idx <- idx + 1
+ }
+ }
+ return (list(data=data, dic=dic, lookup.table=lookup.table))
+ }
+ ```
+Move the tail text:
+
+ ```r
+ drop.tail <- function(X, batch.size) {
+ shape <- dim(X)
+ nstep <- as.integer(shape[2] / batch.size)
+ return (X[, 1:(nstep * batch.size)])
+ }
+ ```
+Get the label of X:
+
+ ```r
+ get.label <- function(X) {
+ label <- array(0, dim=dim(X))
+ d <- dim(X)[1]
+ w <- dim(X)[2]
+ for (i in 0:(w-1)) {
+ for (j in 1:d) {
+ label[i*d+j] <- X[(i*d+j)%%(w*d)+1]
+ }
+ }
+ return (label)
+ }
+ ```
+Get the training data and evaluation data:
+
+ ```r
+ download.data("./data/")
+ ret <- make.data("./data/input.txt", seq.len=seq.len)
+ ```
+
+ ```
+ ## Total unique char: 65
+ ```
+
+ ```r
+ X <- ret$data
+ dic <- ret$dic
+ lookup.table <- ret$lookup.table
+
+ vocab <- length(dic)
+
+ shape <- dim(X)
+ train.val.fraction <- 0.9
+ size <- shape[2]
+
+ X.train.data <- X[, 1:as.integer(size * train.val.fraction)]
+ X.val.data <- X[, -(1:as.integer(size * train.val.fraction))]
+ X.train.data <- drop.tail(X.train.data, batch.size)
+ X.val.data <- drop.tail(X.val.data, batch.size)
+
+ X.train.label <- get.label(X.train.data)
+ X.val.label <- get.label(X.val.data)
+
+ X.train <- list(data=X.train.data, label=X.train.label)
+ X.val <- list(data=X.val.data, label=X.val.label)
+ ```
+
+Train the Model
+--------------
+In `mxnet`, we have a function called `mx.lstm` so that users can build a general LSTM model:
+
+
+ ```r
+ model <- mx.lstm(X.train, X.val,
+ ctx=mx.cpu(),
+ num.round=num.round,
+ update.period=update.period,
+ num.lstm.layer=num.lstm.layer,
+ seq.len=seq.len,
+ num.hidden=num.hidden,
+ num.embed=num.embed,
+ num.label=vocab,
+ batch.size=batch.size,
+ input.size=vocab,
+ initializer=mx.init.uniform(0.1),
+ learning.rate=learning.rate,
+ wd=wd,
+ clip_gradient=clip_gradient)
+ ```
+
+ ```
+ ## Epoch [31] Train: NLL=3.53787130224343, Perp=34.3936275728271
+ ## Epoch [62] Train: NLL=3.43087958036949, Perp=30.903813186055
+ ## Epoch [93] Train: NLL=3.39771238228587, Perp=29.8956319855751
+ ## Epoch [124] Train: NLL=3.37581711716687, Perp=29.2481732041015
+ ## Epoch [155] Train: NLL=3.34523331338447, Perp=28.3671933405139
+ ## Epoch [186] Train: NLL=3.30756356274787, Perp=27.31848454823
+ ## Epoch [217] Train: NLL=3.25642968403829, Perp=25.9566978956055
+ ## Epoch [248] Train: NLL=3.19825967486207, Perp=24.4898727477925
+ ## Epoch [279] Train: NLL=3.14013971549828, Perp=23.1070950525017
+ ## Epoch [310] Train: NLL=3.08747601837462, Perp=21.9216781782189
+ ## Epoch [341] Train: NLL=3.04015595674863, Perp=20.9085038031042
+ ## Epoch [372] Train: NLL=2.99839339255659, Perp=20.0532932584534
+ ## Epoch [403] Train: NLL=2.95940091012609, Perp=19.2864139984503
+ ## Epoch [434] Train: NLL=2.92603311380224, Perp=18.6534872738302
+ ## Epoch [465] Train: NLL=2.89482756896395, Perp=18.0803835531869
+ ## Epoch [496] Train: NLL=2.86668230478397, Perp=17.5786009078994
+ ## Epoch [527] Train: NLL=2.84089368534943, Perp=17.1310684830416
+ ## Epoch [558] Train: NLL=2.81725862932279, Perp=16.7309220880514
+ ## Epoch [589] Train: NLL=2.79518870141492, Perp=16.3657166956952
+ ## Epoch [620] Train: NLL=2.77445683225304, Perp=16.0299176962855
+ ## Epoch [651] Train: NLL=2.75490970113174, Perp=15.719621374694
+ ## Epoch [682] Train: NLL=2.73697900634351, Perp=15.4402696117257
+ ## Epoch [713] Train: NLL=2.72059739336781, Perp=15.1893935780915
+ ## Epoch [744] Train: NLL=2.70462837571585, Perp=14.948760335793
+ ## Epoch [775] Train: NLL=2.68909904683828, Perp=14.7184093476224
+ ## Epoch [806] Train: NLL=2.67460054451836, Perp=14.5065539595711
+ ## Epoch [837] Train: NLL=2.66078997776751, Perp=14.3075873113043
+ ## Epoch [868] Train: NLL=2.6476781639279, Perp=14.1212134100373
+ ## Epoch [899] Train: NLL=2.63529039846876, Perp=13.9473621677371
+ ## Epoch [930] Train: NLL=2.62367693518974, Perp=13.7863219168709
+ ## Epoch [961] Train: NLL=2.61238282674384, Perp=13.6314936713501
+ ## Iter [1] Train: Time: 10301.6818172932 sec, NLL=2.60536539345356, Perp=13.5361704272949
+ ## Iter [1] Val: NLL=2.26093848746227, Perp=9.59208699731232
+ ```
+
+Build Inference from the Model
+--------------------
+Use the helper function for random sample:
+
+ ```r
+ cdf <- function(weights) {
+ total <- sum(weights)
+ result <- c()
+ cumsum <- 0
+ for (w in weights) {
+ cumsum <- cumsum+w
+ result <- c(result, cumsum / total)
+ }
+ return (result)
+ }
+
+ search.val <- function(cdf, x) {
+ l <- 1
+ r <- length(cdf)
+ while (l <= r) {
+ m <- as.integer((l+r)/2)
+ if (cdf[m] < x) {
+ l <- m+1
+ } else {
+ r <- m-1
+ }
+ }
+ return (l)
+ }
+ choice <- function(weights) {
+ cdf.vals <- cdf(as.array(weights))
+ x <- runif(1)
+ idx <- search.val(cdf.vals, x)
+ return (idx)
+ }
+ ```
+Use random output or fixed output by choosing the greatest probability:
+
+ ```r
+ make.output <- function(prob, sample=FALSE) {
+ if (!sample) {
+ idx <- which.max(as.array(prob))
+ }
+ else {
+ idx <- choice(prob)
+ }
+ return (idx)
+
+ }
+ ```
+
+In `mxnet`, we have a function called `mx.lstm.inference` so that users can build an inference from an LSTM model, and then use the `mx.lstm.forward` function to get forward output from the inference.
+
+Build an inference from the model:
+
+ ```r
+ infer.model <- mx.lstm.inference(num.lstm.layer=num.lstm.layer,
+ input.size=vocab,
+ num.hidden=num.hidden,
+ num.embed=num.embed,
+ num.label=vocab,
+ arg.params=model$arg.params,
+ ctx=mx.cpu())
+ ```
+Generate a sequence of 75 characters using the `mx.lstm.forward` function:
+
+ ```r
+ start <- 'a'
+ seq.len <- 75
+ random.sample <- TRUE
+
+ last.id <- dic[[start]]
+ out <- "a"
+ for (i in (1:(seq.len-1))) {
+ input <- c(last.id-1)
+ ret <- mx.lstm.forward(infer.model, input, FALSE)
+ infer.model <- ret$model
+ prob <- ret$prob
+ last.id <- make.output(prob, random.sample)
+ out <- paste0(out, lookup.table[[last.id]])
+ }
+ cat (paste0(out, "\n"))
+ ```
+The result:
+
+ ```
+ ah not a drobl greens
+ Settled asing lately sistering sounted to their hight
+ ```
+
+Create Other RNN Models
+----------------
+In `mxnet`, other RNN models, like custom RNN and GRU, are also provided:
+
+- For a custom RNN model, you can replace `mx.lstm` with `mx.rnn` to train an RNN model. You can replace `mx.lstm.inference` and `mx.lstm.forward` with `mx.rnn.inference` and `mx.rnn.forward` to build inference from an RNN model and get the forward result from the inference model.
+- For a GRU model, you can replace `mx.lstm` with `mx.gru` to train a GRU model. You can replace `mx.lstm.inference` and `mx.lstm.forward` with `mx.gru.inference` and `mx.gru.forward` to build inference from a GRU model and get the forward result from the inference model.
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/versions/master/_sources/tutorials/r/classifyRealImageWithPretrainedModel.txt b/versions/master/_sources/tutorials/r/classifyRealImageWithPretrainedModel.txt
new file mode 100644
index 0000000..4276fde
--- /dev/null
+++ b/versions/master/_sources/tutorials/r/classifyRealImageWithPretrainedModel.txt
@@ -0,0 +1,184 @@
+Classify Images with a PreTrained Model
+=================================================
+MXNet is a flexible and efficient deep learning framework. One of the interesting things that a deep learning
+algorithm can do is classify real world images.
+
+In this tutorial, we show how to use a pre-trained Inception-BatchNorm network to predict the class of an
+image. For information about the network architecture, see [1].
+
+The pre-trained Inception-BatchNorm network is able to be downloaded from [this link](http://data.mxnet.io/mxnet/data/Inception.zip)
+This model gives the recent state-of-art prediction accuracy on image net dataset.
+
+Load the MXNet Package
+---------------
+To get started, load the mxnet package:
+
+ ```r
+ require(mxnet)
+ ```
+
+ ```
+ ## Loading required package: mxnet
+ ## Loading required package: methods
+ ```
+
+Now load the imager package to load and preprocess the images in R:
+
+
+ ```r
+ require(imager)
+ ```
+
+ ```
+ ## Loading required package: imager
+ ## Loading required package: plyr
+ ## Loading required package: magrittr
+ ## Loading required package: stringr
+ ## Loading required package: png
+ ## Loading required package: jpeg
+ ##
+ ## Attaching package: 'imager'
+ ##
+ ## The following object is masked from 'package:magrittr':
+ ##
+ ## add
+ ##
+ ## The following object is masked from 'package:plyr':
+ ##
+ ## liply
+ ##
+ ## The following objects are masked from 'package:stats':
+ ##
+ ## convolve, spectrum
+ ##
+ ## The following object is masked from 'package:graphics':
+ ##
+ ## frame
+ ##
+ ## The following object is masked from 'package:base':
+ ##
+ ## save.image
+ ```
+
+Load the PreTrained Model
+-------------------------
+Make sure you unzip the pre-trained model in the current folder. Use the model
+loading function to load the model into R:
+
+ ```r
+ model = mx.model.load("Inception/Inception_BN", iteration=39)
+ ```
+
+Load in the mean image, which is used for preprocessing using:
+
+
+ ```r
+ mean.img = as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
+ ```
+
+Load and Preprocess the Image
+-----------------------------
+Now, we are ready to classify a real image. In this example, we simply take the parrots image
+from the imager package. You can use another image, if you prefer.
+
+Load and plot the image:
+
+
+```r
+ im <- load.image(system.file("extdata/parrots.png", package="imager"))
+ plot(im)
+ ```
+
+
+
+Before feeding the image to the deep network, we need to perform some preprocessing
+to make the image meet the deep network input requirements. Preprocessing
+includes cropping and subtracting the mean.
+Because MXNet is deeply integrated with R, we can do all the processing in an R function:
+
+
+ ```r
+ preproc.image <- function(im, mean.image) {
+ # crop the image
+ shape <- dim(im)
+ short.edge <- min(shape[1:2])
+ xx <- floor((shape[1] - short.edge) / 2)
+ yy <- floor((shape[2] - short.edge) / 2)
+ cropped <- crop.borders(im, xx, yy)
+ # resize to 224 x 224, needed by input of the model.
+ resized <- resize(cropped, 224, 224)
+ # convert to array (x, y, channel)
+ arr <- as.array(resized) * 255
+ dim(arr) <- c(224, 224, 3)
+ # subtract the mean
+ normed <- arr - mean.img
+ # Reshape to format needed by mxnet (width, height, channel, num)
+ dim(normed) <- c(224, 224, 3, 1)
+ return(normed)
+ }
+ ```
+
+Use the defined preprocessing function to get the normalized image:
+
+
+ ```r
+ normed <- preproc.image(im, mean.img)
+ ```
+
+Classify the Image
+------------------
+Now we are ready to classify the image! Use the ```predict``` function
+to get the probability over classes:
+
+
+ ```r
+ prob <- predict(model, X=normed)
+ dim(prob)
+ ```
+
+ ```
+ ## [1] 1000 1
+ ```
+
+As you can see, ```prob``` is a 1 times 1000 array, which gives the probability
+over the 1000 image classes of the input.
+
+Use the ```max.col``` on the transpose of ```prob``` to get the class index:
+
+ ```r
+ max.idx <- max.col(t(prob))
+ max.idx
+ ```
+
+ ```
+ ## [1] 89
+ ```
+
+The index doesn't make much sense, so let's see what it really means.
+Read the names of the classes from the following file:
+
+
+ ```r
+ synsets <- readLines("Inception/synset.txt")
+ ```
+
+Let's see what the image really is:
+
+
+ ```r
+ print(paste0("Predicted Top-class: ", synsets [[max.idx]]))
+ ```
+
+ ```
+ ## [1] "Predicted Top-class: n01818515 macaw"
+ ```
+
+It's a macaw!
+
+Reference
+---------
+[1] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015).
+
+## Next Steps
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/versions/master/_sources/tutorials/r/fiveMinutesNeuralNetwork.txt b/versions/master/_sources/tutorials/r/fiveMinutesNeuralNetwork.txt
new file mode 100644
index 0000000..9104e8f
--- /dev/null
+++ b/versions/master/_sources/tutorials/r/fiveMinutesNeuralNetwork.txt
@@ -0,0 +1,314 @@
+Develop a Neural Network with MXNet in Five Minutes
+=============================================
+
+This tutorial is designed for new users of the `mxnet` package for R. It shows how to construct a neural network to do regression in 5 minutes. It shows how to perform classification and regression tasks, respectively. The data we use is in the `mlbench` package.
+
+## Classification
+
+
+
+ ```r
+ require(mlbench)
+ ```
+
+ ```
+ ## Loading required package: mlbench
+ ```
+
+ ```r
+ require(mxnet)
+ ```
+
+ ```
+ ## Loading required package: mxnet
+ ## Loading required package: methods
+ ```
+
+ ```r
+ data(Sonar, package="mlbench")
+
+ Sonar[,61] = as.numeric(Sonar[,61])-1
+ train.ind = c(1:50, 100:150)
+ train.x = data.matrix(Sonar[train.ind, 1:60])
+ train.y = Sonar[train.ind, 61]
+ test.x = data.matrix(Sonar[-train.ind, 1:60])
+ test.y = Sonar[-train.ind, 61]
+ ```
+
+We are going to use a multi-layer perceptron as our classifier. In `mxnet`, we have a function called `mx.mlp` for building a general multi-layer neural network to do classification or regression.
+
+`mx.mlp` requires the following parameters:
+
+- Training data and label
+- Number of hidden nodes in each hidden layer
+- Number of nodes in the output layer
+- Type of the activation
+- Type of the output loss
+- The device to train (GPU or CPU)
+- Other parameters for `mx.model.FeedForward.create`
+
+The following code shows an example usage of `mx.mlp`:
+
+
+ ```r
+ mx.set.seed(0)
+ model <- mx.mlp(train.x, train.y, hidden_node=10, out_node=2, out_activation="softmax",
+ num.round=20, array.batch.size=15, learning.rate=0.07, momentum=0.9,
+ eval.metric=mx.metric.accuracy)
+ ```
+
+ ```
+ ## Auto detect layout of input matrix, use rowmajor..
+ ## Start training with 1 devices
+ ## [1] Train-accuracy=0.488888888888889
+ ## [2] Train-accuracy=0.514285714285714
+ ## [3] Train-accuracy=0.514285714285714
+ ## [4] Train-accuracy=0.514285714285714
+ ## [5] Train-accuracy=0.514285714285714
+ ## [6] Train-accuracy=0.523809523809524
+ ## [7] Train-accuracy=0.619047619047619
+ ## [8] Train-accuracy=0.695238095238095
+ ## [9] Train-accuracy=0.695238095238095
+ ## [10] Train-accuracy=0.761904761904762
+ ## [11] Train-accuracy=0.828571428571429
+ ## [12] Train-accuracy=0.771428571428571
+ ## [13] Train-accuracy=0.742857142857143
+ ## [14] Train-accuracy=0.733333333333333
+ ## [15] Train-accuracy=0.771428571428571
+ ## [16] Train-accuracy=0.847619047619048
+ ## [17] Train-accuracy=0.857142857142857
+ ## [18] Train-accuracy=0.838095238095238
+ ## [19] Train-accuracy=0.838095238095238
+ ## [20] Train-accuracy=0.838095238095238
+ ```
+
+Note that `mx.set.seed` controls the random process in `mxnet`. You can see the accuracy in each round during training. It's also easy to make predictions and evaluate.
+
+To get an idea of what is happening, view the computation graph from R:
+
+ ```{r}
+ graph.viz(model$symbol)
+ ```
+
+[<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png">](https://github.com/dmlc/mxnet)
+
+ ```r
+ preds = predict(model, test.x)
+ ```
+
+ ```
+ ## Auto detect layout of input matrix, use rowmajor.
+ ```
+
+ ```r
+ pred.label = max.col(t(preds))-1
+ table(pred.label, test.y)
+ ```
+
+ ```
+ ## test.y
+ ## pred.label 0 1
+ ## 0 24 14
+ ## 1 36 33
+ ```
+
+Note for that for multi-class predictions, mxnet outputs `nclass` x `nexamples`, with each row corresponding to the probability of the class.
+
+## Regression
+
+Again, let us preprocess the data:
+
+
+ ```r
+ data(BostonHousing, package="mlbench")
+
+ train.ind = seq(1, 506, 3)
+ train.x = data.matrix(BostonHousing[train.ind, -14])
+ train.y = BostonHousing[train.ind, 14]
+ test.x = data.matrix(BostonHousing[-train.ind, -14])
+ test.y = BostonHousing[-train.ind, 14]
+ ```
+
+Although we can use `mx.mlp` again to do regression by changing the `out_activation`, this time we are going to introduce a flexible way to configure neural networks in `mxnet`. Configuration is done by the "Symbol" system in `mxnet`. The Symbol system takes care of the links among nodes, activation, dropout ratio, etc. Configure a multi-layer neural network as follows:
+
+
+ ```r
+ # Define the input data
+ data <- mx.symbol.Variable("data")
+ # A fully connected hidden layer
+ # data: input source
+ # num_hidden: number of neurons in this hidden layer
+ fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)
+
+ # Use linear regression for the output layer
+ lro <- mx.symbol.LinearRegressionOutput(fc1)
+ ```
+
+What matters for a regression task is mainly the last function. It enables the new network to optimize for squared loss. Now let's train on this simple data set. In this configuration, we dropped the hidden layer so that the input layer is directly connected to the output layer.
+
+Next, make prediction with this structure and other parameters with `mx.model.FeedForward.create`:
+
+
+ ```r
+ mx.set.seed(0)
+ model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
+ ctx=mx.cpu(), num.round=50, array.batch.size=20,
+ learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse)
+ ```
+
+ ```
+ ## Auto detect layout of input matrix, use rowmajor.
+ ## Start training with 1 devices
+ ## [1] Train-rmse=16.063282524034
+ ## [2] Train-rmse=12.2792375712573
+ ## [3] Train-rmse=11.1984634005885
+ ## [4] Train-rmse=10.2645236892904
+ ## [5] Train-rmse=9.49711005504284
+ ## [6] Train-rmse=9.07733734175182
+ ## [7] Train-rmse=9.07884450847991
+ ## [8] Train-rmse=9.10463850277417
+ ## [9] Train-rmse=9.03977049028532
+ ## [10] Train-rmse=8.96870685004475
+ ## [11] Train-rmse=8.93113287361574
+ ## [12] Train-rmse=8.89937257821847
+ ## [13] Train-rmse=8.87182096922953
+ ## [14] Train-rmse=8.84476075083586
+ ## [15] Train-rmse=8.81464673014974
+ ## [16] Train-rmse=8.78672567900196
+ ## [17] Train-rmse=8.76265872846474
+ ## [18] Train-rmse=8.73946101419974
+ ## [19] Train-rmse=8.71651926303267
+ ## [20] Train-rmse=8.69457600919277
+ ## [21] Train-rmse=8.67354928674563
+ ## [22] Train-rmse=8.65328755392436
+ ## [23] Train-rmse=8.63378039680078
+ ## [24] Train-rmse=8.61488162586984
+ ## [25] Train-rmse=8.5965105183022
+ ## [26] Train-rmse=8.57868133563275
+ ## [27] Train-rmse=8.56135851937663
+ ## [28] Train-rmse=8.5444819772098
+ ## [29] Train-rmse=8.52802114610432
+ ## [30] Train-rmse=8.5119504512622
+ ## [31] Train-rmse=8.49624261719241
+ ## [32] Train-rmse=8.48087453238701
+ ## [33] Train-rmse=8.46582689119887
+ ## [34] Train-rmse=8.45107881002491
+ ## [35] Train-rmse=8.43661331401712
+ ## [36] Train-rmse=8.42241575909639
+ ## [37] Train-rmse=8.40847217331365
+ ## [38] Train-rmse=8.39476931796395
+ ## [39] Train-rmse=8.38129658373974
+ ## [40] Train-rmse=8.36804269059018
+ ## [41] Train-rmse=8.35499817678397
+ ## [42] Train-rmse=8.34215505742154
+ ## [43] Train-rmse=8.32950441908131
+ ## [44] Train-rmse=8.31703985777311
+ ## [45] Train-rmse=8.30475363906755
+ ## [46] Train-rmse=8.29264031506106
+ ## [47] Train-rmse=8.28069372820073
+ ## [48] Train-rmse=8.26890902770415
+ ## [49] Train-rmse=8.25728089053853
+ ## [50] Train-rmse=8.24580511500735
+ ```
+
+It's also easy to make a prediction and evaluate it:
+
+
+ ```r
+ preds = predict(model, test.x)
+ ```
+
+ ```
+ ## Auto detect layout of input matrix, use rowmajor..
+ ```
+
+ ```r
+ sqrt(mean((preds-test.y)^2))
+ ```
+
+ ```
+ ## [1] 7.800502
+ ```
+
+Currently, we have four predefined metrics: "accuracy", "rmse", "mae", and "rmsle". MXNet provides the interface for defining your own metrics:
+
+
+ ```r
+ demo.metric.mae <- mx.metric.custom("mae", function(label, pred) {
+ res <- mean(abs(label-pred))
+ return(res)
+ })
+ ```
+
+This is an example of the mean absolute error metric. Simply plug it into the training function:
+
+
+ ```r
+ mx.set.seed(0)
+ model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
+ ctx=mx.cpu(), num.round=50, array.batch.size=20,
+ learning.rate=2e-6, momentum=0.9, eval.metric=demo.metric.mae)
+ ```
+
+ ```
+ ## Auto detect layout of input matrix, use rowmajor.
+ ## Start training with 1 devices
+ ## [1] Train-mae=13.1889538083225
+ ## [2] Train-mae=9.81431959337658
+ ## [3] Train-mae=9.21576419870059
+ ## [4] Train-mae=8.38071537613869
+ ## [5] Train-mae=7.45462437611487
+ ## [6] Train-mae=6.93423301743136
+ ## [7] Train-mae=6.91432357016537
+ ## [8] Train-mae=7.02742733055105
+ ## [9] Train-mae=7.00618194618469
+ ## [10] Train-mae=6.92541576984028
+ ## [11] Train-mae=6.87530243690643
+ ## [12] Train-mae=6.84757369098564
+ ## [13] Train-mae=6.82966501611388
+ ## [14] Train-mae=6.81151759574811
+ ## [15] Train-mae=6.78394182841811
+ ## [16] Train-mae=6.75914719419347
+ ## [17] Train-mae=6.74180388773481
+ ## [18] Train-mae=6.725853071279
+ ## [19] Train-mae=6.70932178215848
+ ## [20] Train-mae=6.6928868798746
+ ## [21] Train-mae=6.6769521329138
+ ## [22] Train-mae=6.66184809505939
+ ## [23] Train-mae=6.64754504809777
+ ## [24] Train-mae=6.63358514060577
+ ## [25] Train-mae=6.62027640889088
+ ## [26] Train-mae=6.60738245232238
+ ## [27] Train-mae=6.59505546771818
+ ## [28] Train-mae=6.58346195800437
+ ## [29] Train-mae=6.57285477783945
+ ## [30] Train-mae=6.56259003960424
+ ## [31] Train-mae=6.5527790788975
+ ## [32] Train-mae=6.54353428422991
+ ## [33] Train-mae=6.5344172368447
+ ## [34] Train-mae=6.52557652526432
+ ## [35] Train-mae=6.51697905850079
+ ## [36] Train-mae=6.50847898812758
+ ## [37] Train-mae=6.50014844106303
+ ## [38] Train-mae=6.49207674844397
+ ## [39] Train-mae=6.48412070125341
+ ## [40] Train-mae=6.47650500999557
+ ## [41] Train-mae=6.46893867486053
+ ## [42] Train-mae=6.46142131653097
+ ## [43] Train-mae=6.45395035048326
+ ## [44] Train-mae=6.44652914123403
+ ## [45] Train-mae=6.43916216409869
+ ## [46] Train-mae=6.43183777381976
+ ## [47] Train-mae=6.42455544223388
+ ## [48] Train-mae=6.41731406417158
+ ## [49] Train-mae=6.41011292926139
+ ## [50] Train-mae=6.40312503493494
+ ```
+
+Congratulations! You've learned the basics for using MXNet in R. To learn how to use MXNet's advanced features, see the other tutorials.
+
+
+## Next Steps
+* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/versions/master/_sources/tutorials/r/mnistCompetition.txt b/versions/master/_sources/tutorials/r/mnistCompetition.txt
new file mode 100644
index 0000000..04e68cf
--- /dev/null
+++ b/versions/master/_sources/tutorials/r/mnistCompetition.txt
@@ -0,0 +1,339 @@
+Handwritten Digits Classification Competition
+=============================================
+
+[MNIST](http://yann.lecun.com/exdb/mnist/) is a handwritten digits image data set created by Yann LeCun. Every digit is represented by a 28 x 28 pixel image. It's become a standard data set for testing classifiers on simple image input. A neural network is a strong model for image classification tasks. There's a [long-term hosted competition](https://www.kaggle.com/c/digit-recognizer) on Kaggle using this data set.
+This tutorial shows how to use [MXNet](https://github.com/dmlc/mxnet/tree/master/R-package) to compete in this challenge.
+
+## Loading the Data
+
+First, let's download the data from [Kaggle](https://www.kaggle.com/c/digit-recognizer/data) and put it in the `data/` folder in your working directory.
+
+Now we can read it in R and convert it to matrices:
+
+
+ ```r
+ require(mxnet)
+ ```
+
+ ```
+ ## Loading required package: mxnet
+ ## Loading required package: methods
+ ```
+
+ ```r
+ train <- read.csv('data/train.csv', header=TRUE)
+ test <- read.csv('data/test.csv', header=TRUE)
+ train <- data.matrix(train)
+ test <- data.matrix(test)
+
+ train.x <- train[,-1]
+ train.y <- train[,1]
+ ```
+
+Every image is represented as a single row in train/test. The greyscale of each image falls in the range [0, 255]. Linearly transform it into [0,1] by using the following command:
+
+
+ ```r
+ train.x <- t(train.x/255)
+ test <- t(test/255)
+ ```
+Transpose the input matrix to npixel x nexamples, which is the major format for columns accepted by MXNet (and the convention of R).
+
+In the label section, the number of each digit is fairly evenly distributed:
+
+
+ ```r
+ table(train.y)
+ ```
+
+ ```
+ ## train.y
+ ## 0 1 2 3 4 5 6 7 8 9
+ ## 4132 4684 4177 4351 4072 3795 4137 4401 4063 4188
+```
+
+## Configuring the Network
+
+Now that we have the data, let's configure the structure of our network:
+
+
+ ```r
+ data <- mx.symbol.Variable("data")
+ fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=128)
+ act1 <- mx.symbol.Activation(fc1, name="relu1", act_type="relu")
+ fc2 <- mx.symbol.FullyConnected(act1, name="fc2", num_hidden=64)
+ act2 <- mx.symbol.Activation(fc2, name="relu2", act_type="relu")
+ fc3 <- mx.symbol.FullyConnected(act2, name="fc3", num_hidden=10)
+ softmax <- mx.symbol.SoftmaxOutput(fc3, name="sm")
+ ```
+
+1. In `mxnet`, we use the data type `symbol` to configure the network. `data <- mx.symbol.Variable("data")` uses `data` to represent the input data, i.e., the input layer.
+2. We set the first hidden layer with `fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=128)`. This layer has `data` as the input, its name, and the number of hidden neurons.
+3. Activation is set with `act1 <- mx.symbol.Activation(fc1, name="relu1", act_type="relu")`. The activation function takes the output from the first hidden layer, `fc1`.
+4. The second hidden layer takes the result from `act1` as input, with its name as "fc2" and the number of hidden neurons as 64.
+5. The second activation is almost the same as `act1`, except we have a different input source and name.
+6. This generates the output layer. Because there are only 10 digits, we set the number of neurons to 10.
+7. Finally, we set the activation to softmax to get a probabilistic prediction.
+
+## Training
+
+We are almost ready for the training process. Before we start the computation, let's decide which device to use:
+
+
+ ```r
+ devices <- mx.cpu()
+ ```
+
+We assign CPU to `mxnet`. Now, you can run the following command to train the neural network! Note that `mx.set.seed` is the function that controls the random process in `mxnet`:
+
+
+ ```r
+ mx.set.seed(0)
+ model <- mx.model.FeedForward.create(softmax, X=train.x, y=train.y,
+ ctx=devices, num.round=10, array.batch.size=100,
+ learning.rate=0.07, momentum=0.9, eval.metric=mx.metric.accuracy,
+ initializer=mx.init.uniform(0.07),
+ epoch.end.callback=mx.callback.log.train.metric(100))
+ ```
+
+ ```
+ ## Start training with 1 devices
+ ## Batch [100] Train-accuracy=0.6563
+ ## Batch [200] Train-accuracy=0.777999999999999
+ ## Batch [300] Train-accuracy=0.827466666666665
+ ## Batch [400] Train-accuracy=0.855499999999999
+ ## [1] Train-accuracy=0.859832935560859
+ ## Batch [100] Train-accuracy=0.9529
+ ## Batch [200] Train-accuracy=0.953049999999999
+ ## Batch [300] Train-accuracy=0.955866666666666
+ ## Batch [400] Train-accuracy=0.957525000000001
+ ## [2] Train-accuracy=0.958309523809525
+ ## Batch [100] Train-accuracy=0.968
+ ## Batch [200] Train-accuracy=0.9677
+ ## Batch [300] Train-accuracy=0.9696
+ ## Batch [400] Train-accuracy=0.970650000000002
+ ## [3] Train-accuracy=0.970809523809526
+ ## Batch [100] Train-accuracy=0.973
+ ## Batch [200] Train-accuracy=0.974249999999999
+ ## Batch [300] Train-accuracy=0.976
+ ## Batch [400] Train-accuracy=0.977100000000003
+ ## [4] Train-accuracy=0.977452380952384
+ ## Batch [100] Train-accuracy=0.9834
+ ## Batch [200] Train-accuracy=0.981949999999999
+ ## Batch [300] Train-accuracy=0.981900000000001
+ ## Batch [400] Train-accuracy=0.982600000000003
+ ## [5] Train-accuracy=0.983000000000003
+ ## Batch [100] Train-accuracy=0.983399999999999
+ ## Batch [200] Train-accuracy=0.98405
+ ## Batch [300] Train-accuracy=0.985000000000001
+ ## Batch [400] Train-accuracy=0.985725000000003
+ ## [6] Train-accuracy=0.985952380952384
+ ## Batch [100] Train-accuracy=0.988999999999999
+ ## Batch [200] Train-accuracy=0.9876
+ ## Batch [300] Train-accuracy=0.988100000000001
+ ## Batch [400] Train-accuracy=0.988750000000003
+ ## [7] Train-accuracy=0.988880952380955
+ ## Batch [100] Train-accuracy=0.991999999999999
+ ## Batch [200] Train-accuracy=0.9912
+ ## Batch [300] Train-accuracy=0.990066666666668
+ ## Batch [400] Train-accuracy=0.990275000000003
+ ## [8] Train-accuracy=0.990452380952384
+ ## Batch [100] Train-accuracy=0.9937
+ ## Batch [200] Train-accuracy=0.99235
+ ## Batch [300] Train-accuracy=0.991966666666668
+ ## Batch [400] Train-accuracy=0.991425000000003
+ ## [9] Train-accuracy=0.991500000000003
+ ## Batch [100] Train-accuracy=0.9942
+ ## Batch [200] Train-accuracy=0.99245
+ ## Batch [300] Train-accuracy=0.992433333333334
+ ## Batch [400] Train-accuracy=0.992275000000002
+ ## [10] Train-accuracy=0.992380952380955
+ ```
+
+## Making a Prediction and Submitting to the Competition
+
+To make a prediction, type:
+
+
+ ```r
+ preds <- predict(model, test)
+ dim(preds)
+ ```
+
+ ```
+ ## [1] 10 28000
+ ```
+
+It is a matrix with 28000 rows and 10 cols, containing the desired classification probabilities from the output layer. To extract the maximum label for each row, use `max.col`:
+
+
+ ```r
+ pred.label <- max.col(t(preds)) - 1
+ table(pred.label)
+ ```
+
+ ```
+ ## pred.label
+ ## 0 1 2 3 4 5 6 7 8 9
+ ## 2818 3195 2744 2767 2683 2596 2798 2790 2784 2825
+ ```
+
+With a little extra effort to modify the .csv format, our submission is ready for the competition!
+
+
+ ```r
+ submission <- data.frame(ImageId=1:ncol(test), Label=pred.label)
+ write.csv(submission, file='submission.csv', row.names=FALSE, quote=FALSE)
+ ```
+
+## LeNet
+
+Now let's use a new network structure: [LeNet](http://yann.lecun.com/exdb/lenet/). It has been proposed by Yann LeCun for recognizing handwritten digits. We'll demonstrate how to construct and train a LeNet in `mxnet`.
+
+First, we construct the network:
+
+
+```r
+# input
+data <- mx.symbol.Variable('data')
+# first conv
+conv1 <- mx.symbol.Convolution(data=data, kernel=c(5,5), num_filter=20)
+tanh1 <- mx.symbol.Activation(data=conv1, act_type="tanh")
+pool1 <- mx.symbol.Pooling(data=tanh1, pool_type="max",
+ kernel=c(2,2), stride=c(2,2))
+# second conv
+conv2 <- mx.symbol.Convolution(data=pool1, kernel=c(5,5), num_filter=50)
+tanh2 <- mx.symbol.Activation(data=conv2, act_type="tanh")
+pool2 <- mx.symbol.Pooling(data=tanh2, pool_type="max",
+ kernel=c(2,2), stride=c(2,2))
+# first fullc
+flatten <- mx.symbol.Flatten(data=pool2)
+fc1 <- mx.symbol.FullyConnected(data=flatten, num_hidden=500)
+tanh3 <- mx.symbol.Activation(data=fc1, act_type="tanh")
+# second fullc
+fc2 <- mx.symbol.FullyConnected(data=tanh3, num_hidden=10)
+# loss
+lenet <- mx.symbol.SoftmaxOutput(data=fc2)
+```
+
+Then let's reshape the matrices into arrays:
+
+
+```r
+train.array <- train.x
+dim(train.array) <- c(28, 28, 1, ncol(train.x))
+test.array <- test
+dim(test.array) <- c(28, 28, 1, ncol(test))
+```
+
+We want to compare training speed on different devices, so define the devices:
+
+
+```r
+n.gpu <- 1
+device.cpu <- mx.cpu()
+device.gpu <- lapply(0:(n.gpu-1), function(i) {
+ mx.gpu(i)
+})
+```
+
+We can pass a list of devices to ask MXNet to train on multiple GPUs (you can do this for CPUs,
+but because internal computation of CPUs is already multi-threaded, there is less gain than with using GPUs).
+
+Start by training on the CPU first. Because this takes a bit time, we run it for just one iteration.
+
+
+ ```r
+ mx.set.seed(0)
+ tic <- proc.time()
+ model <- mx.model.FeedForward.create(lenet, X=train.array, y=train.y,
+ ctx=device.cpu, num.round=1, array.batch.size=100,
+ learning.rate=0.05, momentum=0.9, wd=0.00001,
+ eval.metric=mx.metric.accuracy,
+ epoch.end.callback=mx.callback.log.train.metric(100))
+ ```
+
+ ```
+ ## Start training with 1 devices
+ ## Batch [100] Train-accuracy=0.1066
+ ## Batch [200] Train-accuracy=0.16495
+ ## Batch [300] Train-accuracy=0.401766666666667
+ ## Batch [400] Train-accuracy=0.537675
+ ## [1] Train-accuracy=0.557136038186157
+ ```
+
+ ```r
+ print(proc.time() - tic)
+ ```
+
+ ```
+ ## user system elapsed
+ ## 130.030 204.976 83.821
+ ```
+
+Train on a GPU:
+
+
+ ```r
+ mx.set.seed(0)
+ tic <- proc.time()
+ model <- mx.model.FeedForward.create(lenet, X=train.array, y=train.y,
+ ctx=device.gpu, num.round=5, array.batch.size=100,
+ learning.rate=0.05, momentum=0.9, wd=0.00001,
+ eval.metric=mx.metric.accuracy,
+ epoch.end.callback=mx.callback.log.train.metric(100))
+ ```
+
+ ```
+ ## Start training with 1 devices
+ ## Batch [100] Train-accuracy=0.1066
+ ## Batch [200] Train-accuracy=0.1596
+ ## Batch [300] Train-accuracy=0.3983
+ ## Batch [400] Train-accuracy=0.533975
+ ## [1] Train-accuracy=0.553532219570405
+ ## Batch [100] Train-accuracy=0.958
+ ## Batch [200] Train-accuracy=0.96155
+ ## Batch [300] Train-accuracy=0.966100000000001
+ ## Batch [400] Train-accuracy=0.968550000000003
+ ## [2] Train-accuracy=0.969071428571432
+ ## Batch [100] Train-accuracy=0.977
+ ## Batch [200] Train-accuracy=0.97715
+ ## Batch [300] Train-accuracy=0.979566666666668
+ ## Batch [400] Train-accuracy=0.980900000000003
+ ## [3] Train-accuracy=0.981309523809527
+ ## Batch [100] Train-accuracy=0.9853
+ ## Batch [200] Train-accuracy=0.985899999999999
+ ## Batch [300] Train-accuracy=0.986966666666668
+ ## Batch [400] Train-accuracy=0.988150000000002
+ ## [4] Train-accuracy=0.988452380952384
+ ## Batch [100] Train-accuracy=0.990199999999999
+ ## Batch [200] Train-accuracy=0.98995
+ ## Batch [300] Train-accuracy=0.990600000000001
+ ## Batch [400] Train-accuracy=0.991325000000002
+ ## [5] Train-accuracy=0.991523809523812
+ ```
+
+ ```r
+ print(proc.time() - tic)
+ ```
+
+ ```
+ ## user system elapsed
+ ## 9.288 1.680 6.889
+ ```
+
+By using a GPU processor, we significantly speed up training!
+Now, we can submit the result to Kaggle to see the improvement of our ranking!
+
+
+ ```r
+ preds <- predict(model, test.array)
+ pred.label <- max.col(t(preds)) - 1
+ submission <- data.frame(ImageId=1:ncol(test), Label=pred.label)
+ write.csv(submission, file='submission.csv', row.names=FALSE, quote=FALSE)
+ ```
+
+
+
+## Next Steps
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/versions/master/_sources/tutorials/r/ndarray.txt b/versions/master/_sources/tutorials/r/ndarray.txt
new file mode 100644
index 0000000..e00f947
--- /dev/null
+++ b/versions/master/_sources/tutorials/r/ndarray.txt
@@ -0,0 +1,206 @@
+# NDArray: Vectorized Tensor Computations on CPUs and GPUs
+
+`NDArray` is the basic vectorized operation unit in MXNet for matrix and tensor computations.
+Users can perform usual calculations as on an R"s array, but with two additional features:
+
+
+
+- Multiple devices: All operations can be run on various devices including
+CPUs and GPUs.
+
+
+- Automatic parallelization: All operations are automatically executed in
+ parallel with each other.
+
+## Create and Initialize
+
+Let"s create `NDArray` on either a GPU or a CPU:
+
+
+```r
+require(mxnet)
+```
+
+```
+## Loading required package: mxnet
+## Loading required package: methods
+```
+
+```r
+a <- mx.nd.zeros(c(2, 3)) # create a 2-by-3 matrix on cpu
+b <- mx.nd.zeros(c(2, 3), mx.cpu()) # create a 2-by-3 matrix on cpu
+# c <- mx.nd.zeros(c(2, 3), mx.gpu(0)) # create a 2-by-3 matrix on gpu 0, if you have CUDA enabled.
+```
+
+Typically for CUDA-enabled devices, the device id of a GPU starts from 0.
+That's why we passed in 0 to the GPU id.
+
+We can initialize an `NDArray` object in various ways:
+
+
+```r
+a <- mx.nd.ones(c(4, 4))
+b <- mx.rnorm(c(4, 5))
+c <- mx.nd.array(1:5)
+```
+
+To check the numbers in an `NDArray`, we can simply run:
+
+
+```r
+a <- mx.nd.ones(c(2, 3))
+b <- as.array(a)
+class(b)
+```
+
+```
+## [1] "matrix"
+```
+
+```r
+b
+```
+
+```
+## [,1] [,2] [,3]
+## [1,] 1 1 1
+## [2,] 1 1 1
+```
+
+## Performing Basic Operations
+
+### Elemental-wise Operations
+
+You can perform elemental-wise operations on `NDArray` objects, as follows:
+
+
+```r
+a <- mx.nd.ones(c(2, 4)) * 2
+b <- mx.nd.ones(c(2, 4)) / 8
+as.array(a)
+```
+
+```
+## [,1] [,2] [,3] [,4]
+## [1,] 2 2 2 2
+## [2,] 2 2 2 2
+```
+
+```r
+as.array(b)
+```
+
+```
+## [,1] [,2] [,3] [,4]
+## [1,] 0.125 0.125 0.125 0.125
+## [2,] 0.125 0.125 0.125 0.125
+```
+
+```r
+c <- a + b
+as.array(c)
+```
+
+```
+## [,1] [,2] [,3] [,4]
+## [1,] 2.125 2.125 2.125 2.125
+## [2,] 2.125 2.125 2.125 2.125
+```
+
+```r
+d <- c / a - 5
+as.array(d)
+```
+
+```
+## [,1] [,2] [,3] [,4]
+## [1,] -3.9375 -3.9375 -3.9375 -3.9375
+## [2,] -3.9375 -3.9375 -3.9375 -3.9375
+```
+
+If two `NDArray`s are located on different devices, we need to explicitly move them to the same one. For instance:
+
+
+```r
+a <- mx.nd.ones(c(2, 3)) * 2
+b <- mx.nd.ones(c(2, 3), mx.gpu()) / 8
+c <- mx.nd.copyto(a, mx.gpu()) * b
+as.array(c)
+```
+
+### Loading and Saving
+
+You can save a list of `NDArray` object to your disk with `mx.nd.save`:
+
+
+```r
+a <- mx.nd.ones(c(2, 3))
+mx.nd.save(list(a), "temp.ndarray")
+```
+
+You can load it back easily:
+
+
+```r
+a <- mx.nd.load("temp.ndarray")
+as.array(a[[1]])
+```
+
+```
+## [,1] [,2] [,3]
+## [1,] 1 1 1
+## [2,] 1 1 1
+```
+
+We can directly save data to and load it from a distributed file system, such as Amazon S3 and HDFS:
+
+
+```r
+mx.nd.save(list(a), "s3://mybucket/mydata.bin")
+mx.nd.save(list(a), "hdfs///users/myname/mydata.bin")
+```
+
+## Automatic Parallelization
+
+`NDArray` can automatically execute operations in parallel. Automatic parallelization is useful when
+using multiple resources, such as CPU cards, GPU cards, and CPU-to-GPU memory bandwidth.
+
+For example, if we write `a <- a + 1` followed by `b <- b + 1`, and `a` is on a CPU and
+`b` is on a GPU, executing them in parallel improves
+efficiency. Furthermore, because copying data between CPUs and GPUs are also expensive, running in parallel with other computations further increases efficiency.
+
+It's hard to find the code that can be executed in parallel by eye. In the
+following example, `a <- a + 1` and `c <- c * 3` can be executed in parallel, but `a <- a + 1` and
+`b <- b * 3` should be in sequential.
+
+
+```r
+a <- mx.nd.ones(c(2,3))
+b <- a
+c <- mx.nd.copyto(a, mx.cpu())
+a <- a + 1
+b <- b * 3
+c <- c * 3
+```
+
+Luckily, MXNet can automatically resolve the dependencies and
+execute operations in parallel accurately. This allows us to write our program assuming there is only a single thread. MXNet will
+automatically dispatch the program to multiple devices.
+
+MXNet achieves this with lazy evaluation. Each operation is issued to an
+internal engine, and then returned. For example, if we run `a <- a + 1`, it
+returns immediately after pushing the plus operator to the engine. This
+asynchronous processing allows us to push more operators to the engine. It determines
+the read and write dependencies and the best way to execute them in
+parallel.
+
+The actual computations are finished, allowing us to copy the results someplace else, such as `as.array(a)` or `mx.nd.save(a, "temp.dat")`. To write highly parallelized codes, we only need to postpone when we need
+the results.
+
+## Next Steps
+* [Symbol](http://mxnet.io/tutorials/r/symbol.html)
+* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunctionTutorial.html)
+* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
\ No newline at end of file
diff --git a/versions/master/_sources/tutorials/r/symbol.txt b/versions/master/_sources/tutorials/r/symbol.txt
new file mode 100644
index 0000000..8333265
--- /dev/null
+++ b/versions/master/_sources/tutorials/r/symbol.txt
@@ -0,0 +1,130 @@
+# Symbol and Automatic Differentiation
+
+The computational unit `NDArray` requires a way to construct neural networks. MXNet provides a symbolic interface, named Symbol, to do this. Symbol combines both flexibility and efficiency.
+
+## Basic Composition of Symbols
+
+The following code creates a two-layer perceptron network:
+
+
+```r
+require(mxnet)
+net <- mx.symbol.Variable("data")
+net <- mx.symbol.FullyConnected(data=net, name="fc1", num_hidden=128)
+net <- mx.symbol.Activation(data=net, name="relu1", act_type="relu")
+net <- mx.symbol.FullyConnected(data=net, name="fc2", num_hidden=64)
+net <- mx.symbol.Softmax(data=net, name="out")
+class(net)
+```
+
+```
+## [1] "Rcpp_MXSymbol"
+## attr(,"package")
+## [1] "mxnet"
+```
+
+Each symbol takes a (unique) string name. *Variable* often defines the inputs,
+or free variables. Other symbols take a symbol as the input (*data*),
+and may accept other hyper parameters, such as the number of hidden neurons (*num_hidden*)
+or the activation type (*act_type*).
+
+A symbol can be viewed as a function that takes several arguments, whose
+names are automatically generated and can be retrieved with the following command:
+
+
+```r
+arguments(net)
+```
+
+```
+## [1] "data" "fc1_weight" "fc1_bias" "fc2_weight" "fc2_bias"
+## [6] "out_label"
+```
+
+The arguments are the parameters need by each symbol:
+
+- *data*: Input data needed by the variable *data*
+- *fc1_weight* and *fc1_bias*: The weight and bias for the first fully connected layer, *fc1*
+- *fc2_weight* and *fc2_bias*: The weight and bias for the second fully connected layer, *fc2*
+- *out_label*: The label needed by the loss
+
+We can also specify the automatically generated names explicitly:
+
+
+```r
+data <- mx.symbol.Variable("data")
+w <- mx.symbol.Variable("myweight")
+net <- mx.symbol.FullyConnected(data=data, weight=w, name="fc1", num_hidden=128)
+arguments(net)
+```
+
+```
+## [1] "data" "myweight" "fc1_bias"
+```
+
+## More Complicated Composition of Symbols
+
+MXNet provides well-optimized symbols for
+commonly used layers in deep learning. You can also define new operators
+in Python. The following example first performs an element-wise add between two
+symbols, then feeds them to the fully connected operator:
+
+
+```r
+lhs <- mx.symbol.Variable("data1")
+rhs <- mx.symbol.Variable("data2")
+net <- mx.symbol.FullyConnected(data=lhs + rhs, name="fc1", num_hidden=128)
+arguments(net)
+```
+
+```
+## [1] "data1" "data2" "fc1_weight" "fc1_bias"
+```
+
+We can construct a symbol more flexibly than by using the single
+forward composition, for example:
+
+
+```r
+net <- mx.symbol.Variable("data")
+net <- mx.symbol.FullyConnected(data=net, name="fc1", num_hidden=128)
+net2 <- mx.symbol.Variable("data2")
+net2 <- mx.symbol.FullyConnected(data=net2, name="net2", num_hidden=128)
+composed.net <- mx.apply(net, data=net2, name="compose")
+arguments(composed.net)
+```
+
+```
+## [1] "data2" "net2_weight" "net2_bias" "fc1_weight" "fc1_bias"
+```
+
+In the example, *net* is used as a function to apply to an existing symbol
+*net*. The resulting *composed.net* will replace the original argument *data* with
+*net2* instead.
+
+## Training a Neural Net
+
+The [model API](../../../R-package/R/model.R) is a thin wrapper around the symbolic executors to support neural net training.
+
+We encourage you to read [Symbolic Configuration and Execution in Pictures for python package](../python/symbol_in_pictures.md)for a detailed explanation of concepts in pictures.
+
+## How Efficient Is the Symbolic API?
+
+The Symbolic API brings the efficient C++
+operations in powerful toolkits, such as CXXNet and Caffe, together with the
+flexible dynamic NDArray operations. All of the memory and computation resources are
+allocated statically during bind operations, to maximize runtime performance and memory
+utilization.
+
+The coarse-grained operators are equivalent to CXXNet layers, which are
+extremely efficient. We also provide fine-grained operators for more flexible
+composition. Because MXNet does more in-place memory allocation, it can
+be more memory efficient than CXXNet and gets to the same runtime with
+greater flexibility.
+
+## Next Steps
+* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunctionTutorial.html)
+* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)
diff --git a/versions/master/_sources/tutorials/scala/char_lstm.txt b/versions/master/_sources/tutorials/scala/char_lstm.txt
new file mode 100644
index 0000000..466d827
--- /dev/null
+++ b/versions/master/_sources/tutorials/scala/char_lstm.txt
@@ -0,0 +1,513 @@
+# Developing a Character-level Language model
+
+This tutorial shows how to train a character-level language model with a multilayer recurrent neural network (RNN) using Scala. This model takes one text file as input and trains an RNN that learns to predict the next character in the sequence. In this tutorial, you train a multilayer LSTM (Long Short-Term Memory) network that generates relevant text using Barack Obama's speech patterns.
+
+There are many documents that explain LSTM concepts. If you aren't familiar with LSTM, refer to the following before you proceed:
+- Christopher Olah's [Understanding LSTM blog post](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
+- [Training a LSTM char-rnn in Julia to Generate Random Sentences](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html)
+- [Bucketing in MXNet in Python](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb)
+- [Bucketing in MXNet](http://mxnet.io/how_to/bucketing.html)
+
+## How to Use This Tutorial
+
+There are three ways to use this tutorial:
+
+1) Run it by copying the provided code snippets and pasting them into the Scala command line, making the appropriate changes to the input file path.
+
+2) Reuse the code by making changes to relevant parameters and running it from command line.
+
+3) [Run the source code directly](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/src/main/scala/ml/dmlc/mxnet/examples/rnn) by running the [provided scripts](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/scripts/rnn).
+
+To run the scripts:
+- Build and train the model with the [run_train_charrnn.sh script](https://github.com/dmlc/mxnet/blob/master/scala-package/examples/scripts/rnn/run_train_charrnn.sh). Edit the script as follows:
+
+Edit the CLASS_PATH variable in the script to include your operating system-specific folder (e.g., linux-x86_64-cpu/linux-x86_64-gpu/osx-x86_64-cpu) in the path. Run the script with the following command:
+
+```bash
+
+ bash run_train_charrnn.sh <which GPU card to use; -1 means CPU> <input data path> <location to save the model>
+
+ e.g.,
+ bash run_train_charrnn.sh -1 ./datas/obama.txt ./models/obama
+
+```
+
+- Run inference with the [run_test_charrnn.sh script](https://github.com/dmlc/mxnet/blob/master/scala-package/examples/scripts/rnn/run_test_charrnn.sh). Edit the script as follows:
+
+Edit the CLASS_PATH variable in the script to include your operating system-specific folder (e.g., linux-x86_64-cpu/linux-x86_64-gpu/osx-x86_64-cpu) in the path. Run the script with the following command:
+
+```bash
+
+ bash run_test_charrnn.sh <input data path> <trained model from previous script>
+
+ e.g.,
+ bash run_test_charrnn.sh ./datas/obama.txt ./models/obama
+```
+
+In this tutorial, you will accomplish the following:
+
+- Build an LSTM network that learns speech patterns from Barack Obama's speeches at the character level. At each time interval, the input is a character.
+- Clean up the dataset.
+- Train a model.
+- Fit the model.
+- Build the inference model.
+
+## Prerequisites
+
+To complete this tutorial, you need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html)
+- [Scala 2.11.8](https://www.scala-lang.org/download/2.11.8.html)
+- [Maven 3](https://maven.apache.org/install.html)
+
+## Download the Data
+
+First, download the data, which contains Barack Obama's speeches. The data is stored in a file called obama.txt and is available on [mxnet.io](http://data.mxnet.io/data/char_lstm.zip)
+
+To download the data which contains Barack Obama's speeches:
+
+1) Download the dataset with the following command:
+
+ ```bash
+ wget http://data.mxnet.io/data/char_lstm.zip
+ ```
+
+2) Unzip the dataset with the following command:
+
+ ```bash
+ unzip char_lstm.zip -d char_lstm/
+ ```
+
+3) The downloaded data contains President Obama's speeches. You can have sneak peek at the dataset with the following command:
+
+ ```bash
+ head -10 obama.txt
+ ```
+
+Output:
+```
+ Call to Renewal Keynote Address Call to Renewal Pt 1Call to Renewal Part 2 TOPIC: Our Past, Our Future & Vision for America June
+ 28, 2006 Call to Renewal' Keynote Address Complete Text Good morning. I appreciate the opportunity to speak here at the Call to R
+ enewal's Building a Covenant for a New America conference. I've had the opportunity to take a look at your Covenant for a New Ame
+ rica. It is filled with outstanding policies and prescriptions for much of what ails this country. So I'd like to congratulate yo
+ u all on the thoughtful presentations you've given so far about poverty and justice in America, and for putting fire under the fe
+ et of the political leadership here in Washington.But today I'd like to talk about the connection between religion and politics a
+ nd perhaps offer some thoughts about how we can sort through some of the often bitter arguments that we've been seeing over the l
+ ast several years.I do so because, as you all know, we can affirm the importance of poverty in the Bible; and we can raise up and
+ pass out this Covenant for a New America. We can talk to the press, and we can discuss the religious call to address poverty and
+ environmental stewardship all we want, but it won't have an impact unless we tackle head-on the mutual suspicion that sometimes
+```
+
+## Prepare the Data
+
+To preprocess the dataset, define the following utility functions:
+
+* `readContent` - Reads data from the data file.
+* `buildVocab` - Maps each character to a unique Integer ID, i.e., a build a vocabulary
+* `text2Id` - Encodes each sentence with an Integer ID.
+
+Then, use these utility functions to generate vocabulary from the input text file (obama.txt).
+
+To prepare the data:
+
+1) Read the dataset with the following function:
+
+ ```scala
+ scala> import scala.io.Source
+
+ import scala.io.Source
+
+ scala> // Read file
+ scala> def readContent(path: String): String = Source.fromFile(path).mkString
+
+ readContent: (path: String)String
+
+ ```
+
+2) Build a vocabulary with the following function:
+
+ ```scala
+ scala> // Build a vocabulary of what char we have in the content
+ scala> def buildVocab(path: String): Map[String, Int] = {
+ val content = readContent(dataPath).split("\n")
+ var idx = 1 // 0 is left for zero padding
+ var theVocab = Map[String, Int]()
+ for (line <- content) {
+ for (char <- line) {
+ val key = s"$char"
+ if (!theVocab.contains(key)) {
+ theVocab = theVocab + (key -> idx)
+ idx += 1
+ }
+ }
+ }
+ theVocab
+ }
+
+ buildVocab: (path: String)Map[String,Int]
+ ```
+
+3) To assign each character a unique numerical ID, use the following function:
+
+ ```scala
+ scala> def text2Id(sentence: String, theVocab: Map[String, Int]): Array[Int] = {
+ val words = for (char <- sentence) yield theVocab(s"$char")
+ words.toArray
+ }
+
+ text2Id: (sentence: String, theVocab: Map[String,Int])Array[Int]
+ ```
+
+4) Now, build a character vocabulary from the dataset (obama.txt). Change the input filepath (dataPath) to reflect your settings.
+
+ ```scala
+ scala> // Give your system path to the "obama.txt" we have downloaded using previous steps.
+ scala> val dataPath = "obama.txt"
+ dataPath: String = obama.txt
+
+ scala> val vocab = buildVocab(dataPath)
+
+ scala> vocab.size
+ res23: Int = 82
+ ```
+
+
+## Build a Multi-layer LSTM model
+
+Now, create a multi-layer LSTM model.
+
+To create the model:
+
+1) Load the helper files (`Lstm.scala`, `BucketIo.scala` and `RnnModel.scala`).
+`Lstm.scala` contains the definition of the LSTM cell. `BucketIo.scala` creates a sentence iterator. `RnnModel.scala` is used for model inference. The helper files are available on the [MXNet site](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/src/main/scala/ml/dmlc/mxnetexamples/rnn).
+To load them, at the Scala command prompt type:
+
+ ```scala
+ scala> :load ../../../scala-package/examples/src/main/scala/ml/dmlc/mxnet/examples/rnn/Lstm.scala
+ scala> :load ../../../scala-package/examples/src/main/scala/ml/dmlc/mxnet/examples/rnn/BucketIo.scala
+ scala> :load ../../../scala-package/examples/src/main/scala/ml/dmlc/mxnet/examples/rnn/RnnModel.scala
+ ```
+
+2) Set the LSTM hyperparameters as follows:
+
+ ```scala
+ scala> // We can support various input lengths.
+ scala> // For this problem, we cut each input sentence to a length of 129 characters.
+ scala> // So we only need a fixed length bucket length.
+ scala> val buckets = Array(129)
+ buckets: Array[Int] = Array(129)
+
+ scala> // hidden unit in LSTM cell
+ scala> val numHidden = 512
+ numHidden: Int = 512
+
+ scala> // The embedding dimension, which maps a char to a 256 dim vector
+ scala> val numEmbed = 256
+ numEmbed: Int = 256
+
+ scala> // The number of lstm layers
+ scala> val numLstmLayer = 3
+ numLstmLayer: Int = 3
+
+ scala> // The batch size for training
+ scala> val batchSize = 32
+ batchSize: Int = 32
+ ```
+
+3) Now, construct the LSTM network as a symbolic computation graph. Type the following to create a graph in which the model is unrolled for a fixed length explicitly in time.
+
+ ```scala
+ scala> // generate symbol for a length
+ scala> def symGen(seqLen: Int): Symbol = {
+ Lstm.lstmUnroll(numLstmLayer, seqLen, vocab.size + 1,
+ numHidden = numHidden, numEmbed = numEmbed,
+ numLabel = vocab.size + 1, dropout = 0.2f)
+ }
+ symGen: (seqLen: Int)ml.dmlc.mxnet.Symbol
+
+ scala> // create the network symbol
+ scala> val symbol = symGen(buckets(0))
+ symbol: ml.dmlc.mxnet.Symbol = ml.dmlc.mxnet.Symbol@3a589eed
+
+ ```
+
+4) To train the model, initialize states for the LSTM and create a data iterator, which groups the data into buckets.
+Note: The BucketSentenceIter data iterator supports various length examples; however, we use only the fixed length version in this tutorial.
+
+ ```scala
+
+ scala> // initialize states for LSTM
+ scala> val initC = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_c", (batchSize, numHidden))
+
+ initC: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_c,(32,512)),
+ (l1_init_c,(32,512)), (l2_init_c,(32,512)))
+
+ scala> val initH = for (l <- 0 until numLstmLayer) yield (s"l${l}_init_h", (batchSize, numHidden))
+
+ initH: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] = Vector((l0_init_h,(32,512)),
+ (l1_init_h,(32,512)), (l2_init_h,(32,512)))
+
+ scala> val initStates = initC ++ initH
+
+ initStates: scala.collection.immutable.IndexedSeq[(String, (Int, Int))] =
+ Vector((l0_init_c,(32,512)), (l1_init_c,(32,512)), (l2_init_c,(32,512)), (l0_init_h,(32,512)),
+ (l1_init_h,(32,512)), (l2_init_h,(32,512)))
+
+ scala> val dataTrain = new BucketIo.BucketSentenceIter(dataPath, vocab, buckets,
+ batchSize, initStates, seperateChar = "\n",
+ text2Id = text2Id, readContent = readContent)
+
+ dataTrain: BucketIo.BucketSentenceIter = non-empty iterator
+
+ ```
+
+5) You can set more than 100 epochs, but for this tutorial, specify 75 epochs. Each epoch can take as long as 4 minutes on a GPU. In this tutorial, you will use the [ADAM optimizer](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.optimizer.Adam):
+
+ ```scala
+ scala> import ml.dmlc.mxnet._
+ import ml.dmlc.mxnet._
+
+ scala> import ml.dmlc.mxnet.Callback.Speedometer
+ import ml.dmlc.mxnet.Callback.Speedometer
+
+ scala> import ml.dmlc.mxnet.optimizer.Adam
+ import ml.dmlc.mxnet.optimizer.Adam
+
+ scala> // and we will see result by training 75 epochs
+ scala> val numEpoch = 75
+ numEpoch: Int = 75
+
+ scala> // learning rate
+ scala> val learningRate = 0.001f
+ learningRate: Float = 0.001
+
+ ```
+
+6) Define the perplexity utility function for the evaluation metric which is used to calculate the negative log-likelihood during training.
+
+ ```scala
+ scala> def perplexity(label: NDArray, pred: NDArray): Float = {
+ val shape = label.shape
+ val size = shape(0) * shape(1)
+ val labelT = {
+ val tmp = label.toArray.grouped(shape(1)).toArray
+ val result = Array.fill[Float](size)(0f)
+ var idx = 0
+ for (i <- 0 until shape(1)) {
+ for (j <- 0 until shape(0)) {
+ result(idx) = tmp(j)(i)
+ idx += 1
+ }
+ }
+ result
+ }
+ var loss = 0f
+ val predArray = pred.toArray.grouped(pred.shape(1)).toArray
+ for (i <- 0 until pred.shape(0)) {
+ loss += -Math.log(Math.max(1e-10, predArray(i)(labelT(i).toInt)).toFloat).toFloat
+ }
+ loss / size
+ }
+
+ perplexity: (label: ml.dmlc.mxnet.NDArray, pred: ml.dmlc.mxnet.NDArray)Float
+
+ scala> def doCheckpoint(prefix: String): EpochEndCallback = new EpochEndCallback {
+ override def invoke(epoch: Int, symbol: Symbol,
+ argParams: Map[String, NDArray],
+ auxStates: Map[String, NDArray]): Unit = {
+ Model.saveCheckpoint(prefix, epoch + 1, symbol, argParams, auxStates)
+ }
+ }
+
+ doCheckpoint: (prefix: String)ml.dmlc.mxnet.EpochEndCallback
+
+ ```
+
+7) Define the initializer that is required for creating a model, as follows:
+
+ ```scala
+ scala> val initializer = new Xavier(factorType = "in", magnitude = 2.34f)
+
+ initializer: ml.dmlc.mxnet.Xavier = ml.dmlc.mxnet.Xavier@54e8f10a
+
+ ```
+
+8) Now, you have implemented all the supporting infrastructures for the char-lstm model. To train the model, use the standard [MXNet high-level API](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.FeedForward). You can train the model on a single GPU or CPU from multiple GPUs or CPUs by changing ```scala .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))``` to ```scala .setContext(Array(Context.gpu(0)))```:
+
+ ```scala
+ scala> val model = FeedForward.newBuilder(symbol)
+ .setContext(Array(Context.gpu(0),Context.gpu(1),Context.gpu(2),Context.gpu(3)))
+ .setNumEpoch(numEpoch)
+ .setOptimizer(new Adam(learningRate = learningRate, wd = 0.00001f))
+ .setInitializer(initializer)
+ .setTrainData(dataTrain)
+ .setEvalMetric(new CustomMetric(perplexity, name = "perplexity"))
+ .setBatchEndCallback(new Speedometer(batchSize, 20))
+ .setEpochEndCallback(doCheckpoint("obama"))
+ .build()
+
+ model: ml.dmlc.mxnet.FeedForward = ml.dmlc.mxnet.FeedForward@4926f6c7
+ ```
+
+Now, you have an LSTM model and you've trained it. Use this model to create the inference.
+
+## Build the Inference Model
+
+You can now sample sentences from the trained model. The sampler works as follows:
+- Takes some fixed character set (e.g., "The United States") and feeds it into the LSTM as the starting input.
+- The LSTM produces an output distribution over the vocabulary and a state in the first time step then, samples a character from the output distribution and fixes it as the second character.
+- In the next time step, feeds the previously sampled character as input.
+- Continues running until it has sampled enough characters. Note we are running mini-batches, so several sentences could be sampled simultaneously.
+
+To build the inference model, define the following utility functions that help MXNet make inferences:
+
+* `makeRevertVocab` - Reverts the key value in the dictionary for easy access to characters while predicting
+* `makeInput` - Uses a given character as input
+* `cdf`, `choice` - `cdf` is a helper function for the `choice` function, which is used to create random samples
+* `makeOutput` - Directs the model to use either random output or fixed output by choosing the option with the greatest probability.
+
+ ```scala
+ scala> import scala.util.Random
+
+ scala> // helper structure for prediction
+ scala> def makeRevertVocab(vocab: Map[String, Int]): Map[Int, String] = {
+ var dic = Map[Int, String]()
+ vocab.foreach { case (k, v) =>
+ dic = dic + (v -> k)
+ }
+ dic
+ }
+
+ makeRevertVocab: (vocab: Map[String,Int])Map[Int,String]
+
+ scala> // make input from char
+ scala> def makeInput(char: Char, vocab: Map[String, Int], arr: NDArray): Unit = {
+ val idx = vocab(s"$char")
+ val tmp = NDArray.zeros(1)
+ tmp.set(idx)
+ arr.set(tmp)
+ }
+
+ makeInput: (char: Char, vocab: Map[String,Int], arr: ml.dmlc.mxnet.NDArray)Unit
+
+ scala> // helper function for random sample
+ scala> def cdf(weights: Array[Float]): Array[Float] = {
+ val total = weights.sum
+ var result = Array[Float]()
+ var cumsum = 0f
+ for (w <- weights) {
+ cumsum += w
+ result = result :+ (cumsum / total)
+ }
+ result
+ }
+
+ cdf: (weights: Array[Float])Array[Float]
+
+ scala> def choice(population: Array[String], weights: Array[Float]): String = {
+ assert(population.length == weights.length)
+ val cdfVals = cdf(weights)
+ val x = Random.nextFloat()
+ var idx = 0
+ var found = false
+ for (i <- 0 until cdfVals.length) {
+ if (cdfVals(i) >= x && !found) {
+ idx = i
+ found = true
+ }
+ }
+ population(idx)
+ }
+
+ choice: (population: Array[String], weights: Array[Float])String
+
+ scala> // we can use random output or fixed output by choosing largest probability
+ scala> def makeOutput(prob: Array[Float], vocab: Map[Int, String],
+ sample: Boolean = false, temperature: Float = 1f): String = {
+ var idx = -1
+ val char = if (sample == false) {
+ idx = ((-1f, -1) /: prob.zipWithIndex) { (max, elem) =>
+ if (max._1 < elem._1) elem else max
+ }._2
+ if (vocab.contains(idx)) vocab(idx)
+ else ""
+ } else {
+ val fixDict = Array("") ++ (1 until vocab.size + 1).map(i => vocab(i))
+ var scaleProb = prob.map(x => if (x < 1e-6) 1e-6 else if (x > 1 - 1e-6) 1 - 1e-6 else x)
+ var rescale = scaleProb.map(x => Math.exp(Math.log(x) / temperature).toFloat)
+ val sum = rescale.sum.toFloat
+ rescale = rescale.map(_ / sum)
+ choice(fixDict, rescale)
+ }
+ char
+ }
+
+ makeOutput: (prob: Array[Float], vocab: Map[Int,String], sample: Boolean, temperature: Float)String
+
+ ```
+
+1) Build the inference model:
+
+ ```scala
+ scala> // load from check-point
+ scala> val (_, argParams, _) = Model.loadCheckpoint("obama", 75)
+
+ scala> // build an inference model
+ scala> val model = new RnnModel.LSTMInferenceModel(numLstmLayer, vocab.size + 1, \
+ numHidden = numHidden, numEmbed = numEmbed, \
+ numLabel = vocab.size + 1, argParams = argParams, \
+ ctx = Context.cpu(), dropout = 0.2f)
+
+ model: RnnModel.LSTMInferenceModel = RnnModel$LSTMInferenceModel@2f0c0319
+ ```
+
+2) Now you can generate a sequence of 1200 characters (you can select any number of characters you want) starting with "The United States" as follows:
+
+ ```scala
+
+ scala> val seqLength = 1200
+ seqLength: Int = 1200
+
+ scala> val inputNdarray = NDArray.zeros(1)
+ inputNdarray: ml.dmlc.mxnet.NDArray = ml.dmlc.mxnet.NDArray@9c231a24
+
+ scala> val revertVocab = makeRevertVocab(vocab)
+
+ scala> // Feel free to change the starter sentence
+
+ scala> var output = "The United States"
+ output: String = The United States
+
+ scala> val randomSample = true
+ randomSample: Boolean = true
+
+ scala> var newSentence = true
+ newSentence: Boolean = true
+
+ scala> val ignoreLength = output.length()
+ ignoreLength: Int = 17
+
+ scala> for (i <- 0 until seqLength) {
+ if (i <= ignoreLength - 1) makeInput(output(i), vocab, inputNdarray)
+ else makeInput(output.takeRight(1)(0), vocab, inputNdarray)
+ val prob = model.forward(inputNdarray, newSentence)
+ newSentence = false
+ val nextChar = makeOutput(prob, revertVocab, randomSample)
+ if (nextChar == "") newSentence = true
+ if (i >= ignoreLength) output = output ++ nextChar
+ }
+
+ scala> output
+
+ res7: String = The United States who have been blessed no companies would be proud that the challenges we face, it's not as directly untelle are in my daughters - you can afford -- life-saving march care and poor information and receiving battle against other speeces and lead its people. After champions of 2006, and because Africa in America, separate has been conferenced by children ation of discrimination, we remember all of this, succeeded in any other feelings of a palently b [...]
+
+ ```
+
+
+You can see the output generated from Obama's speeches. All of the line breaks, punctuation, and uppercase and lowercase letters were produced by the sampler (no post-processing was performed).
+
+
+## Next Steps
+* [Scala API](http://mxnet.io/api/scala/)
+* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/)
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/versions/master/_sources/tutorials/scala/mnist.txt b/versions/master/_sources/tutorials/scala/mnist.txt
new file mode 100644
index 0000000..ad55ee4
--- /dev/null
+++ b/versions/master/_sources/tutorials/scala/mnist.txt
@@ -0,0 +1,117 @@
+# Handwritten Digit Recognition
+
+This Scala tutorial guides you through a classic computer vision application: identifying hand written digits.
+
+Let's train a 3-layer network (i.e multilayer perceptron network) on the MNIST dataset to classify handwritten digits.
+
+## Prerequisites
+To complete this tutorial, we need:
+
+- to compile the latest MXNet version. See the MXNet installation instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html).
+- to compile the Scala API. See Scala API build instructions in [Build](https://github.com/dmlc/mxnet/tree/master/scala-package).
+
+## Define the Network
+
+First, define the neural network's architecture using the Symbol API:
+
+```scala
+import ml.dmlc.mxnet._
+import ml.dmlc.mxnet.optimizer.SGD
+
+// model definition
+val data = Symbol.Variable("data")
+val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
+val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
+val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
+val act2 = Symbol.Activation(name = "relu2")()(Map("data" -> fc2, "act_type" -> "relu"))
+val fc3 = Symbol.FullyConnected(name = "fc3")()(Map("data" -> act2, "num_hidden" -> 10))
+val mlp = Symbol.SoftmaxOutput(name = "sm")()(Map("data" -> fc3))
+```
+
+## Load the Data
+
+Then, load the training and validation data using DataIterators.
+
+You can download the MNIST data using the [get_mnist_data script](https://github.com/dmlc/mxnet/blob/master/scala-package/core/scripts/get_mnist_data.sh). We've already written a DataIterator for the MNIST dataset:
+
+```scala
+// load MNIST dataset
+val trainDataIter = IO.MNISTIter(Map(
+ "image" -> "data/train-images-idx3-ubyte",
+ "label" -> "data/train-labels-idx1-ubyte",
+ "data_shape" -> "(1, 28, 28)",
+ "label_name" -> "sm_label",
+ "batch_size" -> "50",
+ "shuffle" -> "1",
+ "flat" -> "0",
+ "silent" -> "0",
+ "seed" -> "10"))
+
+val valDataIter = IO.MNISTIter(Map(
+ "image" -> "data/t10k-images-idx3-ubyte",
+ "label" -> "data/t10k-labels-idx1-ubyte",
+ "data_shape" -> "(1, 28, 28)",
+ "label_name" -> "sm_label",
+ "batch_size" -> "50",
+ "shuffle" -> "1",
+ "flat" -> "0", "silent" -> "0"))
+```
+
+## Train the model
+
+We can use the FeedForward builder to train our network:
+
+```scala
+// setup model and fit the training data
+val model = FeedForward.newBuilder(mlp)
+ .setContext(Context.cpu())
+ .setNumEpoch(10)
+ .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f))
+ .setTrainData(trainDataIter)
+ .setEvalData(valDataIter)
+ .build()
+```
+
+## Make predictions
+
+Finally, let's make predictions against the validation dataset and compare the predicted labels with the real labels.
+
+```scala
+val probArrays = model.predict(valDataIter)
+// in this case, we do not have multiple outputs
+require(probArrays.length == 1)
+val prob = probArrays(0)
+
+// get real labels
+import scala.collection.mutable.ListBuffer
+valDataIter.reset()
+val labels = ListBuffer.empty[NDArray]
+while (valDataIter.hasNext) {
+ val evalData = valDataIter.next()
+ labels += evalData.label(0).copy()
+}
+val y = NDArray.concatenate(labels)
+
+// get predicted labels
+val predictedY = NDArray.argmax_channel(prob)
+require(y.shape == predictedY.shape)
+
+// calculate accuracy
+var numCorrect = 0
+var numTotal = 0
+for ((labelElem, predElem) <- y.toArray zip predictedY.toArray) {
+ if (labelElem == predElem) {
+ numCorrect += 1
+ }
+ numTotal += 1
+}
+val acc = numCorrect.toFloat / numTotal
+println(s"Final accuracy = $acc")
+```
+
+Check out more MXNet Scala examples below.
+
+## Next Steps
+* [Scala API](http://mxnet.io/api/scala/)
+* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/)
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/versions/master/_sources/tutorials/scala/mxnet_scala_on_intellij.txt b/versions/master/_sources/tutorials/scala/mxnet_scala_on_intellij.txt
new file mode 100644
index 0000000..eb667e9
--- /dev/null
+++ b/versions/master/_sources/tutorials/scala/mxnet_scala_on_intellij.txt
@@ -0,0 +1,62 @@
+# Run MXNet Scala Examples Using the IntelliJ IDE
+
+This tutorial guides you through setting up a Scala project in the IntelliJ IDE and shows how to use an MXNet package from your application.
+
+## Prerequisites:
+To use this tutorial, you need:
+
+- [Maven 3](https://maven.apache.org/install.html).
+- [Scala 2.11.8](https://www.scala-lang.org/download/2.11.8.html).
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html).
+- The MXNet package for Scala. For installation instructions, see [this procedure](http://mxnet.io/get_started/osx_setup.html#install-the-mxnet-package-for-scala).
+- [IntelliJ IDE](https://www.jetbrains.com/idea/).
+
+## Set Up Your Project
+
+- Install the plugin for IntelliJ IDE by following these steps:
+ On **Menu**, choose **Preferences**, choose **Plugins**, type **Scala**, and then choose **Install**.
+
+- Follow the instructions for [Scala plugin setup for IDE](https://www.jetbrains.com/help/idea/2016.3/scala.html).
+
+- When you build the MXNet package with Scala, a JAR file called `mxnet-full_${scala.binary.version}-${platform}` is generated in `native/<your-architecture>/target` directory. You need this file to create an example package that has a dependency on MXNet.
+
+- Specify project dependencies in pom.xml:
+
+```HTML
+ <dependencies>
+ <dependency>
+ <groupId>ml.dmlc.mxnet</groupId>
+ <artifactId>mxnet-full_${scala.binary.version}-${platform}</artifactId>
+ <version>0.1.1</version>
+ <scope>system</scope>
+ <systemPath>`MXNet-Scala-jar-path`</systemPath>
+ </dependency>
+ <dependency>
+ <groupId>args4j</groupId>
+ <artifactId>args4j</artifactId>
+ <version>2.0.29</version>
+ </dependency>
+ </dependencies>
+```
+
+Be sure to change the system path of MXNet-Scala-jar, which is in the `native/<your-architecture>/target` directory.
+
+- Choose the example project, choose Maven, and then reimport. These steps add all of the dependencies in pom.xml as external libraries in your project.
+
+- To build the project, choose Menu, choose Build, and then choose Rebuild Project. If errors are reported in the IDE, address them.
+
+- You can also compile the project by using the following command at the command line.
+
+```bash
+ cd mxnet-scala-example
+ mvn clean package
+```
+
+- This also generates a file called mxnet-scala-example-0.1-SNAPSHOT.jar for your application.
+
+## Next Steps
+For more information about MXNet Scala resources, see the following:
+
+* [Scala API](http://mxnet.io/api/scala/)
+* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/)
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/versions/master/_sources/tutorials/speech_recognition/baidu_warp_ctc.txt b/versions/master/_sources/tutorials/speech_recognition/baidu_warp_ctc.txt
new file mode 100644
index 0000000..6277a19
--- /dev/null
+++ b/versions/master/_sources/tutorials/speech_recognition/baidu_warp_ctc.txt
@@ -0,0 +1,97 @@
+# Using Baidu Warp-CTC with MXNet
+
+
+Baidu-WarpCTC is a CTC implementation by Baidu that supports using GPU processors. It supports using CTC with LSTM to solve label alignment problems in many areas, such as OCR and speech recognition.
+
+You can get the source code for the example on [GitHub](https://github.com/dmlc/mxnet/tree/master/example/warpctc).
+
+## Install Baidu Warp-CTC
+
+```
+ cd ~/
+ git clone https://github.com/baidu-research/warp-ctc
+ cd warp-ctc
+ mkdir build
+ cd build
+ cmake ..
+ make
+ sudo make install
+```
+
+## Enable Warp-CTC in MXNet
+
+```
+ comment out following lines in make/config.mk
+ WARPCTC_PATH = $(HOME)/warp-ctc
+ MXNET_PLUGINS += plugin/warpctc/warpctc.mk
+
+ rebuild mxnet by
+ make clean && make -j4
+```
+
+## Run Examples
+
+There are two examples. One is a toy example that validates CTC integration. The second is an OCR example with LSTM and CTC. You can run it by typing the following code:
+
+```
+ cd examples/warpctc
+ python lstm_ocr.py
+```
+
+The OCR example is constructed as follows:
+
+1. It generates a 80x30-pixel image for a 4-digit captcha using a Python captcha library.
+2. The 80x30 image is used as 80 input for LSTM, and every input is one column of the image (a 30 dim vector).
+3. The output layer use CTC loss.
+
+The following code shows the detailed construction of the net:
+
+```
+ def lstm_unroll(num_lstm_layer, seq_len,
+ num_hidden, num_label):
+ param_cells = []
+ last_states = []
+ for i in range(num_lstm_layer):
+ param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("l%d_i2h_weight" % i),
+ i2h_bias=mx.sym.Variable("l%d_i2h_bias" % i),
+ h2h_weight=mx.sym.Variable("l%d_h2h_weight" % i),
+ h2h_bias=mx.sym.Variable("l%d_h2h_bias" % i)))
+ state = LSTMState(c=mx.sym.Variable("l%d_init_c" % i),
+ h=mx.sym.Variable("l%d_init_h" % i))
+ last_states.append(state)
+ assert(len(last_states) == num_lstm_layer)
+ data = mx.sym.Variable('data')
+ label = mx.sym.Variable('label')
+
+ #every column of image is an input, there are seq_len inputs
+ wordvec = mx.sym.SliceChannel(data=data, num_outputs=seq_len, squeeze_axis=1)
+ hidden_all = []
+ for seqidx in range(seq_len):
+ hidden = wordvec[seqidx]
+ for i in range(num_lstm_layer):
+ next_state = lstm(num_hidden, indata=hidden,
+ prev_state=last_states[i],
+ param=param_cells[i],
+ seqidx=seqidx, layeridx=i)
+ hidden = next_state.h
+ last_states[i] = next_state
+ hidden_all.append(hidden)
+ hidden_concat = mx.sym.Concat(*hidden_all, dim=0)
+ pred = mx.sym.FullyConnected(data=hidden_concat, num_hidden=11)
+
+ # here we do NOT need to transpose label as other lstm examples do
+ label = mx.sym.Reshape(data=label, target_shape=(0,))
+ #label should be int type, so use cast
+ label = mx.sym.Cast(data = label, dtype = 'int32')
+ sm = mx.sym.WarpCTC(data=pred, label=label, label_length = num_label, input_length = seq_len)
+ return sm
+```
+
+## Supporting Multi-label Length
+
+Provide labels with length b. For samples whose label length is smaller than b, append 0 to the label data to make it have length b.
+
+0 is reserved for a blank label.
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/versions/master/_sources/tutorials/speech_recognition/speech_lstm.txt b/versions/master/_sources/tutorials/speech_recognition/speech_lstm.txt
new file mode 100644
index 0000000..17e2ca0
--- /dev/null
+++ b/versions/master/_sources/tutorials/speech_recognition/speech_lstm.txt
@@ -0,0 +1,156 @@
+# Speech LSTM
+You can get the source code for these examples on [GitHub](https://github.com/dmlc/mxnet/tree/master/example/speech-demo).
+
+## Speech Acoustic Modeling Example
+
+The examples folder contains examples for speech recognition:
+
+- [lstm_proj.py](https://github.com/dmlc/mxnet/tree/master/example/speech-demo/lstm_proj.py): Functions for building an LSTM network with and without a projection layer.
+- [io_util.py](https://github.com/dmlc/mxnet/tree/master/example/speech-demo/io_util.py): Wrapper functions for `DataIter` over speech data.
+- [train_lstm_proj.py](https://github.com/dmlc/mxnet/tree/master/example/speech-demo/train_lstm_proj.py): A script for training an LSTM acoustic model.
+- [decode_mxnet.py](https://github.com/dmlc/mxnet/tree/master/example/speech-demo/decode_mxnet.py): A script for decoding an LSTMP acoustic model.
+- [default.cfg](https://github.com/dmlc/mxnet/tree/master/example/speech-demo/default.cfg): Configuration for training on the `AMI` SDM1 dataset. You can use it as a template for writing other configuration files.
+- [python_wrap](https://github.com/dmlc/mxnet/tree/master/example/speech-demo/python_wrap): C wrappers for Kaldi C++ code, built into an .so file. Python code that loads the .so file and calls the C wrapper functions in `io_func/feat_readers/reader_kaldi.py`.
+
+Connect to Kaldi:
+
+- [decode_mxnet.sh](https://github.com/dmlc/mxnet/tree/master/example/speech-demo/decode_mxnet.sh): Called by Kaldi to decode an acoustic model trained by MXNet (select the `simple` method for decoding).
+
+A full receipt:
+
+- [run_ami.sh](https://github.com/dmlc/mxnet/tree/master/example/speech-demo/run_ami.sh): A full receipt to train and decode an acoustic model on AMI. It takes features and alignment from Kaldi to train an acoustic model and decode it.
+
+To create the speech acoustic modeling example, use the following steps.
+
+### Build Kaldi
+
+Build Kaldi as shared libraries if you have not already done so.
+
+```bash
+cd kaldi/src
+./configure --shared # and other options that you need
+make depend
+make
+```
+
+### Build the Python Wrapper
+
+1. Copy or link the attached `python_wrap` folder to `kaldi/src`.
+2. Compile python_wrap/.
+
+```
+cd kaldi/src/python_wrap/
+make
+```
+
+### Extract Features and Prepare Frame-level Labels
+
+The acoustic models use Mel filter-bank or MFCC as input features. They also need to use Kaldi to perform force-alignment to generate frame-level labels from the text transcriptions. For example, if you want to work on the `AMI` data `SDM1`, you can run `kaldi/egs/ami/s5/run_sdm.sh`. Before you can run the examples, you need to configure some paths in `kaldi/egs/ami/s5/cmd.sh` and `kaldi/egs/ami/s5/run_sdm.sh`. Refer to Kaldi's documentation for details.
+
+The default `run_sdm.sh` script generates the force-alignment labels in their stage 7, and saves the force-aligned labels in `exp/sdm1/tri3a_ali`. The default script generates MFCC features (13-dimensional). You can try training with the MFCC features, or you can create Mel filter-bank features by yourself. For example, you can use a script like this to compute Mel filter-bank features using Kaldi:
+
+```bash
+#!/bin/bash -u
+
+. ./cmd.sh
+. ./path.sh
+
+# SDM - Single Distant Microphone
+micid=1 #which mic from array should be used?
+mic=sdm$micid
+
+# Set bash to 'debug' mode, it prints the commands (option '-x') and exits on :
+# -e 'error', -u 'undefined variable', -o pipefail 'error in pipeline',
+set -euxo pipefail
+
+# Path where AMI gets downloaded (or where locally available):
+AMI_DIR=$PWD/wav_db # Default,
+data_dir=$PWD/data/$mic
+
+# make filter bank data
+for dset in train dev eval; do
+ steps/make_fbank.sh --nj 48 --cmd "$train_cmd" $data_dir/$dset \
+ $data_dir/$dset/log $data_dir/$dset/data-fbank
+ steps/compute_cmvn_stats.sh $data_dir/$dset \
+ $data_dir/$dset/log $data_dir/$dset/data
+
+ apply-cmvn --utt2spk=ark:$data_dir/$dset/utt2spk \
+ scp:$data_dir/$dset/cmvn.scp scp:$data_dir/$dset/feats.scp \
+ ark,scp:$data_dir/$dset/feats-cmvn.ark,$data_dir/$dset/feats-cmvn.scp
+
+ mv $data_dir/$dset/feats-cmvn.scp $data_dir/$dset/feats.scp
+done
+```
+`apply-cmvn` provides mean-variance normalization. The default setup was applied per speaker. It's more common to perform mean-variance normalization for the whole corpus, and then feed the results to the neural networks:
+
+```
+ compute-cmvn-stats scp:data/sdm1/train_fbank/feats.scp data/sdm1/train_fbank/cmvn_g.ark
+ apply-cmvn --norm-vars=true data/sdm1/train_fbank/cmvn_g.ark scp:data/sdm1/train_fbank/feats.scp ark,scp:data/sdm1/train_fbank_gcmvn/feats.ark,data/sdm1/train_fbank_gcmvn/feats.scp
+```
+Note that Kaldi always tries to find features in `feats.scp`. Ensure that the normalized features are organized as Kaldi expects them during decoding.
+
+Finally, put the features and labels together in a file so that MXNet can find them. More specifically, for each data set (train, dev, eval), you will need to create a file similar to `train_mxnet.feats`, with the following contents:
+
+```
+TRANSFORM scp:feat.scp
+scp:label.scp
+```
+
+`TRANSFORM` is the transformation you want to apply to the features. By default, we use `NO_FEATURE_TRANSFORM`. The `scp:` syntax is from Kaldi. `feat.scp` is typically the file from `data/sdm1/train/feats.scp`, and `label.scp` is converted from the force-aligned labels located in `exp/sdm1/tri3a_ali`. Because the force-alignments are generated only on the training data, we split the training set in two, using a 90/10 ratio, and then use the 1/10 holdout as the dev set (validation set). [...]
+
+### Run MXNet Acoustic Model Training
+
+1. Return to the speech demo directory in MXNet. Make a copy of `default.cfg`, and edit the necessary parameters, such as the path to the dataset you just prepared.
+2. Run `python train_lstm.py --configfile=your-config.cfg`. For help, use `python train_lstm.py --help`. You can set all of the configuration parameters in `default.cfg`, the customized config file, and through the command line (e.g., using `--train_batch_size=50`). The latter values overwrite the former ones.
+
+Here are some example outputs from training on the TIMIT dataset:
+
+```
+Example output for TIMIT:
+Summary of dataset ==================
+bucket of len 100 : 3 samples
+bucket of len 200 : 346 samples
+bucket of len 300 : 1496 samples
+bucket of len 400 : 974 samples
+bucket of len 500 : 420 samples
+bucket of len 600 : 90 samples
+bucket of len 700 : 11 samples
+bucket of len 800 : 2 samples
+Summary of dataset ==================
+bucket of len 100 : 0 samples
+bucket of len 200 : 28 samples
+bucket of len 300 : 169 samples
+bucket of len 400 : 107 samples
+bucket of len 500 : 41 samples
+bucket of len 600 : 6 samples
+bucket of len 700 : 3 samples
+bucket of len 800 : 0 samples
+2016-04-21 20:02:40,904 Epoch[0] Train-Acc_exlude_padding=0.154763
+2016-04-21 20:02:40,904 Epoch[0] Time cost=91.574
+2016-04-21 20:02:44,419 Epoch[0] Validation-Acc_exlude_padding=0.353552
+2016-04-21 20:04:17,290 Epoch[1] Train-Acc_exlude_padding=0.447318
+2016-04-21 20:04:17,290 Epoch[1] Time cost=92.870
+2016-04-21 20:04:20,738 Epoch[1] Validation-Acc_exlude_padding=0.506458
+2016-04-21 20:05:53,127 Epoch[2] Train-Acc_exlude_padding=0.557543
+2016-04-21 20:05:53,128 Epoch[2] Time cost=92.390
+2016-04-21 20:05:56,568 Epoch[2] Validation-Acc_exlude_padding=0.548100
+```
+
+The final frame accuracy was approximately 62%.
+
+### Run Decode on the Trained Acoustic Model
+
+1. Estimate senone priors by running `python make_stats.py --configfile=your-config.cfg | copy-feats ark:- ark:label_mean.ark` (edit necessary items, such as the path to the training dataset). This command generates the label counts in `label_mean.ark`.
+2. Link to the necessary Kaldi decode setup, e.g., `local/` and `utils/` and run `./run_ami.sh --model prefix model --num_epoch num`.
+
+Here are the results for the TIMIT and AMI test sets (using the default setup, three-layer LSTM with projection layers):
+
+ | Corpus | WER |
+ |--------|-----|
+ |TIMIT | 18.9|
+ |AMI | 51.7 (42.2) |
+
+For AMI 42.2 was evaluated non-overlapped speech. The Kaldi-HMM baseline was 67.2%, and DNN was 57.5%.
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
diff --git a/versions/master/_sources/tutorials/unsupervised_learning/auto_encoders.txt b/versions/master/_sources/tutorials/unsupervised_learning/auto_encoders.txt
new file mode 100644
index 0000000..7dfefac
--- /dev/null
+++ b/versions/master/_sources/tutorials/unsupervised_learning/auto_encoders.txt
@@ -0,0 +1,5 @@
+# Autoencoders
+Get the source code for an example of autoencoders running on MXNet on GitHub in the [autoencoder](https://github.com/dmlc/mxnet/tree/master/example/autoencoder) folder.
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
\ No newline at end of file
diff --git a/versions/master/_sources/tutorials/unsupervised_learning/gan.txt b/versions/master/_sources/tutorials/unsupervised_learning/gan.txt
new file mode 100644
index 0000000..6491806
--- /dev/null
+++ b/versions/master/_sources/tutorials/unsupervised_learning/gan.txt
@@ -0,0 +1,5 @@
+# Generative Adversarial Network
+Get the source code for an example of a generative adversarial network (GAN) running on MXNet on GitHub in the [gan](https://github.com/dmlc/mxnet/tree/master/example/gan) folder.
+
+## Next Steps
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
\ No newline at end of file
diff --git a/versions/master/_sources/tutorials/vision/large_scale_classification.txt b/versions/master/_sources/tutorials/vision/large_scale_classification.txt
new file mode 100644
index 0000000..1cf2270
--- /dev/null
+++ b/versions/master/_sources/tutorials/vision/large_scale_classification.txt
@@ -0,0 +1,255 @@
+# Large Scale Image Classification
+
+Training a neural network with a large number of images presents several challenges. Even with the latest GPUs, it is not possible to train large networks using a large number of images in a reasonable amount of time using a single GPU. This problem can be somewhat mitigated by using multiple GPUs in a single machine. But there is a limit to the number of GPUs that can be attached to one machine (typically 8 or 16). This tutorial explains how to train large networks with terabytes of dat [...]
+
+## Prerequisites
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html).
+
+- [OpenCV Python library](http://opencv.org/opencv-3-2.html)
+
+```
+$ pip install opencv-python
+```
+
+## Preprocessing
+
+### Disk space
+The first step in training with large data is downloading the data and preprocessing it. For this tutorial, we will be using the full ImageNet dataset. Note that, at least 2 TB of disk space is required to download and preprocess this data. It is strongly recommended to use SSD instead of HDD. SSD is much better at dealing with a large number of small image files. After the preprocessing completes and images are packed into recordIO files, HDD should be fine for training.
+
+In this tutorial, we will use an AWS storage instance for data preprocessing. The storage instance `i3.4xlarge` has 3.8 TB of disk space across two NVMe SSD disks. We will use software RAID to combine them into one disk and mount it at `~/data`.
+
+```
+sudo mdadm --create --verbose /dev/md0 --level=stripe --raid-devices=2 \
+ /dev/nvme0n1 /dev/nvme1n1
+sudo mkfs /dev/md0
+sudo mkdir ~/data
+sudo mount /dev/md0 ~/data
+sudo chown ${whoami} ~/data
+```
+
+We now have sufficient disk space to download and preprocess the data.
+
+### Download ImageNet
+
+In this tutorial, we will be using the full ImageNet dataset which can be downloaded from http://www.image-net.org/download-images. `fall11_whole.tar` contains all the images. This file is 1.2 TB in size and could take a long time to download.
+
+After downloading, untar the file.
+```
+export ROOT=full
+mkdir $ROOT
+tar -xvf fall11_whole.tar -C $ROOT
+```
+
+That should give you a collection of tar files. Each tar file represents a category and contains all images belonging to that category. We can unzip each tar file and copy the images into a folder named after the name of the tar file.
+
+```
+for i in $ROOT/*.tar; do j=${i%.*}; echo $j; mkdir -p $j; tar -xf $i -C $j; done
+rm $ROOT/*.tar
+
+ls $ROOT | head
+n00004475
+n00005787
+n00006024
+n00006484
+n00007846
+n00015388
+n00017222
+n00021265
+n00021939
+n00120010
+```
+
+### Remove uncommon classes for transfer learning (optional)
+A common reason to train a network on ImageNet data is to use it for transfer learning (including feature extraction or fine-tuning other models). According to [this](https://arxiv.org/pdf/1608.08614v1.pdf) study, classes with too few images don’t help in transfer learning. So, we could remove classes with fewer than a certain number of images. The following code will remove classes with less than 500 images.
+
+```
+BAK=${ROOT}_filtered
+mkdir -p ${BAK}
+for c in ${ROOT}/n*; do
+ count=`ls $c/*.JPEG | wc -l`
+ if [ "$count" -gt "500" ]; then
+ echo "keep $c, count = $count"
+ else
+ echo "remove $c, $count"
+ mv $c ${BAK}/
+ fi
+done
+```
+
+### Generate a validation set
+To ensure we don’t overfit the data, we will create a validation set separate from the training set. During training, we will monitor loss on the validation set frequently. We create the validation set by picking fifty random images from each class and moving them to the validation set.
+```
+VAL_ROOT=${ROOT}_val
+mkdir -p ${VAL_ROOT}
+for i in ${ROOT}/n*; do
+ c=`basename $i`
+ echo $c
+ mkdir -p ${VAL_ROOT}/$c
+ for j in `ls $i/*.JPEG | shuf | head -n 50`; do
+ mv $j ${VAL_ROOT}/$c/
+ done
+done
+```
+
+### Pack images into record files
+While MXNet can read image files directly, it is recommended to pack the image files into a recordIO file for increased performance. MXNet provides a tool (tools/im2rec.py) to do this. To use this tool, MXNet and OpenCV’s python module needs to be installed in the system.
+
+Set the environment variable `MXNET` to point to the MXNet installation directory and `NAME` to the name of the dataset. Here, we assume MXNet is installed at `~/mxnet`
+
+```
+MXNET=~/mxnet
+NAME=full_imagenet_500_filtered
+```
+
+To create the recordIO files, we first create a list of images we want in the recordIO files and then use `im2rec` to pack images in the list into recordIO files. We create this list in `train_meta`. Training data is around 1TB. We split it into 8 parts, with each part roughly 100 GB in size.
+
+```
+mkdir -p train_meta
+python ${MXNET}/tools/im2rec.py --list True --chunks 8 --recursive True \
+train_meta/${NAME} ${ROOT}
+```
+
+We then resize the images such that the short edge is 480 pixels long and pack the images into recordIO files. Since most of the work is disk I/O, we use multiple (16) threads to get the work done faster.
+
+```
+python ${MXNET}/tools/im2rec.py --resize 480 --quality 90 \
+--num-thread 16 train_meta/${NAME} ${ROOT}
+```
+
+Once done, we move the rec files into a folder named `train`.
+
+```
+mkdir -p train
+mv train_meta/*.rec train/
+```
+
+We do similar preprocessing for the validation set.
+
+```
+mkdir -p val_meta
+python ${MXNET}/tools/im2rec.py --list True --recursive True \
+val_meta/${NAME} ${VAL_ROOT}
+python ${MXNET}/tools/im2rec.py --resize 480 --quality 90 \
+--num-thread 16 val_meta/${NAME} ${VAL_ROOT}
+mkdir -p val
+mv val_meta/*.rec val/
+```
+
+We now have all training and validation images in recordIO format in `train` and `val` directories respectively. We can now use these `.rec` files for training.
+
+## Training
+
+[ResNet](https://arxiv.org/abs/1512.03385) has shown its effectiveness on ImageNet competition. Our experiments also [reproduced](https://github.com/tornadomeet/ResNet) the results reported in the paper. As we increase the number of layers from 18 to 152, we see steady improvement in validation accuracy. Given this is a huge dataset, we will use Resnet with 152 layers.
+
+Due to the huge computational complexity, even the fastest GPU needs more than one day for a single pass of the data. We often need tens of epochs before the training converges to good validation accuracy. While we can use multiple GPUs in a machine, the number of GPUs in a machine is often limited to 8 or 16. For faster training, in this tutorial, we will use multiple machines each containing multiple GPUs to train the model.
+
+### Setup
+
+We will use 16 machines (P2.16x instances), each containing 16 GPUs (Tesla K80). These machines are interconnected via 20 Gbps ethernet.
+
+AWS CloudFormation makes it very easy to create deep learning clusters. We follow instructions from [this](https://aws.amazon.com/blogs/compute/distributed-deep-learning-made-easy/) page and create a deep learning cluster with 16 P2.16x instances.
+
+We load the data and code in the first machine (we’ll refer to this machine as master). We share both the data and code to other machines using EFS.
+
+If you are setting up your cluster manually, without using AWS CloudFormation, remember to do the following:
+1. Compile MXNet using `USE_DIST_KVSTORE=1` to enable distributed training.
+2. Create a hosts file in the master that contains the host names of all the machines in the cluster. For example,
+ ```
+ $ head -3 hosts
+ deeplearning-worker1
+ deeplearning-worker2
+ deeplearning-worker3
+ ```
+ It should be possible to ssh into any of these machines from the master by invoking `ssh` with just a hostname from the file. For example,
+ ```
+ $ ssh deeplearning-worker2
+ ===================================
+ Deep Learning AMI for Ubuntu
+ ===================================
+ ...
+ ubuntu@ip-10-0-1-199:~$
+ ```
+ One way to do this is to use ssh agent forwarding. Please check [this](https://aws.amazon.com/blogs/security/securely-connect-to-linux-instances-running-in-a-private-amazon-vpc/) page to learn how to set this up. In short, you’ll configure all machines to login using a particular certificate (mycert.pem) which is present on your local machine. You then login to the master using the certificate and the `-A` switch to enable agent forwarding. Now, from the master, you should be able to [...]
+
+### Run Training
+After the cluster is setup, login to master and run the following command from ${MXNET}/example/image-classification
+
+```
+../../tools/launch.py -n 16 -H $DEEPLEARNING_WORKERS_PATH python train_imagenet.py --network resnet \
+--num-layers 152 --data-train ~/data/train --data-val ~/data/val/ --gpus 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 \
+--batch-size 8192 --model ~/data/model/resnet152 --num-epochs 1 --kv-store dist_sync
+```
+
+launch.py launches the command it is provided in all the machine in the cluster. List of machines in the cluster must be provided to launch.py using the `-H` switch. Here is description of options used for launch.py.
+
+|Option | Description|
+|-------|------------|
+|n | specifies the number of worker jobs to run on each machine. We run 16 workers since we have 16 machines in the cluster.|
+|H | specifies the path to a file that has a list of hostnames of machines in the cluster. Since we created the cluster using the AWS deep learning CloudFormation template, the environment variable `$DEEPLEARNING_WORKERS_PATH` points to the required file.|
+
+train_imagenet.py trains the network provided by the `--network` option using the data provided by the `--data-train` and `--data-val` options. Here is description of the options used with train_imagenet.py.
+
+|Option | Description|
+|-----------|------------|
+|network | The network to train. Could be any of the network available in `${MXNET}/example/image-classification`. For this tutorial, we use Resnet.|
+|num-layers | Number of layers to use in the network. We use 152 layer Resnet.|
+|data-train | Directory containing the training images. We point to the EFS location (`~/data/train/`) where we stored the training images.|
+|data-val | Directory containing the validation images. We point to the EFS location (`~/data/val`) where we stored the validation images.|
+|gpus | Comma separated list of gpu indices to use for training on each machine. We use all 16 GPUs.|
+|batch-size | Batch size across all GPUs. This is equal to batch size per GPU * total number of GPUs. We use a batch size of 32 images per GPU. So, effective batch size is 32 * 16 * 16 = 8192.|
+|model | Path prefix for the model file created by the training.|
+|num-epochs | Number of epochs to train.|
+|kv-store | Key/Value store for parameter synchronization. We use distributed kv store since we are doing distributed training.|
+
+After training is complete, trained models are available in the directory specified by the `--model` option. Models are saved in two parts: model-symbol.json for the network definition and model-n.params for the parameters saved after the n'th epoch.
+
+## Scalability
+One common concern using large number of machines for training is the scalability. We have benchmarked scalability running several popular networks on clusters with up to 256 GPUs and the speedup is very close to ideal.
+
+This scalability test was run on sixteen P2.16xl instances with 256 GPUs in total. We used AWS deep learning AMI with CUDA 7.5 and CUDNN 5.1 installed.
+
+We fixed the batch size per GPU constant and doubled the number of GPUs for every subsequent test. Synchronized SGD (--kv-store dist_device_sync) was used. The CNNs used are located [here](https://github.com/dmlc/mxnet/tree/master/example/image-classification/symbols).
+
+| | alexnet | inception-v3 | resnet-152|
+|---------------------|---------|--------------|-----------|
+|batch size per GPU | 512 | 32 | 32 |
+|model size (MB) | 203 | 95 | 240 |
+
+ Number of images processed per second is shown in the following table:
+
+ |Number of GPUs | Alexnet | Inception-v3 | Resnet-152|
+ |----------------|---------|--------------|-----------|
+ |1 |457.07 |30.4 |20.8 |
+ |2 |870.43 |59.61 |38.76 |
+ |4 |1514.8 |117.9 |77.01 |
+ |8 |2852.5 |233.39 |153.07 |
+ |16 |4244.18 |447.61 |298.03 |
+ |32 |7945.57 |882.57 |595.53 |
+ |64 |15840.52 |1761.24 |1179.86 |
+ |128 |31334.88 |3416.2 |2333.47 |
+ |256 |61938.36 |6660.98 |4630.42 |
+
+ The following figure shows speedup against the number of GPUs used and compares it with ideal speedup.
+
+ 
+
+## Troubleshooting guidelines
+
+### Validation accuracy
+It is often straightforward to achieve a reasonable validation accuracy, but achieving the state-of-the-art numbers reported in papers can sometimes be very hard. Here are few things you can try to improve validation accuracy.
+- Adding more data augmentations often reduces the gap between training and validation accuracy. Data augmentation could be reduced in epochs closer to the end.
+- Start with a large learning rate and keep it large for a long time. For example, in CIFAR10, you could keep the learning rate at 0.1 for the first 200 epochs and then reduce it to 0.01.
+- Do not use a batch size that is too large, especially batch size >> number of classes.
+
+### Speed
+- Distributed training improves speed when computation cost of a batch is high. So, make sure your workload is not too small (like LeNet on MNIST). Make sure batch size is reasonably large.
+- Make sure data-read and preprocessing is not the bottleneck. Use the `--test-io 1` flag to check how many images can be pre-processed per second.
+- Increase --data-nthreads (default is 4) to use more threads for data preprocessing.
+- Data preprocessing is done by opencv. If opencv is compiled from source code, check if it is configured correctly.
+- Use `--benchmark 1` to use randomly generated data rather than real data to narrow down where the bottleneck is.
+- Check [this](http://mxnet.io/how_to/perf.html) page for more details.
+
+### Memory
+If the batch size is too big, it can exhaust GPU memory. If this happens, you’ll see the error message “cudaMalloc failed: out of memory” or something similar. There are a couple of ways to fix this:
+- Reduce the batch size.
+- Set the environment variable `MXNET_BACKWARD_DO_MIRROR` to 1. It reduces the memory consumption by trading off speed. For example, with batch size 64, inception-v3 uses 10G memory and trains 30 image/sec on a single K80 GPU. When mirroring is enabled, with 10G GPU memory consumption, we can run inception-v3 using batch size of 128. The cost is that, the speed reduces to 27 images/sec.
diff --git a/versions/master/_static/basic.css b/versions/master/_static/basic.css
index 7ed0e58..c89fc7e 100644
--- a/versions/master/_static/basic.css
+++ b/versions/master/_static/basic.css
@@ -52,8 +52,6 @@ div.sphinxsidebar {
width: 230px;
margin-left: -100%;
font-size: 90%;
- word-wrap: break-word;
- overflow-wrap : break-word;
}
div.sphinxsidebar ul {
@@ -85,6 +83,10 @@ div.sphinxsidebar #searchbox input[type="text"] {
width: 170px;
}
+div.sphinxsidebar #searchbox input[type="submit"] {
+ width: 30px;
+}
+
img {
border: 0;
max-width: 100%;
@@ -122,8 +124,6 @@ ul.keywordmatches li.goodmatch a {
table.contentstable {
width: 90%;
- margin-left: auto;
- margin-right: auto;
}
table.contentstable p.biglink {
@@ -151,14 +151,9 @@ table.indextable td {
vertical-align: top;
}
-table.indextable ul {
+table.indextable dl, table.indextable dd {
margin-top: 0;
margin-bottom: 0;
- list-style-type: none;
-}
-
-table.indextable > tbody > tr > td > ul {
- padding-left: 0em;
}
table.indextable tr.pcap {
@@ -190,22 +185,8 @@ div.genindex-jumpbox {
padding: 0.4em;
}
-/* -- domain module index --------------------------------------------------- */
-
-table.modindextable td {
- padding: 2px;
- border-collapse: collapse;
-}
-
/* -- general body styles --------------------------------------------------- */
-div.body p, div.body dd, div.body li, div.body blockquote {
- -moz-hyphens: auto;
- -ms-hyphens: auto;
- -webkit-hyphens: auto;
- hyphens: auto;
-}
-
a.headerlink {
visibility: hidden;
}
@@ -231,6 +212,10 @@ div.body td {
text-align: left;
}
+.field-list ul {
+ padding-left: 1em;
+}
+
.first {
margin-top: 0 !important;
}
@@ -347,6 +332,10 @@ table.docutils td, table.docutils th {
border-bottom: 1px solid #aaa;
}
+table.field-list td, table.field-list th {
+ border: 0 !important;
+}
+
table.footnote td, table.footnote th {
border: 0 !important;
}
@@ -383,20 +372,6 @@ div.figure p.caption span.caption-number {
div.figure p.caption span.caption-text {
}
-/* -- field list styles ----------------------------------------------------- */
-
-table.field-list td, table.field-list th {
- border: 0 !important;
-}
-
-.field-list ul {
- margin: 0;
- padding-left: 1em;
-}
-
-.field-list p {
- margin: 0;
-}
/* -- other body styles ----------------------------------------------------- */
@@ -447,6 +422,15 @@ dl.glossary dt {
font-size: 1.1em;
}
+.field-list ul {
+ margin: 0;
+ padding-left: 1em;
+}
+
+.field-list p {
+ margin: 0;
+}
+
.optional {
font-size: 1.3em;
}
@@ -505,13 +489,6 @@ pre {
overflow-y: hidden; /* fixes display issues on Chrome browsers */
}
-span.pre {
- -moz-hyphens: none;
- -ms-hyphens: none;
- -webkit-hyphens: none;
- hyphens: none;
-}
-
td.linenos pre {
padding: 5px 0px;
border: 0;
@@ -603,16 +580,6 @@ span.eqno {
float: right;
}
-span.eqno a.headerlink {
- position: relative;
- left: 0px;
- z-index: 1;
-}
-
-div.math:hover a.headerlink {
- visibility: visible;
-}
-
/* -- printout stylesheet --------------------------------------------------- */
@media print {
diff --git a/versions/master/_static/comment-bright.png b/versions/master/_static/comment-bright.png
index 15e27ed..551517b 100644
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diff --git a/versions/master/_static/comment-close.png b/versions/master/_static/comment-close.png
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diff --git a/versions/master/_static/doctools.js b/versions/master/_static/doctools.js
index 8163495..e2e70cc 100644
--- a/versions/master/_static/doctools.js
+++ b/versions/master/_static/doctools.js
@@ -124,7 +124,6 @@ var Documentation = {
this.fixFirefoxAnchorBug();
this.highlightSearchWords();
this.initIndexTable();
-
},
/**
@@ -253,29 +252,6 @@ var Documentation = {
});
var url = parts.join('/');
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
- },
-
- initOnKeyListeners: function() {
- $(document).keyup(function(event) {
- var activeElementType = document.activeElement.tagName;
- // don't navigate when in search box or textarea
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
- switch (event.keyCode) {
- case 37: // left
- var prevHref = $('link[rel="prev"]').prop('href');
- if (prevHref) {
- window.location.href = prevHref;
- return false;
- }
- case 39: // right
- var nextHref = $('link[rel="next"]').prop('href');
- if (nextHref) {
- window.location.href = nextHref;
- return false;
- }
- }
- }
- });
}
};
@@ -284,4 +260,4 @@ _ = Documentation.gettext;
$(document).ready(function() {
Documentation.init();
-});
\ No newline at end of file
+});
diff --git a/versions/master/_static/down-pressed.png b/versions/master/_static/down-pressed.png
index 5756c8c..7c30d00 100644
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diff --git a/versions/master/_static/jquery.js b/versions/master/_static/jquery.js
index f6a6a99..ab28a24 100644
--- a/versions/master/_static/jquery.js
+++ b/versions/master/_static/jquery.js
@@ -1,4 +1,4 @@
-/*! jQuery v3.1.0 | (c) jQuery Foundation | jquery.org/license */
-!function(a,b){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){"use strict";var c=[],d=a.document,e=Object.getPrototypeOf,f=c.slice,g=c.concat,h=c.push,i=c.indexOf,j={},k=j.toString,l=j.hasOwnProperty,m=l.toString,n=m.call(Object),o={};function p(a,b){b=b||d;var c=b.createElem [...]
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+/*! jQuery v1.11.1 | (c) 2005, 2014 jQuery Foundation, Inc. | jquery.org/license */
+!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=c.slice,e=c.concat,f=c.push,g=c.indexOf,h={},i=h.toString,j=h.hasOwnProperty,k={},l="1.11.1",m=function(a,b){return new m.fn.init(a,b)},n=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,o=/^-ms-/,p=/-([\da-z])/gi,q=function(a,b [...]
+if(k&&j[k]&&(e||j[k].data)||void 0!==d||"string"!=typeof b)return k||(k=i?a[h]=c.pop()||m.guid++:h),j[k]||(j[k]=i?{}:{toJSON:m.noop}),("object"==typeof b||"function"==typeof b)&&(e?j[k]=m.extend(j[k],b):j[k].data=m.extend(j[k].data,b)),g=j[k],e||(g.data||(g.data={}),g=g.data),void 0!==d&&(g[m.camelCase(b)]=d),"string"==typeof b?(f=g[b],null==f&&(f=g[m.camelCase(b)])):f=g,f}}function R(a,b,c){if(m.acceptData(a)){var d,e,f=a.nodeType,g=f?m.cache:a,h=f?a[m.expando]:m.expando;if(g[h]){if(b&& [...]
+},cur:function(){var a=Zb.propHooks[this.prop];return a&&a.get?a.get(this):Zb.propHooks._default.get(this)},run:function(a){var b,c=Zb.propHooks[this.prop];return this.pos=b=this.options.duration?m.easing[this.easing](a,this.options.duration*a,0,1,this.options.duration):a,this.now=(this.end-this.start)*b+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),c&&c.set?c.set(this):Zb.propHooks._default.set(this),this}},Zb.prototype.init.prototype=Zb.prototype,Zb.prop [...]
diff --git a/versions/master/_static/minus.png b/versions/master/_static/minus.png
index d96755f..0f22b16 100644
Binary files a/versions/master/_static/minus.png and b/versions/master/_static/minus.png differ
diff --git a/versions/master/_static/mxnet-theme/index.html b/versions/master/_static/mxnet-theme/index.html
index fafb92a..c4f3c48 100644
--- a/versions/master/_static/mxnet-theme/index.html
+++ b/versions/master/_static/mxnet-theme/index.html
@@ -18,6 +18,12 @@
<div class="container">
<div class="row">
<div class="col-lg-4 col-sm-12">
+ <h3>Introducing - Gluon</h3>
+ <p>We’re happy to introduce a new elegant, easy to use, imperative interface for MXNet.
+ </p>
+ <a href="http://gluon.mxnet.io">Learn More</a>
+ </div>
+ <div class="col-lg-4 col-sm-12">
<h3>MXNet 0.10.0 Released</h3>
<p>We're excited to announce the release of MXNet 0.10.0! Check out the release notes for latest updates.</p>
<a href="https://github.com/dmlc/mxnet/releases">Learn More</a>
@@ -28,12 +34,6 @@
</p>
<a href="http://incubator.apache.org/projects/mxnet.html">Learn More</a>
</div>
- <div class="col-lg-4 col-sm-12">
- <h3>MXNet in AWS re:Invent 2016</h3>
- <p> Learn how to use MXNet to build neural network models for recommendation systems.
- </p>
- <a href="https://www.youtube.com/watch?v=cftJAuwKWkA">Watch Video</a>
- </div>
</div>
</div>
</div>
diff --git a/versions/master/_static/mxnet-theme/navbar.html b/versions/master/_static/mxnet-theme/navbar.html
index 64298f5..81c2762 100644
--- a/versions/master/_static/mxnet-theme/navbar.html
+++ b/versions/master/_static/mxnet-theme/navbar.html
@@ -68,7 +68,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="{{url_root}}architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="{{url_root}}community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "{{url_root}}" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -90,7 +90,7 @@ Previous Navbar Layout End -->
{% endfor %}
<li><a href="{{url_root}}architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
diff --git a/versions/master/_static/plus.png b/versions/master/_static/plus.png
index 7107cec..0cfe084 100644
Binary files a/versions/master/_static/plus.png and b/versions/master/_static/plus.png differ
diff --git a/versions/master/_static/searchtools.js b/versions/master/_static/searchtools.js
index bbfb3ac..cb74467 100644
--- a/versions/master/_static/searchtools.js
+++ b/versions/master/_static/searchtools.js
@@ -2,7 +2,7 @@
* searchtools.js_t
* ~~~~~~~~~~~~~~~~
*
- * Sphinx JavaScript utilities for the full-text search.
+ * Sphinx JavaScript utilties for the full-text search.
*
* :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
@@ -226,106 +226,6 @@ var Scorer = {
};
-
-
-
-var splitChars = (function() {
- var result = {};
- var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
- 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
- 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
- 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
- 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
- 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
- 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
- 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
- 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
- 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
- var i, j, start, end;
- for (i = 0; i < singles.length; i++) {
- result[singles[i]] = true;
- }
- var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
- [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
- [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
- [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
- [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
- [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
- [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
- [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
- [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
- [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
- [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
- [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
- [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
- [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
- [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
- [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
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- [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
- [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
- [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
- [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
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- [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
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- [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
- [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
- [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
- [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
- [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
- [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
- [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
- [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
- [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
- [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
- [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
- [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
- [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
- [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
- [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
- [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
- [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
- [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
- [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
- [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
- [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
- for (i = 0; i < ranges.length; i++) {
- start = ranges[i][0];
- end = ranges[i][1];
- for (j = start; j <= end; j++) {
- result[j] = true;
- }
- }
- return result;
-})();
-
-function splitQuery(query) {
- var result = [];
- var start = -1;
- for (var i = 0; i < query.length; i++) {
- if (splitChars[query.charCodeAt(i)]) {
- if (start !== -1) {
- result.push(query.slice(start, i));
- start = -1;
- }
- } else if (start === -1) {
- start = i;
- }
- }
- if (start !== -1) {
- result.push(query.slice(start));
- }
- return result;
-}
-
-
-
-
/**
* Search Module
*/
@@ -424,7 +324,7 @@ var Search = {
var searchterms = [];
var excluded = [];
var hlterms = [];
- var tmp = splitQuery(query);
+ var tmp = query.split(/\s+/);
var objectterms = [];
for (i = 0; i < tmp.length; i++) {
if (tmp[i] !== "") {
@@ -438,10 +338,6 @@ var Search = {
}
// stem the word
var word = stemmer.stemWord(tmp[i].toLowerCase());
- // prevent stemmer from cutting word smaller than two chars
- if(word.length < 3 && tmp[i].length >= 3) {
- word = tmp[i];
- }
var toAppend;
// select the correct list
if (word[0] == '-') {
@@ -539,8 +435,7 @@ var Search = {
displayNextItem();
});
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
- var suffix = DOCUMENTATION_OPTIONS.SOURCELINK_SUFFIX;
- $.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[5] + (item[5].slice(-suffix.length) === suffix ? '' : suffix),
+ $.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[0] + '.txt',
dataType: "text",
complete: function(jqxhr, textstatus) {
var data = jqxhr.responseText;
@@ -579,7 +474,6 @@ var Search = {
*/
performObjectSearch : function(object, otherterms) {
var filenames = this._index.filenames;
- var docnames = this._index.docnames;
var objects = this._index.objects;
var objnames = this._index.objnames;
var titles = this._index.titles;
@@ -633,7 +527,7 @@ var Search = {
} else {
score += Scorer.objPrioDefault;
}
- results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
+ results.push([filenames[match[0]], fullname, '#'+anchor, descr, score]);
}
}
}
@@ -645,7 +539,6 @@ var Search = {
* search for full-text terms in the index
*/
performTermsSearch : function(searchterms, excluded, terms, titleterms) {
- var docnames = this._index.docnames;
var filenames = this._index.filenames;
var titles = this._index.titles;
@@ -720,7 +613,7 @@ var Search = {
// select one (max) score for the file.
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
- results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
+ results.push([filenames[file], titles[file], '', null, score]);
}
}
return results;
@@ -730,7 +623,7 @@ var Search = {
* helper function to return a node containing the
* search summary for a given text. keywords is a list
* of stemmed words, hlwords is the list of normal, unstemmed
- * words. the first one is used to find the occurrence, the
+ * words. the first one is used to find the occurance, the
* latter for highlighting it.
*/
makeSearchSummary : function(text, keywords, hlwords) {
diff --git a/versions/master/_static/up-pressed.png b/versions/master/_static/up-pressed.png
index acee3b6..99e7210 100644
Binary files a/versions/master/_static/up-pressed.png and b/versions/master/_static/up-pressed.png differ
diff --git a/versions/master/_static/up.png b/versions/master/_static/up.png
index 2a940a7..26de002 100644
Binary files a/versions/master/_static/up.png and b/versions/master/_static/up.png differ
diff --git a/versions/master/_static/websupport.js b/versions/master/_static/websupport.js
index 98e7f40..ffd9b2b 100644
--- a/versions/master/_static/websupport.js
+++ b/versions/master/_static/websupport.js
@@ -2,7 +2,7 @@
* websupport.js
* ~~~~~~~~~~~~~
*
- * sphinx.websupport utilities for all documentation.
+ * sphinx.websupport utilties for all documentation.
*
* :copyright: Copyright 2007-2016 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
diff --git a/versions/master/api/c++/index.html b/versions/master/api/c++/index.html
index a8354e2..dc80ba4 100644
--- a/versions/master/api/c++/index.html
+++ b/versions/master/api/c++/index.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet - C++ API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
<!-- -->
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-<link href="../../search.html" rel="search" title="Search"/>
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+<link href="../scala/index.html" rel="next" title="MXNet - Scala API">
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
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@@ -153,7 +151,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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@@ -180,7 +178,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
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<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
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@@ -226,7 +224,7 @@ Previous Navbar Layout End -->
<li class="toctree-l1"><a class="reference internal" href="../python/index.html">Python Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../r/index.html">R Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../julia/index.html">Julia Documents</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">C++ Documents</a><ul>
+<li class="toctree-l1 current"><a class="current reference internal" href="">C++ Documents</a><ul>
<li class="toctree-l2"><a class="reference external" href="http://mxnet.io/doxygen/namespaces.html">Namespaces</a></li>
<li class="toctree-l2"><a class="reference external" href="http://mxnet.io/doxygen/annotated.html">Classes</a></li>
<li class="toctree-l2"><a class="reference external" href="http://mxnet.io/doxygen/files.html">Code Files</a></li>
diff --git a/versions/master/api/julia/index.html b/versions/master/api/julia/index.html
index 95688cf..121602f 100644
--- a/versions/master/api/julia/index.html
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@@ -2,15 +2,15 @@
<html lang="en">
<head>
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-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet - Julia API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
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-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
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<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
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<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../c++/index.html" rel="next" title="MXNet - C++ API"/>
+<link href="../c++/index.html" rel="next" title="MXNet - C++ API">
<link href="../r/index.html" rel="prev" title="MXNet - R API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -153,7 +151,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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@@ -180,7 +178,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
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@@ -190,11 +188,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
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@@ -225,7 +223,7 @@ Previous Navbar Layout End -->
<ul class="current">
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<li class="toctree-l1"><a class="reference internal" href="../r/index.html">R Documents</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Julia Documents</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="">Julia Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../c++/index.html">C++ Documents</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../perl/index.html">Perl Documents</a></li>
diff --git a/versions/master/api/perl/index.html b/versions/master/api/perl/index.html
index 2498c1e..b738653 100644
--- a/versions/master/api/perl/index.html
+++ b/versions/master/api/perl/index.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet - Perl API — mxnet documentation</title>
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+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
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URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
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<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
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-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../../how_to/index.html" rel="next" title="MXNet How To"/>
+<link href="../../how_to/index.html" rel="next" title="MXNet How To">
<link href="../scala/index.html" rel="prev" title="MXNet - Scala API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -153,7 +151,7 @@ Previous Navbar Layout End -->
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<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -180,7 +178,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -190,11 +188,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -228,7 +226,7 @@ Previous Navbar Layout End -->
<li class="toctree-l1"><a class="reference internal" href="../julia/index.html">Julia Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../c++/index.html">C++ Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../scala/index.html">Scala Documents</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Perl Documents</a><ul>
+<li class="toctree-l1 current"><a class="current reference internal" href="">Perl Documents</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#perl-api-reference">Perl API Reference</a></li>
</ul>
</li>
@@ -295,11 +293,11 @@ transferred to PDL objects in order to allow you to use all the glory and power
<div class="section" id="perl-api-reference">
<span id="perl-api-reference"></span><h2>Perl API Reference<a class="headerlink" href="#perl-api-reference" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
-<li><a class="reference internal" href="module.html"><span class="doc">Module API</span></a> is a flexible high-level interface for training neural networks.</li>
-<li><a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a> performs operations on NDArrays to assemble neural networks from layers.</li>
-<li><a class="reference internal" href="io.html"><span class="doc">IO Data Loading API</span></a> performs parsing and data loading.</li>
-<li><a class="reference internal" href="ndarray.html"><span class="doc">NDArray API</span></a> performs vector/matrix/tensor operations.</li>
-<li><a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> performs multi-GPU and multi-host distributed training.</li>
+<li><a class="reference internal" href="module.html"><em>Module API</em></a> is a flexible high-level interface for training neural networks.</li>
+<li><a class="reference internal" href="symbol.html"><em>Symbolic API</em></a> performs operations on NDArrays to assemble neural networks from layers.</li>
+<li><a class="reference internal" href="io.html"><em>IO Data Loading API</em></a> performs parsing and data loading.</li>
+<li><a class="reference internal" href="ndarray.html"><em>NDArray API</em></a> performs vector/matrix/tensor operations.</li>
+<li><a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> performs multi-GPU and multi-host distributed training.</li>
</ul>
</div>
</div>
diff --git a/versions/master/api/perl/io.html b/versions/master/api/perl/io.html
index ef3a6bc..d1e3c52 100644
--- a/versions/master/api/perl/io.html
+++ b/versions/master/api/perl/io.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Data Loading API — mxnet documentation</title>
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-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
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<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/api/perl/kvstore.html b/versions/master/api/perl/kvstore.html
index 0936ced..2dc190e 100644
--- a/versions/master/api/perl/kvstore.html
+++ b/versions/master/api/perl/kvstore.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>KVStore API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/api/perl/module.html b/versions/master/api/perl/module.html
index a3b69ba..3762919 100644
--- a/versions/master/api/perl/module.html
+++ b/versions/master/api/perl/module.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Module API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/api/perl/ndarray.html b/versions/master/api/perl/ndarray.html
index 7c3c2ca..58dcec2 100644
--- a/versions/master/api/perl/ndarray.html
+++ b/versions/master/api/perl/ndarray.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>NDArray API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/api/perl/symbol.html b/versions/master/api/perl/symbol.html
index decb276..b55cdcc 100644
--- a/versions/master/api/perl/symbol.html
+++ b/versions/master/api/perl/symbol.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet Perl Symbolic API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
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-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
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<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -275,7 +273,7 @@ pdl> $net = mx->symbol->SoftmaxOutput(data => $fc2, name => "out")
</pre></div>
</div>
<p>For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use <code class="docutils literal"><span class="pre">->attr($key)</span></code>:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> $data->attr("mood")
+<div class="highlight-python"><div class="highlight"><pre><span></span> $data->attr("mood")
</pre></div>
</div>
<p>To attach attributes, you can use <code class="docutils literal"><span class="pre">AI::MXNet::AttrScope</span></code>. <code class="docutils literal"><span class="pre">AI::MXNet::AttrScopeAttrScope</span></code> automatically adds
@@ -318,12 +316,12 @@ The symbol is saved in JSON format. You can also get a JSON string directly usin
<span id="executing-symbols"></span><h2>Executing Symbols<a class="headerlink" href="#executing-symbols" title="Permalink to this headline">¶</a></h2>
<p>After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
If you are training a neural network, this is typically
-handled by the high-level <a class="reference internal" href="module.html"><span class="doc">AI::MXNet::Module package</span></a> and the [<code class="docutils literal"><span class="pre">fit()</span></code>] function.</p>
+handled by the high-level <a class="reference internal" href="module.html"><em>AI::MXNet::Module package</em></a> and the [<code class="docutils literal"><span class="pre">fit()</span></code>] function.</p>
<p>For neural networks used in “feed-forward”, “prediction”, or “inference” mode (all terms for the same
thing: running a trained network), the input arguments are the
input data, and the weights of the neural network that were learned during training.</p>
<p>To manually execute a set of symbols, you need to create an [<code class="docutils literal"><span class="pre">AI::MXNet::Executor</span></code>] object,
-which is typically constructed by calling the [<code class="docutils literal"><span class="pre">simple_bind(<parameters>)</parameters></span></code>] method on a AI::MXNet::Symbol.</p>
+which is typically constructed by calling the [<code class="docutils literal"><span class="pre">simple_bind(<parameters>)</span></code>] method on a AI::MXNet::Symbol.</p>
</div>
<div class="section" id="multiple-outputs">
<span id="multiple-outputs"></span><h2>Multiple Outputs<a class="headerlink" href="#multiple-outputs" title="Permalink to this headline">¶</a></h2>
diff --git a/versions/master/api/r/index.html b/versions/master/api/python/autograd.html
similarity index 70%
copy from versions/master/api/r/index.html
copy to versions/master/api/python/autograd.html
index 8458a1e..8d41512 100644
--- a/versions/master/api/r/index.html
+++ b/versions/master/api/python/autograd.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
-<title>MXNet - R API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
+<title>Autograd Package — mxnet documentation</title>
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../julia/index.html" rel="next" title="MXNet - Julia API"/>
-<link href="../python/metric.html" rel="prev" title="Evaluation Metric API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="index.html" rel="up" title="MXNet - Python API">
+<link href="gluon.html" rel="next" title="Gluon Package"/>
+<link href="module.html" rel="prev" title="Module API"/>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
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<div class="container">
@@ -153,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -180,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -190,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -223,15 +222,28 @@ Previous Navbar Layout End -->
<div aria-label="main navigation" class="sphinxsidebar leftsidebar" role="navigation">
<div class="sphinxsidebarwrapper">
<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../python/index.html">Python Documents</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">R Documents</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#resources">Resources</a><ul>
-<li class="toctree-l3"><a class="reference external" href="https://media.readthedocs.org/pdf/mxnet-test/latest/mxnet-test.pdf">MXNet R Reference Manual</a></li>
-<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/tutorials/index.html#R-Tutorials">MXNet for R Tutorials</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="index.html">Python Documents</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="index.html#table-of-contents">Table of contents</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Autograd Package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#autograd">Autograd</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
+<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kvstore.html">KVStore API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="io.html">Data Loading API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image.html">Image API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html">Optimization: initialize and update weights</a></li>
+<li class="toctree-l3"><a class="reference internal" href="callback.html">Callback API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="metric.html">Evaluation Metric API</a></li>
</ul>
</li>
</ul>
</li>
+<li class="toctree-l1"><a class="reference internal" href="../r/index.html">R Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../julia/index.html">Julia Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../c++/index.html">C++ Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../scala/index.html">Scala Documents</a></li>
@@ -243,34 +255,40 @@ Previous Navbar Layout End -->
</div>
</div>
<div class="content">
-<div class="section" id="mxnet-r-api">
-<span id="mxnet-r-api"></span><h1>MXNet - R API<a class="headerlink" href="#mxnet-r-api" title="Permalink to this headline">¶</a></h1>
-<p>See the <a class="reference external" href="https://media.readthedocs.org/pdf/mxnet-test/latest/mxnet-test.pdf">MXNet R Reference Manual</a>.</p>
-<p>MXNet supports the R programming language. The MXNet R package brings flexible and efficient GPU
-computing and state-of-art deep learning to R. It enables you to write seamless tensor/matrix computation with multiple GPUs in R. It also lets you construct and customize the state-of-art deep learning models in R,
-and apply them to tasks, such as image classification and data science challenges.</p>
-<p>You can perform tensor or matrix computation in R:</p>
-<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="o">></span> <span class="kn">require</span><span class="p">(</span>mxnet<span class="p">)</span>
- Loading required package<span class="o">:</span> mxnet
- <span class="o">></span> a <span class="o"><-</span> mx.nd.ones<span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="m">2</span><span class="p">,</span><span class="m">3</span><span class="p">))</span>
- <span class="o">></span> a
- <span class="p">[,</span><span class="m">1</span><span class="p">]</span> <span class="p">[,</span><span class="m">2</span><span class="p">]</span> <span class="p">[,</span><span class="m">3</span><span class="p">]</span>
- <span class="p">[</span><span class="m">1</span><span class="p">,]</span> <span class="m">1</span> <span class="m">1</span> <span class="m">1</span>
- <span class="p">[</span><span class="m">2</span><span class="p">,]</span> <span class="m">1</span> <span class="m">1</span> <span class="m">1</span>
- <span class="o">></span> a <span class="o">+</span> <span class="m">1</span>
- <span class="p">[,</span><span class="m">1</span><span class="p">]</span> <span class="p">[,</span><span class="m">2</span><span class="p">]</span> <span class="p">[,</span><span class="m">3</span><span class="p">]</span>
- <span class="p">[</span><span class="m">1</span><span class="p">,]</span> <span class="m">2</span> <span class="m">2</span> <span class="m">2</span>
- <span class="p">[</span><span class="m">2</span><span class="p">,]</span> <span class="m">2</span> <span class="m">2</span> <span class="m">2</span>
-</pre></div>
-</div>
-<div class="section" id="resources">
-<span id="resources"></span><h2>Resources<a class="headerlink" href="#resources" title="Permalink to this headline">¶</a></h2>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference external" href="https://media.readthedocs.org/pdf/mxnet-test/latest/mxnet-test.pdf">MXNet R Reference Manual</a></li>
-<li class="toctree-l1"><a class="reference external" href="http://mxnet.io/tutorials/index.html#R-Tutorials">MXNet for R Tutorials</a></li>
-</ul>
+<div class="section" id="autograd-package">
+<span id="autograd-package"></span><h1>Autograd Package<a class="headerlink" href="#autograd-package" title="Permalink to this headline">¶</a></h1>
+<div class="admonition warning">
+<p class="first admonition-title">Warning</p>
+<p class="last">This package is currently experimental and may change in the near future.</p>
</div>
+<script src="../../_static/js/auto_module_index.js" type="text/javascript"></script><div class="section" id="autograd">
+<span id="autograd"></span><h2>Autograd<a class="headerlink" href="#autograd" title="Permalink to this headline">¶</a></h2>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">record</span></code></td>
+<td>Returns a training scope context to be used in ‘with’ statement and captures training code.</td>
+</tr>
+<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">pause</span></code></td>
+<td>Returns a testing scope context to be used in ‘with’ statement and captures testing code.</td>
+</tr>
+<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">mark_variables</span></code></td>
+<td>Mark NDArrays as variables to compute gradient for autograd.</td>
+</tr>
+<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">backward</span></code></td>
+<td>Compute the gradients of heads w.r.t previously marked variables.</td>
+</tr>
+<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">set_training</span></code></td>
+<td>Set status to training/not training.</td>
+</tr>
+<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">set_recording</span></code></td>
+<td>Set status to recording/not recording.</td>
+</tr>
+</tbody>
+</table>
</div>
</div>
<div class="container">
@@ -283,8 +301,8 @@ and apply them to tasks, such as image classification and data science challenge
<div class="sphinxsidebarwrapper">
<h3><a href="../../index.html">Table Of Contents</a></h3>
<ul>
-<li><a class="reference internal" href="#">MXNet - R API</a><ul>
-<li><a class="reference internal" href="#resources">Resources</a></li>
+<li><a class="reference internal" href="#">Autograd Package</a><ul>
+<li><a class="reference internal" href="#autograd">Autograd</a></li>
</ul>
</li>
</ul>
diff --git a/versions/master/api/python/callback.html b/versions/master/api/python/callback.html
index ce34094..69c20d8 100644
--- a/versions/master/api/python/callback.html
+++ b/versions/master/api/python/callback.html
@@ -2,15 +2,15 @@
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+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
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@@ -18,7 +18,7 @@
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+ SOURCELINK_SUFFIX: ''
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@@ -48,13 +48,11 @@
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-<li class="toctree-l3 current"><a class="current reference internal" href="#">Callback API</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Callback API</a><ul>
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@@ -264,8 +263,8 @@ Previous Navbar Layout End -->
<p>This document lists the routines of the callback package</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#module-mxnet.callback" title="mxnet.callback"><code class="xref py py-obj docutils literal"><span class="pre">mxnet.callback</span></code></a></td>
@@ -282,8 +281,8 @@ Previous Navbar Layout End -->
<code class="descclassname">mxnet.callback.</code><code class="descname">module_checkpoint</code><span class="sig-paren">(</span><em>mod</em>, <em>prefix</em>, <em>period=1</em>, <em>save_optimizer_states=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.callback.module_checkpoint" title="Permalink to this definition">¶</a></dt>
<dd><p>Callback to checkpoint Module to prefix every epoch.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>mod</strong> (<em>subclass of BaseModule</em>) – The module to checkpoint.</li>
@@ -293,7 +292,8 @@ Previous Navbar Layout End -->
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>callback</strong> – The callback function that can be passed as iter_end_callback to fit.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>callback</strong> –
+The callback function that can be passed as iter_end_callback to fit.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">function</p>
@@ -310,16 +310,17 @@ Each checkpoint is made up of a couple of binary files: a model description file
parameters (weights and biases) file. The model description file is named
<cite>prefix</cite>–symbol.json and the parameters file is named <cite>prefix</cite>-<cite>epoch_number</cite>.params</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>prefix</strong> (<em>str</em>) – Prefix for the checkpoint filenames.</li>
-<li><strong>period</strong> (<em>int</em><em>, </em><em>optional</em>) – Interval (number of epochs) between checkpoints. Default <cite>period</cite> is 1.</li>
+<li><strong>period</strong> (<em>int, optional</em>) – Interval (number of epochs) between checkpoints. Default <cite>period</cite> is 1.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>callback</strong> – A callback function that can be passed as <cite>epoch_end_callback</cite> to fit.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>callback</strong> –
+A callback function that can be passed as <cite>epoch_end_callback</cite> to fit.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">function</p>
@@ -328,7 +329,7 @@ parameters (weights and biases) file. The model description file is named
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">iterator</span><span class="p">,</span> <span class="n">num_epoch</span><span class="o">=</span><span class="n">n_epoch</span><span class="p">,</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">iterator</span><span class="p">,</span> <span class="n">num_epoch</span><span class="o">=</span><span class="n">n_epoch</span><span class="p">,</span>
<span class="gp">... </span><span class="n">epoch_end_callback</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">callback</span><span class="o">.</span><span class="n">do_checkpoint</span><span class="p">(</span><span class="s2">"mymodel"</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="go">Start training with [cpu(0)]</span>
<span class="go">Epoch[0] Resetting Data Iterator</span>
@@ -345,8 +346,8 @@ parameters (weights and biases) file. The model description file is named
<code class="descclassname">mxnet.callback.</code><code class="descname">log_train_metric</code><span class="sig-paren">(</span><em>period</em>, <em>auto_reset=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.callback.log_train_metric" title="Permalink to this definition">¶</a></dt>
<dd><p>Callback to log the training evaluation result every period.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>period</strong> (<em>int</em>) – The number of batch to log the training evaluation metric.</li>
@@ -354,7 +355,8 @@ parameters (weights and biases) file. The model description file is named
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>callback</strong> – The callback function that can be passed as iter_epoch_callback to fit.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>callback</strong> –
+The callback function that can be passed as iter_epoch_callback to fit.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">function</p>
@@ -368,8 +370,8 @@ parameters (weights and biases) file. The model description file is named
<em class="property">class </em><code class="descclassname">mxnet.callback.</code><code class="descname">Speedometer</code><span class="sig-paren">(</span><em>batch_size</em>, <em>frequent=50</em>, <em>auto_reset=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.callback.Speedometer" title="Permalink to this definition">¶</a></dt>
<dd><p>Logs training speed and evaluation metrics periodically.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>batch_size</strong> (<em>int</em>) – Batch size of data.</li>
@@ -382,7 +384,7 @@ must be logged. Default behavior is to log once every 50 batches.</li>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Print training speed and evaluation metrics every ten batches. Batch size is one.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Print training speed and evaluation metrics every ten batches. Batch size is one.</span>
<span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">iterator</span><span class="p">,</span> <span class="n">num_epoch</span><span class="o">=</span><span class="n">n_epoch</span><span class="p">,</span>
<span class="gp">... </span><span class="n">batch_end_callback</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">callback</span><span class="o">.</span><span class="n">Speedometer</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="go">Epoch[0] Batch [10] Speed: 1910.41 samples/sec Train-accuracy=0.200000</span>
@@ -390,14 +392,19 @@ must be logged. Default behavior is to log once every 50 batches.</li>
<span class="go">Epoch[0] Batch [30] Speed: 1740.59 samples/sec Train-accuracy=0.500000</span>
</pre></div>
</div>
+<dl class="method">
+<dt id="mxnet.callback.Speedometer.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>param</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.callback.Speedometer.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Callback to Show speed.</p>
+</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.callback.ProgressBar">
<em class="property">class </em><code class="descclassname">mxnet.callback.</code><code class="descname">ProgressBar</code><span class="sig-paren">(</span><em>total</em>, <em>length=80</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.callback.ProgressBar" title="Permalink to this definition">¶</a></dt>
<dd><p>Displays a progress bar, indicating the percentage of batches processed within each epoch.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>total</strong> (<em>int</em>) – total number of batches per epoch</li>
@@ -408,12 +415,17 @@ must be logged. Default behavior is to log once every 50 batches.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">progress_bar</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">callback</span><span class="o">.</span><span class="n">ProgressBar</span><span class="p">(</span><span class="n">total</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">progress_bar</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">callback</span><span class="o">.</span><span class="n">ProgressBar</span><span class="p">(</span><span class="n">total</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">num_epoch</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">batch_end_callback</span><span class="o">=</span><span class="n">progress_bar</span><span class="p">)</span>
<span class="go">[========--------] 50.0%</span>
<span class="go">[================] 100.0%</span>
</pre></div>
</div>
+<dl class="method">
+<dt id="mxnet.callback.ProgressBar.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>param</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.callback.ProgressBar.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Callback to Show progress bar.</p>
+</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.callback.LogValidationMetricsCallback">
diff --git a/versions/master/api/python/gluon.html b/versions/master/api/python/gluon.html
index eb8cbc6..2751eb0 100644
--- a/versions/master/api/python/gluon.html
+++ b/versions/master/api/python/gluon.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Gluon Package — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
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+<link href="index.html" rel="up" title="MXNet - Python API">
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-<link href="module.html" rel="prev" title="Module API"/>
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+<link href="autograd.html" rel="prev" title="Autograd Package"/>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
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@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
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+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
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@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
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<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
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@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
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<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
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@@ -229,7 +227,8 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Gluon Package</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Gluon Package</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
<li class="toctree-l4"><a class="reference internal" href="#parameter">Parameter</a></li>
<li class="toctree-l4"><a class="reference internal" href="#containers">Containers</a></li>
@@ -238,6 +237,9 @@ Previous Navbar Layout End -->
<li class="toctree-l4"><a class="reference internal" href="#trainer">Trainer</a></li>
<li class="toctree-l4"><a class="reference internal" href="#loss-functions">Loss functions</a></li>
<li class="toctree-l4"><a class="reference internal" href="#utilities">Utilities</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#data">Data</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#model-zoo">Model Zoo</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#api-reference">API Reference</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
@@ -278,14 +280,600 @@ in Python and then deploy with symbolic graph in C++ and Scala.</p>
</div>
<div class="section" id="parameter">
<span id="parameter"></span><h2>Parameter<a class="headerlink" href="#parameter" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.Parameter" title="mxnet.gluon.Parameter"><code class="xref py py-obj docutils literal"><span class="pre">Parameter</span></code></a></td>
+<td>A Container holding parameters (weights) of <a href="#id3"><span class="problematic" id="id4">`</span></a>Block`s.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><code class="xref py py-obj docutils literal"><span class="pre">ParameterDict</span></code></a></td>
+<td>A dictionary managing a set of parameters.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="containers">
+<span id="containers"></span><h2>Containers<a class="headerlink" href="#containers" title="Permalink to this headline">¶</a></h2>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.Block" title="mxnet.gluon.Block"><code class="xref py py-obj docutils literal"><span class="pre">Block</span></code></a></td>
+<td>Base class for all neural network layers and models.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.HybridBlock" title="mxnet.gluon.HybridBlock"><code class="xref py py-obj docutils literal"><span class="pre">HybridBlock</span></code></a></td>
+<td><cite>HybridBlock</cite> supports forwarding with both Symbol and NDArray.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.SymbolBlock" title="mxnet.gluon.SymbolBlock"><code class="xref py py-obj docutils literal"><span class="pre">SymbolBlock</span></code></a></td>
+<td>Construct block from symbol.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="neural-network-layers">
+<span id="neural-network-layers"></span><h2>Neural Network Layers<a class="headerlink" href="#neural-network-layers" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="containers">
+<span id="id1"></span><h3>Containers<a class="headerlink" href="#containers" title="Permalink to this headline">¶</a></h3>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.Sequential" title="mxnet.gluon.nn.Sequential"><code class="xref py py-obj docutils literal"><span class="pre">Sequential</span></code></a></td>
+<td>Stacks <a href="#id5"><span class="problematic" id="id6">`</span></a>Block`s sequentially.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.HybridSequential" title="mxnet.gluon.nn.HybridSequential"><code class="xref py py-obj docutils literal"><span class="pre">HybridSequential</span></code></a></td>
+<td>Stacks <a href="#id7"><span class="problematic" id="id8">`</span></a>HybridBlock`s sequentially.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="basic-layers">
+<span id="basic-layers"></span><h3>Basic Layers<a class="headerlink" href="#basic-layers" title="Permalink to this headline">¶</a></h3>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.Dense" title="mxnet.gluon.nn.Dense"><code class="xref py py-obj docutils literal"><span class="pre">Dense</span></code></a></td>
+<td>Just your regular densely-connected NN layer.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.Activation" title="mxnet.gluon.nn.Activation"><code class="xref py py-obj docutils literal"><span class="pre">Activation</span></code></a></td>
+<td>Applies an activation function to input.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.Dropout" title="mxnet.gluon.nn.Dropout"><code class="xref py py-obj docutils literal"><span class="pre">Dropout</span></code></a></td>
+<td>Applies Dropout to the input.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.BatchNorm" title="mxnet.gluon.nn.BatchNorm"><code class="xref py py-obj docutils literal"><span class="pre">BatchNorm</span></code></a></td>
+<td>Batch normalization layer (Ioffe and Szegedy, 2014).</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.LeakyReLU" title="mxnet.gluon.nn.LeakyReLU"><code class="xref py py-obj docutils literal"><span class="pre">LeakyReLU</span></code></a></td>
+<td>Leaky version of a Rectified Linear Unit.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.Embedding" title="mxnet.gluon.nn.Embedding"><code class="xref py py-obj docutils literal"><span class="pre">Embedding</span></code></a></td>
+<td>Turns non-negative integers (indexes/tokens) into dense vectors of fixed size.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="convolutional-layers">
+<span id="convolutional-layers"></span><h3>Convolutional Layers<a class="headerlink" href="#convolutional-layers" title="Permalink to this headline">¶</a></h3>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.Conv1D" title="mxnet.gluon.nn.Conv1D"><code class="xref py py-obj docutils literal"><span class="pre">Conv1D</span></code></a></td>
+<td>1D convolution layer (e.g. temporal convolution).</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.Conv2D" title="mxnet.gluon.nn.Conv2D"><code class="xref py py-obj docutils literal"><span class="pre">Conv2D</span></code></a></td>
+<td>2D convolution layer (e.g. spatial convolution over images).</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.Conv3D" title="mxnet.gluon.nn.Conv3D"><code class="xref py py-obj docutils literal"><span class="pre">Conv3D</span></code></a></td>
+<td>3D convolution layer (e.g. spatial convolution over volumes).</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.Conv1DTranspose" title="mxnet.gluon.nn.Conv1DTranspose"><code class="xref py py-obj docutils literal"><span class="pre">Conv1DTranspose</span></code></a></td>
+<td>Transposed 1D convolution layer (sometimes called Deconvolution).</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.Conv2DTranspose" title="mxnet.gluon.nn.Conv2DTranspose"><code class="xref py py-obj docutils literal"><span class="pre">Conv2DTranspose</span></code></a></td>
+<td>Transposed 2D convolution layer (sometimes called Deconvolution).</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.Conv3DTranspose" title="mxnet.gluon.nn.Conv3DTranspose"><code class="xref py py-obj docutils literal"><span class="pre">Conv3DTranspose</span></code></a></td>
+<td>Transposed 3D convolution layer (sometimes called Deconvolution).</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="pooling-layers">
+<span id="pooling-layers"></span><h3>Pooling Layers<a class="headerlink" href="#pooling-layers" title="Permalink to this headline">¶</a></h3>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.MaxPool1D" title="mxnet.gluon.nn.MaxPool1D"><code class="xref py py-obj docutils literal"><span class="pre">MaxPool1D</span></code></a></td>
+<td>Max pooling operation for one dimensional data.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.MaxPool2D" title="mxnet.gluon.nn.MaxPool2D"><code class="xref py py-obj docutils literal"><span class="pre">MaxPool2D</span></code></a></td>
+<td>Max pooling operation for two dimensional (spatial) data.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.MaxPool3D" title="mxnet.gluon.nn.MaxPool3D"><code class="xref py py-obj docutils literal"><span class="pre">MaxPool3D</span></code></a></td>
+<td>Max pooling operation for 3D data (spatial or spatio-temporal).</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.AvgPool1D" title="mxnet.gluon.nn.AvgPool1D"><code class="xref py py-obj docutils literal"><span class="pre">AvgPool1D</span></code></a></td>
+<td>Average pooling operation for temporal data.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.AvgPool2D" title="mxnet.gluon.nn.AvgPool2D"><code class="xref py py-obj docutils literal"><span class="pre">AvgPool2D</span></code></a></td>
+<td>Average pooling operation for spatial data.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.AvgPool3D" title="mxnet.gluon.nn.AvgPool3D"><code class="xref py py-obj docutils literal"><span class="pre">AvgPool3D</span></code></a></td>
+<td>Average pooling operation for 3D data (spatial or spatio-temporal).</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.GlobalMaxPool1D" title="mxnet.gluon.nn.GlobalMaxPool1D"><code class="xref py py-obj docutils literal"><span class="pre">GlobalMaxPool1D</span></code></a></td>
+<td>Global max pooling operation for temporal data.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.GlobalMaxPool2D" title="mxnet.gluon.nn.GlobalMaxPool2D"><code class="xref py py-obj docutils literal"><span class="pre">GlobalMaxPool2D</span></code></a></td>
+<td>Global max pooling operation for spatial data.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.GlobalMaxPool3D" title="mxnet.gluon.nn.GlobalMaxPool3D"><code class="xref py py-obj docutils literal"><span class="pre">GlobalMaxPool3D</span></code></a></td>
+<td>Global max pooling operation for 3D data.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.GlobalAvgPool1D" title="mxnet.gluon.nn.GlobalAvgPool1D"><code class="xref py py-obj docutils literal"><span class="pre">GlobalAvgPool1D</span></code></a></td>
+<td>Global average pooling operation for temporal data.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.nn.GlobalAvgPool2D" title="mxnet.gluon.nn.GlobalAvgPool2D"><code class="xref py py-obj docutils literal"><span class="pre">GlobalAvgPool2D</span></code></a></td>
+<td>Global average pooling operation for spatial data.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.nn.GlobalAvgPool3D" title="mxnet.gluon.nn.GlobalAvgPool3D"><code class="xref py py-obj docutils literal"><span class="pre">GlobalAvgPool3D</span></code></a></td>
+<td>Global max pooling operation for 3D data.</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+<div class="section" id="recurrent-layers">
+<span id="recurrent-layers"></span><h2>Recurrent Layers<a class="headerlink" href="#recurrent-layers" title="Permalink to this headline">¶</a></h2>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><code class="xref py py-obj docutils literal"><span class="pre">RecurrentCell</span></code></a></td>
+<td>Abstract base class for RNN cells</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.rnn.RNN" title="mxnet.gluon.rnn.RNN"><code class="xref py py-obj docutils literal"><span class="pre">RNN</span></code></a></td>
+<td>Applies a multi-layer Elman RNN with <cite>tanh</cite> or <cite>ReLU</cite> non-linearity to an input sequence.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.rnn.LSTM" title="mxnet.gluon.rnn.LSTM"><code class="xref py py-obj docutils literal"><span class="pre">LSTM</span></code></a></td>
+<td>Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.rnn.GRU" title="mxnet.gluon.rnn.GRU"><code class="xref py py-obj docutils literal"><span class="pre">GRU</span></code></a></td>
+<td>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.rnn.RNNCell" title="mxnet.gluon.rnn.RNNCell"><code class="xref py py-obj docutils literal"><span class="pre">RNNCell</span></code></a></td>
+<td>Simple recurrent neural network cell.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.rnn.LSTMCell" title="mxnet.gluon.rnn.LSTMCell"><code class="xref py py-obj docutils literal"><span class="pre">LSTMCell</span></code></a></td>
+<td>Long-Short Term Memory (LSTM) network cell.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.rnn.GRUCell" title="mxnet.gluon.rnn.GRUCell"><code class="xref py py-obj docutils literal"><span class="pre">GRUCell</span></code></a></td>
+<td>Gated Rectified Unit (GRU) network cell.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.rnn.SequentialRNNCell" title="mxnet.gluon.rnn.SequentialRNNCell"><code class="xref py py-obj docutils literal"><span class="pre">SequentialRNNCell</span></code></a></td>
+<td>Sequentially stacking multiple RNN cells.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.rnn.BidirectionalCell" title="mxnet.gluon.rnn.BidirectionalCell"><code class="xref py py-obj docutils literal"><span class="pre">BidirectionalCell</span></code></a></td>
+<td>Bidirectional RNN cell.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.rnn.DropoutCell" title="mxnet.gluon.rnn.DropoutCell"><code class="xref py py-obj docutils literal"><span class="pre">DropoutCell</span></code></a></td>
+<td>Applies dropout on input.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.rnn.ZoneoutCell" title="mxnet.gluon.rnn.ZoneoutCell"><code class="xref py py-obj docutils literal"><span class="pre">ZoneoutCell</span></code></a></td>
+<td>Applies Zoneout on base cell.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.rnn.ResidualCell" title="mxnet.gluon.rnn.ResidualCell"><code class="xref py py-obj docutils literal"><span class="pre">ResidualCell</span></code></a></td>
+<td>Adds residual connection as described in Wu et al, 2016 (<a class="reference external" href="https://arxiv.org/abs/1609.08144">https://arxiv.org/abs/1609.08144</a>).</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="trainer">
+<span id="trainer"></span><h2>Trainer<a class="headerlink" href="#trainer" title="Permalink to this headline">¶</a></h2>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.Trainer" title="mxnet.gluon.Trainer"><code class="xref py py-obj docutils literal"><span class="pre">Trainer</span></code></a></td>
+<td>Applies an <cite>Optimizer</cite> on a set of Parameters.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="loss-functions">
+<span id="loss-functions"></span><h2>Loss functions<a class="headerlink" href="#loss-functions" title="Permalink to this headline">¶</a></h2>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.loss.L2Loss" title="mxnet.gluon.loss.L2Loss"><code class="xref py py-obj docutils literal"><span class="pre">L2Loss</span></code></a></td>
+<td>Calculates the mean squared error between output and label:</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.loss.L1Loss" title="mxnet.gluon.loss.L1Loss"><code class="xref py py-obj docutils literal"><span class="pre">L1Loss</span></code></a></td>
+<td>Calculates the mean absolute error between output and label:</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss" title="mxnet.gluon.loss.SoftmaxCrossEntropyLoss"><code class="xref py py-obj docutils literal"><span class="pre">SoftmaxCrossEntropyLoss</span></code></a></td>
+<td>Computes the softmax cross entropy loss.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.loss.KLDivLoss" title="mxnet.gluon.loss.KLDivLoss"><code class="xref py py-obj docutils literal"><span class="pre">KLDivLoss</span></code></a></td>
+<td>The Kullback-Leibler divergence loss.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="utilities">
+<span id="utilities"></span><h2>Utilities<a class="headerlink" href="#utilities" title="Permalink to this headline">¶</a></h2>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.utils.split_data" title="mxnet.gluon.utils.split_data"><code class="xref py py-obj docutils literal"><span class="pre">split_data</span></code></a></td>
+<td>Splits an NDArray into <cite>num_slice</cite> slices along <cite>batch_axis</cite>.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.utils.split_and_load" title="mxnet.gluon.utils.split_and_load"><code class="xref py py-obj docutils literal"><span class="pre">split_and_load</span></code></a></td>
+<td>Splits an NDArray into <cite>len(ctx_list)</cite> slices along <cite>batch_axis</cite> and loads each slice to one context in <cite>ctx_list</cite>.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.utils.clip_global_norm" title="mxnet.gluon.utils.clip_global_norm"><code class="xref py py-obj docutils literal"><span class="pre">clip_global_norm</span></code></a></td>
+<td>Rescales NDArrays so that the sum of their 2-norm is smaller than <cite>max_norm</cite>.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="data">
+<span id="data"></span><h2>Data<a class="headerlink" href="#data" title="Permalink to this headline">¶</a></h2>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset"><code class="xref py py-obj docutils literal"><span class="pre">Dataset</span></code></a></td>
+<td>Abstract dataset class.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.data.ArrayDataset" title="mxnet.gluon.data.ArrayDataset"><code class="xref py py-obj docutils literal"><span class="pre">ArrayDataset</span></code></a></td>
+<td>A dataset with a data array and a label array.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.data.RecordFileDataset" title="mxnet.gluon.data.RecordFileDataset"><code class="xref py py-obj docutils literal"><span class="pre">RecordFileDataset</span></code></a></td>
+<td>A dataset wrapping over a RecordIO (.rec) file.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.data.ImageRecordDataset" title="mxnet.gluon.data.ImageRecordDataset"><code class="xref py py-obj docutils literal"><span class="pre">ImageRecordDataset</span></code></a></td>
+<td>A dataset wrapping over a RecordIO file containing images.</td>
+</tr>
+</tbody>
+</table>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.data.Sampler" title="mxnet.gluon.data.Sampler"><code class="xref py py-obj docutils literal"><span class="pre">Sampler</span></code></a></td>
+<td>Base class for samplers.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.data.SequentialSampler" title="mxnet.gluon.data.SequentialSampler"><code class="xref py py-obj docutils literal"><span class="pre">SequentialSampler</span></code></a></td>
+<td>Samples elements from [0, length) sequentially.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.data.RandomSampler" title="mxnet.gluon.data.RandomSampler"><code class="xref py py-obj docutils literal"><span class="pre">RandomSampler</span></code></a></td>
+<td>Samples elements from [0, length) randomly without replacement.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.data.BatchSampler" title="mxnet.gluon.data.BatchSampler"><code class="xref py py-obj docutils literal"><span class="pre">BatchSampler</span></code></a></td>
+<td>Wraps over another <cite>Sampler</cite> and return mini-batches of samples.</td>
+</tr>
+</tbody>
+</table>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.data.DataLoader" title="mxnet.gluon.data.DataLoader"><code class="xref py py-obj docutils literal"><span class="pre">DataLoader</span></code></a></td>
+<td>Loads data from a dataset and returns mini-batches of data.</td>
+</tr>
+</tbody>
+</table>
+<div class="section" id="vision">
+<span id="vision"></span><h3>Vision<a class="headerlink" href="#vision" title="Permalink to this headline">¶</a></h3>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.data.vision.MNIST" title="mxnet.gluon.data.vision.MNIST"><code class="xref py py-obj docutils literal"><span class="pre">MNIST</span></code></a></td>
+<td>MNIST handwritten digits dataset from <a href="#id109"><span class="problematic" id="id110">`http://yann.lecun.com/exdb/mnist`_</span></a>.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.data.vision.CIFAR10" title="mxnet.gluon.data.vision.CIFAR10"><code class="xref py py-obj docutils literal"><span class="pre">CIFAR10</span></code></a></td>
+<td>CIFAR10 image classification dataset from <a href="#id111"><span class="problematic" id="id112">`https://www.cs.toronto.edu/~kriz/cifar.html`_</span></a>.</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+<div class="section" id="model-zoo">
+<span id="model-zoo"></span><h2>Model Zoo<a class="headerlink" href="#model-zoo" title="Permalink to this headline">¶</a></h2>
+<p>Model zoo provides pre-defined and pre-trained models to help bootstrap machine learning applications.</p>
+<div class="section" id="vision">
+<span id="id2"></span><h3>Vision<a class="headerlink" href="#vision" title="Permalink to this headline">¶</a></h3>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_model" title="mxnet.gluon.model_zoo.vision.get_model"><code class="xref py py-obj docutils literal"><span class="pre">get_model</span></code></a></td>
+<td>Returns a pre-defined model by name</td>
+</tr>
+</tbody>
+</table>
+<div class="section" id="resnet">
+<span id="resnet"></span><h4>ResNet<a class="headerlink" href="#resnet" title="Permalink to this headline">¶</a></h4>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet18_v1" title="mxnet.gluon.model_zoo.vision.resnet18_v1"><code class="xref py py-obj docutils literal"><span class="pre">resnet18_v1</span></code></a></td>
+<td>ResNet-18 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet34_v1" title="mxnet.gluon.model_zoo.vision.resnet34_v1"><code class="xref py py-obj docutils literal"><span class="pre">resnet34_v1</span></code></a></td>
+<td>ResNet-34 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet50_v1" title="mxnet.gluon.model_zoo.vision.resnet50_v1"><code class="xref py py-obj docutils literal"><span class="pre">resnet50_v1</span></code></a></td>
+<td>ResNet-50 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet101_v1" title="mxnet.gluon.model_zoo.vision.resnet101_v1"><code class="xref py py-obj docutils literal"><span class="pre">resnet101_v1</span></code></a></td>
+<td>ResNet-101 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet152_v1" title="mxnet.gluon.model_zoo.vision.resnet152_v1"><code class="xref py py-obj docutils literal"><span class="pre">resnet152_v1</span></code></a></td>
+<td>ResNet-152 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet18_v2" title="mxnet.gluon.model_zoo.vision.resnet18_v2"><code class="xref py py-obj docutils literal"><span class="pre">resnet18_v2</span></code></a></td>
+<td>ResNet-18 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet34_v2" title="mxnet.gluon.model_zoo.vision.resnet34_v2"><code class="xref py py-obj docutils literal"><span class="pre">resnet34_v2</span></code></a></td>
+<td>ResNet-34 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet50_v2" title="mxnet.gluon.model_zoo.vision.resnet50_v2"><code class="xref py py-obj docutils literal"><span class="pre">resnet50_v2</span></code></a></td>
+<td>ResNet-50 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet101_v2" title="mxnet.gluon.model_zoo.vision.resnet101_v2"><code class="xref py py-obj docutils literal"><span class="pre">resnet101_v2</span></code></a></td>
+<td>ResNet-101 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet152_v2" title="mxnet.gluon.model_zoo.vision.resnet152_v2"><code class="xref py py-obj docutils literal"><span class="pre">resnet152_v2</span></code></a></td>
+<td>ResNet-152 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.ResNetV1" title="mxnet.gluon.model_zoo.vision.ResNetV1"><code class="xref py py-obj docutils literal"><span class="pre">ResNetV1</span></code></a></td>
+<td>ResNet V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.ResNetV2" title="mxnet.gluon.model_zoo.vision.ResNetV2"><code class="xref py py-obj docutils literal"><span class="pre">ResNetV2</span></code></a></td>
+<td>ResNet V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BasicBlockV1" title="mxnet.gluon.model_zoo.vision.BasicBlockV1"><code class="xref py py-obj docutils literal"><span class="pre">BasicBlockV1</span></code></a></td>
+<td>BasicBlock V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BasicBlockV2" title="mxnet.gluon.model_zoo.vision.BasicBlockV2"><code class="xref py py-obj docutils literal"><span class="pre">BasicBlockV2</span></code></a></td>
+<td>BasicBlock V2 from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BottleneckV1" title="mxnet.gluon.model_zoo.vision.BottleneckV1"><code class="xref py py-obj docutils literal"><span class="pre">BottleneckV1</span></code></a></td>
+<td>Bottleneck V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BottleneckV2" title="mxnet.gluon.model_zoo.vision.BottleneckV2"><code class="xref py py-obj docutils literal"><span class="pre">BottleneckV2</span></code></a></td>
+<td>Bottleneck V2 from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_resnet" title="mxnet.gluon.model_zoo.vision.get_resnet"><code class="xref py py-obj docutils literal"><span class="pre">get_resnet</span></code></a></td>
+<td>ResNet V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="vgg">
+<span id="vgg"></span><h4>VGG<a class="headerlink" href="#vgg" title="Permalink to this headline">¶</a></h4>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg11" title="mxnet.gluon.model_zoo.vision.vgg11"><code class="xref py py-obj docutils literal"><span class="pre">vgg11</span></code></a></td>
+<td>VGG-11 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg13" title="mxnet.gluon.model_zoo.vision.vgg13"><code class="xref py py-obj docutils literal"><span class="pre">vgg13</span></code></a></td>
+<td>VGG-13 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg16" title="mxnet.gluon.model_zoo.vision.vgg16"><code class="xref py py-obj docutils literal"><span class="pre">vgg16</span></code></a></td>
+<td>VGG-16 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg19" title="mxnet.gluon.model_zoo.vision.vgg19"><code class="xref py py-obj docutils literal"><span class="pre">vgg19</span></code></a></td>
+<td>VGG-19 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg11_bn" title="mxnet.gluon.model_zoo.vision.vgg11_bn"><code class="xref py py-obj docutils literal"><span class="pre">vgg11_bn</span></code></a></td>
+<td>VGG-11 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg13_bn" title="mxnet.gluon.model_zoo.vision.vgg13_bn"><code class="xref py py-obj docutils literal"><span class="pre">vgg13_bn</span></code></a></td>
+<td>VGG-13 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg16_bn" title="mxnet.gluon.model_zoo.vision.vgg16_bn"><code class="xref py py-obj docutils literal"><span class="pre">vgg16_bn</span></code></a></td>
+<td>VGG-16 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg19_bn" title="mxnet.gluon.model_zoo.vision.vgg19_bn"><code class="xref py py-obj docutils literal"><span class="pre">vgg19_bn</span></code></a></td>
+<td>VGG-19 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.VGG" title="mxnet.gluon.model_zoo.vision.VGG"><code class="xref py py-obj docutils literal"><span class="pre">VGG</span></code></a></td>
+<td>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_vgg" title="mxnet.gluon.model_zoo.vision.get_vgg"><code class="xref py py-obj docutils literal"><span class="pre">get_vgg</span></code></a></td>
+<td>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="alexnet">
+<span id="alexnet"></span><h4>Alexnet<a class="headerlink" href="#alexnet" title="Permalink to this headline">¶</a></h4>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.alexnet" title="mxnet.gluon.model_zoo.vision.alexnet"><code class="xref py py-obj docutils literal"><span class="pre">alexnet</span></code></a></td>
+<td>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick...”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.AlexNet" title="mxnet.gluon.model_zoo.vision.AlexNet"><code class="xref py py-obj docutils literal"><span class="pre">AlexNet</span></code></a></td>
+<td>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick...”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="densenet">
+<span id="densenet"></span><h4>DenseNet<a class="headerlink" href="#densenet" title="Permalink to this headline">¶</a></h4>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet121" title="mxnet.gluon.model_zoo.vision.densenet121"><code class="xref py py-obj docutils literal"><span class="pre">densenet121</span></code></a></td>
+<td>Densenet-BC 121-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet161" title="mxnet.gluon.model_zoo.vision.densenet161"><code class="xref py py-obj docutils literal"><span class="pre">densenet161</span></code></a></td>
+<td>Densenet-BC 161-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet169" title="mxnet.gluon.model_zoo.vision.densenet169"><code class="xref py py-obj docutils literal"><span class="pre">densenet169</span></code></a></td>
+<td>Densenet-BC 169-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet201" title="mxnet.gluon.model_zoo.vision.densenet201"><code class="xref py py-obj docutils literal"><span class="pre">densenet201</span></code></a></td>
+<td>Densenet-BC 201-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.DenseNet" title="mxnet.gluon.model_zoo.vision.DenseNet"><code class="xref py py-obj docutils literal"><span class="pre">DenseNet</span></code></a></td>
+<td>Densenet-BC model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="squeezenet">
+<span id="squeezenet"></span><h4>SqueezeNet<a class="headerlink" href="#squeezenet" title="Permalink to this headline">¶</a></h4>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.squeezenet1_0" title="mxnet.gluon.model_zoo.vision.squeezenet1_0"><code class="xref py py-obj docutils literal"><span class="pre">squeezenet1_0</span></code></a></td>
+<td>SqueezeNet 1.0 model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”</a> paper.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.squeezenet1_1" title="mxnet.gluon.model_zoo.vision.squeezenet1_1"><code class="xref py py-obj docutils literal"><span class="pre">squeezenet1_1</span></code></a></td>
+<td>SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>.</td>
+</tr>
+</tbody>
+</table>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.SqueezeNet" title="mxnet.gluon.model_zoo.vision.SqueezeNet"><code class="xref py py-obj docutils literal"><span class="pre">SqueezeNet</span></code></a></td>
+<td>SqueezeNet model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section" id="inception">
+<span id="inception"></span><h4>Inception<a class="headerlink" href="#inception" title="Permalink to this headline">¶</a></h4>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.inception_v3" title="mxnet.gluon.model_zoo.vision.inception_v3"><code class="xref py py-obj docutils literal"><span class="pre">inception_v3</span></code></a></td>
+<td>Inception v3 model from <a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.Inception3" title="mxnet.gluon.model_zoo.vision.Inception3"><code class="xref py py-obj docutils literal"><span class="pre">Inception3</span></code></a></td>
+<td>Inception v3 model from <a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+</div>
+<div class="section" id="api-reference">
+<span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline">¶</a></h2>
+<script src="../../_static/js/auto_module_index.js" type="text/javascript"></script><dl class="class">
<dt id="mxnet.gluon.Parameter">
-<em class="property">class </em><code class="descclassname">mxnet.gluon.</code><code class="descname">Parameter</code><span class="sig-paren">(</span><em>name</em>, <em>grad_req='write'</em>, <em>shape=None</em>, <em>dtype=<type 'numpy.float32'=""></type></em>, <em>lr_mult=1.0</em>, <em>wd_mult=1.0</em>, <em>init=None</em>, <em>allow_deferred_init=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Parameter" title="Permalink to this definition">¶</a></dt>
-<dd><p>A Container holding parameters (weights) of <a href="#id2"><span class="problematic" id="id3">`</span></a>Block`s.</p>
-<p><cite>Parameter</cite> holds a copy of the the parameter on each <cite>Context</cite> after
+<em class="property">class </em><code class="descclassname">mxnet.gluon.</code><code class="descname">Parameter</code><span class="sig-paren">(</span><em>name</em>, <em>grad_req='write'</em>, <em>shape=None</em>, <em>dtype=<type 'numpy.float32'></em>, <em>lr_mult=1.0</em>, <em>wd_mult=1.0</em>, <em>init=None</em>, <em>allow_deferred_init=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Parameter" title="Permalink to this definition">¶</a></dt>
+<dd><p>A Container holding parameters (weights) of <a href="#id40"><span class="problematic" id="id41">`</span></a>Block`s.</p>
+<p><cite>Parameter</cite> holds a copy of the parameter on each <cite>Context</cite> after
it is initialized with <cite>Parameter.initialize(...)</cite>. If <cite>grad_req</cite> is
not <cite>null</cite>, it will also hold a gradient array on each <cite>Context</cite>:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">ctx</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ctx</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">16</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span>
<span class="n">w</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="s1">'fc_weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">init</span><span class="o">=< [...]
<span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="s1">'fc_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">64</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">mx</span><span class="o">.</spa [...]
@@ -295,12 +883,12 @@ not <cite>null</cite>, it will also hold a gradient array on each <cite>Context<
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this parameter.</li>
-<li><strong>grad_req</strong> (<em>{'write'</em><em>, </em><em>'add'</em><em>, </em><em>'null'}</em><em>, </em><em>default 'write'</em>) – <p>Specifies how to update gradient to grad arrays.</p>
+<li><strong>grad_req</strong> (<em>{'write', 'add', 'null'}, default 'write'</em>) – <p>Specifies how to update gradient to grad arrays.</p>
<ul>
<li>‘write’ means everytime gradient is written to grad <cite>NDArray</cite>.</li>
<li>‘add’ means everytime gradient is added to the grad <cite>NDArray</cite>. You need
@@ -310,14 +898,14 @@ iteration when using this option.</li>
will not be allocated.</li>
</ul>
</li>
-<li><strong>shape</strong> (<em>tuple of int</em><em>, </em><em>default None</em>) – Shape of this parameter. By default shape is not specified. Parameter with
+<li><strong>shape</strong> (<em>tuple of int, default None</em>) – Shape of this parameter. By default shape is not specified. Parameter with
unknown shape can be used for <cite>Symbol</cite> API, but <cite>init</cite> will throw an error
when using <cite>NDArray</cite> API.</li>
-<li><strong>dtype</strong> (<em>numpy.dtype</em><em> or </em><em>str</em><em>, </em><em>default 'float32'</em>) – Data type of this parameter. For example, numpy.float32 or ‘float32’.</li>
-<li><strong>lr_mult</strong> (<em>float</em><em>, </em><em>default 1.0</em>) – Learning rate multiplier. Learning rate will be multiplied by lr_mult
+<li><strong>dtype</strong> (<em>numpy.dtype or str, default 'float32'</em>) – Data type of this parameter. For example, numpy.float32 or ‘float32’.</li>
+<li><strong>lr_mult</strong> (<em>float, default 1.0</em>) – Learning rate multiplier. Learning rate will be multiplied by lr_mult
when updating this parameter with optimizer.</li>
-<li><strong>wd_mult</strong> (<em>float</em><em>, </em><em>default 1.0</em>) – Weight decay multiplier (L2 regularizer coefficient). Works similar to lr_mult.</li>
-<li><strong>init</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default None</em>) – Initializer of this parameter. Will use the global initializer by default.</li>
+<li><strong>wd_mult</strong> (<em>float, default 1.0</em>) – Weight decay multiplier (L2 regularizer coefficient). Works similar to lr_mult.</li>
+<li><strong>init</strong> (<em>Initializer, default None</em>) – Initializer of this parameter. Will use the global initializer by default.</li>
</ul>
</td>
</tr>
@@ -325,11 +913,11 @@ when updating this parameter with optimizer.</li>
</table>
<dl class="method">
<dt id="mxnet.gluon.Parameter.initialize">
-<code class="descname">initialize</code><span class="sig-paren">(</span><em>init=None</em>, <em>ctx=None</em>, <em>default_init=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Parameter.initialize" title="Permalink to this definition">¶</a></dt>
+<code class="descname">initialize</code><span class="sig-paren">(</span><em>init=None</em>, <em>ctx=None</em>, <em>default_init=<mxnet.initializer.Uniform object></em>, <em>force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Parameter.initialize" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes parameter and gradient arrays. Only used for <cite>NDArray</cite> API.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>init</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – The initializer to use. Overrides <cite>Parameter.init</cite> and default_init.</li>
@@ -342,35 +930,46 @@ copy will be made for each context.</p>
<p>their values consistent when updating. Normally <cite>gluon.Trainer</cite> does this for you.</p>
</li>
<li><strong>default_init</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Default initializer is used when both <cite>init</cite> and <cite>Parameter.init</cite> are <cite>None</cite>.</li>
+<li><strong>force_reinit</strong> (<em>bool, default False</em>) – Whether to force re-initialization if parameter is already initialized.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weight</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weight</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span [...]
<span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">()</span>
<span class="go">[[-0.01068833 0.01729892]</span>
<span class="go"> [ 0.02042518 -0.01618656]]</span>
-<span class="go"><ndarray 2x2="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2x2 @cpu(0)></span>
<span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">grad</span><span class="p">()</span>
<span class="go">[[ 0. 0.]</span>
<span class="go"> [ 0. 0.]]</span>
-<span class="go"><ndarray 2x2="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2x2 @cpu(0)></span>
<span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">1</span><span [...]
<span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="go">[[-0.00873779 -0.02834515]</span>
<span class="go"> [ 0.05484822 -0.06206018]]</span>
-<span class="go"><ndarray 2x2="" @gpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2x2 @gpu(0)></span>
<span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="go">[[-0.00873779 -0.02834515]</span>
<span class="go"> [ 0.05484822 -0.06206018]]</span>
-<span class="go"><ndarray 2x2="" @gpu(1)=""></ndarray></span>
+<span class="go"><NDArray 2x2 @gpu(1)></span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
+<dt id="mxnet.gluon.Parameter.reset_ctx">
+<code class="descname">reset_ctx</code><span class="sig-paren">(</span><em>ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Parameter.reset_ctx" title="Permalink to this definition">¶</a></dt>
+<dd><p>Re-assign Parameter to other contexts.</p>
+<dl class="docutils">
+<dt>ctx <span class="classifier-delimiter">:</span> <span class="classifier">Context or list of Context, default <cite>context.current_context()</cite>.</span></dt>
+<dd>Assign Parameter to given context. If ctx is a list of Context, a
+copy will be made for each context.</dd>
+</dl>
+</dd></dl>
+<dl class="method">
<dt id="mxnet.gluon.Parameter.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Parameter.set_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets this parameter’s value on all contexts to data.</p>
@@ -381,8 +980,8 @@ copy will be made for each context.</p>
<dd><p>Returns a copy of this parameter on one context. Must have been
initialized on this context before.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>ctx</strong> (<em>Context</em>) – Desired context.</td>
</tr>
@@ -404,8 +1003,8 @@ as creation.</p>
<code class="descname">grad</code><span class="sig-paren">(</span><em>ctx=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Parameter.grad" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a gradient buffer for this parameter on one context.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>ctx</strong> (<em>Context</em>) – Desired context.</td>
</tr>
@@ -440,12 +1039,12 @@ parameter is uninitialized or doesn’t require gradient.</p>
<em class="property">class </em><code class="descclassname">mxnet.gluon.</code><code class="descname">ParameterDict</code><span class="sig-paren">(</span><em>prefix=''</em>, <em>shared=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.ParameterDict" title="Permalink to this definition">¶</a></dt>
<dd><p>A dictionary managing a set of parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>prefix</strong> (<em>str</em><em>, </em><em>default ''</em>) – The prefix to be prepended to all Parameters’ name created by this dict.</li>
-<li><strong>shared</strong> (<a class="reference internal" href="#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><em>ParameterDict</em></a><em> or </em><em>None</em>) – If not <cite>None</cite>, when this dict’s <cite>get</cite> method creates a new parameter, will
+<li><strong>prefix</strong> (<em>str, default ''</em>) – The prefix to be prepended to all Parameters’ names created by this dict.</li>
+<li><strong>shared</strong> (<em>ParameterDict or None</em>) – If not <cite>None</cite>, when this dict’s <cite>get</cite> method creates a new parameter, will
first try to retrieve it from <cite>shared</cite> dict. Usually used for sharing
parameters with another <cite>Block</cite>.</li>
</ul>
@@ -467,13 +1066,14 @@ with <cite>get</cite>.</p>
found, <cite>get</cite> will create a new <cite>Parameter</cite> with key-word arguments and
insert it to self.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>str</em>) – Name of the desired Parameter. It will be prepended with this dictionary’s
prefix.</li>
-<li><strong>**kwargs</strong> (<em>dict</em>) – The rest of key-word arguments for the created <cite>Parameter</cite>.</li>
+<li><strong>**kwargs</strong> – <p>The rest of key-word arguments for the created <cite>Parameter</cite>.</p>
+</li>
</ul>
</td>
</tr>
@@ -493,17 +1093,18 @@ prefix.</li>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.ParameterDict.initialize">
-<code class="descname">initialize</code><span class="sig-paren">(</span><em>init=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, <em>ctx=None</em>, <em>verbose=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.ParameterDict.initialize" title="Permalink to this definition">¶</a></dt>
+<code class="descname">initialize</code><span class="sig-paren">(</span><em>init=<mxnet.initializer.Uniform object></em>, <em>ctx=None</em>, <em>verbose=False</em>, <em>force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.ParameterDict.initialize" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes all Parameters managed by this dictionary to be used for <cite>NDArray</cite>
API. It has no effect when using <cite>Symbol</cite> API.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>init</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <cite>Parameter.init</cite> is <cite>None</cite>.
Otherwise, <cite>Parameter.init</cite> takes precedence.</li>
-<li><strong>ctx</strong> (<em>Context</em><em> or </em><em>list of Context</em>) – Keeps a copy of Parameters on one or many context(s).</li>
+<li><strong>ctx</strong> (<em>Context or list of Context</em>) – Keeps a copy of Parameters on one or many context(s).</li>
+<li><strong>force_reinit</strong> (<em>bool, default False</em>) – Whether to force re-initialization if parameter is already initialized.</li>
</ul>
</td>
</tr>
@@ -516,14 +1117,24 @@ Otherwise, <cite>Parameter.init</cite> takes precedence.</li>
<dd><p>Sets all Parameters’ gradient buffer to 0.</p>
</dd></dl>
<dl class="method">
+<dt id="mxnet.gluon.ParameterDict.reset_ctx">
+<code class="descname">reset_ctx</code><span class="sig-paren">(</span><em>ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.ParameterDict.reset_ctx" title="Permalink to this definition">¶</a></dt>
+<dd><p>Re-assign all Parameters to other contexts.</p>
+<dl class="docutils">
+<dt>ctx <span class="classifier-delimiter">:</span> <span class="classifier">Context or list of Context, default <cite>context.current_context()</cite>.</span></dt>
+<dd>Assign Parameter to given context. If ctx is a list of Context, a
+copy will be made for each context.</dd>
+</dl>
+</dd></dl>
+<dl class="method">
<dt id="mxnet.gluon.ParameterDict.save">
<code class="descname">save</code><span class="sig-paren">(</span><em>filename</em>, <em>strip_prefix=''</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.ParameterDict.save" title="Permalink to this definition">¶</a></dt>
<dd><p>Save parameters to file.</p>
<dl class="docutils">
-<dt>filename</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">str</span><dd>Path to parameter file.</dd>
-<dt>strip_prefix</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">str, default ‘’</span><dd>Strip prefix from parameter names before saving.</dd>
+<dt>filename <span class="classifier-delimiter">:</span> <span class="classifier">str</span></dt>
+<dd>Path to parameter file.</dd>
+<dt>strip_prefix <span class="classifier-delimiter">:</span> <span class="classifier">str, default ‘’</span></dt>
+<dd>Strip prefix from parameter names before saving.</dd>
</dl>
</dd></dl>
<dl class="method">
@@ -531,23 +1142,20 @@ Otherwise, <cite>Parameter.init</cite> takes precedence.</li>
<code class="descname">load</code><span class="sig-paren">(</span><em>filename</em>, <em>ctx</em>, <em>allow_missing=False</em>, <em>ignore_extra=False</em>, <em>restore_prefix=''</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.ParameterDict.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Load parameters from file.</p>
<dl class="docutils">
-<dt>filename</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">str</span><dd>Path to parameter file.</dd>
-<dt>ctx</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">Context or list of Context</span><dd>Context(s) initialize loaded parameters on.</dd>
-<dt>allow_missing</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">bool, default False</span><dd>Whether to silently skip loading parameters not represents in the file.</dd>
-<dt>ignore_extra</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">bool, default False</span><dd>Whether to silently ignore parameters from the file that are not
+<dt>filename <span class="classifier-delimiter">:</span> <span class="classifier">str</span></dt>
+<dd>Path to parameter file.</dd>
+<dt>ctx <span class="classifier-delimiter">:</span> <span class="classifier">Context or list of Context</span></dt>
+<dd>Context(s) initialize loaded parameters on.</dd>
+<dt>allow_missing <span class="classifier-delimiter">:</span> <span class="classifier">bool, default False</span></dt>
+<dd>Whether to silently skip loading parameters not represents in the file.</dd>
+<dt>ignore_extra <span class="classifier-delimiter">:</span> <span class="classifier">bool, default False</span></dt>
+<dd>Whether to silently ignore parameters from the file that are not
present in this ParameterDict.</dd>
-<dt>restore_prefix</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">str, default ‘’</span><dd>prepend prefix to names of stored parameters before loading.</dd>
+<dt>restore_prefix <span class="classifier-delimiter">:</span> <span class="classifier">str, default ‘’</span></dt>
+<dd>prepend prefix to names of stored parameters before loading.</dd>
</dl>
</dd></dl>
</dd></dl>
-</div>
-<div class="section" id="containers">
-<span id="containers"></span><h2>Containers<a class="headerlink" href="#containers" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="mxnet.gluon.Block">
<em class="property">class </em><code class="descclassname">mxnet.gluon.</code><code class="descname">Block</code><span class="sig-paren">(</span><em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block" title="Permalink to this definition">¶</a></dt>
@@ -555,11 +1163,11 @@ present in this ParameterDict.</dd>
subclass this class.</p>
<p><cite>Block</cite> can be nested recursively in a tree structure. You can create and
assign child <cite>Block</cite> as regular attributes:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="k">import</span> <span class="n">Block</span><span class="p">,</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">mxnet</span> <span class="k">import</span> <span class="n">ndarray</span> <span class="k">as</span> <span class="n">F</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">Block</span><span class="p">,</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">ndarray</span> <span class="k">as</span> <span class="n">F</span>
<span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">Block</span><span class="p">):</span>
- <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+ <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Model</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="c1"># use name_scope to give child Blocks appropriate names.</span>
<span class="c1"># It also allows sharing Parameters between Blocks recursively.</span>
@@ -579,16 +1187,16 @@ assign child <cite>Block</cite> as regular attributes:</p>
<p>Child <cite>Block</cite> assigned this way will be registered and <cite>collect_params</cite>
will collect their Parameters recursively.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>prefix</strong> (<em>str</em>) – Prefix acts like a name space. It will be prepended to the names of all
Parameters and child <cite>Block`s in this `Block</cite>‘s <cite>name_scope</cite>. Prefix
should be unique within one model to prevent name collisions.</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><em>ParameterDict</em></a><em> or </em><em>None</em>) – <p><cite>ParameterDict</cite> for sharing weights with the new <cite>Block</cite>. For example,
+<li><strong>params</strong> (<em>ParameterDict or None</em>) – <p><cite>ParameterDict</cite> for sharing weights with the new <cite>Block</cite>. For example,
if you want <cite>dense1</cite> to share <cite>dense0</cite>‘s weights, you can do:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
<span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span>
</pre></div>
</div>
@@ -599,18 +1207,9 @@ if you want <cite>dense1</cite> to share <cite>dense0</cite>‘s weights, you ca
</tbody>
</table>
<dl class="method">
-<dt id="mxnet.gluon.Block.forward">
-<code class="descname">forward</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.forward" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overrides to implement forward computation using <cite>NDArray</cite>. Only
-accepts positional arguments.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>*args</strong> (<em>list of NDArray</em>) – Input tensors.</td>
-</tr>
-</tbody>
-</table>
+<dt id="mxnet.gluon.Block.__setattr__">
+<code class="descname">__setattr__</code><span class="sig-paren">(</span><em>name</em>, <em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.__setattr__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Registers parameters.</p>
</dd></dl>
<dl class="attribute">
<dt id="mxnet.gluon.Block.prefix">
@@ -627,7 +1226,7 @@ accepts positional arguments.</p>
<code class="descname">name_scope</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.name_scope" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a name space object managing a child <cite>Block</cite> and parameter
names. Should be used within a <cite>with</cite> statement:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dense</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
</pre></div>
</div>
@@ -649,8 +1248,8 @@ children’s Parameters.</p>
<code class="descname">save_params</code><span class="sig-paren">(</span><em>filename</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.save_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Save parameters to file.</p>
<dl class="docutils">
-<dt>filename</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">str</span><dd>Path to file.</dd>
+<dt>filename <span class="classifier-delimiter">:</span> <span class="classifier">str</span></dt>
+<dd>Path to file.</dd>
</dl>
</dd></dl>
<dl class="method">
@@ -658,53 +1257,59 @@ children’s Parameters.</p>
<code class="descname">load_params</code><span class="sig-paren">(</span><em>filename</em>, <em>ctx</em>, <em>allow_missing=False</em>, <em>ignore_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.load_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Load parameters from file.</p>
<dl class="docutils">
-<dt>filename</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">str</span><dd>Path to parameter file.</dd>
-<dt>ctx</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">Context or list of Context</span><dd>Context(s) initialize loaded parameters on.</dd>
-<dt>allow_missing</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">bool, default False</span><dd>Whether to silently skip loading parameters not represents in the file.</dd>
-<dt>ignore_extra</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">bool, default False</span><dd>Whether to silently ignore parameters from the file that are not
+<dt>filename <span class="classifier-delimiter">:</span> <span class="classifier">str</span></dt>
+<dd>Path to parameter file.</dd>
+<dt>ctx <span class="classifier-delimiter">:</span> <span class="classifier">Context or list of Context</span></dt>
+<dd>Context(s) initialize loaded parameters on.</dd>
+<dt>allow_missing <span class="classifier-delimiter">:</span> <span class="classifier">bool, default False</span></dt>
+<dd>Whether to silently skip loading parameters not represents in the file.</dd>
+<dt>ignore_extra <span class="classifier-delimiter">:</span> <span class="classifier">bool, default False</span></dt>
+<dd>Whether to silently ignore parameters from the file that are not
present in this Block.</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.Block.register_child">
<code class="descname">register_child</code><span class="sig-paren">(</span><em>block</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.register_child" title="Permalink to this definition">¶</a></dt>
-<dd><p>Registers block as a child of self. <a href="#id4"><span class="problematic" id="id5">`</span></a>Block`s assigned to self as
+<dd><p>Registers block as a child of self. <a href="#id46"><span class="problematic" id="id47">`</span></a>Block`s assigned to self as
attributes will be registered automatically.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.Block.initialize">
-<code class="descname">initialize</code><span class="sig-paren">(</span><em>init=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, <em>ctx=None</em>, <em>verbose=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.initialize" title="Permalink to this definition">¶</a></dt>
+<code class="descname">initialize</code><span class="sig-paren">(</span><em>init=<mxnet.initializer.Uniform object></em>, <em>ctx=None</em>, <em>verbose=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.initialize" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes <cite>Parameter`s of this `Block</cite> and its children.</p>
<p>Equivalent to <cite>block.collect_params().initialize(...)</cite></p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.Block.hybridize">
<code class="descname">hybridize</code><span class="sig-paren">(</span><em>active=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.hybridize" title="Permalink to this definition">¶</a></dt>
-<dd><p>Activates or deactivates <a href="#id6"><span class="problematic" id="id7">`</span></a>HybridBlock`s recursively. Has no effect on
+<dd><p>Activates or deactivates <a href="#id48"><span class="problematic" id="id49">`</span></a>HybridBlock`s recursively. Has no effect on
non-hybrid children.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>active</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether to turn hybrid on or off.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>active</strong> (<em>bool, default True</em>) – Whether to turn hybrid on or off.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
-<dt>
-<code class="descname">forward</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span></dt>
+<dt id="mxnet.gluon.Block.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Calls forward. Only accepts positional arguments.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.Block.forward">
+<code class="descname">forward</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Block.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Overrides to implement forward computation using <cite>NDArray</cite>. Only
accepts positional arguments.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>*args</strong> (<em>list of NDArray</em>) – Input tensors.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>*args</strong> – <p>Input tensors.</p>
+</td>
</tr>
</tbody>
</table>
@@ -726,21 +1331,9 @@ the cached graph will be used instead of <cite>hybrid_forward</cite>.</p>
<p>Refer <a class="reference external" href="http://mxnet.io/tutorials/gluon/hybrid.html">Hybrid tutorial</a> to see
the end-to-end usage.</p>
<dl class="method">
-<dt id="mxnet.gluon.HybridBlock.hybrid_forward">
-<code class="descname">hybrid_forward</code><span class="sig-paren">(</span><em>F</em>, <em>x</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.HybridBlock.hybrid_forward" title="Permalink to this definition">¶</a></dt>
-<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>x</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</li>
-<li><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
+<dt id="mxnet.gluon.HybridBlock.__setattr__">
+<code class="descname">__setattr__</code><span class="sig-paren">(</span><em>name</em>, <em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.HybridBlock.__setattr__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Registers parameters.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.HybridBlock.infer_shape">
@@ -754,16 +1347,17 @@ the end-to-end usage.</p>
<cite>NDArray</cite> or <cite>Symbol</cite>.</p>
</dd></dl>
<dl class="method">
-<dt>
-<code class="descname">hybrid_forward</code><span class="sig-paren">(</span><em>F</em>, <em>x</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dt id="mxnet.gluon.HybridBlock.hybrid_forward">
+<code class="descname">hybrid_forward</code><span class="sig-paren">(</span><em>F</em>, <em>x</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.HybridBlock.hybrid_forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>x</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</li>
-<li><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</li>
+<li><strong>x</strong> (<em>Symbol or NDArray</em>) – The first input tensor.</li>
+<li><strong>*args</strong> – <p>Additional input tensors.</p>
+</li>
</ul>
</td>
</tr>
@@ -771,17 +1365,50 @@ the end-to-end usage.</p>
</table>
</dd></dl>
</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.SymbolBlock">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.</code><code class="descname">SymbolBlock</code><span class="sig-paren">(</span><em>outputs</em>, <em>inputs</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock" title="Permalink to this definition">¶</a></dt>
+<dd><p>Construct block from symbol. This is useful for using pre-trained models
+as feature extractors. For example, you may want to extract get the output
+from fc2 layer in AlexNet.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>outputs</strong> (<em>Symbol or list of Symbol</em>) – The desired output for SymbolBlock.</li>
+<li><strong>inputs</strong> (<em>Symbol or list of Symbol</em>) – The Variables in output’s argument that should be used as inputs.</li>
+<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><em>ParameterDict</em></a>) – Parameter dictionary for arguments and auxililary states of outputs
+that are not inputs.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># To extract the feature from fc1 and fc2 layers of AlexNet:</span>
+<span class="gp">>>> </span><span class="n">alexnet</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">model_zoo</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span cl [...]
+<span class="go"> prefix='model_')</span>
+<span class="gp">>>> </span><span class="n">inputs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">alexnet</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">internals</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">get_internals</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">internals</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">())</span>
+<span class="go">['data', ..., 'model_dense0_relu_fwd_output', ..., 'model_dense1_relu_fwd_output', ...]</span>
+<span class="gp">>>> </span><span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">internals</span><span class="p">[</span><span class="s1">'model_dense0_relu_fwd_output'</span><span class="p">],</span>
+<span class="go"> internals['model_dense1_relu_fwd_output']]</span>
+<span class="gp">>>> </span><span class="c1"># Create SymbolBlock that shares parameters with alexnet</span>
+<span class="gp">>>> </span><span class="n">feat_model</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">SymbolBlock</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">alexnet</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span>
+<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span> [...]
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">feat_model</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+</pre></div>
</div>
-<div class="section" id="neural-network-layers">
-<span id="neural-network-layers"></span><h2>Neural Network Layers<a class="headerlink" href="#neural-network-layers" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="containers">
-<span id="id1"></span><h3>Containers<a class="headerlink" href="#containers" title="Permalink to this headline">¶</a></h3>
+</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.nn.Sequential">
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">Sequential</code><span class="sig-paren">(</span><em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.Sequential" title="Permalink to this definition">¶</a></dt>
-<dd><p>Stacks <a href="#id8"><span class="problematic" id="id9">`</span></a>Block`s sequentially.</p>
+<dd><p>Stacks <a href="#id58"><span class="problematic" id="id59">`</span></a>Block`s sequentially.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="c1"># use net's name_scope to give child Blocks appropriate names.</span>
<span class="k">with</span> <span class="n">net</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span>
@@ -797,9 +1424,9 @@ the end-to-end usage.</p>
<dl class="class">
<dt id="mxnet.gluon.nn.HybridSequential">
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">HybridSequential</code><span class="sig-paren">(</span><em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.HybridSequential" title="Permalink to this definition">¶</a></dt>
-<dd><p>Stacks <a href="#id10"><span class="problematic" id="id11">`</span></a>HybridBlock`s sequentially.</p>
+<dd><p>Stacks <a href="#id60"><span class="problematic" id="id61">`</span></a>HybridBlock`s sequentially.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="c1"># use net's name_scope to give child Blocks appropriate names.</span>
<span class="k">with</span> <span class="n">net</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span>
@@ -812,9 +1439,6 @@ the end-to-end usage.</p>
<dd><p>Adds block on top of the stack.</p>
</dd></dl>
</dd></dl>
-</div>
-<div class="section" id="basic-layers">
-<span id="basic-layers"></span><h3>Basic Layers<a class="headerlink" href="#basic-layers" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="mxnet.gluon.nn.Dense">
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">Dense</code><span class="sig-paren">(</span><em>units</em>, <em>activation=None</em>, <em>use_bias=True</em>, <em>weight_initializer=None</em>, <em>bias_initializer='zeros'</em>, <em>in_units=0</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.Dense" title="Permalink to this definition">¶</a></dt>
@@ -828,8 +1452,8 @@ created by the layer, and <cite>bias</cite> is a bias vector created by the laye
<p>Note: the input must be a tensor with rank 2. Use <cite>flatten</cite> to convert it
to rank 2 manually if necessary.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>units</strong> (<em>int</em>) – Dimensionality of the output space.</li>
@@ -839,11 +1463,11 @@ If you don’t specify anything, no activation is applied
<li><strong>use_bias</strong> (<em>bool</em>) – Whether the layer uses a bias vector.</li>
<li><strong>weight_initializer</strong> (str or <cite>Initializer</cite>) – Initializer for the <cite>kernel</cite> weights matrix.</li>
<li><strong>bias_initializer</strong> (str or <cite>Initializer</cite>) – Initializer for the bias vector.</li>
-<li><strong>in_units</strong> (<em>int</em><em>, </em><em>optional</em>) – Size of the input data. If not specified, initialization will be
+<li><strong>in_units</strong> (<em>int, optional</em>) – Size of the input data. If not specified, initialization will be
deferred to the first time <cite>forward</cite> is called and <cite>in_units</cite>
will be inferred from the shape of input data.</li>
-<li><strong>prefix</strong> (<em>str</em><em> or </em><em>None</em>) – See document of <cite>Block</cite>.</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><em>ParameterDict</em></a><em> or </em><em>None</em>) – See document of <cite>Block</cite>.</li>
+<li><strong>prefix</strong> (<em>str or None</em>) – See document of <cite>Block</cite>.</li>
+<li><strong>params</strong> (<em>ParameterDict or None</em>) – See document of <cite>Block</cite>.</li>
</ul>
</td>
</tr>
@@ -861,8 +1485,8 @@ will be inferred from the shape of input data.</li>
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">Activation</code><span class="sig-paren">(</span><em>activation</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.Activation" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies an activation function to input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>activation</strong> (<em>str</em>) – Name of activation function to use.
See <a class="reference internal" href="ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a> for available choices.</td>
@@ -883,8 +1507,8 @@ See <a class="reference internal" href="ndarray.html#mxnet.ndarray.Activation" t
<p>Dropout consists in randomly setting a fraction <cite>rate</cite> of input units
to 0 at each update during training time, which helps prevent overfitting.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>rate</strong> (<em>float</em>) – Fraction of the input units to drop. Must be a number between 0 and 1.</td>
</tr>
@@ -901,24 +1525,24 @@ to 0 at each update during training time, which helps prevent overfitting.</p>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.nn.BatchNorm">
-<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">BatchNorm</code><span class="sig-paren">(</span><em>axis=1</em>, <em>momentum=0.9</em>, <em>epsilon=0.001</em>, <em>center=True</em>, <em>scale=True</em>, <em>beta_initializer='zeros'</em>, <em>gamma_initializer='ones'</em>, <em>running_mean_initializer='zeros'</em>, <em>running_variance_initializer='ones'</em>, <em>in_channels=0</em>, <em>**kwargs</em><span class="sig-paren">)</span> [...]
+<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">BatchNorm</code><span class="sig-paren">(</span><em>axis=1</em>, <em>momentum=0.9</em>, <em>epsilon=1e-05</em>, <em>center=True</em>, <em>scale=True</em>, <em>beta_initializer='zeros'</em>, <em>gamma_initializer='ones'</em>, <em>running_mean_initializer='zeros'</em>, <em>running_variance_initializer='ones'</em>, <em>in_channels=0</em>, <em>**kwargs</em><span class="sig-paren">)</span> [...]
<dd><p>Batch normalization layer (Ioffe and Szegedy, 2014).
Normalizes the input at each batch, i.e. applies a transformation
that maintains the mean activation close to 0 and the activation
standard deviation close to 1.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>default 1</em>) – The axis that should be normalized. This is typically the channels
+<li><strong>axis</strong> (<em>int, default 1</em>) – The axis that should be normalized. This is typically the channels
(C) axis. For instance, after a <cite>Conv2D</cite> layer with <cite>layout=’NCHW’</cite>,
set <cite>axis=1</cite> in <cite>BatchNorm</cite>. If <cite>layout=’NHWC’</cite>, then set <cite>axis=3</cite>.</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>default 0.9</em>) – Momentum for the moving average.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>default 1e-3</em>) – Small float added to variance to avoid dividing by zero.</li>
-<li><strong>center</strong> (<em>bool</em><em>, </em><em>default True</em>) – If True, add offset of <cite>beta</cite> to normalized tensor.
+<li><strong>momentum</strong> (<em>float, default 0.9</em>) – Momentum for the moving average.</li>
+<li><strong>epsilon</strong> (<em>float, default 1e-5</em>) – Small float added to variance to avoid dividing by zero.</li>
+<li><strong>center</strong> (<em>bool, default True</em>) – If True, add offset of <cite>beta</cite> to normalized tensor.
If False, <cite>beta</cite> is ignored.</li>
-<li><strong>scale</strong> (<em>bool</em><em>, </em><em>default True</em>) – If True, multiply by <cite>gamma</cite>. If False, <cite>gamma</cite> is not used.
+<li><strong>scale</strong> (<em>bool, default True</em>) – If True, multiply by <cite>gamma</cite>. If False, <cite>gamma</cite> is not used.
When the next layer is linear (also e.g. <cite>nn.relu</cite>),
this can be disabled since the scaling
will be done by the next layer.</li>
@@ -926,7 +1550,7 @@ will be done by the next layer.</li>
<li><strong>gamma_initializer</strong> (str or <cite>Initializer</cite>, default ‘ones’) – Initializer for the gamma weight.</li>
<li><strong>moving_mean_initializer</strong> (str or <cite>Initializer</cite>, default ‘zeros’) – Initializer for the moving mean.</li>
<li><strong>moving_variance_initializer</strong> (str or <cite>Initializer</cite>, default ‘ones’) – Initializer for the moving variance.</li>
-<li><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – Number of channels (feature maps) in input data. If not specified,
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – Number of channels (feature maps) in input data. If not specified,
initialization will be deferred to the first time <cite>forward</cite> is called
and <cite>in_channels</cite> will be inferred from the shape of input data.</li>
</ul>
@@ -946,13 +1570,13 @@ and <cite>in_channels</cite> will be inferred from the shape of input data.</li>
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">LeakyReLU</code><span class="sig-paren">(</span><em>alpha</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.LeakyReLU" title="Permalink to this definition">¶</a></dt>
<dd><p>Leaky version of a Rectified Linear Unit.</p>
<p>It allows a small gradient when the unit is not active:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>`f(x) = alpha * x for x < 0`,
+<div class="highlight-python"><div class="highlight"><pre><span></span>`f(x) = alpha * x for x < 0`,
`f(x) = x for x >= 0`.
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>alpha</strong> (<em>float</em>) – slope coefficient for the negative half axis. Must be >= 0.</td>
</tr>
@@ -971,13 +1595,13 @@ and <cite>in_channels</cite> will be inferred from the shape of input data.</li>
<dd><p>Turns non-negative integers (indexes/tokens) into dense vectors
of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>input_dim</strong> (<em>int</em>) – Size of the vocabulary, i.e. maximum integer index + 1.</li>
<li><strong>output_dim</strong> (<em>int</em>) – Dimension of the dense embedding.</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>np.dtype</em><em>, </em><em>default 'float32'</em>) – Data type of output embeddings.</li>
+<li><strong>dtype</strong> (<em>str or np.dtype, default 'float32'</em>) – Data type of output embeddings.</li>
<li><strong>weight_initializer</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the <cite>embeddings</cite> matrix.</li>
</ul>
</td>
@@ -991,9 +1615,6 @@ of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]</p>
<dd>3D tensor with shape: <cite>(N, M, output_dim)</cite>.</dd>
</dl>
</dd></dl>
-</div>
-<div class="section" id="convolutional-layers">
-<span id="convolutional-layers"></span><h3>Convolutional Layers<a class="headerlink" href="#convolutional-layers" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="mxnet.gluon.nn.Conv1D">
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">Conv1D</code><span class="sig-paren">(</span><em>channels</em>, <em>kernel_size</em>, <em>strides=1</em>, <em>padding=0</em>, <em>dilation=1</em>, <em>groups=1</em>, <em>layout='NCW'</em>, <em>activation=None</em>, <em>use_bias=True</em>, <em>weight_initializer=None</em>, <em>bias_initializer='zeros'</em>, <em>in_channels=0</em>, <em>**kwargs</em><span class="sig-paren">)</span><a cla [...]
@@ -1008,26 +1629,26 @@ it is applied to the outputs as well.</p>
deferred to the first time <cite>forward</cite> is called and <cite>in_channels</cite> will be
inferred from the shape of input data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>channels</strong> (<em>int</em>) – The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.</li>
-<li><strong>kernel_size</strong> (<em>int</em><em> or </em><em>tuple/list of 1 int</em>) – Specifies the dimensions of the convolution window.</li>
-<li><strong>strides</strong> (<em>int</em><em> or </em><em>tuple/list of 1 int</em><em>,</em><em></em>) – Specify the strides of the convolution.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>a tuple/list of 1 int</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly zero-padded
+<li><strong>kernel_size</strong> (<em>int or tuple/list of 1 int</em>) – Specifies the dimensions of the convolution window.</li>
+<li><strong>strides</strong> (<em>int or tuple/list of 1 int,</em>) – Specify the strides of the convolution.</li>
+<li><strong>padding</strong> (<em>int or a tuple/list of 1 int,</em>) – If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points</li>
-<li><strong>dilation</strong> (<em>int</em><em> or </em><em>tuple/list of 1 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
+<li><strong>dilation</strong> (<em>int or tuple/list of 1 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
<li><strong>groups</strong> (<em>int</em>) – Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Dimension ordering of data and weight. Can be ‘NCW’, ‘NWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCW'</em>) – Dimension ordering of data and weight. Can be ‘NCW’, ‘NWC’, etc.
‘N’, ‘C’, ‘W’ stands for batch, channel, and width (time) dimensions
respectively. Convolution is applied on the ‘W’ dimension.</li>
-<li><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of input channels to this layer. If not specified,
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – The number of input channels to this layer. If not specified,
initialization will be deferred to the first time <cite>forward</cite> is called
and <cite>in_channels</cite> will be inferred from the shape of input data.</li>
<li><strong>activation</strong> (<em>str</em>) – Activation function to use. See <a class="reference internal" href="ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a>.
@@ -1049,7 +1670,7 @@ If you don’t specify anything, no activation is applied
<dd><p class="first">This depends on the <cite>layout</cite> parameter. Output is 3D array of shape
(batch_size, channels, out_width) if <cite>layout</cite> is <cite>NCW</cite>.
out_width is calculated as:</p>
-<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="o">-</span><span class="n">dilation</span><span class="o">*</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">-</span><span class= [...]
+<div class="last highlight-python"><div class="highlight"><pre><span></span><span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="o">-</span><span class="n">dilation</span><span class="o">*</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">-</span><span class=" [...]
</pre></div>
</div>
</dd>
@@ -1068,27 +1689,27 @@ a bias vector is created and added to the outputs. Finally, if
deferred to the first time <cite>forward</cite> is called and <cite>in_channels</cite> will be
inferred from the shape of input data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>channels</strong> (<em>int</em>) – The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.</li>
-<li><strong>kernel_size</strong> (<em>int</em><em> or </em><em>tuple/list of 2 int</em>) – Specifies the dimensions of the convolution window.</li>
-<li><strong>strides</strong> (<em>int</em><em> or </em><em>tuple/list of 2 int</em><em>,</em><em></em>) – Specify the strides of the convolution.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>a tuple/list of 2 int</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly zero-padded
+<li><strong>kernel_size</strong> (<em>int or tuple/list of 2 int</em>) – Specifies the dimensions of the convolution window.</li>
+<li><strong>strides</strong> (<em>int or tuple/list of 2 int,</em>) – Specify the strides of the convolution.</li>
+<li><strong>padding</strong> (<em>int or a tuple/list of 2 int,</em>) – If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points</li>
-<li><strong>dilation</strong> (<em>int</em><em> or </em><em>tuple/list of 2 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
+<li><strong>dilation</strong> (<em>int or tuple/list of 2 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
<li><strong>groups</strong> (<em>int</em>) – Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Dimension ordering of data and weight. Can be ‘NCHW’, ‘NHWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCHW'</em>) – Dimension ordering of data and weight. Can be ‘NCHW’, ‘NHWC’, etc.
‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width
dimensions respectively. Convolution is applied on the ‘H’ and
‘W’ dimensions.</li>
-<li><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of input channels to this layer. If not specified,
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – The number of input channels to this layer. If not specified,
initialization will be deferred to the first time <cite>forward</cite> is called
and <cite>in_channels</cite> will be inferred from the shape of input data.</li>
<li><strong>activation</strong> (<em>str</em>) – Activation function to use. See <a class="reference internal" href="ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a>.
@@ -1110,7 +1731,7 @@ If you don’t specify anything, no activation is applied
<dd><p class="first">This depends on the <cite>layout</cite> parameter. Output is 4D array of shape
(batch_size, channels, out_height, out_width) if <cite>layout</cite> is <cite>NCHW</cite>.</p>
<p>out_height and out_width are calculated as:</p>
-<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">height</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">dilation</span><span class="p">[</span><span class="mi">0< [...]
+<div class="last highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">height</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">dilation</span><span class="p">[</span><span class="mi">0</ [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">dilation</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span>< [...]
</pre></div>
</div>
@@ -1130,27 +1751,27 @@ a bias vector is created and added to the outputs. Finally, if
deferred to the first time <cite>forward</cite> is called and <cite>in_channels</cite> will be
inferred from the shape of input data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>channels</strong> (<em>int</em>) – The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.</li>
-<li><strong>kernel_size</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em>) – Specifies the dimensions of the convolution window.</li>
-<li><strong>strides</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em><em>,</em><em></em>) – Specify the strides of the convolution.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>a tuple/list of 3 int</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly zero-padded
+<li><strong>kernel_size</strong> (<em>int or tuple/list of 3 int</em>) – Specifies the dimensions of the convolution window.</li>
+<li><strong>strides</strong> (<em>int or tuple/list of 3 int,</em>) – Specify the strides of the convolution.</li>
+<li><strong>padding</strong> (<em>int or a tuple/list of 3 int,</em>) – If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points</li>
-<li><strong>dilation</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
+<li><strong>dilation</strong> (<em>int or tuple/list of 3 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
<li><strong>groups</strong> (<em>int</em>) – Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Dimension ordering of data and weight. Can be ‘NCDHW’, ‘NDHWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCDHW'</em>) – Dimension ordering of data and weight. Can be ‘NCDHW’, ‘NDHWC’, etc.
‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and
depth dimensions respectively. Convolution is applied on the ‘D’,
‘H’ and ‘W’ dimensions.</li>
-<li><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of input channels to this layer. If not specified,
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – The number of input channels to this layer. If not specified,
initialization will be deferred to the first time <cite>forward</cite> is called
and <cite>in_channels</cite> will be inferred from the shape of input data.</li>
<li><strong>activation</strong> (<em>str</em>) – Activation function to use. See <a class="reference internal" href="ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a>.
@@ -1173,7 +1794,7 @@ If you don’t specify anything, no activation is applied
(batch_size, channels, out_depth, out_height, out_width) if <cite>layout</cite> is
<cite>NCDHW</cite>.</p>
<p>out_depth, out_height and out_width are calculated as:</p>
-<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="n">out_depth</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">depth</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">dilation</span><span class="p">[</span><span class="mi">0</s [...]
+<div class="last highlight-python"><div class="highlight"><pre><span></span><span class="n">out_depth</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">depth</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">dilation</span><span class="p">[</span><span class="mi">0</sp [...]
<span class="n">out_height</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">height</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">dilation</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">-</span><span class="n">dilation</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="p">(</span>< [...]
</pre></div>
@@ -1195,26 +1816,26 @@ said convolution.</p>
deferred to the first time <cite>forward</cite> is called and <cite>in_channels</cite> will be
inferred from the shape of input data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>channels</strong> (<em>int</em>) – The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.</li>
-<li><strong>kernel_size</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em>) – Specifies the dimensions of the convolution window.</li>
-<li><strong>strides</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em><em>,</em><em></em>) – Specify the strides of the convolution.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>a tuple/list of 3 int</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly zero-padded
+<li><strong>kernel_size</strong> (<em>int or tuple/list of 3 int</em>) – Specifies the dimensions of the convolution window.</li>
+<li><strong>strides</strong> (<em>int or tuple/list of 3 int,</em>) – Specify the strides of the convolution.</li>
+<li><strong>padding</strong> (<em>int or a tuple/list of 3 int,</em>) – If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points</li>
-<li><strong>dilation</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
+<li><strong>dilation</strong> (<em>int or tuple/list of 3 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
<li><strong>groups</strong> (<em>int</em>) – Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Dimension ordering of data and weight. Can be ‘NCW’, ‘NWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCW'</em>) – Dimension ordering of data and weight. Can be ‘NCW’, ‘NWC’, etc.
‘N’, ‘C’, ‘W’ stands for batch, channel, and width (time) dimensions
respectively. Convolution is applied on the ‘W’ dimension.</li>
-<li><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of input channels to this layer. If not specified,
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – The number of input channels to this layer. If not specified,
initialization will be deferred to the first time <cite>forward</cite> is called
and <cite>in_channels</cite> will be inferred from the shape of input data.</li>
<li><strong>activation</strong> (<em>str</em>) – Activation function to use. See <a class="reference internal" href="ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a>.
@@ -1236,7 +1857,7 @@ If you don’t specify anything, no activation is applied
<dd><p class="first">This depends on the <cite>layout</cite> parameter. Output is 3D array of shape
(batch_size, channels, out_width) if <cite>layout</cite> is <cite>NCW</cite>.</p>
<p>out_width is calculated as:</p>
-<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="n">out_width</span> <span class="o">=</span> <span class="p">(</span><span class="n">width</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">strides</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="o">+</span><span class="n">kernel_size</span><span class="o">+ [...]
+<div class="last highlight-python"><div class="highlight"><pre><span></span><span class="n">out_width</span> <span class="o">=</span> <span class="p">(</span><span class="n">width</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">strides</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="o">+</span><span class="n">kernel_size</span><span class="o">+< [...]
</pre></div>
</div>
</dd>
@@ -1256,27 +1877,27 @@ said convolution.</p>
deferred to the first time <cite>forward</cite> is called and <cite>in_channels</cite> will be
inferred from the shape of input data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>channels</strong> (<em>int</em>) – The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.</li>
-<li><strong>kernel_size</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em>) – Specifies the dimensions of the convolution window.</li>
-<li><strong>strides</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em><em>,</em><em></em>) – Specify the strides of the convolution.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>a tuple/list of 3 int</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly zero-padded
+<li><strong>kernel_size</strong> (<em>int or tuple/list of 3 int</em>) – Specifies the dimensions of the convolution window.</li>
+<li><strong>strides</strong> (<em>int or tuple/list of 3 int,</em>) – Specify the strides of the convolution.</li>
+<li><strong>padding</strong> (<em>int or a tuple/list of 3 int,</em>) – If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points</li>
-<li><strong>dilation</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
+<li><strong>dilation</strong> (<em>int or tuple/list of 3 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
<li><strong>groups</strong> (<em>int</em>) – Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Dimension ordering of data and weight. Can be ‘NCHW’, ‘NHWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCHW'</em>) – Dimension ordering of data and weight. Can be ‘NCHW’, ‘NHWC’, etc.
‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width
dimensions respectively. Convolution is applied on the ‘H’ and
‘W’ dimensions.</li>
-<li><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of input channels to this layer. If not specified,
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – The number of input channels to this layer. If not specified,
initialization will be deferred to the first time <cite>forward</cite> is called
and <cite>in_channels</cite> will be inferred from the shape of input data.</li>
<li><strong>activation</strong> (<em>str</em>) – Activation function to use. See <a class="reference internal" href="ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a>.
@@ -1298,7 +1919,7 @@ If you don’t specify anything, no activation is applied
<dd><p class="first">This depends on the <cite>layout</cite> parameter. Output is 4D array of shape
(batch_size, channels, out_height, out_width) if <cite>layout</cite> is <cite>NCHW</cite>.</p>
<p>out_height and out_width are calculated as:</p>
-<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="p">(</span><span class="n">height</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">strides</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span> [...]
+<div class="last highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="p">(</span><span class="n">height</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">strides</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span>< [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="p">(</span><span class="n">width</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">strides</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span [...]
</pre></div>
</div>
@@ -1319,27 +1940,27 @@ said convolution.</p>
deferred to the first time <cite>forward</cite> is called and <cite>in_channels</cite> will be
inferred from the shape of input data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>channels</strong> (<em>int</em>) – The dimensionality of the output space, i.e. the number of output
channels (filters) in the convolution.</li>
-<li><strong>kernel_size</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em>) – Specifies the dimensions of the convolution window.</li>
-<li><strong>strides</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em><em>,</em><em></em>) – Specify the strides of the convolution.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>a tuple/list of 3 int</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly zero-padded
+<li><strong>kernel_size</strong> (<em>int or tuple/list of 3 int</em>) – Specifies the dimensions of the convolution window.</li>
+<li><strong>strides</strong> (<em>int or tuple/list of 3 int,</em>) – Specify the strides of the convolution.</li>
+<li><strong>padding</strong> (<em>int or a tuple/list of 3 int,</em>) – If padding is non-zero, then the input is implicitly zero-padded
on both sides for padding number of points</li>
-<li><strong>dilation</strong> (<em>int</em><em> or </em><em>tuple/list of 3 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
+<li><strong>dilation</strong> (<em>int or tuple/list of 3 int</em>) – Specifies the dilation rate to use for dilated convolution.</li>
<li><strong>groups</strong> (<em>int</em>) – Controls the connections between inputs and outputs.
At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv
layers side by side, each seeing half the input channels, and producing
half the output channels, and both subsequently concatenated.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Dimension ordering of data and weight. Can be ‘NCDHW’, ‘NDHWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCDHW'</em>) – Dimension ordering of data and weight. Can be ‘NCDHW’, ‘NDHWC’, etc.
‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and
depth dimensions respectively. Convolution is applied on the ‘D’,
‘H’, and ‘W’ dimensions.</li>
-<li><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of input channels to this layer. If not specified,
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – The number of input channels to this layer. If not specified,
initialization will be deferred to the first time <cite>forward</cite> is called
and <cite>in_channels</cite> will be inferred from the shape of input data.</li>
<li><strong>activation</strong> (<em>str</em>) – Activation function to use. See <a class="reference internal" href="ndarray.html#mxnet.ndarray.Activation" title="mxnet.ndarray.Activation"><code class="xref py py-func docutils literal"><span class="pre">Activation()</span></code></a>.
@@ -1361,7 +1982,7 @@ If you don’t specify anything, no activation is applied
<dd><p class="first">This depends on the <cite>layout</cite> parameter. Output is 5D array of shape
(batch_size, channels, out_depth, out_height, out_width) if <cite>layout</cite> is <cite>NCDHW</cite>.
out_depth, out_height and out_width are calculated as:</p>
-<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="n">out_depth</span> <span class="o">=</span> <span class="p">(</span><span class="n">depth</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">strides</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><s [...]
+<div class="last highlight-python"><div class="highlight"><pre><span></span><span class="n">out_depth</span> <span class="o">=</span> <span class="p">(</span><span class="n">depth</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">strides</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><sp [...]
<span class="n">out_height</span> <span class="o">=</span> <span class="p">(</span><span class="n">height</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">strides</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><spa [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="p">(</span><span class="n">width</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">strides</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span [...]
</pre></div>
@@ -1369,27 +1990,24 @@ out_depth, out_height and out_width are calculated as:</p>
</dd>
</dl>
</dd></dl>
-</div>
-<div class="section" id="pooling-layers">
-<span id="pooling-layers"></span><h3>Pooling Layers<a class="headerlink" href="#pooling-layers" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="mxnet.gluon.nn.MaxPool1D">
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">MaxPool1D</code><span class="sig-paren">(</span><em>pool_size=2</em>, <em>strides=None</em>, <em>padding=0</em>, <em>layout='NCW'</em>, <em>ceil_mode=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.MaxPool1D" title="Permalink to this definition">¶</a></dt>
<dd><p>Max pooling operation for one dimensional data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>pool_size</strong> (<em>int</em>) – Size of the max pooling windows.</li>
-<li><strong>strides</strong> (<em>int</em><em>, or </em><em>None</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
+<li><strong>strides</strong> (<em>int, or None</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
If <cite>None</cite>, it will default to <cite>pool_size</cite>.</li>
<li><strong>padding</strong> (<em>int</em>) – If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Dimension ordering of data and weight. Can be ‘NCW’, ‘NWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCW'</em>) – Dimension ordering of data and weight. Can be ‘NCW’, ‘NWC’, etc.
‘N’, ‘C’, ‘W’ stands for batch, channel, and width (time) dimensions
respectively. Pooling is applied on the W dimension.</li>
-<li><strong>ceil_mode</strong> (<em>bool</em><em>, </em><em>default False</em>) – When <cite>True</cite>, will use ceil instead of floor to compute the output shape.</li>
+<li><strong>ceil_mode</strong> (<em>bool, default False</em>) – When <cite>True</cite>, will use ceil instead of floor to compute the output shape.</li>
</ul>
</td>
</tr>
@@ -1403,7 +2021,7 @@ respectively. Pooling is applied on the W dimension.</li>
<dd><p class="first">This depends on the <cite>layout</cite> parameter. Output is 3D array of shape
(batch_size, channels, out_width) if <cite>layout</cite> is <cite>NCW</cite>.</p>
<p>out_width is calculated as:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="o">-</span><span class="n">pool_size</span><span class="p">)</span><span class="o">/</span><span class="n">strides</span><span class="p">)</span><span class="o">+</s [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="o">-</span><span class="n">pool_size</span><span class="p">)</span><span class="o">/</span><span class="n">strides</span><span class="p">)</span><span class="o">+</sp [...]
</pre></div>
</div>
<p class="last">When <cite>ceil_mode</cite> is <cite>True</cite>, ceil will be used instead of floor in this
@@ -1416,19 +2034,19 @@ equation.</p>
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">MaxPool2D</code><span class="sig-paren">(</span><em>pool_size=(2</em>, <em>2)</em>, <em>strides=None</em>, <em>padding=0</em>, <em>layout='NCHW'</em>, <em>ceil_mode=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.MaxPool2D" title="Permalink to this definition">¶</a></dt>
<dd><p>Max pooling operation for two dimensional (spatial) data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>pool_size</strong> (<em>int</em><em> or </em><em>list/tuple of 2 ints</em><em>,</em><em></em>) – Size of the max pooling windows.</li>
-<li><strong>strides</strong> (<em>int</em><em>, </em><em>list/tuple of 2 ints</em><em>, or </em><em>None.</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
+<li><strong>pool_size</strong> (<em>int or list/tuple of 2 ints,</em>) – Size of the max pooling windows.</li>
+<li><strong>strides</strong> (<em>int, list/tuple of 2 ints, or None.</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
If <cite>None</cite>, it will default to <cite>pool_size</cite>.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>list/tuple of 2 ints</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly
+<li><strong>padding</strong> (<em>int or list/tuple of 2 ints,</em>) – If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Dimension ordering of data and weight. Can be ‘NCHW’, ‘NHWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCHW'</em>) – Dimension ordering of data and weight. Can be ‘NCHW’, ‘NHWC’, etc.
‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width
dimensions respectively. padding is applied on ‘H’ and ‘W’ dimension.</li>
-<li><strong>ceil_mode</strong> (<em>bool</em><em>, </em><em>default False</em>) – When <cite>True</cite>, will use ceil instead of floor to compute the output shape.</li>
+<li><strong>ceil_mode</strong> (<em>bool, default False</em>) – When <cite>True</cite>, will use ceil instead of floor to compute the output shape.</li>
</ul>
</td>
</tr>
@@ -1442,7 +2060,7 @@ dimensions respectively. padding is applied on ‘H’ and ‘W’ dimension.</l
<dd><p class="first">This depends on the <cite>layout</cite> parameter. Output is 4D array of shape
(batch_size, channels, out_height, out_width) if <cite>layout</cite> is <cite>NCHW</cite>.</p>
<p>out_height and out_width are calculated as:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">height</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">0</spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">height</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">0</span [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="n">strides [...]
</pre></div>
</div>
@@ -1456,20 +2074,20 @@ equation.</p>
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">MaxPool3D</code><span class="sig-paren">(</span><em>pool_size=(2</em>, <em>2</em>, <em>2)</em>, <em>strides=None</em>, <em>padding=0</em>, <em>ceil_mode=False</em>, <em>layout='NCDHW'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.MaxPool3D" title="Permalink to this definition">¶</a></dt>
<dd><p>Max pooling operation for 3D data (spatial or spatio-temporal).</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>pool_size</strong> (<em>int</em><em> or </em><em>list/tuple of 3 ints</em><em>,</em><em></em>) – Size of the max pooling windows.</li>
-<li><strong>strides</strong> (<em>int</em><em>, </em><em>list/tuple of 3 ints</em><em>, or </em><em>None.</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
+<li><strong>pool_size</strong> (<em>int or list/tuple of 3 ints,</em>) – Size of the max pooling windows.</li>
+<li><strong>strides</strong> (<em>int, list/tuple of 3 ints, or None.</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
If <cite>None</cite>, it will default to <cite>pool_size</cite>.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>list/tuple of 3 ints</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly
+<li><strong>padding</strong> (<em>int or list/tuple of 3 ints,</em>) – If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Dimension ordering of data and weight. Can be ‘NCDHW’, ‘NDHWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCDHW'</em>) – Dimension ordering of data and weight. Can be ‘NCDHW’, ‘NDHWC’, etc.
‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and
depth dimensions respectively. padding is applied on ‘D’, ‘H’ and ‘W’
dimension.</li>
-<li><strong>ceil_mode</strong> (<em>bool</em><em>, </em><em>default False</em>) – When <cite>True</cite>, will use ceil instead of floor to compute the output shape.</li>
+<li><strong>ceil_mode</strong> (<em>bool, default False</em>) – When <cite>True</cite>, will use ceil instead of floor to compute the output shape.</li>
</ul>
</td>
</tr>
@@ -1484,7 +2102,7 @@ dimension.</li>
(batch_size, channels, out_depth, out_height, out_width) if <cite>layout</cite>
is <cite>NCDHW</cite>.</p>
<p>out_depth, out_height and out_width are calculated as</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_depth</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">depth</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">0</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_depth</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">depth</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">0</span>< [...]
<span class="n">out_height</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">height</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="n">strid [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span><span class="o">/</span><span class="n">strides [...]
</pre></div>
@@ -1499,19 +2117,19 @@ equation.</p>
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">AvgPool1D</code><span class="sig-paren">(</span><em>pool_size=2</em>, <em>strides=None</em>, <em>padding=0</em>, <em>layout='NCW'</em>, <em>ceil_mode=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.AvgPool1D" title="Permalink to this definition">¶</a></dt>
<dd><p>Average pooling operation for temporal data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>pool_size</strong> (<em>int</em>) – Size of the max pooling windows.</li>
-<li><strong>strides</strong> (<em>int</em><em>, or </em><em>None</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
+<li><strong>strides</strong> (<em>int, or None</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
If <cite>None</cite>, it will default to <cite>pool_size</cite>.</li>
<li><strong>padding</strong> (<em>int</em>) – If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCW'</em>) – Dimension ordering of data and weight. Can be ‘NCW’, ‘NWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCW'</em>) – Dimension ordering of data and weight. Can be ‘NCW’, ‘NWC’, etc.
‘N’, ‘C’, ‘W’ stands for batch, channel, and width (time) dimensions
respectively. padding is applied on ‘W’ dimension.</li>
-<li><strong>ceil_mode</strong> (<em>bool</em><em>, </em><em>default False</em>) – When <cite>True</cite>, will use ceil instead of floor to compute the output shape.</li>
+<li><strong>ceil_mode</strong> (<em>bool, default False</em>) – When <cite>True</cite>, will use ceil instead of floor to compute the output shape.</li>
</ul>
</td>
</tr>
@@ -1525,7 +2143,7 @@ respectively. padding is applied on ‘W’ dimension.</li>
<dd><p class="first">This depends on the <cite>layout</cite> parameter. Output is 3D array of shape
(batch_size, channels, out_width) if <cite>layout</cite> is <cite>NCW</cite>.</p>
<p>out_width is calculated as:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="o">-</span><span class="n">pool_size</span><span class="p">)</span><span class="o">/</span><span class="n">strides</span><span class="p">)</span><span class="o">+</s [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="o">-</span><span class="n">pool_size</span><span class="p">)</span><span class="o">/</span><span class="n">strides</span><span class="p">)</span><span class="o">+</sp [...]
</pre></div>
</div>
<p class="last">When <cite>ceil_mode</cite> is <cite>True</cite>, ceil will be used instead of floor in this
@@ -1538,19 +2156,19 @@ equation.</p>
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">AvgPool2D</code><span class="sig-paren">(</span><em>pool_size=(2</em>, <em>2)</em>, <em>strides=None</em>, <em>padding=0</em>, <em>ceil_mode=False</em>, <em>layout='NCHW'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.AvgPool2D" title="Permalink to this definition">¶</a></dt>
<dd><p>Average pooling operation for spatial data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>pool_size</strong> (<em>int</em><em> or </em><em>list/tuple of 2 ints</em><em>,</em><em></em>) – Size of the max pooling windows.</li>
-<li><strong>strides</strong> (<em>int</em><em>, </em><em>list/tuple of 2 ints</em><em>, or </em><em>None.</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
+<li><strong>pool_size</strong> (<em>int or list/tuple of 2 ints,</em>) – Size of the max pooling windows.</li>
+<li><strong>strides</strong> (<em>int, list/tuple of 2 ints, or None.</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
If <cite>None</cite>, it will default to <cite>pool_size</cite>.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>list/tuple of 2 ints</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly
+<li><strong>padding</strong> (<em>int or list/tuple of 2 ints,</em>) – If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCHW'</em>) – Dimension ordering of data and weight. Can be ‘NCHW’, ‘NHWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCHW'</em>) – Dimension ordering of data and weight. Can be ‘NCHW’, ‘NHWC’, etc.
‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width
dimensions respectively. padding is applied on ‘H’ and ‘W’ dimension.</li>
-<li><strong>ceil_mode</strong> (<em>bool</em><em>, </em><em>default False</em>) – When True, will use ceil instead of floor to compute the output shape.</li>
+<li><strong>ceil_mode</strong> (<em>bool, default False</em>) – When True, will use ceil instead of floor to compute the output shape.</li>
</ul>
</td>
</tr>
@@ -1564,7 +2182,7 @@ dimensions respectively. padding is applied on ‘H’ and ‘W’ dimension.</l
<dd><p class="first">This depends on the <cite>layout</cite> parameter. Output is 4D array of shape
(batch_size, channels, out_height, out_width) if <cite>layout</cite> is <cite>NCHW</cite>.</p>
<p>out_height and out_width are calculated as:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">height</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">0</spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">height</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">0</span [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="n">strides [...]
</pre></div>
</div>
@@ -1578,20 +2196,20 @@ equation.</p>
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">AvgPool3D</code><span class="sig-paren">(</span><em>pool_size=(2</em>, <em>2</em>, <em>2)</em>, <em>strides=None</em>, <em>padding=0</em>, <em>ceil_mode=False</em>, <em>layout='NCDHW'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.AvgPool3D" title="Permalink to this definition">¶</a></dt>
<dd><p>Average pooling operation for 3D data (spatial or spatio-temporal).</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>pool_size</strong> (<em>int</em><em> or </em><em>list/tuple of 3 ints</em><em>,</em><em></em>) – Size of the max pooling windows.</li>
-<li><strong>strides</strong> (<em>int</em><em>, </em><em>list/tuple of 3 ints</em><em>, or </em><em>None.</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
+<li><strong>pool_size</strong> (<em>int or list/tuple of 3 ints,</em>) – Size of the max pooling windows.</li>
+<li><strong>strides</strong> (<em>int, list/tuple of 3 ints, or None.</em>) – Factor by which to downscale. E.g. 2 will halve the input size.
If <cite>None</cite>, it will default to <cite>pool_size</cite>.</li>
-<li><strong>padding</strong> (<em>int</em><em> or </em><em>list/tuple of 3 ints</em><em>,</em><em></em>) – If padding is non-zero, then the input is implicitly
+<li><strong>padding</strong> (<em>int or list/tuple of 3 ints,</em>) – If padding is non-zero, then the input is implicitly
zero-padded on both sides for padding number of points.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NCDHW'</em>) – Dimension ordering of data and weight. Can be ‘NCDHW’, ‘NDHWC’, etc.
+<li><strong>layout</strong> (<em>str, default 'NCDHW'</em>) – Dimension ordering of data and weight. Can be ‘NCDHW’, ‘NDHWC’, etc.
‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and
depth dimensions respectively. padding is applied on ‘D’, ‘H’ and ‘W’
dimension.</li>
-<li><strong>ceil_mode</strong> (<em>bool</em><em>, </em><em>default False</em>) – When True, will use ceil instead of floor to compute the output shape.</li>
+<li><strong>ceil_mode</strong> (<em>bool, default False</em>) – When True, will use ceil instead of floor to compute the output shape.</li>
</ul>
</td>
</tr>
@@ -1606,7 +2224,7 @@ dimension.</li>
(batch_size, channels, out_depth, out_height, out_width) if <cite>layout</cite>
is <cite>NCDHW</cite>.</p>
<p>out_depth, out_height and out_width are calculated as</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_depth</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">depth</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">0</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_depth</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">depth</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">0</span>< [...]
<span class="n">out_height</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">height</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="n">strid [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">padding</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">-</span><span class="n">pool_size</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span><span class="o">/</span><span class="n">strides [...]
</pre></div>
@@ -1646,23 +2264,19 @@ equation.</p>
<em class="property">class </em><code class="descclassname">mxnet.gluon.nn.</code><code class="descname">GlobalAvgPool3D</code><span class="sig-paren">(</span><em>layout='NCDHW'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.nn.GlobalAvgPool3D" title="Permalink to this definition">¶</a></dt>
<dd><p>Global max pooling operation for 3D data.</p>
</dd></dl>
-</div>
-</div>
-<div class="section" id="recurrent-layers">
-<span id="recurrent-layers"></span><h2>Recurrent Layers<a class="headerlink" href="#recurrent-layers" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="mxnet.gluon.rnn.RecurrentCell">
<em class="property">class </em><code class="descclassname">mxnet.gluon.rnn.</code><code class="descname">RecurrentCell</code><span class="sig-paren">(</span><em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Abstract base class for RNN cells</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>prefix</strong> (<em>str</em><em>, </em><em>optional</em>) – Prefix for names of <cite>Block`s
+<li><strong>prefix</strong> (<em>str, optional</em>) – Prefix for names of <cite>Block`s
(this prefix is also used for names of weights if `params</cite> is <cite>None</cite>
i.e. if <cite>params</cite> are being created and not reused)</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.Parameter" title="mxnet.gluon.Parameter"><em>Parameter</em></a><em> or </em><em>None</em><em>, </em><em>optional</em>) – Container for weight sharing between cells.
+<li><strong>params</strong> (<em>Parameter or None, optional</em>) – Container for weight sharing between cells.
A new Parameter container is created if <cite>params</cite> is <cite>None</cite>.</li>
</ul>
</td>
@@ -1670,11 +2284,6 @@ A new Parameter container is created if <cite>params</cite> is <cite>None</cite>
</tbody>
</table>
<dl class="method">
-<dt id="mxnet.gluon.rnn.RecurrentCell.__call__">
-<code class="descname">__call__</code><span class="sig-paren">(</span><em>*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.__call__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Calls forward. Only accepts positional arguments.</p>
-</dd></dl>
-<dl class="method">
<dt id="mxnet.gluon.rnn.RecurrentCell.reset">
<code class="descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.reset" title="Permalink to this definition">¶</a></dt>
<dd><p>Reset before re-using the cell for another graph.</p>
@@ -1686,27 +2295,29 @@ A new Parameter container is created if <cite>params</cite> is <cite>None</cite>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.rnn.RecurrentCell.begin_state">
-<code class="descname">begin_state</code><span class="sig-paren">(</span><em>batch_size=0</em>, <em>func=<function zeros=""></function></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.begin_state" title="Permalink to this definition">¶</a></dt>
+<code class="descname">begin_state</code><span class="sig-paren">(</span><em>batch_size=0</em>, <em>func=<function zeros></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.begin_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Initial state for this cell.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – <p>Function for creating initial state.</p>
+<li><strong>func</strong> (<em>callable, default symbol.zeros</em>) – <p>Function for creating initial state.</p>
<p>For Symbol API, func can be <cite>symbol.zeros</cite>, <cite>symbol.uniform</cite>,
<cite>symbol.var etc</cite>. Use <cite>symbol.var</cite> if you want to directly
feed input as states.</p>
<p>For NDArray API, func can be <cite>ndarray.zeros</cite>, <cite>ndarray.ones</cite>, etc.</p>
</li>
-<li><strong>batch_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout)
+<li><strong>batch_size</strong> (<em>int, default 0</em>) – Only required for NDArray API. Size of the batch (‘N’ in layout)
dimension of input.</li>
-<li><strong>**kwargs</strong> – Additional keyword arguments passed to func. For example
-<cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</li>
+<li><strong>**kwargs</strong> – <p>Additional keyword arguments passed to func. For example
+<cite>mean</cite>, <cite>std</cite>, <cite>dtype</cite>, etc.</p>
+</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>states</strong> – Starting states for the first RNN step.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>states</strong> –
+Starting states for the first RNN step.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">nested list of Symbol</p>
@@ -1720,12 +2331,12 @@ dimension of input.</li>
<code class="descname">unroll</code><span class="sig-paren">(</span><em>length</em>, <em>inputs</em>, <em>begin_state=None</em>, <em>layout='NTC'</em>, <em>merge_outputs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.unroll" title="Permalink to this definition">¶</a></dt>
<dd><p>Unrolls an RNN cell across time steps.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>length</strong> (<em>int</em>) – Number of steps to unroll.</li>
-<li><strong>inputs</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output
+<li><strong>inputs</strong> (<em>Symbol, list of Symbol, or None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output
of Embedding symbol), it should have shape
(batch_size, length, ...) if <cite>layout</cite> is ‘NTC’,
or (length, batch_size, ...) if <cite>layout</cite> is ‘TNC’.</p>
@@ -1733,12 +2344,12 @@ or (length, batch_size, ...) if <cite>layout</cite> is ‘TNC’.</p>
previous unroll), they should all have shape
(batch_size, ...).</p>
</li>
-<li><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>optional</em>) – Input states created by <cite>begin_state()</cite>
+<li><strong>begin_state</strong> (<em>nested list of Symbol, optional</em>) – Input states created by <cite>begin_state()</cite>
or output state of another cell.
Created from <cite>begin_state()</cite> if <cite>None</cite>.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs
+<li><strong>layout</strong> (<em>str, optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs
is a single Symbol.</li>
-<li><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols.
+<li><strong>merge_outputs</strong> (<em>bool, optional</em>) – If <cite>False</cite>, returns outputs as a list of Symbols.
If <cite>True</cite>, concatenates output across time steps
and returns a single symbol with shape
(batch_size, length, ...) if layout is ‘NTC’,
@@ -1748,10 +2359,12 @@ If <cite>None</cite>, output whatever is faster.</li>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols
+<li><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) –
+Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols
(if <cite>merge_outputs</cite> is False) corresponding to the output from
the RNN from this unrolling.</li>
-<li><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling.
+<li><strong>states</strong> (<em>list of Symbol</em>) –
+The new state of this RNN after this unrolling.
The type of this symbol is same as the output of <cite>begin_state()</cite>.</li>
</ul>
</p>
@@ -1765,8 +2378,8 @@ The type of this symbol is same as the output of <cite>begin_state()</cite>.</li
<code class="descname">forward</code><span class="sig-paren">(</span><em>inputs</em>, <em>states</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.RecurrentCell.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Unrolls the recurrent cell for one time step.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>inputs</strong> (<em>sym.Variable</em>) – Input symbol, 2D, of shape (batch_size * num_units).</li>
@@ -1775,9 +2388,11 @@ The type of this symbol is same as the output of <cite>begin_state()</cite>.</li
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling
+<li><strong>output</strong> (<em>Symbol</em>) –
+Symbol corresponding to the output from the RNN when unrolling
for a single time step.</li>
-<li><strong>states</strong> (<em>list of Symbol</em>) – The new state of this RNN after this unrolling.
+<li><strong>states</strong> (<em>list of Symbol</em>) –
+The new state of this RNN after this unrolling.
The type of this symbol is same as the output of <cite>begin_state()</cite>.
This can be used as an input state to the next time step
of this RNN.</li>
@@ -1790,9 +2405,9 @@ of this RNN.</li>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
-<dt><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.begin_state" title="mxnet.gluon.rnn.RecurrentCell.begin_state"><code class="xref py py-meth docutils literal"><span class="pre">begin_state()</span></code></a></dt>
+<dt><code class="xref py py-meth docutils literal"><span class="pre">begin_state()</span></code></dt>
<dd>This function can provide the states for the first time step.</dd>
-<dt><a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell.unroll" title="mxnet.gluon.rnn.RecurrentCell.unroll"><code class="xref py py-meth docutils literal"><span class="pre">unroll()</span></code></a></dt>
+<dt><code class="xref py py-meth docutils literal"><span class="pre">unroll()</span></code></dt>
<dd>This function unrolls an RNN for a given number of (>=1) time steps.</dd>
</dl>
</div>
@@ -1810,28 +2425,28 @@ function:</p>
state of the previous layer at time <cite>t</cite> or <span class="math">\(input_t\)</span> for the first layer.
If nonlinearity=’relu’, then <cite>ReLU</cite> is used instead of <cite>tanh</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>hidden_size</strong> (<em>int</em>) – The number of features in the hidden state h.</li>
-<li><strong>num_layers</strong> (<em>int</em><em>, </em><em>default 1</em>) – Number of recurrent layers.</li>
-<li><strong>activation</strong> (<em>{'relu'</em><em> or </em><em>'tanh'}</em><em>, </em><em>default 'tanh'</em>) – The activation function to use.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'TNC'</em>) – The format of input and output tensors. T, N and C stand for
+<li><strong>num_layers</strong> (<em>int, default 1</em>) – Number of recurrent layers.</li>
+<li><strong>activation</strong> (<em>{'relu' or 'tanh'}, default 'tanh'</em>) – The activation function to use.</li>
+<li><strong>layout</strong> (<em>str, default 'TNC'</em>) – The format of input and output tensors. T, N and C stand for
sequence length, batch size, and feature dimensions respectively.</li>
-<li><strong>dropout</strong> (<em>float</em><em>, </em><em>default 0</em>) – If non-zero, introduces a dropout layer on the outputs of each
+<li><strong>dropout</strong> (<em>float, default 0</em>) – If non-zero, introduces a dropout layer on the outputs of each
RNN layer except the last layer.</li>
-<li><strong>bidirectional</strong> (<em>bool</em><em>, </em><em>default False</em>) – If <cite>True</cite>, becomes a bidirectional RNN.</li>
-<li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear
+<li><strong>bidirectional</strong> (<em>bool, default False</em>) – If <cite>True</cite>, becomes a bidirectional RNN.</li>
+<li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the linear
transformation of the inputs.</li>
-<li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear
+<li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the linear
transformation of the recurrent state.</li>
-<li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>input_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of expected features in the input x.
+<li><strong>i2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>h2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>input_size</strong> (<em>int, default 0</em>) – The number of expected features in the input x.
If not specified, it will be inferred from input.</li>
-<li><strong>prefix</strong> (<em>str</em><em> or </em><em>None</em>) – Prefix of this <cite>Block</cite>.</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><em>ParameterDict</em></a><em> or </em><em>None</em>) – Shared Parameters for this <cite>Block</cite>.</li>
+<li><strong>prefix</strong> (<em>str or None</em>) – Prefix of this <cite>Block</cite>.</li>
+<li><strong>params</strong> (<em>ParameterDict or None</em>) – Shared Parameters for this <cite>Block</cite>.</li>
</ul>
</td>
</tr>
@@ -1849,10 +2464,10 @@ If <cite>bidirectional</cite> is True, output shape will instead be
<dt>Recurrent state shape:</dt>
<dd>The recurrent state’s shape is <cite>(num_layers, batch_size, num_hidden)</cite>.
If <cite>bidirectional</cite> is True, state shape will instead be
-<cite>(num_layers, batch_size, 2*num_hidden)</cite></dd>
+<cite>(2*num_layers, batch_size, num_hidden)</cite></dd>
</dl>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">layer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">RNN</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">layer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">RNN</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">layer</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">h0</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
@@ -1881,28 +2496,28 @@ layer at time <cite>t</cite> or <span class="math">\(input_t\)</span> for the fi
<span class="math">\(f_t\)</span>, <span class="math">\(g_t\)</span>, <span class="math">\(o_t\)</span> are the input, forget, cell, and
out gates, respectively.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>hidden_size</strong> (<em>int</em>) – The number of features in the hidden state h.</li>
-<li><strong>num_layers</strong> (<em>int</em><em>, </em><em>default 1</em>) – Number of recurrent layers.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'TNC'</em>) – The format of input and output tensors. T, N and C stand for
+<li><strong>num_layers</strong> (<em>int, default 1</em>) – Number of recurrent layers.</li>
+<li><strong>layout</strong> (<em>str, default 'TNC'</em>) – The format of input and output tensors. T, N and C stand for
sequence length, batch size, and feature dimensions respectively.</li>
-<li><strong>dropout</strong> (<em>float</em><em>, </em><em>default 0</em>) – If non-zero, introduces a dropout layer on the outputs of each
+<li><strong>dropout</strong> (<em>float, default 0</em>) – If non-zero, introduces a dropout layer on the outputs of each
RNN layer except the last layer.</li>
-<li><strong>bidirectional</strong> (<em>bool</em><em>, </em><em>default False</em>) – If <cite>True</cite>, becomes a bidirectional RNN.</li>
-<li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear
+<li><strong>bidirectional</strong> (<em>bool, default False</em>) – If <cite>True</cite>, becomes a bidirectional RNN.</li>
+<li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the linear
transformation of the inputs.</li>
-<li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear
+<li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the linear
transformation of the recurrent state.</li>
-<li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'lstmbias'</em>) – Initializer for the bias vector. By default, bias for the forget
+<li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default 'lstmbias'</em>) – Initializer for the bias vector. By default, bias for the forget
gate is initialized to 1 while all other biases are initialized
to zero.</li>
-<li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>input_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of expected features in the input x.
+<li><strong>h2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>input_size</strong> (<em>int, default 0</em>) – The number of expected features in the input x.
If not specified, it will be inferred from input.</li>
-<li><strong>prefix</strong> (<em>str</em><em> or </em><em>None</em>) – Prefix of this <cite>Block</cite>.</li>
+<li><strong>prefix</strong> (<em>str or None</em>) – Prefix of this <cite>Block</cite>.</li>
<li><strong>params</strong> (<cite>ParameterDict</cite> or <cite>None</cite>) – Shared Parameters for this <cite>Block</cite>.</li>
</ul>
</td>
@@ -1922,10 +2537,10 @@ If <cite>bidirectional</cite> is True, output shape will instead be
<dd>The recurrent state is a list of two NDArrays. Both has shape
<cite>(num_layers, batch_size, num_hidden)</cite>.
If <cite>bidirectional</cite> is True, state shape will instead be
-<cite>(num_layers, batch_size, 2*num_hidden)</cite>.</dd>
+<cite>(2*num_layers, batch_size, num_hidden)</cite>.</dd>
</dl>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">layer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">layer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">layer</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">h0</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
@@ -1951,27 +2566,27 @@ h_t = (1 - i_t) * n_t + i_t * h_{(t-1)} \\
state of the previous layer at time <cite>t</cite> or <span class="math">\(input_t\)</span> for the first layer,
and <span class="math">\(r_t\)</span>, <span class="math">\(i_t\)</span>, <span class="math">\(n_t\)</span> are the reset, input, and new gates, respectively.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>hidden_size</strong> (<em>int</em>) – The number of features in the hidden state h</li>
-<li><strong>num_layers</strong> (<em>int</em><em>, </em><em>default 1</em>) – Number of recurrent layers.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>default 'TNC'</em>) – The format of input and output tensors. T, N and C stand for
+<li><strong>num_layers</strong> (<em>int, default 1</em>) – Number of recurrent layers.</li>
+<li><strong>layout</strong> (<em>str, default 'TNC'</em>) – The format of input and output tensors. T, N and C stand for
sequence length, batch size, and feature dimensions respectively.</li>
-<li><strong>dropout</strong> (<em>float</em><em>, </em><em>default 0</em>) – If non-zero, introduces a dropout layer on the outputs of each
+<li><strong>dropout</strong> (<em>float, default 0</em>) – If non-zero, introduces a dropout layer on the outputs of each
RNN layer except the last layer</li>
-<li><strong>bidirectional</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, becomes a bidirectional RNN.</li>
-<li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear
+<li><strong>bidirectional</strong> (<em>bool, default False</em>) – If True, becomes a bidirectional RNN.</li>
+<li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the linear
transformation of the inputs.</li>
-<li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear
+<li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the linear
transformation of the recurrent state.</li>
-<li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>input_size</strong> (<em>int</em><em>, </em><em>default 0</em>) – The number of expected features in the input x.
+<li><strong>i2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>h2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>input_size</strong> (<em>int, default 0</em>) – The number of expected features in the input x.
If not specified, it will be inferred from input.</li>
-<li><strong>prefix</strong> (<em>str</em><em> or </em><em>None</em>) – Prefix of this <cite>Block</cite>.</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><em>ParameterDict</em></a><em> or </em><em>None</em>) – Shared Parameters for this <cite>Block</cite>.</li>
+<li><strong>prefix</strong> (<em>str or None</em>) – Prefix of this <cite>Block</cite>.</li>
+<li><strong>params</strong> (<em>ParameterDict or None</em>) – Shared Parameters for this <cite>Block</cite>.</li>
</ul>
</td>
</tr>
@@ -1989,10 +2604,10 @@ If <cite>bidirectional</cite> is True, output shape will instead be
<dt>Recurrent state shape:</dt>
<dd>The recurrent state’s shape is <cite>(num_layers, batch_size, num_hidden)</cite>.
If <cite>bidirectional</cite> is True, state shape will instead be
-<cite>(num_layers, batch_size, 2*num_hidden)</cite></dd>
+<cite>(2*num_layers, batch_size, num_hidden)</cite></dd>
</dl>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">layer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">GRU</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">layer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">GRU</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">layer</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">h0</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
@@ -2005,21 +2620,21 @@ If <cite>bidirectional</cite> is True, state shape will instead be
<em class="property">class </em><code class="descclassname">mxnet.gluon.rnn.</code><code class="descname">RNNCell</code><span class="sig-paren">(</span><em>hidden_size</em>, <em>activation='tanh'</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>input_size=0</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluo [...]
<dd><p>Simple recurrent neural network cell.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>hidden_size</strong> (<em>int</em>) – Number of units in output symbol</li>
-<li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function.</li>
-<li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear
+<li><strong>activation</strong> (<em>str or Symbol, default 'tanh'</em>) – Type of activation function.</li>
+<li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the linear
transformation of the inputs.</li>
-<li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear
+<li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the linear
transformation of the recurrent state.</li>
-<li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>prefix</strong> (str, default ‘<a href="#id12"><span class="problematic" id="id13">rnn_</span></a>‘) – Prefix for name of <cite>Block`s
+<li><strong>i2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>h2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>prefix</strong> (str, default ‘<a href="#id113"><span class="problematic" id="id114">rnn_</span></a>‘) – Prefix for name of <cite>Block`s
(and name of weight if params is `None</cite>).</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.Parameter" title="mxnet.gluon.Parameter"><em>Parameter</em></a><em> or </em><em>None</em>) – Container for weight sharing between cells.
+<li><strong>params</strong> (<em>Parameter or None</em>) – Container for weight sharing between cells.
Created if <cite>None</cite>.</li>
</ul>
</td>
@@ -2032,22 +2647,22 @@ Created if <cite>None</cite>.</li>
<em class="property">class </em><code class="descclassname">mxnet.gluon.rnn.</code><code class="descname">LSTMCell</code><span class="sig-paren">(</span><em>hidden_size</em>, <em>i2h_weight_initializer=None</em>, <em>h2h_weight_initializer=None</em>, <em>i2h_bias_initializer='zeros'</em>, <em>h2h_bias_initializer='zeros'</em>, <em>input_size=0</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.LSTMCell" title="Perm [...]
<dd><p>Long-Short Term Memory (LSTM) network cell.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>hidden_size</strong> (<em>int</em>) – Number of units in output symbol.</li>
-<li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear
+<li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the linear
transformation of the inputs.</li>
-<li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear
+<li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the linear
transformation of the recurrent state.</li>
-<li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default 'lstmbias'</em>) – Initializer for the bias vector. By default, bias for the forget
+<li><strong>i2h_bias_initializer</strong> (<em>str or Initializer, default 'lstmbias'</em>) – Initializer for the bias vector. By default, bias for the forget
gate is initialized to 1 while all other biases are initialized
to zero.</li>
-<li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>prefix</strong> (str, default ‘<a href="#id14"><span class="problematic" id="id15">lstm_</span></a>‘) – Prefix for name of <cite>Block`s
+<li><strong>h2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>prefix</strong> (str, default ‘<a href="#id115"><span class="problematic" id="id116">lstm_</span></a>‘) – Prefix for name of <cite>Block`s
(and name of weight if params is `None</cite>).</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.Parameter" title="mxnet.gluon.Parameter"><em>Parameter</em></a><em> or </em><em>None</em>) – Container for weight sharing between cells.
+<li><strong>params</strong> (<em>Parameter or None</em>) – Container for weight sharing between cells.
Created if <cite>None</cite>.</li>
</ul>
</td>
@@ -2062,20 +2677,20 @@ Created if <cite>None</cite>.</li>
Note: this is an implementation of the cuDNN version of GRUs
(slight modification compared to Cho et al. 2014).</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>hidden_size</strong> (<em>int</em>) – Number of units in output symbol.</li>
-<li><strong>i2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the input weights matrix, used for the linear
+<li><strong>i2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the input weights matrix, used for the linear
transformation of the inputs.</li>
-<li><strong>h2h_weight_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the recurrent weights matrix, used for the linear
+<li><strong>h2h_weight_initializer</strong> (<em>str or Initializer</em>) – Initializer for the recurrent weights matrix, used for the linear
transformation of the recurrent state.</li>
-<li><strong>i2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>h2h_bias_initializer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Initializer for the bias vector.</li>
-<li><strong>prefix</strong> (str, default ‘<a href="#id16"><span class="problematic" id="id17">gru_</span></a>‘) – prefix for name of <cite>Block`s
+<li><strong>i2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>h2h_bias_initializer</strong> (<em>str or Initializer</em>) – Initializer for the bias vector.</li>
+<li><strong>prefix</strong> (str, default ‘<a href="#id117"><span class="problematic" id="id118">gru_</span></a>‘) – prefix for name of <cite>Block`s
(and name of weight if params is `None</cite>).</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.Parameter" title="mxnet.gluon.Parameter"><em>Parameter</em></a><em> or </em><em>None</em>) – Container for weight sharing between cells.
+<li><strong>params</strong> (<em>Parameter or None</em>) – Container for weight sharing between cells.
Created if <cite>None</cite>.</li>
</ul>
</td>
@@ -2092,8 +2707,8 @@ Created if <cite>None</cite>.</li>
<code class="descname">add</code><span class="sig-paren">(</span><em>cell</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.SequentialRNNCell.add" title="Permalink to this definition">¶</a></dt>
<dd><p>Appends a cell into the stack.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>cell</strong> (<em>rnn cell</em>) – </td>
</tr>
@@ -2106,8 +2721,8 @@ Created if <cite>None</cite>.</li>
<em class="property">class </em><code class="descclassname">mxnet.gluon.rnn.</code><code class="descname">BidirectionalCell</code><span class="sig-paren">(</span><em>l_cell</em>, <em>r_cell</em>, <em>output_prefix='bi_'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.BidirectionalCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Bidirectional RNN cell.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>l_cell</strong> (<a class="reference internal" href="#mxnet.gluon.rnn.RecurrentCell" title="mxnet.gluon.rnn.RecurrentCell"><em>RecurrentCell</em></a>) – Cell for forward unrolling</li>
@@ -2120,13 +2735,13 @@ Created if <cite>None</cite>.</li>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.rnn.DropoutCell">
-<em class="property">class </em><code class="descclassname">mxnet.gluon.rnn.</code><code class="descname">DropoutCell</code><span class="sig-paren">(</span><em>dropout</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">mxnet.gluon.rnn.</code><code class="descname">DropoutCell</code><span class="sig-paren">(</span><em>rate</em>, <em>prefix=None</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.rnn.DropoutCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies dropout on input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dropout</strong> (<em>float</em>) – Percentage of elements to drop out, which
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>rate</strong> (<em>float</em>) – Percentage of elements to drop out, which
is 1 - percentage to retain.</td>
</tr>
</tbody>
@@ -2144,24 +2759,26 @@ is 1 - percentage to retain.</td>
(<a class="reference external" href="https://arxiv.org/abs/1609.08144">https://arxiv.org/abs/1609.08144</a>).
Output of the cell is output of the base cell plus input.</p>
</dd></dl>
-</div>
-<div class="section" id="trainer">
-<span id="trainer"></span><h2>Trainer<a class="headerlink" href="#trainer" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="mxnet.gluon.Trainer">
-<em class="property">class </em><code class="descclassname">mxnet.gluon.</code><code class="descname">Trainer</code><span class="sig-paren">(</span><em>params</em>, <em>optimizer</em>, <em>optimizer_params</em>, <em>kvstore='device'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Trainer" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">mxnet.gluon.</code><code class="descname">Trainer</code><span class="sig-paren">(</span><em>params</em>, <em>optimizer</em>, <em>optimizer_params=None</em>, <em>kvstore='device'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.Trainer" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies an <cite>Optimizer</cite> on a set of Parameters. Trainer should
be used together with <cite>autograd</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>params</strong> (<a class="reference internal" href="#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><em>ParameterDict</em></a>) – The set of parameters to optimize.</li>
-<li><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – The optimizer to use.</li>
+<li><strong>optimizer</strong> (<em>str or Optimizer</em>) – The optimizer to use. See
+<a class="reference external" href="http://mxnet.io/api/python/optimization.html#the-mxnet-optimizer-package">help</a>
+on Optimizer for a list of available optimizers.</li>
<li><strong>optimizer_params</strong> (<em>dict</em>) – Key-word arguments to be passed to optimizer constructor. For example,
-<cite>{‘learning_rate’: 0.1}</cite></li>
-<li><strong>kvstore</strong> (<em>str</em><em> or </em><a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><em>KVStore</em></a>) – kvstore type for multi-gpu and distributed training.</li>
+<cite>{‘learning_rate’: 0.1}</cite>. All optimizers accept learning_rate, wd (weight decay),
+clip_gradient, and lr_scheduler. See each optimizer’s
+constructor for a list of additional supported arguments.</li>
+<li><strong>kvstore</strong> (<em>str or KVStore</em>) – kvstore type for multi-gpu and distributed training. See help on
+<a class="reference internal" href="kvstore.html#mxnet.kvstore.create" title="mxnet.kvstore.create"><code class="xref any py py-func docutils literal"><span class="pre">mxnet.kvstore.create</span></code></a> for more information.</li>
</ul>
</td>
</tr>
@@ -2173,13 +2790,13 @@ be used together with <cite>autograd</cite>.</p>
<dd><p>Makes one step of parameter update. Should be called after
<cite>autograd.compute_gradient</cite> and outside of <cite>record()</cite> scope.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>batch_size</strong> (<em>int</em>) – Batch size of data processed. Gradient will be normalized by <cite>1/batch_size</cite>.
Set this to 1 if you normalized loss manually with <cite>loss = mean(loss)</cite>.</li>
-<li><strong>ignore_stale_grad</strong> (<em>bool</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true, ignores Parameters with stale gradient (gradient that has not
+<li><strong>ignore_stale_grad</strong> (<em>bool, optional, default=False</em>) – If true, ignores Parameters with stale gradient (gradient that has not
been updated by <cite>backward</cite> after last step) and skip update.</li>
</ul>
</td>
@@ -2188,9 +2805,6 @@ been updated by <cite>backward</cite> after last step) and skip update.</li>
</table>
</dd></dl>
</dd></dl>
-</div>
-<div class="section" id="loss-functions">
-<span id="loss-functions"></span><h2>Loss functions<a class="headerlink" href="#loss-functions" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="mxnet.gluon.loss.L2Loss">
<em class="property">class </em><code class="descclassname">mxnet.gluon.loss.</code><code class="descname">L2Loss</code><span class="sig-paren">(</span><em>weight=1.0</em>, <em>batch_axis=0</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.loss.L2Loss" title="Permalink to this definition">¶</a></dt>
@@ -2200,16 +2814,16 @@ been updated by <cite>backward</cite> after last step) and skip update.</li>
<p>Output and label can have arbitrary shape as long as they have the same
number of elements.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</li>
-<li><strong>sample_weight</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>None</em>) – Per sample weighting. Must be broadcastable to
+<li><strong>weight</strong> (<em>float or None</em>) – Global scalar weight for loss.</li>
+<li><strong>sample_weight</strong> (<em>Symbol or None</em>) – Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch, <cite>sample_weight</cite> should have shape (64, 1).</li>
-<li><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</li>
+<li><strong>batch_axis</strong> (<em>int, default 0</em>) – The axis that represents mini-batch.</li>
</ul>
</td>
</tr>
@@ -2224,16 +2838,16 @@ in the batch, <cite>sample_weight</cite> should have shape (64, 1).</li>
\[L = \frac{1}{2}\sum_i \vert {output}_i - {label}_i \vert.\]</div>
<p>Output and label must have the same shape.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</li>
-<li><strong>sample_weight</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>None</em>) – Per sample weighting. Must be broadcastable to
+<li><strong>weight</strong> (<em>float or None</em>) – Global scalar weight for loss.</li>
+<li><strong>sample_weight</strong> (<em>Symbol or None</em>) – Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch, <cite>sample_weight</cite> should have shape (64, 1).</li>
-<li><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</li>
+<li><strong>batch_axis</strong> (<em>int, default 0</em>) – The axis that represents mini-batch.</li>
</ul>
</td>
</tr>
@@ -2246,28 +2860,28 @@ in the batch, <cite>sample_weight</cite> should have shape (64, 1).</li>
<dd><p>Computes the softmax cross entropy loss.</p>
<p>If <cite>sparse_label</cite> is <cite>True</cite>, label should contain integer category indicators:</p>
<div class="math">
-\[ \begin{align}\begin{aligned}p = {softmax}({output})\\L = -\sum_i {log}(p_{i,{label}_i})\end{aligned}\end{align} \]</div>
+\[p = {softmax}({output})\]\[L = -\sum_i {log}(p_{i,{label}_i})\]</div>
<p>Label’s shape should be output’s shape without the <cite>axis</cite> dimension. i.e. for
<cite>output.shape</cite> = (1,2,3,4) and axis = 2, <cite>label.shape</cite> should be (1,2,4).</p>
<p>If <cite>sparse_label</cite> is <cite>False</cite>, label should contain probability distribution
with the same shape as output:</p>
<div class="math">
-\[ \begin{align}\begin{aligned}p = {softmax}({output})\\L = -\sum_i \sum_j {label}_j {log}(p_{ij})\end{aligned}\end{align} \]</div>
+\[p = {softmax}({output})\]\[L = -\sum_i \sum_j {label}_j {log}(p_{ij})\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>default -1</em>) – The axis to sum over when computing softmax and entropy.</li>
-<li><strong>sparse_label</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether label is an integer array instead of probability distribution.</li>
-<li><strong>from_logits</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether input is a log probability (usually from log_softmax) instead
+<li><strong>axis</strong> (<em>int, default -1</em>) – The axis to sum over when computing softmax and entropy.</li>
+<li><strong>sparse_label</strong> (<em>bool, default True</em>) – Whether label is an integer array instead of probability distribution.</li>
+<li><strong>from_logits</strong> (<em>bool, default False</em>) – Whether input is a log probability (usually from log_softmax) instead
of unnormalized numbers.</li>
-<li><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</li>
-<li><strong>sample_weight</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>None</em>) – Per sample weighting. Must be broadcastable to
+<li><strong>weight</strong> (<em>float or None</em>) – Global scalar weight for loss.</li>
+<li><strong>sample_weight</strong> (<em>Symbol or None</em>) – Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch, <cite>sample_weight</cite> should have shape (64, 1).</li>
-<li><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</li>
+<li><strong>batch_axis</strong> (<em>int, default 0</em>) – The axis that represents mini-batch.</li>
</ul>
</td>
</tr>
@@ -2285,27 +2899,24 @@ and is often useful when performing direct regression over the space of
\[L = 1/n \sum_i (label_i * (log(label_i) - output_i))\]</div>
<p>Label’s shape should be the same as output’s.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>from_logits</strong> (bool, default is <cite>True</cite>) – Whether the input is log probability (usually from log_softmax) instead
of unnormalized numbers.</li>
-<li><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</li>
-<li><strong>sample_weight</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>None</em>) – Per sample weighting. Must be broadcastable to
+<li><strong>weight</strong> (<em>float or None</em>) – Global scalar weight for loss.</li>
+<li><strong>sample_weight</strong> (<em>Symbol or None</em>) – Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch, <cite>sample_weight</cite> should have shape (64, 1).</li>
-<li><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</li>
+<li><strong>batch_axis</strong> (<em>int, default 0</em>) – The axis that represents mini-batch.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
-</div>
-<div class="section" id="utilities">
-<span id="utilities"></span><h2>Utilities<a class="headerlink" href="#utilities" title="Permalink to this headline">¶</a></h2>
<dl class="method">
<dt id="mxnet.gluon.utils.split_data">
<code class="descclassname">utils.</code><code class="descname">split_data</code><span class="sig-paren">(</span><em>data</em>, <em>num_slice</em>, <em>batch_axis=0</em>, <em>even_split=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.utils.split_data" title="Permalink to this definition">¶</a></dt>
@@ -2313,14 +2924,14 @@ in the batch, <cite>sample_weight</cite> should have shape (64, 1).</li>
Usually used for data parallelism where each slices is sent
to one device (i.e. GPU).</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – A batch of data.</li>
<li><strong>num_slice</strong> (<em>int</em>) – Number of desired slices.</li>
-<li><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis along which to slice.</li>
-<li><strong>even_split</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether to force all slices to have the same number of elements.
+<li><strong>batch_axis</strong> (<em>int, default 0</em>) – The axis along which to slice.</li>
+<li><strong>even_split</strong> (<em>bool, default True</em>) – Whether to force all slices to have the same number of elements.
If <cite>True</cite>, an error will be raised when <cite>num_slice</cite> does not evenly
divide <cite>data.shape[batch_axis]</cite>.</li>
</ul>
@@ -2341,14 +2952,14 @@ divide <cite>data.shape[batch_axis]</cite>.</li>
<dd><p>Splits an NDArray into <cite>len(ctx_list)</cite> slices along <cite>batch_axis</cite> and loads
each slice to one context in <cite>ctx_list</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – A batch of data.</li>
<li><strong>ctx_list</strong> (<em>list of Context</em>) – A list of Contexts.</li>
-<li><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis along which to slice.</li>
-<li><strong>even_split</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether to force all slices to have the same number of elements.</li>
+<li><strong>batch_axis</strong> (<em>int, default 0</em>) – The axis along which to slice.</li>
+<li><strong>even_split</strong> (<em>bool, default True</em>) – Whether to force all slices to have the same number of elements.</li>
</ul>
</td>
</tr>
@@ -2366,33 +2977,1012 @@ each slice to one context in <cite>ctx_list</cite>.</p>
<code class="descclassname">utils.</code><code class="descname">clip_global_norm</code><span class="sig-paren">(</span><em>arrays</em>, <em>max_norm</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.utils.clip_global_norm" title="Permalink to this definition">¶</a></dt>
<dd><p>Rescales NDArrays so that the sum of their 2-norm is smaller than <cite>max_norm</cite>.</p>
</dd></dl>
-<script>auto_index("api-reference");</script></div>
-</div>
-<div class="container">
-<div class="footer">
-<p> © 2015-2017 DMLC. All rights reserved. </p>
-</div>
+<dl class="class">
+<dt id="mxnet.gluon.data.Dataset">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.</code><code class="descname">Dataset</code><a class="headerlink" href="#mxnet.gluon.data.Dataset" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract dataset class. All datasets should have this interface.</p>
+<p>Subclasses need to override <cite>__getitem__</cite>, which returns the i-th
+element, and <cite>__len__</cite>, which returns the total number elements.</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last">An mxnet or numpy array can be directly used as a dataset.</p>
</div>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.data.ArrayDataset">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.</code><code class="descname">ArrayDataset</code><span class="sig-paren">(</span><em>data</em>, <em>label</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.ArrayDataset" title="Permalink to this definition">¶</a></dt>
+<dd><p>A dataset with a data array and a label array.</p>
+<p>The i-th sample is <cite>(data[i], lable[i])</cite>.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>data</strong> (<em>array-like object</em>) – The data array. Can be mxnet or numpy array.</li>
+<li><strong>label</strong> (<em>array-like object</em>) – The label array. Can be mxnet or numpy array.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.data.RecordFileDataset">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.</code><code class="descname">RecordFileDataset</code><span class="sig-paren">(</span><em>filename</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.RecordFileDataset" title="Permalink to this definition">¶</a></dt>
+<dd><p>A dataset wrapping over a RecordIO (.rec) file.</p>
+<p>Each sample is a string representing the raw content of an record.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>filename</strong> (<em>str</em>) – Path to rec file.</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.data.ImageRecordDataset">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.</code><code class="descname">ImageRecordDataset</code><span class="sig-paren">(</span><em>filename</em>, <em>flag=1</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.ImageRecordDataset" title="Permalink to this definition">¶</a></dt>
+<dd><p>A dataset wrapping over a RecordIO file containing images.</p>
+<p>Each sample is an image and its corresponding label.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>filename</strong> (<em>str</em>) – Path to rec file.</li>
+<li><strong>flag</strong> (<em>{0, 1}, default 1</em>) – <p>If 0, always convert images to greyscale.</p>
+<p>If 1, always convert images to colored (RGB).</p>
+</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.data.Sampler">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.</code><code class="descname">Sampler</code><a class="headerlink" href="#mxnet.gluon.data.Sampler" title="Permalink to this definition">¶</a></dt>
+<dd><p>Base class for samplers.</p>
+<p>All samplers should subclass <cite>Sampler</cite> and define <cite>__iter__</cite> and <cite>__len__</cite>
+methods.</p>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.data.SequentialSampler">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.</code><code class="descname">SequentialSampler</code><span class="sig-paren">(</span><em>length</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.SequentialSampler" title="Permalink to this definition">¶</a></dt>
+<dd><p>Samples elements from [0, length) sequentially.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>length</strong> (<em>int</em>) – Length of the sequence.</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.data.RandomSampler">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.</code><code class="descname">RandomSampler</code><span class="sig-paren">(</span><em>length</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.RandomSampler" title="Permalink to this definition">¶</a></dt>
+<dd><p>Samples elements from [0, length) randomly without replacement.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>length</strong> (<em>int</em>) – Length of the sequence.</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.data.BatchSampler">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.</code><code class="descname">BatchSampler</code><span class="sig-paren">(</span><em>sampler</em>, <em>batch_size</em>, <em>last_batch='keep'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.BatchSampler" title="Permalink to this definition">¶</a></dt>
+<dd><p>Wraps over another <cite>Sampler</cite> and return mini-batches of samples.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>sampler</strong> (<a class="reference internal" href="#mxnet.gluon.data.Sampler" title="mxnet.gluon.data.Sampler"><em>Sampler</em></a>) – The source Sampler.</li>
+<li><strong>batch_size</strong> (<em>int</em>) – Size of mini-batch.</li>
+<li><strong>last_batch</strong> (<em>{'keep', 'discard', 'rollover'}</em>) – <p>Specifies how the last batch is handled if batch_size does not evenly
+divide sequence length.</p>
+<p>If ‘keep’, the last batch will be returned directly, but will contain
+less element than <cite>batch_size</cite> requires.</p>
+<p>If ‘discard’, the last batch will be discarded.</p>
+<p>If ‘rollover’, the remaining elements will be rolled over to the next
+iteration.</p>
+</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">SequentialSampler</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">batch_sampler</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">BatchSampler</span><span class="p">(</span><span class="n">sampler</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">'keep'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">batch_sampler</span><span class="p">)</span>
+<span class="go">[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]</span>
+</pre></div>
</div>
-<div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation">
-<div class="sphinxsidebarwrapper">
-<h3><a href="../../index.html">Table Of Contents</a></h3>
-<ul>
-<li><a class="reference internal" href="#">Gluon Package</a><ul>
-<li><a class="reference internal" href="#overview">Overview</a></li>
-<li><a class="reference internal" href="#parameter">Parameter</a></li>
-<li><a class="reference internal" href="#containers">Containers</a></li>
-<li><a class="reference internal" href="#neural-network-layers">Neural Network Layers</a><ul>
-<li><a class="reference internal" href="#containers">Containers</a></li>
-<li><a class="reference internal" href="#basic-layers">Basic Layers</a></li>
-<li><a class="reference internal" href="#convolutional-layers">Convolutional Layers</a></li>
-<li><a class="reference internal" href="#pooling-layers">Pooling Layers</a></li>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.data.DataLoader">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.</code><code class="descname">DataLoader</code><span class="sig-paren">(</span><em>dataset</em>, <em>batch_size=None</em>, <em>shuffle=False</em>, <em>sampler=None</em>, <em>last_batch=None</em>, <em>batch_sampler=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.DataLoader" title="Permalink to this definition">¶</a></dt>
+<dd><p>Loads data from a dataset and returns mini-batches of data.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>dataset</strong> (<a class="reference internal" href="#mxnet.gluon.data.Dataset" title="mxnet.gluon.data.Dataset"><em>Dataset</em></a>) – Source dataset. Note that numpy and mxnet arrays can be directly used
+as a Dataset.</li>
+<li><strong>batch_size</strong> (<em>int</em>) – Size of mini-batch.</li>
+<li><strong>shuffle</strong> (<em>bool</em>) – Whether to shuffle the samples.</li>
+<li><strong>sampler</strong> (<a class="reference internal" href="#mxnet.gluon.data.Sampler" title="mxnet.gluon.data.Sampler"><em>Sampler</em></a>) – The sampler to use. Either specify sampler or shuffle, not both.</li>
+<li><strong>batch_sampler</strong> (<a class="reference internal" href="#mxnet.gluon.data.Sampler" title="mxnet.gluon.data.Sampler"><em>Sampler</em></a>) – A sampler that returns mini-batches. Do not specify batch_size,
+shuffle, sampler, and last_batch if batch_sampler is specified.</li>
</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<span class="target" id="module-mxnet.gluon.data.vision"></span><p>Dataset container.</p>
+<dl class="class">
+<dt id="mxnet.gluon.data.vision.MNIST">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.vision.</code><code class="descname">MNIST</code><span class="sig-paren">(</span><em>root</em>, <em>train=True</em>, <em>transform=<function <lambda>></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.MNIST" title="Permalink to this definition">¶</a></dt>
+<dd><p>MNIST handwritten digits dataset from <a href="#id119"><span class="problematic" id="id120">`http://yann.lecun.com/exdb/mnist`_</span></a>.</p>
+<p>Each sample is an image (in 3D NDArray) with shape (28, 28, 1).</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>root</strong> (<em>str</em>) – Path to temp folder for storing data.</li>
+<li><strong>train</strong> (<em>bool</em>) – Whether to load the training or testing set.</li>
+<li><strong>transform</strong> (<em>function</em>) – <p>A user defined callback that transforms each instance. For example:</p>
+<p>transform=lambda data, label: (data.astype(np.float32)/255, label)</p>
</li>
-<li><a class="reference internal" href="#recurrent-layers">Recurrent Layers</a></li>
-<li><a class="reference internal" href="#trainer">Trainer</a></li>
-<li><a class="reference internal" href="#loss-functions">Loss functions</a></li>
-<li><a class="reference internal" href="#utilities">Utilities</a></li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.data.vision.CIFAR10">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.data.vision.</code><code class="descname">CIFAR10</code><span class="sig-paren">(</span><em>root</em>, <em>train=True</em>, <em>transform=<function <lambda>></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.CIFAR10" title="Permalink to this definition">¶</a></dt>
+<dd><p>CIFAR10 image classification dataset from <a href="#id121"><span class="problematic" id="id122">`https://www.cs.toronto.edu/~kriz/cifar.html`_</span></a>.</p>
+<p>Each sample is an image (in 3D NDArray) with shape (32, 32, 1).</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>root</strong> (<em>str</em>) – Path to temp folder for storing data.</li>
+<li><strong>train</strong> (<em>bool</em>) – Whether to load the training or testing set.</li>
+<li><strong>transform</strong> (<em>function</em>) – <p>A user defined callback that transforms each instance. For example:</p>
+<p>transform=lambda data, label: (data.astype(np.float32)/255, label)</p>
+</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<span class="target" id="module-mxnet.gluon.model_zoo.vision"></span><p>Module for pre-defined neural network models.
+This module contains definitions for the following model architectures:
+- <a class="reference external" href="https://arxiv.org/abs/1404.5997">AlexNet</a>
+- <a class="reference external" href="https://arxiv.org/abs/1608.06993">DenseNet</a>
+- <a class="reference external" href="http://arxiv.org/abs/1512.00567">Inception V3</a>
+- <a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet V1</a>
+- <a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet V2</a>
+- <a class="reference external" href="https://arxiv.org/abs/1602.07360">SqueezeNet</a>
+- <a class="reference external" href="https://arxiv.org/abs/1409.1556">VGG</a>
+You can construct a model with random weights by calling its constructor:
+.. code:: python</p>
+<blockquote>
+<div>import mxnet.gluon.models as models
+resnet18 = models.resnet18_v1()
+alexnet = models.alexnet()
+squeezenet = models.squeezenet1_0()
+densenet = models.densenet_161()</div></blockquote>
+<p>We provide pre-trained models for all the models except ResNet V2.
+These can constructed by passing
+<code class="docutils literal"><span class="pre">pretrained=True</span></code>:
+.. code:: python</p>
+<blockquote>
+<div>import mxnet.gluon.models as models
+resnet18 = models.resnet18_v1(pretrained=True)
+alexnet = models.alexnet(pretrained=True)</div></blockquote>
+<p>Pretrained model is converted from torchvision.
+All pre-trained models expect input images normalized in the same way,
+i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W),
+where N is the batch size, and H and W are expected to be at least 224.
+The images have to be loaded in to a range of [0, 1] and then normalized
+using <code class="docutils literal"><span class="pre">mean</span> <span class="pre">=</span> <span class="pre">[0.485,</span> <span class="pre">0.456,</span> <span class="pre">0.406]</span></code> and <code class="docutils literal"><span class="pre">std</span> <span class="pre">=</span> <span class="pre">[0.229,</span> <span class="pre">0.224,</span> <span class="pre">0.225]</span></code>.
+The transformation should preferrably happen at preprocessing. You can use
+<code class="docutils literal"><span class="pre">mx.image.color_normalize</span></code> for such transformation:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">/</span><span class="mi">255</span>
+<span class="n">normalized</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">color_normalize</span><span class="p">(</span><span class="n">image</span><span class="p">,</span>
+ <span class="n">mean</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]),</span>
+ <span class="n">std</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]))</span>
+</pre></div>
+</div>
+<dl class="function">
+<dt id="mxnet.gluon.model_zoo.vision.get_model">
+<code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">get_model</code><span class="sig-paren">(</span><em>name</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.get_model" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a pre-defined model by name</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>name</strong> (<em>str</em>) – Name of the model.</li>
+<li><strong>pretrained</strong> (<em>bool</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>classes</strong> (<em>int</em>) – Number of classes for the output layer.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The model.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.gluon.HybridBlock" title="mxnet.gluon.HybridBlock">HybridBlock</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet18_v1">
+<code class="descclassname">vision.</code><code class="descname">resnet18_v1</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet18_v1" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-18 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet34_v1">
+<code class="descclassname">vision.</code><code class="descname">resnet34_v1</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet34_v1" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-34 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet50_v1">
+<code class="descclassname">vision.</code><code class="descname">resnet50_v1</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet50_v1" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-50 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet101_v1">
+<code class="descclassname">vision.</code><code class="descname">resnet101_v1</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet101_v1" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-101 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet152_v1">
+<code class="descclassname">vision.</code><code class="descname">resnet152_v1</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet152_v1" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-152 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet18_v2">
+<code class="descclassname">vision.</code><code class="descname">resnet18_v2</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet18_v2" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-18 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet34_v2">
+<code class="descclassname">vision.</code><code class="descname">resnet34_v2</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet34_v2" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-34 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet50_v2">
+<code class="descclassname">vision.</code><code class="descname">resnet50_v2</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet50_v2" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-50 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet101_v2">
+<code class="descclassname">vision.</code><code class="descname">resnet101_v2</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet101_v2" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-101 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.resnet152_v2">
+<code class="descclassname">vision.</code><code class="descname">resnet152_v2</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet152_v2" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet-152 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.get_resnet">
+<code class="descclassname">vision.</code><code class="descname">get_resnet</code><span class="sig-paren">(</span><em>version</em>, <em>num_layers</em>, <em>pretrained=False</em>, <em>ctx=cpu(0)</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.get_resnet" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.
+ResNet V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>version</strong> (<em>int</em>) – Version of ResNet. Options are 1, 2.</li>
+<li><strong>num_layers</strong> (<em>int</em>) – Numbers of layers. Options are 18, 34, 50, 101, 152.</li>
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.ResNetV1">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">ResNetV1</code><span class="sig-paren">(</span><em>block</em>, <em>layers</em>, <em>channels</em>, <em>classes=1000</em>, <em>thumbnail=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.ResNetV1" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet V1 model from
+<a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>block</strong> (<a class="reference internal" href="#mxnet.gluon.HybridBlock" title="mxnet.gluon.HybridBlock"><em>HybridBlock</em></a>) – Class for the residual block. Options are BasicBlockV1, BottleneckV1.</li>
+<li><strong>layers</strong> (<em>list of int</em>) – Numbers of layers in each block</li>
+<li><strong>channels</strong> (<em>list of int</em>) – Numbers of channels in each block. Length should be one larger than layers list.</li>
+<li><strong>classes</strong> (<em>int, default 1000</em>) – Number of classification classes.</li>
+<li><strong>thumbnail</strong> (<em>bool, default False</em>) – Enable thumbnail.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.BasicBlockV1">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">BasicBlockV1</code><span class="sig-paren">(</span><em>channels</em>, <em>stride</em>, <em>downsample=False</em>, <em>in_channels=0</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BasicBlockV1" title="Permalink to this definition">¶</a></dt>
+<dd><p>BasicBlock V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.
+This is used for ResNet V1 for 18, 34 layers.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>channels</strong> (<em>int</em>) – Number of output channels.</li>
+<li><strong>stride</strong> (<em>int</em>) – Stride size.</li>
+<li><strong>downsample</strong> (<em>bool, default False</em>) – Whether to downsample the input.</li>
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – Number of input channels. Default is 0, to infer from the graph.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.BottleneckV1">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">BottleneckV1</code><span class="sig-paren">(</span><em>channels</em>, <em>stride</em>, <em>downsample=False</em>, <em>in_channels=0</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BottleneckV1" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bottleneck V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.
+This is used for ResNet V1 for 50, 101, 152 layers.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>channels</strong> (<em>int</em>) – Number of output channels.</li>
+<li><strong>stride</strong> (<em>int</em>) – Stride size.</li>
+<li><strong>downsample</strong> (<em>bool, default False</em>) – Whether to downsample the input.</li>
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – Number of input channels. Default is 0, to infer from the graph.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.ResNetV2">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">ResNetV2</code><span class="sig-paren">(</span><em>block</em>, <em>layers</em>, <em>channels</em>, <em>classes=1000</em>, <em>thumbnail=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.ResNetV2" title="Permalink to this definition">¶</a></dt>
+<dd><p>ResNet V2 model from
+<a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>block</strong> (<a class="reference internal" href="#mxnet.gluon.HybridBlock" title="mxnet.gluon.HybridBlock"><em>HybridBlock</em></a>) – Class for the residual block. Options are BasicBlockV1, BottleneckV1.</li>
+<li><strong>layers</strong> (<em>list of int</em>) – Numbers of layers in each block</li>
+<li><strong>channels</strong> (<em>list of int</em>) – Numbers of channels in each block. Length should be one larger than layers list.</li>
+<li><strong>classes</strong> (<em>int, default 1000</em>) – Number of classification classes.</li>
+<li><strong>thumbnail</strong> (<em>bool, default False</em>) – Enable thumbnail.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.BasicBlockV2">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">BasicBlockV2</code><span class="sig-paren">(</span><em>channels</em>, <em>stride</em>, <em>downsample=False</em>, <em>in_channels=0</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BasicBlockV2" title="Permalink to this definition">¶</a></dt>
+<dd><p>BasicBlock V2 from
+<a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.
+This is used for ResNet V2 for 18, 34 layers.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>channels</strong> (<em>int</em>) – Number of output channels.</li>
+<li><strong>stride</strong> (<em>int</em>) – Stride size.</li>
+<li><strong>downsample</strong> (<em>bool, default False</em>) – Whether to downsample the input.</li>
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – Number of input channels. Default is 0, to infer from the graph.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.BottleneckV2">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">BottleneckV2</code><span class="sig-paren">(</span><em>channels</em>, <em>stride</em>, <em>downsample=False</em>, <em>in_channels=0</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BottleneckV2" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bottleneck V2 from
+<a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.
+This is used for ResNet V2 for 50, 101, 152 layers.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>channels</strong> (<em>int</em>) – Number of output channels.</li>
+<li><strong>stride</strong> (<em>int</em>) – Stride size.</li>
+<li><strong>downsample</strong> (<em>bool, default False</em>) – Whether to downsample the input.</li>
+<li><strong>in_channels</strong> (<em>int, default 0</em>) – Number of input channels. Default is 0, to infer from the graph.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.vgg11">
+<code class="descclassname">vision.</code><code class="descname">vgg11</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg11" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG-11 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.vgg13">
+<code class="descclassname">vision.</code><code class="descname">vgg13</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg13" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG-13 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.vgg16">
+<code class="descclassname">vision.</code><code class="descname">vgg16</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg16" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG-16 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.vgg19">
+<code class="descclassname">vision.</code><code class="descname">vgg19</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg19" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG-19 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.vgg11_bn">
+<code class="descclassname">vision.</code><code class="descname">vgg11_bn</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg11_bn" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG-11 model with batch normalization from the
+<a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.vgg13_bn">
+<code class="descclassname">vision.</code><code class="descname">vgg13_bn</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg13_bn" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG-13 model with batch normalization from the
+<a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.vgg16_bn">
+<code class="descclassname">vision.</code><code class="descname">vgg16_bn</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg16_bn" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG-16 model with batch normalization from the
+<a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.vgg19_bn">
+<code class="descclassname">vision.</code><code class="descname">vgg19_bn</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg19_bn" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG-19 model with batch normalization from the
+<a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.get_vgg">
+<code class="descclassname">vision.</code><code class="descname">get_vgg</code><span class="sig-paren">(</span><em>num_layers</em>, <em>pretrained=False</em>, <em>ctx=cpu(0)</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.get_vgg" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>num_layers</strong> (<em>int</em>) – Number of layers for the variant of densenet. Options are 11, 13, 16, 19.</li>
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.VGG">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">VGG</code><span class="sig-paren">(</span><em>layers</em>, <em>filters</em>, <em>classes=1000</em>, <em>batch_norm=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.VGG" title="Permalink to this definition">¶</a></dt>
+<dd><p>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>layers</strong> (<em>list of int</em>) – Numbers of layers in each feature block.</li>
+<li><strong>filters</strong> (<em>list of int</em>) – Numbers of filters in each feature block. List length should match the layers.</li>
+<li><strong>classes</strong> (<em>int, default 1000</em>) – Number of classification classes.</li>
+<li><strong>batch_norm</strong> (<em>bool, default False</em>) – Use batch normalization.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.alexnet">
+<code class="descclassname">vision.</code><code class="descname">alexnet</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>ctx=cpu(0)</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.alexnet" title="Permalink to this definition">¶</a></dt>
+<dd><p>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick...”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.AlexNet">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">AlexNet</code><span class="sig-paren">(</span><em>classes=1000</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.AlexNet" title="Permalink to this definition">¶</a></dt>
+<dd><p>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick...”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>classes</strong> (<em>int, default 1000</em>) – Number of classes for the output layer.</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.densenet121">
+<code class="descclassname">vision.</code><code class="descname">densenet121</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.densenet121" title="Permalink to this definition">¶</a></dt>
+<dd><p>Densenet-BC 121-layer model from the
+<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.densenet161">
+<code class="descclassname">vision.</code><code class="descname">densenet161</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.densenet161" title="Permalink to this definition">¶</a></dt>
+<dd><p>Densenet-BC 161-layer model from the
+<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.densenet169">
+<code class="descclassname">vision.</code><code class="descname">densenet169</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.densenet169" title="Permalink to this definition">¶</a></dt>
+<dd><p>Densenet-BC 169-layer model from the
+<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.densenet201">
+<code class="descclassname">vision.</code><code class="descname">densenet201</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.densenet201" title="Permalink to this definition">¶</a></dt>
+<dd><p>Densenet-BC 201-layer model from the
+<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.DenseNet">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">DenseNet</code><span class="sig-paren">(</span><em>num_init_features</em>, <em>growth_rate</em>, <em>block_config</em>, <em>bn_size=4</em>, <em>dropout=0</em>, <em>classes=1000</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.DenseNet" title="Permalink to this definition">¶</a></dt>
+<dd><p>Densenet-BC model from the
+<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>num_init_features</strong> (<em>int</em>) – Number of filters to learn in the first convolution layer.</li>
+<li><strong>growth_rate</strong> (<em>int</em>) – Number of filters to add each layer (<cite>k</cite> in the paper).</li>
+<li><strong>block_config</strong> (<em>list of int</em>) – List of integers for numbers of layers in each pooling block.</li>
+<li><strong>bn_size</strong> (<em>int, default 4</em>) – Multiplicative factor for number of bottle neck layers.
+(i.e. bn_size * k features in the bottleneck layer)</li>
+<li><strong>dropout</strong> (<em>float, default 0</em>) – Rate of dropout after each dense layer.</li>
+<li><strong>classes</strong> (<em>int, default 1000</em>) – Number of classification classes.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.squeezenet1_0">
+<code class="descclassname">vision.</code><code class="descname">squeezenet1_0</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.squeezenet1_0" title="Permalink to this definition">¶</a></dt>
+<dd><p>SqueezeNet 1.0 model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
+and <0.5MB model size”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.squeezenet1_1">
+<code class="descclassname">vision.</code><code class="descname">squeezenet1_1</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.squeezenet1_1" title="Permalink to this definition">¶</a></dt>
+<dd><p>SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>.
+SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
+than SqueezeNet 1.0, without sacrificing accuracy.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.SqueezeNet">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">SqueezeNet</code><span class="sig-paren">(</span><em>version</em>, <em>classes=1000</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.SqueezeNet" title="Permalink to this definition">¶</a></dt>
+<dd><p>SqueezeNet model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
+and <0.5MB model size”</a> paper.
+SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>.
+SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
+than SqueezeNet 1.0, without sacrificing accuracy.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>version</strong> (<em>str</em>) – Version of squeezenet. Options are ‘1.0’, ‘1.1’.</li>
+<li><strong>classes</strong> (<em>int, default 1000</em>) – Number of classification classes.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.gluon.model_zoo.vision.inception_v3">
+<code class="descclassname">vision.</code><code class="descname">inception_v3</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>ctx=cpu(0)</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.inception_v3" title="Permalink to this definition">¶</a></dt>
+<dd><p>Inception v3 model from
+<a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>pretrained</strong> (<em>bool, default False</em>) – Whether to load the pretrained weights for model.</li>
+<li><strong>ctx</strong> (<em>Context, default CPU</em>) – The context in which to load the pretrained weights.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="class">
+<dt id="mxnet.gluon.model_zoo.vision.Inception3">
+<em class="property">class </em><code class="descclassname">mxnet.gluon.model_zoo.vision.</code><code class="descname">Inception3</code><span class="sig-paren">(</span><em>classes=1000</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.Inception3" title="Permalink to this definition">¶</a></dt>
+<dd><p>Inception v3 model from
+<a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>classes</strong> (<em>int, default 1000</em>) – Number of classification classes.</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<script>auto_index("api-reference");</script></div>
+</div>
+<div class="container">
+<div class="footer">
+<p> © 2015-2017 DMLC. All rights reserved. </p>
+</div>
+</div>
+</div>
+<div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation">
+<div class="sphinxsidebarwrapper">
+<h3><a href="../../index.html">Table Of Contents</a></h3>
+<ul>
+<li><a class="reference internal" href="#">Gluon Package</a><ul>
+<li><a class="reference internal" href="#overview">Overview</a></li>
+<li><a class="reference internal" href="#parameter">Parameter</a></li>
+<li><a class="reference internal" href="#containers">Containers</a></li>
+<li><a class="reference internal" href="#neural-network-layers">Neural Network Layers</a><ul>
+<li><a class="reference internal" href="#containers">Containers</a></li>
+<li><a class="reference internal" href="#basic-layers">Basic Layers</a></li>
+<li><a class="reference internal" href="#convolutional-layers">Convolutional Layers</a></li>
+<li><a class="reference internal" href="#pooling-layers">Pooling Layers</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#recurrent-layers">Recurrent Layers</a></li>
+<li><a class="reference internal" href="#trainer">Trainer</a></li>
+<li><a class="reference internal" href="#loss-functions">Loss functions</a></li>
+<li><a class="reference internal" href="#utilities">Utilities</a></li>
+<li><a class="reference internal" href="#data">Data</a><ul>
+<li><a class="reference internal" href="#vision">Vision</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#model-zoo">Model Zoo</a><ul>
+<li><a class="reference internal" href="#vision">Vision</a><ul>
+<li><a class="reference internal" href="#resnet">ResNet</a></li>
+<li><a class="reference internal" href="#vgg">VGG</a></li>
+<li><a class="reference internal" href="#alexnet">Alexnet</a></li>
+<li><a class="reference internal" href="#densenet">DenseNet</a></li>
+<li><a class="reference internal" href="#squeezenet">SqueezeNet</a></li>
+<li><a class="reference internal" href="#inception">Inception</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li><a class="reference internal" href="#api-reference">API Reference</a></li>
</ul>
</li>
</ul>
diff --git a/versions/master/api/python/image.html b/versions/master/api/python/image.html
index ac995ca..57f8733 100644
--- a/versions/master/api/python/image.html
+++ b/versions/master/api/python/image.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Image API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="index.html" rel="up" title="MXNet - Python API"/>
+<link href="index.html" rel="up" title="MXNet - Python API">
<link href="optimization.html" rel="next" title="Optimization: initialize and update weights"/>
<link href="io.html" rel="prev" title="Data Loading API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
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-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
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<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
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+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
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<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
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@@ -229,11 +227,12 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
<li class="toctree-l3"><a class="reference internal" href="kvstore.html">KVStore API</a></li>
<li class="toctree-l3"><a class="reference internal" href="io.html">Data Loading API</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Image API</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Image API</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
<li class="toctree-l4"><a class="reference internal" href="#image-processing-functions">Image processing functions</a></li>
<li class="toctree-l4"><a class="reference internal" href="#image-iterators">Image iterators</a></li>
@@ -267,8 +266,8 @@ Previous Navbar Layout End -->
images provided in</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#module-mxnet.image" title="mxnet.image"><code class="xref py py-obj docutils literal"><span class="pre">mxnet.image</span></code></a></td>
@@ -281,8 +280,8 @@ images provided in</p>
<span id="image-processing-functions"></span><h2>Image processing functions<a class="headerlink" href="#image-processing-functions" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.image.imdecode" title="mxnet.image.imdecode"><code class="xref py py-obj docutils literal"><span class="pre">image.imdecode</span></code></a></td>
@@ -317,8 +316,8 @@ images provided in</p>
<p>Iterators support loading image from binary <code class="docutils literal"><span class="pre">Record</span> <span class="pre">IO</span></code> and raw image files.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.image.ImageIter" title="mxnet.image.ImageIter"><code class="xref py py-obj docutils literal"><span class="pre">image.ImageIter</span></code></a></td>
@@ -348,8 +347,8 @@ images provided in</p>
<p>We use helper function to initialize augmenters</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.image.CreateAugmenter" title="mxnet.image.CreateAugmenter"><code class="xref py py-obj docutils literal"><span class="pre">image.CreateAugmenter</span></code></a></td>
@@ -360,8 +359,8 @@ images provided in</p>
<p>A list of supporting augmenters</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.image.Augmenter" title="mxnet.image.Augmenter"><code class="xref py py-obj docutils literal"><span class="pre">image.Augmenter</span></code></a></td>
@@ -420,8 +419,8 @@ images provided in</p>
<p>Similar to <code class="docutils literal"><span class="pre">ImageIter</span></code>, <code class="docutils literal"><span class="pre">ImageDetIter</span></code> is designed for <code class="docutils literal"><span class="pre">Object</span> <span class="pre">Detection</span></code> tasks.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.image.ImageDetIter" title="mxnet.image.ImageDetIter"><code class="xref py py-obj docutils literal"><span class="pre">image.ImageDetIter</span></code></a></td>
@@ -442,13 +441,13 @@ images provided in</p>
<p>Unlike object classification with fixed label_width, object count may vary from
image to image. Thus we have special format for object detection labels.
Usually the <code class="docutils literal"><span class="pre">lst</span></code> file generated by <code class="docutils literal"><span class="pre">tools/im2rec.py</span></code> is a list of</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">index_0</span> <span class="n">label_0</span> <span class="n">image_path_0</span>
-<span class="n">index_1</span> <span class="n">label_1</span> <span class="n">image_path_1</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>index_0 label_0 image_path_0
+index_1 label_1 image_path_1
</pre></div>
</div>
<p>Where <code class="docutils literal"><span class="pre">label_N</span></code> is a number a of fixed-width vector.
The format of label used in object detection is a variable length vector</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="n">B</span> <span class="p">[</span><span class="n">header</span><span class="p">]</span> <span class="p">[(</span><span class="n">object0</span><span class="p">),</span> <span class="p">(</span><span class="n">object1</span><span class="p">),</span> <span class="o">...</span> <span class="p">(</span><span class="n">objectN</span><span class="p">)]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>A B [header] [(object0), (object1), ... (objectN)]
</pre></div>
</div>
<p>Where A is the width of header, B is the width of each object.
@@ -456,17 +455,17 @@ Header is optional and used for inserting helper information such as (width, hei
Each object is usually 5 or 6 numbers describing the object properties, for example:
[id, xmin, ymin, xmax, ymax, difficulty]
Putting all together, we have a <code class="docutils literal"><span class="pre">lst</span></code> file for object detection:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="mi">0</span> <span class="mi">2</span> <span class="mi">5</span> <span class="mi">640</span> <span class="mi">480</span> <span class="mi">1</span> <span class="mf">0.1</span> <span class="mf">0.2</span> <span class="mf">0.8</span> <span class="mf">0.9</span> <span class="mi">2</span> <span class="mf">0.5</span> <span class="mf">0.3</span> <span class="mf">0.6</span> <span class="mf">0.8</sp [...]
-<span class="mi">1</span> <span class="mi">2</span> <span class="mi">5</span> <span class="mi">480</span> <span class="mi">640</span> <span class="mi">3</span> <span class="mf">0.05</span> <span class="mf">0.16</span> <span class="mf">0.75</span> <span class="mf">0.9</span> <span class="n">data</span><span class="o">/</span><span class="n">xxx</span><span class="o">.</span><span class="n">jpg</span>
-<span class="mi">2</span> <span class="mi">2</span> <span class="mi">5</span> <span class="mi">500</span> <span class="mi">600</span> <span class="mi">2</span> <span class="mf">0.6</span> <span class="mf">0.1</span> <span class="mf">0.7</span> <span class="mf">0.5</span> <span class="mi">0</span> <span class="mf">0.1</span> <span class="mf">0.3</span> <span class="mf">0.2</span> <span class="mf">0.4</span> <span class="mi">3</span> <span class="mf">0.25</span> <span clas [...]
-<span class="o">...</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>0 2 5 640 480 1 0.1 0.2 0.8 0.9 2 0.5 0.3 0.6 0.8 data/xxx.jpg
+1 2 5 480 640 3 0.05 0.16 0.75 0.9 data/xxx.jpg
+2 2 5 500 600 2 0.6 0.1 0.7 0.5 0 0.1 0.3 0.2 0.4 3 0.25 0.25 0.3 0.3 data/xxx.jpg
+...
</pre></div>
</div>
<p>A helper function to initialize Augmenters for <code class="docutils literal"><span class="pre">Object</span> <span class="pre">detection</span></code> task</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.image.CreateDetAugmenter" title="mxnet.image.CreateDetAugmenter"><code class="xref py py-obj docutils literal"><span class="pre">image.CreateDetAugmenter</span></code></a></td>
@@ -479,8 +478,8 @@ to image that introduced localization shift will require correction to label,
and a list of augmenters specific for <code class="docutils literal"><span class="pre">Object</span> <span class="pre">detection</span></code> is provided</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.image.DetBorrowAug" title="mxnet.image.DetBorrowAug"><code class="xref py py-obj docutils literal"><span class="pre">image.DetBorrowAug</span></code></a></td>
@@ -513,14 +512,14 @@ This iterator supports reading from both .rec files and raw image files.</p>
files, use <cite>path_imglist</cite> and <cite>path_root</cite> parameters.</p>
<p>To use data partition (for distributed training) or shuffling, specify <cite>path_imgidx</cite> parameter.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>batch_size</strong> (<em>int</em>) – Number of examples per batch.</li>
<li><strong>data_shape</strong> (<em>tuple</em>) – Data shape in (channels, height, width) format.
For now, only RGB image with 3 channels is supported.</li>
-<li><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of labels per example. The default label width is 1.</li>
+<li><strong>label_width</strong> (<em>int, optional</em>) – Number of labels per example. The default label width is 1.</li>
<li><strong>path_imgrec</strong> (<em>str</em>) – Path to image record file (.rec).
Created with tools/im2rec.py or bin/im2rec.</li>
<li><strong>path_imglist</strong> (<em>str</em>) – Path to image list (.lst).
@@ -577,7 +576,7 @@ See mx.img.imdecode for more details.</p>
<dt id="mxnet.image.ImageIter.read_image">
<code class="descname">read_image</code><span class="sig-paren">(</span><em>fname</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.ImageIter.read_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Reads an input image <cite>fname</cite> and returns the decoded raw bytes.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">dataIter</span><span class="o">.</span><span class="n">read_image</span><span class="p">(</span><span class="s1">'Face.jpg'</span><span class="p">)</span> <span class="c1"># returns decoded raw bytes.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">dataIter</span><span class="o">.</span><span class="n">read_image</span><span class="p">(</span><span class="s1">'Face.jpg'</span><span class="p">)</span> <span class="c1"># returns decoded raw bytes.</span>
</pre></div>
</div>
</dd></dl>
@@ -599,14 +598,14 @@ See mx.img.imdecode for more details.</p>
<p>Note: <cite>imdecode</cite> uses OpenCV (not the CV2 Python library).
MXNet must have been built with OpenCV for <cite>imdecode</cite> to work.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>buf</strong> (<em>str/bytes</em><em> or </em><em>numpy.ndarray</em>) – Binary image data as string or numpy ndarray.</li>
-<li><strong>flag</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – 1 for three channel color output. 0 for grayscale output.</li>
-<li><strong>to_rgb</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – 1 for RGB formatted output (MXNet default). 0 for BGR formatted output (OpenCV default).</li>
-<li><strong>out</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Output buffer. Use <cite>None</cite> for automatic allocation.</li>
+<li><strong>buf</strong> (<em>str/bytes or numpy.ndarray</em>) – Binary image data as string or numpy ndarray.</li>
+<li><strong>flag</strong> (<em>int, optional, default=1</em>) – 1 for three channel color output. 0 for grayscale output.</li>
+<li><strong>to_rgb</strong> (<em>int, optional, default=1</em>) – 1 for RGB formatted output (MXNet default). 0 for BGR formatted output (OpenCV default).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – Output buffer. Use <cite>None</cite> for automatic allocation.</li>
</ul>
</td>
</tr>
@@ -619,30 +618,30 @@ MXNet must have been built with OpenCV for <cite>imdecode</cite> to work.</p>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">str_image</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">img</span><span class="o">.</span><span class="n">imdecode</span><span class="p">(</span><span class="n">str_image</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">image</span>
-<span class="go"><ndarray 224x224x3="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 224x224x3 @cpu(0)></span>
</pre></div>
</div>
<p>Set <cite>flag</cite> parameter to 0 to get grayscale output</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">str_image</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">img</span><span class="o">.</span><span class="n">imdecode</span><span class="p">(</span><span class="n">str_image</span><span class="p">,</span> <span class="n">flag</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">image</span>
-<span class="go"><ndarray 224x224x1="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 224x224x1 @cpu(0)></span>
</pre></div>
</div>
<p>Set <cite>to_rgb</cite> parameter to 0 to get output in OpenCV format (BGR)</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">str_image</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">img</span><span class="o">.</span><span class="n">imdecode</span><span class="p">(</span><span class="n">str_image</span><span class="p">,</span> <span class="n">to_rgb</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">image</span>
-<span class="go"><ndarray 224x224x3="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 224x224x3 @cpu(0)></span>
</pre></div>
</div>
</dd></dl>
@@ -653,8 +652,8 @@ MXNet must have been built with OpenCV for <cite>imdecode</cite> to work.</p>
<p>If width/height of the crop is larger than the width/height of the image,
sets the width/height to the width/height of the image.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>src_size</strong> (<em>tuple of int</em>) – Size of the image in (width, height) format.</li>
@@ -671,7 +670,7 @@ sets the width/height to the width/height of the image.</p>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">src_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">640</span><span class="p">,</span><span class="mi">480</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">src_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">640</span><span class="p">,</span><span class="mi">480</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">720</span><span class="p">,</span><span class="mi">120</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">new_size</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">img</span><span class="o">.</span><span class="n">scale_down</span><span class="p">(</span><span class="n">src_size</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">new_size</span>
@@ -689,13 +688,13 @@ MXNet must have been built with OpenCV for <cite>resize_short</cite> to work.</p
and setting the longer edge accordingly.
Resizing function is called from OpenCV.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>src</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The original image.</li>
<li><strong>size</strong> (<em>int</em>) – The length to be set for the shorter edge.</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method used for resizing the image.
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method used for resizing the image.
Possible values:
0: Nearest Neighbors Interpolation.
1: Bilinear interpolation.
@@ -725,16 +724,16 @@ More details can be found in the documentation of OpenCV, please refer to
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpeg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpeg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">str_image</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">img</span><span class="o">.</span><span class="n">imdecode</span><span class="p">(</span><span class="n">str_image</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">image</span>
-<span class="go"><ndarray 2321x3482x3="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2321x3482x3 @cpu(0)></span>
<span class="gp">>>> </span><span class="n">size</span> <span class="o">=</span> <span class="mi">640</span>
<span class="gp">>>> </span><span class="n">new_image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">img</span><span class="o">.</span><span class="n">resize_short</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">new_image</span>
-<span class="go"><ndarray 2321x3482x3="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2321x3482x3 @cpu(0)></span>
</pre></div>
</div>
</dd></dl>
@@ -743,8 +742,8 @@ More details can be found in the documentation of OpenCV, please refer to
<code class="descclassname">image.</code><code class="descname">fixed_crop</code><span class="sig-paren">(</span><em>src</em>, <em>x0</em>, <em>y0</em>, <em>w</em>, <em>h</em>, <em>size=None</em>, <em>interp=2</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.fixed_crop" title="Permalink to this definition">¶</a></dt>
<dd><p>Crop src at fixed location, and (optionally) resize it to size.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>src</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input image</li>
@@ -752,8 +751,8 @@ More details can be found in the documentation of OpenCV, please refer to
<li><strong>y0</strong> (<em>int</em>) – Top boundary of the cropping area</li>
<li><strong>w</strong> (<em>int</em>) – Width of the cropping area</li>
<li><strong>h</strong> (<em>int</em>) – Height of the cropping area</li>
-<li><strong>size</strong> (<em>tuple of</em><em> (</em><em>w</em><em>, </em><em>h</em><em>)</em><em></em>) – Optional, resize to new size after cropping</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method. See resize_short for details.</li>
+<li><strong>size</strong> (<em>tuple of (w, h)</em>) – Optional, resize to new size after cropping</li>
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method. See resize_short for details.</li>
</ul>
</td>
</tr>
@@ -772,19 +771,21 @@ More details can be found in the documentation of OpenCV, please refer to
<dd><p>Randomly crop <cite>src</cite> with <cite>size</cite> (width, height).
Upsample result if <cite>src</cite> is smaller than <cite>size</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>src</strong> (Source image <cite>NDArray</cite>) – </li>
<li><strong>size</strong> (Size of the crop formatted as (width, height). If the <cite>size</cite> is larger) – than the image, then the source image is upsampled to <cite>size</cite> and returned.</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method. See resize_short for details.</li>
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method. See resize_short for details.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><em>NDArray</em> – An <cite>NDArray</cite> containing the cropped image.</li>
-<li><em>Tuple</em> – A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the
+<li><em>NDArray</em> –
+An <cite>NDArray</cite> containing the cropped image.</li>
+<li><em>Tuple</em> –
+A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the
original image and (width, height) are the dimensions of the cropped image.</li>
</ul>
</p>
@@ -793,11 +794,11 @@ original image and (width, height) are the dimensions of the cropped image.</li>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">im</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">im</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">cropped_im</span><span class="p">,</span> <span class="n">rect</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">random_crop</span><span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">cropped_im</span>
-<span class="go"><ndarray 100x100x1="" @cpu(0)=""></ndarray></span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">rect</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">cropped_im</span>
+<span class="go"><NDArray 100x100x1 @cpu(0)></span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">rect</span>
<span class="go">(20, 21, 100, 100)</span>
</pre></div>
</div>
@@ -813,19 +814,21 @@ than <cite>size</cite>.</p>
<p class="last">This requires MXNet to be compiled with USE_OPENCV.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>src</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Binary source image data.</li>
-<li><strong>size</strong> (<em>list</em><em> or </em><em>tuple of int</em>) – The desired output image size.</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method. See resize_short for details.</li>
+<li><strong>size</strong> (<em>list or tuple of int</em>) – The desired output image size.</li>
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method. See resize_short for details.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><em>NDArray</em> – The cropped image.</li>
-<li><em>Tuple</em> – (x, y, width, height) where x, y are the positions of the crop in the
+<li><em>NDArray</em> –
+The cropped image.</li>
+<li><em>Tuple</em> –
+(x, y, width, height) where x, y are the positions of the crop in the
original image and width, height the dimensions of the crop.</li>
</ul>
</p>
@@ -834,15 +837,15 @@ original image and width, height the dimensions of the crop.</li>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"flower.jpg"</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">str_image</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">imdecode</span><span class="p">(</span><span class="n">str_image</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">image</span>
-<span class="go"><ndarray 2321x3482x3="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2321x3482x3 @cpu(0)></span>
<span class="gp">>>> </span><span class="n">cropped_image</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">)</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">center_crop</span><span class="p [...]
<span class="gp">>>> </span><span class="n">cropped_image</span>
-<span class="go"><ndarray 500x1000x3="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 500x1000x3 @cpu(0)></span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">height</span>
<span class="go">(1241, 910, 1000, 500)</span>
</pre></div>
@@ -853,8 +856,8 @@ original image and width, height the dimensions of the crop.</li>
<code class="descclassname">image.</code><code class="descname">color_normalize</code><span class="sig-paren">(</span><em>src</em>, <em>mean</em>, <em>std=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.color_normalize" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize src with mean and std.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>src</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input image</li>
@@ -877,21 +880,23 @@ original image and width, height the dimensions of the crop.</li>
<code class="descclassname">image.</code><code class="descname">random_size_crop</code><span class="sig-paren">(</span><em>src</em>, <em>size</em>, <em>min_area</em>, <em>ratio</em>, <em>interp=2</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.random_size_crop" title="Permalink to this definition">¶</a></dt>
<dd><p>Randomly crop src with size. Randomize area and aspect ratio.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>src</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input image</li>
-<li><strong>size</strong> (<em>tuple of</em><em> (</em><em>int</em><em>, </em><em>int</em><em>)</em><em></em>) – Size of the crop formatted as (width, height).</li>
+<li><strong>size</strong> (<em>tuple of (int, int)</em>) – Size of the crop formatted as (width, height).</li>
<li><strong>min_area</strong> (<em>int</em>) – Minimum area to be maintained after cropping</li>
-<li><strong>ratio</strong> (<em>tuple of</em><em> (</em><em>float</em><em>, </em><em>float</em><em>)</em><em></em>) – Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method. See resize_short for details.</li>
+<li><strong>ratio</strong> (<em>tuple of (float, float)</em>) – Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)</li>
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method. See resize_short for details.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><em>NDArray</em> – An <cite>NDArray</cite> containing the cropped image.</li>
-<li><em>Tuple</em> – A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the
+<li><em>NDArray</em> –
+An <cite>NDArray</cite> containing the cropped image.</li>
+<li><em>Tuple</em> –
+A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the
original image and (width, height) are the dimensions of the cropped image.</li>
</ul>
</p>
@@ -909,8 +914,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<code class="descname">dumps</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.Augmenter.dumps" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves the Augmenter to string</p>
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<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">JSON formatted string that describes the Augmenter.</td>
</tr>
@@ -919,18 +924,23 @@ original image and (width, height) are the dimensions of the cropped image.</li>
</tbody>
</table>
</dd></dl>
+<dl class="method">
+<dt id="mxnet.image.Augmenter.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>src</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.Augmenter.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract implementation body</p>
+</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.image.ResizeAug">
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">ResizeAug</code><span class="sig-paren">(</span><em>size</em>, <em>interp=2</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.ResizeAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Make resize shorter edge to size augmenter.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>size</strong> (<em>int</em>) – The length to be set for the shorter edge.</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method. See resize_short for details.</li>
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method. See resize_short for details.</li>
</ul>
</td>
</tr>
@@ -942,12 +952,12 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">ForceResizeAug</code><span class="sig-paren">(</span><em>size</em>, <em>interp=2</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.ForceResizeAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Force resize to size regardless of aspect ratio</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>size</strong> (<em>tuple of</em><em> (</em><em>int</em><em>, </em><em>int</em><em>)</em><em></em>) – The desired size as in (width, height)</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method. See resize_short for details.</li>
+<li><strong>size</strong> (<em>tuple of (int, int)</em>) – The desired size as in (width, height)</li>
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method. See resize_short for details.</li>
</ul>
</td>
</tr>
@@ -959,12 +969,12 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">RandomCropAug</code><span class="sig-paren">(</span><em>size</em>, <em>interp=2</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.RandomCropAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Make random crop augmenter</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>size</strong> (<em>int</em>) – The length to be set for the shorter edge.</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method. See resize_short for details.</li>
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method. See resize_short for details.</li>
</ul>
</td>
</tr>
@@ -976,14 +986,14 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">RandomSizedCropAug</code><span class="sig-paren">(</span><em>size</em>, <em>min_area</em>, <em>ratio</em>, <em>interp=2</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.RandomSizedCropAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Make random crop with random resizing and random aspect ratio jitter augmenter.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>size</strong> (<em>tuple of</em><em> (</em><em>int</em><em>, </em><em>int</em><em>)</em><em></em>) – Size of the crop formatted as (width, height).</li>
+<li><strong>size</strong> (<em>tuple of (int, int)</em>) – Size of the crop formatted as (width, height).</li>
<li><strong>min_area</strong> (<em>int</em>) – Minimum area to be maintained after cropping</li>
-<li><strong>ratio</strong> (<em>tuple of</em><em> (</em><em>float</em><em>, </em><em>float</em><em>)</em><em></em>) – Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method. See resize_short for details.</li>
+<li><strong>ratio</strong> (<em>tuple of (float, float)</em>) – Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)</li>
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method. See resize_short for details.</li>
</ul>
</td>
</tr>
@@ -995,12 +1005,12 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">CenterCropAug</code><span class="sig-paren">(</span><em>size</em>, <em>interp=2</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.CenterCropAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Make center crop augmenter.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>size</strong> (<em>list</em><em> or </em><em>tuple of int</em>) – The desired output image size.</li>
-<li><strong>interp</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Interpolation method. See resize_short for details.</li>
+<li><strong>size</strong> (<em>list or tuple of int</em>) – The desired output image size.</li>
+<li><strong>interp</strong> (<em>int, optional, default=2</em>) – Interpolation method. See resize_short for details.</li>
</ul>
</td>
</tr>
@@ -1012,8 +1022,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">RandomOrderAug</code><span class="sig-paren">(</span><em>ts</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.RandomOrderAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply list of augmenters in random order</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>ts</strong> (<em>list of augmenters</em>) – A series of augmenters to be applied in random order</td>
</tr>
@@ -1025,8 +1035,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">BrightnessJitterAug</code><span class="sig-paren">(</span><em>brightness</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.BrightnessJitterAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Random brightness jitter augmentation.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>brightness</strong> (<em>float</em>) – The brightness jitter ratio range, [0, 1]</td>
</tr>
@@ -1038,8 +1048,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">ContrastJitterAug</code><span class="sig-paren">(</span><em>contrast</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.ContrastJitterAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Random contrast jitter augmentation.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>contrast</strong> (<em>float</em>) – The contrast jitter ratio range, [0, 1]</td>
</tr>
@@ -1051,8 +1061,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">SaturationJitterAug</code><span class="sig-paren">(</span><em>saturation</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.SaturationJitterAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Random saturation jitter augmentation.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>saturation</strong> (<em>float</em>) – The saturation jitter ratio range, [0, 1]</td>
</tr>
@@ -1064,8 +1074,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">HueJitterAug</code><span class="sig-paren">(</span><em>hue</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.HueJitterAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Random hue jitter augmentation.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>hue</strong> (<em>float</em>) – The hue jitter ratio range, [0, 1]</td>
</tr>
@@ -1077,8 +1087,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">ColorJitterAug</code><span class="sig-paren">(</span><em>brightness</em>, <em>contrast</em>, <em>saturation</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.ColorJitterAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply random brightness, contrast and saturation jitter in random order.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>brightness</strong> (<em>float</em>) – The brightness jitter ratio range, [0, 1]</li>
@@ -1095,8 +1105,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">LightingAug</code><span class="sig-paren">(</span><em>alphastd</em>, <em>eigval</em>, <em>eigvec</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.LightingAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Add PCA based noise.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>alphastd</strong> (<em>float</em>) – Noise level</li>
@@ -1113,8 +1123,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">ColorNormalizeAug</code><span class="sig-paren">(</span><em>mean</em>, <em>std</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.ColorNormalizeAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Mean and std normalization.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>mean</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – RGB mean to be subtracted</li>
@@ -1130,8 +1140,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">RandomGrayAug</code><span class="sig-paren">(</span><em>p</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.RandomGrayAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Randomly convert to gray image.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>p</strong> (<em>float</em>) – Probability to convert to grayscale</td>
</tr>
@@ -1143,8 +1153,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">HorizontalFlipAug</code><span class="sig-paren">(</span><em>p</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.HorizontalFlipAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Random horizontal flip.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>p</strong> (<em>float</em>) – Probability to flip image horizontally</td>
</tr>
@@ -1161,8 +1171,8 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<code class="descclassname">image.</code><code class="descname">CreateAugmenter</code><span class="sig-paren">(</span><em>data_shape</em>, <em>resize=0</em>, <em>rand_crop=False</em>, <em>rand_resize=False</em>, <em>rand_mirror=False</em>, <em>mean=None</em>, <em>std=None</em>, <em>brightness=0</em>, <em>contrast=0</em>, <em>saturation=0</em>, <em>hue=0</em>, <em>pca_noise=0</em>, <em>rand_gray=0</em>, <em>inter_method=2</em><span class="sig-paren">)</span><a class="headerlink" href="#mx [...]
<dd><p>Creates an augmenter list.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_shape</strong> (<em>tuple of int</em>) – shape for output data</li>
@@ -1170,14 +1180,14 @@ original image and (width, height) are the dimensions of the cropped image.</li>
<li><strong>rand_resize</strong> (<em>float</em>) – [0, 1], probability to apply random resizing</li>
<li><strong>rand_gray</strong> (<em>float</em>) – [0, 1], probability to convert to grayscale for all channels</li>
<li><strong>rand_mirror</strong> (<em>bool</em>) – whether apply horizontal flip to image with probability 0.5</li>
-<li><strong>mean</strong> (<em>np.ndarray</em><em> or </em><em>None</em>) – mean pixel values for [r, g, b]</li>
-<li><strong>std</strong> (<em>np.ndarray</em><em> or </em><em>None</em>) – standard deviations for [r, g, b]</li>
+<li><strong>mean</strong> (<em>np.ndarray or None</em>) – mean pixel values for [r, g, b]</li>
+<li><strong>std</strong> (<em>np.ndarray or None</em>) – standard deviations for [r, g, b]</li>
<li><strong>brightness</strong> (<em>float</em>) – brightness jittering range (percent)</li>
<li><strong>contrast</strong> (<em>float</em>) – contrast jittering range</li>
<li><strong>saturation</strong> (<em>float</em>) – saturation jittering range</li>
<li><strong>hue</strong> (<em>float</em>) – hue jittering range</li>
<li><strong>pca_noise</strong> (<em>float</em>) – pca noise level</li>
-<li><strong>inter_method</strong> (<em>int</em><em>, </em><em>default=2</em><em>(</em><em>Area-based</em><em>)</em><em></em>) – <p>interpolation method for all resizing operations</p>
+<li><strong>inter_method</strong> (<em>int, default=2(Area-based)</em>) – <p>interpolation method for all resizing operations</p>
<p>Possible values:
0: Nearest Neighbors Interpolation.
1: Bilinear interpolation.
@@ -1200,9 +1210,9 @@ with Bicubic (slow) or Bilinear (faster but still looks OK).</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of creating multiple augmenters</span>
-<span class="gp">>>> </span><span class="n">augs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">CreateAugmenter</span><span class="p">(</span><span class="n">data_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">),</span> <span class= [...]
-<span class="gp">... </span> <span class="n">mean</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">brightness</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">contrast</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">rand_gray</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of creating multiple augmenters</span>
+<span class="gp">>>> </span><span class="n">augs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">CreateAugmenter</span><span class="p">(</span><span class="n">data_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">),</span> <span class= [...]
+<span class="gp">... </span> <span class="n">mean</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">brightness</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">contrast</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">rand_gray</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">saturation</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">pca_noise</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">inter_method</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># dump the details</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">aug</span> <span class="ow">in</span> <span class="n">augs</span><span class="p">:</span>
@@ -1215,11 +1225,11 @@ with Bicubic (slow) or Bilinear (faster but still looks OK).</p>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">ImageDetIter</code><span class="sig-paren">(</span><em>batch_size</em>, <em>data_shape</em>, <em>path_imgrec=None</em>, <em>path_imglist=None</em>, <em>path_root=None</em>, <em>path_imgidx=None</em>, <em>shuffle=False</em>, <em>part_index=0</em>, <em>num_parts=1</em>, <em>aug_list=None</em>, <em>imglist=None</em>, <em>data_name='data'</em>, <em>label_name='label'</em>, <em>**kwargs</em>< [...]
<dd><p>Image iterator with a large number of augmentation choices for detection.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>aug_list</strong> (<em>list</em><em> or </em><em>None</em>) – augmenter list for generating distorted images</li>
+<li><strong>aug_list</strong> (<em>list or None</em>) – augmenter list for generating distorted images</li>
<li><strong>batch_size</strong> (<em>int</em>) – Number of examples per batch.</li>
<li><strong>data_shape</strong> (<em>tuple</em>) – Data shape in (channels, height, width) format.
For now, only RGB image with 3 channels is supported.</li>
@@ -1249,12 +1259,12 @@ Can be slow for HDD.</li>
<code class="descname">reshape</code><span class="sig-paren">(</span><em>data_shape=None</em>, <em>label_shape=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.ImageDetIter.reshape" title="Permalink to this definition">¶</a></dt>
<dd><p>Reshape iterator for data_shape or label_shape.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data_shape</strong> (<em>tuple</em><em> or </em><em>None</em>) – reshape the data_shape to the new shape if not None</li>
-<li><strong>label_shape</strong> (<em>tuple</em><em> or </em><em>None</em>) – reshape label shape to new shape if not None</li>
+<li><strong>data_shape</strong> (<em>tuple or None</em>) – reshape the data_shape to the new shape if not None</li>
+<li><strong>label_shape</strong> (<em>tuple or None</em>) – reshape label shape to new shape if not None</li>
</ul>
</td>
</tr>
@@ -1281,16 +1291,16 @@ Can be slow for HDD.</li>
<code class="descname">draw_next</code><span class="sig-paren">(</span><em>color=None</em>, <em>thickness=2</em>, <em>mean=None</em>, <em>std=None</em>, <em>clip=True</em>, <em>waitKey=None</em>, <em>window_name='draw_next'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.ImageDetIter.draw_next" title="Permalink to this definition">¶</a></dt>
<dd><p>Display next image with bounding boxes drawn.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>color</strong> (<em>tuple</em>) – Bounding box color in RGB, use None for random color</li>
<li><strong>thickness</strong> (<em>int</em>) – Bounding box border thickness</li>
-<li><strong>mean</strong> (<em>True</em><em> or </em><em>numpy.ndarray</em>) – Compensate for the mean to have better visual effect</li>
-<li><strong>std</strong> (<em>True</em><em> or </em><em>numpy.ndarray</em>) – Revert standard deviations</li>
+<li><strong>mean</strong> (<em>True or numpy.ndarray</em>) – Compensate for the mean to have better visual effect</li>
+<li><strong>std</strong> (<em>True or numpy.ndarray</em>) – Revert standard deviations</li>
<li><strong>clip</strong> (<em>bool</em>) – If true, clip to [0, 255] for better visual effect</li>
-<li><strong>waitKey</strong> (<em>None</em><em> or </em><em>int</em>) – Hold the window for waitKey milliseconds if set, skip ploting if None</li>
+<li><strong>waitKey</strong> (<em>None or int</em>) – Hold the window for waitKey milliseconds if set, skip ploting if None</li>
<li><strong>window_name</strong> (<em>str</em>) – Plot window name if waitKey is set.</li>
</ul>
</td>
@@ -1304,9 +1314,9 @@ Can be slow for HDD.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># use draw_next to get images with bounding boxes drawn</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># use draw_next to get images with bounding boxes drawn</span>
<span class="gp">>>> </span><span class="n">iterator</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">ImageDetIter</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">600</span><span class="p">,</span> <span class="mi">600</span><span class="p">),</span> <span class="n">pa [...]
-<span class="gp">>>> </span><span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">iterator</span><span class="o">.</span><span class="n">draw_next</span><span class="p">(</span><span class="n">waitKey</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="gp">>>> </span><span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">iterator</span><span class="o">.</span><span class="n">draw_next</span><span class="p">(</span><span class="n">waitKey</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="gp">... </span> <span class="c1"># display image</span>
<span class="gp">>>> </span><span class="c1"># or let draw_next display using cv2 module</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">iterator</span><span class="o">.</span><span class="n">draw_next</span><span class="p">(</span><span class="n">waitKey</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">window_name</span><span class="o">=</span><span class="s1">'disp'</span><span class="p">):</span>
@@ -1320,8 +1330,8 @@ Can be slow for HDD.</li>
<dd><p>Synchronize label shape with the input iterator. This is useful when
train/validation iterators have different label padding.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>it</strong> (<a class="reference internal" href="#mxnet.image.ImageDetIter" title="mxnet.image.ImageDetIter"><em>ImageDetIter</em></a>) – The other iterator to synchronize</li>
@@ -1338,13 +1348,13 @@ train/validation iterators have different label padding.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">train_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">ImageDetIter</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</sp [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">train_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">ImageDetIter</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</spa [...]
<span class="gp">>>> </span><span class="n">val_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">ImageDetIter</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">),</span> <span class="n">p [...]
<span class="gp">>>> </span><span class="n">train_iter</span><span class="o">.</span><span class="n">label_shape</span>
<span class="go">(30, 6)</span>
<span class="gp">>>> </span><span class="n">val_iter</span><span class="o">.</span><span class="n">label_shape</span>
<span class="go">(25, 6)</span>
-<span class="gp">>>> </span><span class="n">val_iter</span> <span class="o">=</span> <span class="n">train_iter</span><span class="o">.</span><span class="n">sync_label_shape</span><span class="p">(</span><span class="n">val_iter</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">val_iter</span> <span class="o">=</span> <span class="n">train_iter</span><span class="o">.</span><span class="n">sync_label_shape</span><span class="p">(</span><span class="n">val_iter</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">train_iter</span><span class="o">.</span><span class="n">label_shape</span>
<span class="go">(30, 6)</span>
<span class="gp">>>> </span><span class="n">val_iter</span><span class="o">.</span><span class="n">label_shape</span>
@@ -1362,8 +1372,8 @@ train/validation iterators have different label padding.</p>
<code class="descname">dumps</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.DetAugmenter.dumps" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves the Augmenter to string</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">JSON formatted string that describes the Augmenter.</td>
</tr>
@@ -1372,6 +1382,11 @@ train/validation iterators have different label padding.</p>
</tbody>
</table>
</dd></dl>
+<dl class="method">
+<dt id="mxnet.image.DetAugmenter.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>src</em>, <em>label</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.DetAugmenter.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract implementation body</p>
+</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.image.DetBorrowAug">
@@ -1379,8 +1394,8 @@ train/validation iterators have different label padding.</p>
<dd><p>Borrow standard augmenter from image classification.
Which is good once you know label won’t be affected after this augmenter.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>augmenter</strong> (<em>mx.image.Augmenter</em>) – The borrowed standard augmenter which has no effect on label</td>
</tr>
@@ -1392,8 +1407,8 @@ Which is good once you know label won’t be affected after this augmenter.</p>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">DetRandomSelectAug</code><span class="sig-paren">(</span><em>aug_list</em>, <em>skip_prob=0</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.DetRandomSelectAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Randomly select one augmenter to apply, with chance to skip all.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>aug_list</strong> (<em>list of DetAugmenter</em>) – The random selection will be applied to one of the augmenters</li>
@@ -1409,8 +1424,8 @@ Which is good once you know label won’t be affected after this augmenter.</p>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">DetHorizontalFlipAug</code><span class="sig-paren">(</span><em>p</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.DetHorizontalFlipAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Random horizontal flipping.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>p</strong> (<em>float</em>) – chance [0, 1] to flip</td>
</tr>
@@ -1422,21 +1437,21 @@ Which is good once you know label won’t be affected after this augmenter.</p>
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">DetRandomCropAug</code><span class="sig-paren">(</span><em>min_object_covered=0.1</em>, <em>aspect_ratio_range=(0.75</em>, <em>1.33)</em>, <em>area_range=(0.05</em>, <em>1.0)</em>, <em>min_eject_coverage=0.3</em>, <em>max_attempts=50</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.DetRandomCropAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Random cropping with constraints</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>min_object_covered</strong> (<em>float</em><em>, </em><em>default=0.1</em>) – The cropped area of the image must contain at least this fraction of
+<li><strong>min_object_covered</strong> (<em>float, default=0.1</em>) – The cropped area of the image must contain at least this fraction of
any bounding box supplied. The value of this parameter should be non-negative.
In the case of 0, the cropped area does not need to overlap any of the
bounding boxes supplied.</li>
-<li><strong>min_eject_coverage</strong> (<em>float</em><em>, </em><em>default=0.3</em>) – The minimum coverage of cropped sample w.r.t its original size. With this
+<li><strong>min_eject_coverage</strong> (<em>float, default=0.3</em>) – The minimum coverage of cropped sample w.r.t its original size. With this
constraint, objects that have marginal area after crop will be discarded.</li>
-<li><strong>aspect_ratio_range</strong> (<em>tuple of floats</em><em>, </em><em>default=</em><em>(</em><em>0.75</em><em>, </em><em>1.33</em><em>)</em><em></em>) – The cropped area of the image must have an aspect ratio = width / height
+<li><strong>aspect_ratio_range</strong> (<em>tuple of floats, default=(0.75, 1.33)</em>) – The cropped area of the image must have an aspect ratio = width / height
within this range.</li>
-<li><strong>area_range</strong> (<em>tuple of floats</em><em>, </em><em>default=</em><em>(</em><em>0.05</em><em>, </em><em>1.0</em><em>)</em><em></em>) – The cropped area of the image must contain a fraction of the supplied
+<li><strong>area_range</strong> (<em>tuple of floats, default=(0.05, 1.0)</em>) – The cropped area of the image must contain a fraction of the supplied
image within in this range.</li>
-<li><strong>max_attempts</strong> (<em>int</em><em>, </em><em>default=50</em>) – Number of attempts at generating a cropped/padded region of the image of the
+<li><strong>max_attempts</strong> (<em>int, default=50</em>) – Number of attempts at generating a cropped/padded region of the image of the
specified constraints. After max_attempts failures, return the original image.</li>
</ul>
</td>
@@ -1449,16 +1464,16 @@ specified constraints. After max_attempts failures, return the original image.</
<em class="property">class </em><code class="descclassname">mxnet.image.</code><code class="descname">DetRandomPadAug</code><span class="sig-paren">(</span><em>aspect_ratio_range=(0.75</em>, <em>1.33)</em>, <em>area_range=(1.0</em>, <em>3.0)</em>, <em>max_attempts=50</em>, <em>pad_val=(128</em>, <em>128</em>, <em>128)</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.image.DetRandomPadAug" title="Permalink to this definition">¶</a></dt>
<dd><p>Random padding augmenter.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>aspect_ratio_range</strong> (<em>tuple of floats</em><em>, </em><em>default=</em><em>(</em><em>0.75</em><em>, </em><em>1.33</em><em>)</em><em></em>) – The padded area of the image must have an aspect ratio = width / height
+<li><strong>aspect_ratio_range</strong> (<em>tuple of floats, default=(0.75, 1.33)</em>) – The padded area of the image must have an aspect ratio = width / height
within this range.</li>
-<li><strong>area_range</strong> (<em>tuple of floats</em><em>, </em><em>default=</em><em>(</em><em>1.0</em><em>, </em><em>3.0</em><em>)</em><em></em>) – The padded area of the image must be larger than the original area</li>
-<li><strong>max_attempts</strong> (<em>int</em><em>, </em><em>default=50</em>) – Number of attempts at generating a padded region of the image of the
+<li><strong>area_range</strong> (<em>tuple of floats, default=(1.0, 3.0)</em>) – The padded area of the image must be larger than the original area</li>
+<li><strong>max_attempts</strong> (<em>int, default=50</em>) – Number of attempts at generating a padded region of the image of the
specified constraints. After max_attempts failures, return the original image.</li>
-<li><strong>pad_val</strong> (<em>float</em><em> or </em><em>tuple of float</em><em>, </em><em>default=</em><em>(</em><em>128</em><em>, </em><em>128</em><em>, </em><em>128</em><em>)</em><em></em>) – pixel value to be filled when padding is enabled.</li>
+<li><strong>pad_val</strong> (<em>float or tuple of float, default=(128, 128, 128)</em>) – pixel value to be filled when padding is enabled.</li>
</ul>
</td>
</tr>
@@ -1470,8 +1485,8 @@ specified constraints. After max_attempts failures, return the original image.</
<code class="descclassname">image.</code><code class="descname">CreateDetAugmenter</code><span class="sig-paren">(</span><em>data_shape</em>, <em>resize=0</em>, <em>rand_crop=0</em>, <em>rand_pad=0</em>, <em>rand_gray=0</em>, <em>rand_mirror=False</em>, <em>mean=None</em>, <em>std=None</em>, <em>brightness=0</em>, <em>contrast=0</em>, <em>saturation=0</em>, <em>pca_noise=0</em>, <em>hue=0</em>, <em>inter_method=2</em>, <em>min_object_covered=0.1</em>, <em>aspect_ratio_range=(0.75</em>, < [...]
<dd><p>Create augmenters for detection.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_shape</strong> (<em>tuple of int</em>) – shape for output data</li>
@@ -1480,14 +1495,14 @@ specified constraints. After max_attempts failures, return the original image.</
<li><strong>rand_pad</strong> (<em>float</em>) – [0, 1], probability to apply random padding</li>
<li><strong>rand_gray</strong> (<em>float</em>) – [0, 1], probability to convert to grayscale for all channels</li>
<li><strong>rand_mirror</strong> (<em>bool</em>) – whether apply horizontal flip to image with probability 0.5</li>
-<li><strong>mean</strong> (<em>np.ndarray</em><em> or </em><em>None</em>) – mean pixel values for [r, g, b]</li>
-<li><strong>std</strong> (<em>np.ndarray</em><em> or </em><em>None</em>) – standard deviations for [r, g, b]</li>
+<li><strong>mean</strong> (<em>np.ndarray or None</em>) – mean pixel values for [r, g, b]</li>
+<li><strong>std</strong> (<em>np.ndarray or None</em>) – standard deviations for [r, g, b]</li>
<li><strong>brightness</strong> (<em>float</em>) – brightness jittering range (percent)</li>
<li><strong>contrast</strong> (<em>float</em>) – contrast jittering range</li>
<li><strong>saturation</strong> (<em>float</em>) – saturation jittering range</li>
<li><strong>hue</strong> (<em>float</em>) – hue jittering range</li>
<li><strong>pca_noise</strong> (<em>float</em>) – pca noise level</li>
-<li><strong>inter_method</strong> (<em>int</em><em>, </em><em>default=2</em><em>(</em><em>Area-based</em><em>)</em><em></em>) – <p>interpolation method for all resizing operations</p>
+<li><strong>inter_method</strong> (<em>int, default=2(Area-based)</em>) – <p>interpolation method for all resizing operations</p>
<p>Possible values:
0: Nearest Neighbors Interpolation.
1: Bilinear interpolation.
@@ -1524,9 +1539,9 @@ be subtracted by mean and divided by std if applicable.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of creating multiple augmenters</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of creating multiple augmenters</span>
<span class="gp">>>> </span><span class="n">augs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">CreateDetAugmenter</span><span class="p">(</span><span class="n">data_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">),</span> <span cla [...]
-<span class="gp">... </span> <span class="n">rand_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">rand_mirror</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">brightness</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">contrast</sp [...]
+<span class="gp">... </span> <span class="n">rand_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">rand_mirror</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">brightness</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">contrast</sp [...]
<span class="gp">... </span> <span class="n">saturation</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">pca_noise</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">inter_method</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_object_covered</span><span class="o">=</span><span class="p">[</span><span class="mf">0.3</span><span class="p" [...]
<span class="gp">... </span> <span class="n">area_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">))</span>
<span class="gp">>>> </span><span class="c1"># dump the details</span>
diff --git a/versions/master/api/python/index.html b/versions/master/api/python/index.html
index 2c57707..c4b8191 100644
--- a/versions/master/api/python/index.html
+++ b/versions/master/api/python/index.html
@@ -2,15 +2,15 @@
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+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
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+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
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@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="ndarray.html" rel="next" title="NDArray API"/>
+<link href="ndarray.html" rel="next" title="NDArray API">
<link href="../../index.html" rel="prev" title="Contents"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
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+<input class="form-control" name="q" placeholder="Search" type="text"/>
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-</form>
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@@ -223,11 +221,12 @@ Previous Navbar Layout End -->
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<div class="sphinxsidebarwrapper">
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-<li class="toctree-l1 current"><a class="current reference internal" href="#">Python Documents</a><ul>
+<li class="toctree-l1 current"><a class="current reference internal" href="">Python Documents</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#table-of-contents">Table of contents</a><ul>
<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
<li class="toctree-l3"><a class="reference internal" href="kvstore.html">KVStore API</a></li>
@@ -280,6 +279,7 @@ for more details.</p>
<li class="toctree-l2"><a class="reference internal" href="ndarray.html#array-manipulation-routines">Array manipulation routines</a></li>
<li class="toctree-l2"><a class="reference internal" href="ndarray.html#mathematical-functions">Mathematical functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="ndarray.html#neural-network">Neural network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ndarray.html#contrib">Contrib</a></li>
<li class="toctree-l2"><a class="reference internal" href="ndarray.html#api-reference">API Reference</a></li>
</ul>
</li>
@@ -290,6 +290,7 @@ for more details.</p>
<li class="toctree-l2"><a class="reference internal" href="symbol.html#symbol-manipulation-routines">Symbol manipulation routines</a></li>
<li class="toctree-l2"><a class="reference internal" href="symbol.html#mathematical-functions">Mathematical functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="symbol.html#neural-network">Neural network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="symbol.html#contrib">Contrib</a></li>
<li class="toctree-l2"><a class="reference internal" href="symbol.html#api-reference">API Reference</a></li>
</ul>
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@@ -300,6 +301,10 @@ for more details.</p>
<li class="toctree-l2"><a class="reference internal" href="module.html#api-reference">API Reference</a></li>
</ul>
</li>
+<li class="toctree-l1"><a class="reference internal" href="autograd.html">Autograd Package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="autograd.html#autograd">Autograd</a></li>
+</ul>
+</li>
<li class="toctree-l1"><a class="reference internal" href="gluon.html">Gluon Package</a><ul>
<li class="toctree-l2"><a class="reference internal" href="gluon.html#overview">Overview</a></li>
<li class="toctree-l2"><a class="reference internal" href="gluon.html#parameter">Parameter</a></li>
@@ -309,6 +314,9 @@ for more details.</p>
<li class="toctree-l2"><a class="reference internal" href="gluon.html#trainer">Trainer</a></li>
<li class="toctree-l2"><a class="reference internal" href="gluon.html#loss-functions">Loss functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="gluon.html#utilities">Utilities</a></li>
+<li class="toctree-l2"><a class="reference internal" href="gluon.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="gluon.html#model-zoo">Model Zoo</a></li>
+<li class="toctree-l2"><a class="reference internal" href="gluon.html#api-reference">API Reference</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="rnn.html">RNN Cell API</a><ul>
diff --git a/versions/master/api/python/io.html b/versions/master/api/python/io.html
index 608c95e..67cb7f0 100644
--- a/versions/master/api/python/io.html
+++ b/versions/master/api/python/io.html
@@ -2,15 +2,15 @@
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+<meta content="width=device-width, initial-scale=1" name="viewport"/>
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+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
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@@ -18,7 +18,7 @@
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+ SOURCELINK_SUFFIX: ''
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@@ -48,13 +48,11 @@
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-<link href="index.html" rel="up" title="MXNet - Python API"/>
+<link href="index.html" rel="up" title="MXNet - Python API">
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+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
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-<li class="toctree-l3 current"><a class="current reference internal" href="#">Data Loading API</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Data Loading API</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
<li class="toctree-l4"><a class="reference internal" href="#data-iterators">Data iterators</a></li>
<li class="toctree-l4"><a class="reference internal" href="#helper-classes-and-functions">Helper classes and functions</a></li>
@@ -268,8 +267,8 @@ Previous Navbar Layout End -->
data including</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#module-mxnet.io" title="mxnet.io"><code class="xref py py-obj docutils literal"><span class="pre">mxnet.io</span></code></a></td>
@@ -292,9 +291,9 @@ shapes and name.</p>
<span class="gp">... </span> <span class="n">label</span><span class="o">=</span><span class="p">{</span><span class="s1">'softmax_label'</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">100</span><span class="p">,))},</span>
<span class="gp">... </span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">nd_iter</span><span class="o">.</span><span class="n">provide_data</span><span class="p">)</span>
-<span class="go">[DataDesc[data,(25, 10L),<type 'numpy.float32'="">,NCHW]]</type></span>
+<span class="go">[DataDesc[data,(25, 10L),<type 'numpy.float32'>,NCHW]]</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">nd_iter</span><span class="o">.</span><span class="n">provide_label</span><span class="p">)</span>
-<span class="go">[DataDesc[softmax_label,(25,),<type 'numpy.float32'="">,NCHW]]</type></span>
+<span class="go">[DataDesc[softmax_label,(25,),<type 'numpy.float32'>,NCHW]]</span>
</pre></div>
</div>
<p>Let’s see a complete example of how to use data iterator in model training.</p>
@@ -314,8 +313,8 @@ shapes and name.</p>
<span id="data-iterators"></span><h2>Data iterators<a class="headerlink" href="#data-iterators" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.io.NDArrayIter" title="mxnet.io.NDArrayIter"><code class="xref py py-obj docutils literal"><span class="pre">io.NDArrayIter</span></code></a></td>
@@ -353,8 +352,8 @@ shapes and name.</p>
<p>Data structures and other iterators provided in the <code class="docutils literal"><span class="pre">mxnet.io</span></code> packages.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.io.DataDesc" title="mxnet.io.DataDesc"><code class="xref py py-obj docutils literal"><span class="pre">io.DataDesc</span></code></a></td>
@@ -380,8 +379,8 @@ shapes and name.</p>
<p>Functions to read and write RecordIO files.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.recordio.pack" title="mxnet.recordio.pack"><code class="xref py py-obj docutils literal"><span class="pre">recordio.pack</span></code></a></td>
@@ -463,15 +462,15 @@ C is number of channels, H is the height and W is the width of the image.</p>
N is number of examples in the batch, T the temporal axis representing time
and C is the number of channels.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>cls</strong> (<a class="reference internal" href="#mxnet.io.DataDesc" title="mxnet.io.DataDesc"><em>DataDesc</em></a>) – The class.</li>
<li><strong>name</strong> (<em>str</em>) – Data name.</li>
<li><strong>shape</strong> (<em>tuple of int</em>) – Data shape.</li>
-<li><strong>dtype</strong> (<em>np.dtype</em><em>, </em><em>optional</em>) – Data type.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – Data layout.</li>
+<li><strong>dtype</strong> (<em>np.dtype, optional</em>) – Data type.</li>
+<li><strong>layout</strong> (<em>str, optional</em>) – Data layout.</li>
</ul>
</td>
</tr>
@@ -485,8 +484,8 @@ and C is the number of channels.</p>
concatenated along the batch-size dimension. Axis can be -1, which means
the whole array will be copied for each data-parallelism device.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>layout</strong> (<em>str</em>) – layout string. For example, “NCHW”.</td>
</tr>
@@ -502,12 +501,12 @@ the whole array will be copied for each data-parallelism device.</p>
<em class="property">static </em><code class="descname">get_list</code><span class="sig-paren">(</span><em>shapes</em>, <em>types</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.DataDesc.get_list" title="Permalink to this definition">¶</a></dt>
<dd><p>Get DataDesc list from attribute lists.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>shapes</strong> (<em>a tuple of</em><em> (</em><a class="reference internal" href="symbol.html#mxnet.symbol.Symbol.name" title="mxnet.symbol.Symbol.name"><em>name</em></a><em>, </em><a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray.shape" title="mxnet.ndarray.NDArray.shape"><em>shape</em></a><em>)</em><em></em>) – </li>
-<li><strong>types</strong> (<em>a tuple of</em><em> (</em><a class="reference internal" href="symbol.html#mxnet.symbol.Symbol.name" title="mxnet.symbol.Symbol.name"><em>name</em></a><em>, </em><a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore.type" title="mxnet.kvstore.KVStore.type"><em>type</em></a><em>)</em><em></em>) – </li>
+<li><strong>shapes</strong> (<em>a tuple of (name, shape)</em>) – </li>
+<li><strong>types</strong> (<em>a tuple of (name, type)</em>) – </li>
</ul>
</td>
</tr>
@@ -529,17 +528,17 @@ If <cite>layout</cite> is set to ‘NHWC’ then, images should be stored in a 4
of shape <code class="docutils literal"><span class="pre">(batch_size,</span> <span class="pre">height,</span> <span class="pre">width,</span> <span class="pre">num_channel)</span></code>.
The channels are often in RGB order.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> (list of <cite>NDArray</cite>, each array containing <cite>batch_size</cite> examples.) – A list of input data.</li>
<li><strong>label</strong> (list of <cite>NDArray</cite>, each array often containing a 1-dimensional array. optional) – A list of input labels.</li>
-<li><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of examples padded at the end of a batch. It is used when the
+<li><strong>pad</strong> (<em>int, optional</em>) – The number of examples padded at the end of a batch. It is used when the
total number of examples read is not divisible by the <cite>batch_size</cite>.
These extra padded examples are ignored in prediction.</li>
-<li><strong>index</strong> (<em>numpy.array</em><em>, </em><em>optional</em>) – The example indices in this batch.</li>
-<li><strong>bucket_key</strong> (<em>int</em><em>, </em><em>optional</em>) – The bucket key, used for bucketing module.</li>
+<li><strong>index</strong> (<em>numpy.array, optional</em>) – The example indices in this batch.</li>
+<li><strong>bucket_key</strong> (<em>int, optional</em>) – The bucket key, used for bucketing module.</li>
<li><strong>provide_data</strong> (list of <cite>DataDesc</cite>, optional) – A list of <cite>DataDesc</cite> objects. <cite>DataDesc</cite> is used to store
name, shape, type and layout information of the data.
The <em>i</em>-th element describes the name and shape of <code class="docutils literal"><span class="pre">data[i]</span></code>.</li>
@@ -561,10 +560,10 @@ in MXNet are similar to standard-iterators in Python. On each call to <cite>next
they return a <cite>DataBatch</cite> which represents the next batch of data. When
there is no more data to return, it raises a <cite>StopIteration</cite> exception.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>batch_size</strong> (<em>int</em><em>, </em><em>optional</em>) – The batch size, namely the number of items in the batch.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>batch_size</strong> (<em>int, optional</em>) – The batch size, namely the number of items in the batch.</td>
</tr>
</tbody>
</table>
@@ -589,14 +588,15 @@ there is no more data to return, it raises a <cite>StopIteration</cite> exceptio
<code class="descname">next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.DataIter.next" title="Permalink to this definition">¶</a></dt>
<dd><p>Get next data batch from iterator.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The data of next batch.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#mxnet.io.DataBatch" title="mxnet.io.DataBatch">DataBatch</a></td>
</tr>
-<tr class="field-odd field"><th class="field-name">Raises:</th><td class="field-body"><code class="xref py py-exc docutils literal"><span class="pre">StopIteration</span></code> – If the end of the data is reached.</td>
+<tr class="field-odd field"><th class="field-name">Raises:</th><td class="field-body"><code class="xref py py-exc docutils literal"><span class="pre">StopIteration</span></code> –
+If the end of the data is reached.</td>
</tr>
</tbody>
</table>
@@ -606,8 +606,8 @@ there is no more data to return, it raises a <cite>StopIteration</cite> exceptio
<code class="descname">iter_next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.DataIter.iter_next" title="Permalink to this definition">¶</a></dt>
<dd><p>Move to the next batch.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Whether the move is successful.</td>
</tr>
@@ -621,8 +621,8 @@ there is no more data to return, it raises a <cite>StopIteration</cite> exceptio
<code class="descname">getdata</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.DataIter.getdata" title="Permalink to this definition">¶</a></dt>
<dd><p>Get data of current batch.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The data of the current batch.</td>
</tr>
@@ -636,8 +636,8 @@ there is no more data to return, it raises a <cite>StopIteration</cite> exceptio
<code class="descname">getlabel</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.DataIter.getlabel" title="Permalink to this definition">¶</a></dt>
<dd><p>Get label of the current batch.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The label of the current batch.</td>
</tr>
@@ -651,10 +651,11 @@ there is no more data to return, it raises a <cite>StopIteration</cite> exceptio
<code class="descname">getindex</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.DataIter.getindex" title="Permalink to this definition">¶</a></dt>
<dd><p>Get index of the current batch.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>index</strong> – The indices of examples in the current batch.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>index</strong> –
+The indices of examples in the current batch.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">numpy.array</td>
</tr>
@@ -666,8 +667,8 @@ there is no more data to return, it raises a <cite>StopIteration</cite> exceptio
<code class="descname">getpad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.DataIter.getpad" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the number of padding examples in the current batch.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Number of padding examples in the current batch.</td>
</tr>
@@ -682,8 +683,8 @@ there is no more data to return, it raises a <cite>StopIteration</cite> exceptio
<em class="property">class </em><code class="descclassname">mxnet.io.</code><code class="descname">ResizeIter</code><span class="sig-paren">(</span><em>data_iter</em>, <em>size</em>, <em>reset_internal=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.ResizeIter" title="Permalink to this definition">¶</a></dt>
<dd><p>Resize a data iterator to a given number of batches.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_iter</strong> (<a class="reference internal" href="#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a>) – The data iterator to be resized.</li>
@@ -695,12 +696,12 @@ there is no more data to return, it raises a <cite>StopIteration</cite> exceptio
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nd_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">100</span><sp [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nd_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">100</span><spa [...]
<span class="gp">>>> </span><span class="n">resize_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ResizeIter</span><span class="p">(</span><span class="n">nd_iter</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">resize_iter</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
-<span class="go">[<ndarray 25x10="" @cpu(0)="">]</ndarray></span>
-<span class="go">[<ndarray 25x10="" @cpu(0)="">]</ndarray></span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+<span class="go">[<NDArray 25x10 @cpu(0)>]</span>
+<span class="go">[<NDArray 25x10 @cpu(0)>]</span>
</pre></div>
</div>
</dd></dl>
@@ -712,28 +713,28 @@ there is no more data to return, it raises a <cite>StopIteration</cite> exceptio
store the data in memory. It potentially accelerates the data read, at the
cost of more memory usage.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>iters</strong> (<a class="reference internal" href="#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a><em> or </em><em>list of DataIter</em>) – The data iterators to be pre-fetched.</li>
-<li><strong>rename_data</strong> (<em>None</em><em> or </em><em>list of dict</em>) – The <em>i</em>-th element is a renaming map for the <em>i</em>-th iter, in the form of
+<li><strong>iters</strong> (<em>DataIter or list of DataIter</em>) – The data iterators to be pre-fetched.</li>
+<li><strong>rename_data</strong> (<em>None or list of dict</em>) – The <em>i</em>-th element is a renaming map for the <em>i</em>-th iter, in the form of
{‘original_name’ : ‘new_name’}. Should have one entry for each entry
in iter[i].provide_data.</li>
-<li><strong>rename_label</strong> (<em>None</em><em> or </em><em>list of dict</em>) – Similar to <code class="docutils literal"><span class="pre">rename_data</span></code>.</li>
+<li><strong>rename_label</strong> (<em>None or list of dict</em>) – Similar to <code class="docutils literal"><span class="pre">rename_data</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">iter1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">({</span><span class="s1">'data'</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><s [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">iter1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">({</span><span class="s1">'data'</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><sp [...]
<span class="gp">>>> </span><span class="n">iter2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">({</span><span class="s1">'data'</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">100</span><span class="p">,</spa [...]
<span class="gp">>>> </span><span class="n">piter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">PrefetchingIter</span><span class="p">([</span><span class="n">iter1</span><span class="p">,</span> <span class="n">iter2</span><span class="p">],</span>
<span class="gp">... </span> <span class="n">rename_data</span><span class="o">=</span><span class="p">[{</span><span class="s1">'data'</span><span class="p">:</span> <span class="s1">'data_1'</span><span class="p">},</span> <span class="p">{</span><span class="s1">'data'</span><span class="p">:</span> <span class="s1">'data_2'</span><span class="p">}])</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">piter</span><span class="o">.</span><span class="n">provide_data</span><span class="p">)</span>
-<span class="go">[DataDesc[data_1,(25, 10L),<type 'numpy.float32'="">,NCHW],</type></span>
-<span class="go"> DataDesc[data_2,(25, 10L),<type 'numpy.float32'="">,NCHW]]</type></span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">piter</span><span class="o">.</span><span class="n">provide_data</span><span class="p">)</span>
+<span class="go">[DataDesc[data_1,(25, 10L),<type 'numpy.float32'>,NCHW],</span>
+<span class="go"> DataDesc[data_2,(25, 10L),<type 'numpy.float32'>,NCHW]]</span>
</pre></div>
</div>
</dd></dl>
@@ -741,11 +742,11 @@ in iter[i].provide_data.</li>
<dt id="mxnet.io.NDArrayIter">
<em class="property">class </em><code class="descclassname">mxnet.io.</code><code class="descname">NDArrayIter</code><span class="sig-paren">(</span><em>data</em>, <em>label=None</em>, <em>batch_size=1</em>, <em>shuffle=False</em>, <em>last_batch_handle='pad'</em>, <em>data_name='data'</em>, <em>label_name='softmax_label'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.NDArrayIter" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an iterator for <code class="docutils literal"><span class="pre">mx.nd.NDArray</span></code>, <code class="docutils literal"><span class="pre">numpy.ndarray</span></code> or <code class="docutils literal"><span class="pre">h5py.Dataset</span></code>.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">40</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">40</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span clas [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">last_batch_handle</span><s [...]
+<span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="bp">True</span><span class="p">,</span> <span class="n">last_batch_handle</span><s [...]
<span class="gp">>>> </span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataiter</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span> <span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">... </span> <span class="k">print</span> <span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="gp">... </span> <span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
<span class="gp">...</span>
<span class="go">[[[ 36. 37.]</span>
@@ -770,22 +771,22 @@ in iter[i].provide_data.</li>
<span class="go"> [ 30. 31.]]]</span>
<span class="go">(3L, 2L, 2L)</span>
<span class="gp">>>> </span><span class="n">dataiter</span><span class="o">.</span><span class="n">provide_data</span> <span class="c1"># Returns a list of `DataDesc`</span>
-<span class="go">[DataDesc[data,(3, 2L, 2L),<type 'numpy.float32'="">,NCHW]]</type></span>
+<span class="go">[DataDesc[data,(3, 2L, 2L),<type 'numpy.float32'>,NCHW]]</span>
<span class="gp">>>> </span><span class="n">dataiter</span><span class="o">.</span><span class="n">provide_label</span> <span class="c1"># Returns a list of `DataDesc`</span>
-<span class="go">[DataDesc[softmax_label,(3, 1L),<type 'numpy.float32'="">,NCHW]]</type></span>
+<span class="go">[DataDesc[softmax_label,(3, 1L),<type 'numpy.float32'>,NCHW]]</span>
</pre></div>
</div>
<p>In the above example, data is shuffled as <cite>shuffle</cite> parameter is set to <cite>True</cite>
and remaining examples are discarded as <cite>last_batch_handle</cite> parameter is set to <cite>discard</cite>.</p>
<p>Usage of <cite>last_batch_handle</cite> parameter:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="kc">True</ [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="bp">True</s [...]
<span class="gp">>>> </span><span class="n">batchidx</span> <span class="o">=</span> <span class="mi">0</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataiter</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">batchidx</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">batchidx</span> <span class="c1"># Padding added after the examples read are over. So, 10/3+1 batches are created.</span>
<span class="go">4</span>
-<span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">last_batch_handle</span><s [...]
+<span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="bp">True</span><span class="p">,</span> <span class="n">last_batch_handle</span><s [...]
<span class="gp">>>> </span><span class="n">batchidx</span> <span class="o">=</span> <span class="mi">0</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataiter</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">batchidx</span> <span class="o">+=</span> <span class="mi">1</span>
@@ -795,26 +796,26 @@ and remaining examples are discarded as <cite>last_batch_handle</cite> parameter
</pre></div>
</div>
<p><cite>NDArrayIter</cite> also supports multiple input and labels.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'data1'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'data1'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span cla [...]
<span class="gp">>>> </span><span class="n">label</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'label1'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span> <span class="s1">'label2'</span><span class="p">:</ [...]
-<span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">last_batch_handle</span><sp [...]
+<span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="bp">True</span><span class="p">,</span> <span class="n">last_batch_handle</span><sp [...]
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a><em> or </em><em>list of array</em><em> or </em><em>dict of string to array</em>) – The input data.</li>
-<li><strong>label</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a><em> or </em><em>list of array</em><em> or </em><em>dict of string to array</em><em>, </em><em>optional</em>) – The input label.</li>
+<li><strong>data</strong> (<em>array or list of array or dict of string to array</em>) – The input data.</li>
+<li><strong>label</strong> (<em>array or list of array or dict of string to array, optional</em>) – The input label.</li>
<li><strong>batch_size</strong> (<em>int</em>) – Batch size of data.</li>
-<li><strong>shuffle</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to shuffle the data.
+<li><strong>shuffle</strong> (<em>bool, optional</em>) – Whether to shuffle the data.
Only supported if no h5py.Dataset inputs are used.</li>
-<li><strong>last_batch_handle</strong> (<em>str</em><em>, </em><em>optional</em>) – How to handle the last batch. This parameter can be ‘pad’, ‘discard’ or
+<li><strong>last_batch_handle</strong> (<em>str, optional</em>) – How to handle the last batch. This parameter can be ‘pad’, ‘discard’ or
‘roll_over’. ‘roll_over’ is intended for training and can cause problems
if used for prediction.</li>
-<li><strong>data_name</strong> (<em>str</em><em>, </em><em>optional</em>) – The data name.</li>
-<li><strong>label_name</strong> (<em>str</em><em>, </em><em>optional</em>) – The label name.</li>
+<li><strong>data_name</strong> (<em>str, optional</em>) – The data name.</li>
+<li><strong>label_name</strong> (<em>str, optional</em>) – The label name.</li>
</ul>
</td>
</tr>
@@ -849,13 +850,13 @@ underlying C++ data iterators.</p>
implementing your own data iterators in C++. To do that, please refer to
examples under the <cite>src/io</cite> folder.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>handle</strong> (<em>DataIterHandle</em><em>, </em><em>required</em>) – The handle to the underlying C++ Data Iterator.</li>
-<li><strong>data_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Data name. Default to “data”.</li>
-<li><strong>label_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Label name. Default to “softmax_label”.</li>
+<li><strong>handle</strong> (<em>DataIterHandle, required</em>) – The handle to the underlying C++ Data Iterator.</li>
+<li><strong>data_name</strong> (<em>str, optional</em>) – Data name. Default to “data”.</li>
+<li><strong>label_name</strong> (<em>str, optional</em>) – Label name. Default to “softmax_label”.</li>
</ul>
</td>
</tr>
@@ -881,7 +882,7 @@ the call is ignored and remaining examples are returned in the second round.</p>
to set <cite>round_batch</cite> to False.</p>
<p>If <code class="docutils literal"><span class="pre">data_csv</span> <span class="pre">=</span> <span class="pre">'data/'</span></code> is set, then all the files in this directory will be read.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>// Contents of CSV file ``data/data.csv``.
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Contents of CSV file ``data/data.csv``.
1,2,3
2,3,4
3,4,5
@@ -935,18 +936,18 @@ batch_size = 3, round_batch=False)
</div>
<p>Defined in src/io/iter_csv.cc:L202</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data_csv</strong> (<em>string</em><em>, </em><em>required</em>) – The input CSV file or a directory path.</li>
-<li><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The shape of one example.</li>
-<li><strong>label_csv</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='NULL'</em>) – The input CSV file or a directory path. If NULL, all labels will be returned as 0.</li>
-<li><strong>label_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>1</em><em>,</em><em></em><em>)</em><em></em>) – The shape of one label.</li>
-<li><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Batch size.</li>
-<li><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
-<li><strong>prefetch_buffer</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</li>
-<li><strong>dtype</strong> (<em>{None</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
+<li><strong>data_csv</strong> (<em>string, required</em>) – The input CSV file or a directory path.</li>
+<li><strong>data_shape</strong> (<em>Shape(tuple), required</em>) – The shape of one example.</li>
+<li><strong>label_csv</strong> (<em>string, optional, default='NULL'</em>) – The input CSV file or a directory path. If NULL, all labels will be returned as 0.</li>
+<li><strong>label_shape</strong> (<em>Shape(tuple), optional, default=(1,)</em>) – The shape of one label.</li>
+<li><strong>batch_size</strong> (<em>int (non-negative), required</em>) – Batch size.</li>
+<li><strong>round_batch</strong> (<em>boolean, optional, default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
+<li><strong>prefetch_buffer</strong> (<em>long (non-negative), optional, default=4</em>) – Maximum number of batches to prefetch.</li>
+<li><strong>dtype</strong> (<em>{None, 'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
</ul>
</td>
</tr>
@@ -964,69 +965,69 @@ batch_size = 3, round_batch=False)
<code class="descclassname">mxnet.io.</code><code class="descname">ImageDetRecordIter</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.ImageDetRecordIter" title="Permalink to this definition">¶</a></dt>
<dd><p>Create iterator for image detection dataset packed in recordio.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Dataset Param: Path to image list.</li>
-<li><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='./data/imgrec.rec'</em>) – Dataset Param: Path to image record file.</li>
-<li><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='det_aug_default'</em>) – Augmentation Param: the augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters. Make sure you don’t use normal augmenters for detection tasks.</li>
-<li><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Dataset Param: How many labels for an image, -1 for variable label size.</li>
-<li><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – Dataset Param: Shape of each instance generated by the DataIter.</li>
-<li><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – Backend Param: Number of thread to do preprocessing.</li>
-<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Auxiliary Param: Whether to output parser information.</li>
-<li><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – partition the data into multiple parts</li>
-<li><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – the index of the part will read</li>
-<li><strong>shuffle_chunk_size</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the size(MB) of the shuffle chunk, used with shuffle=True, it can enable global shuffling</li>
-<li><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – the seed for chunk shuffling</li>
-<li><strong>label_pad_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – pad output label width if set larger than 0, -1 for auto estimate</li>
-<li><strong>label_pad_value</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – label padding value if enabled</li>
-<li><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Augmentation Param: Whether to shuffle data.</li>
-<li><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Random Seed.</li>
-<li><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Batch size.</li>
-<li><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
-<li><strong>prefetch_buffer</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</li>
-<li><strong>dtype</strong> (<em>{None</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
-<li><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Augmentation Param: scale shorter edge to size before applying other augmentations, -1 to disable.</li>
-<li><strong>rand_crop_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability of random cropping, <= 0 to disable</li>
-<li><strong>min_crop_scales</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Min crop scales.</li>
-<li><strong>max_crop_scales</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>1</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Max crop scales.</li>
-<li><strong>min_crop_aspect_ratios</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>1</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Min crop aspect ratios.</li>
-<li><strong>max_crop_aspect_ratios</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>1</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Max crop aspect ratios.</li>
-<li><strong>min_crop_overlaps</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Minimum crop IOU between crop_box and ground-truths.</li>
-<li><strong>max_crop_overlaps</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>1</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Maximum crop IOU between crop_box and ground-truth.</li>
-<li><strong>min_crop_sample_coverages</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Minimum ratio of intersect/crop_area between crop box and ground-truths.</li>
-<li><strong>max_crop_sample_coverages</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>1</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Maximum ratio of intersect/crop_area between crop box and ground-truths.</li>
-<li><strong>min_crop_object_coverages</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Minimum ratio of intersect/gt_area between crop box and ground-truths.</li>
-<li><strong>max_crop_object_coverages</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>1</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Maximum ratio of intersect/gt_area between crop box and ground-truths.</li>
-<li><strong>num_crop_sampler</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Augmentation Param: Number of crop samplers.</li>
-<li><strong>crop_emit_mode</strong> (<em>{'center'</em><em>, </em><em>'overlap'}</em><em>,</em><em>optional</em><em>, </em><em>default='center'</em>) – Augmentation Param: Emition mode for invalid ground-truths after crop. center: emit if centroid of object is out of crop region; overlap: emit if overlap is less than emit_overlap_thresh.</li>
-<li><strong>emit_overlap_thresh</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.3</em>) – Augmentation Param: Emit overlap thresh for emit mode overlap only.</li>
-<li><strong>max_crop_trials</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>25</em><em>,</em><em></em><em>)</em><em></em>) – Augmentation Param: Skip cropping if fail crop trail count exceeds this number.</li>
-<li><strong>rand_pad_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability for random padding.</li>
-<li><strong>max_pad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Maximum padding scale.</li>
-<li><strong>max_random_hue</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Maximum random value of H channel in HSL color space.</li>
-<li><strong>random_hue_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply random hue.</li>
-<li><strong>max_random_saturation</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Maximum random value of S channel in HSL color space.</li>
-<li><strong>random_saturation_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply random saturation.</li>
-<li><strong>max_random_illumination</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Maximum random value of L channel in HSL color space.</li>
-<li><strong>random_illumination_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply random illumination.</li>
-<li><strong>max_random_contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Maximum random value of delta contrast.</li>
-<li><strong>random_contrast_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply random contrast.</li>
-<li><strong>rand_mirror_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply horizontal flip aka. mirror.</li>
-<li><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='127'</em>) – Augmentation Param: Filled color value while padding.</li>
-<li><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Augmentation Param: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
-<li><strong>resize_mode</strong> (<em>{'fit'</em><em>, </em><em>'force'</em><em>, </em><em>'shrink'}</em><em>,</em><em>optional</em><em>, </em><em>default='force'</em>) – Augmentation Param: How image data fit in data_shape. force: force reshape to data_shape regardless of aspect ratio; shrink: ensure each side fit in data_shape, preserve aspect ratio; fit: fit image to data_shape, preserve ratio, will upscale if applicable.</li>
-<li><strong>mean_img</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Augmentation Param: Mean Image to be subtracted.</li>
-<li><strong>mean_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Mean value on R channel.</li>
-<li><strong>mean_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Mean value on G channel.</li>
-<li><strong>mean_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Mean value on B channel.</li>
-<li><strong>mean_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Mean value on Alpha channel.</li>
-<li><strong>std_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Standard deviation on R channel.</li>
-<li><strong>std_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Standard deviation on G channel.</li>
-<li><strong>std_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Standard deviation on B channel.</li>
-<li><strong>std_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Standard deviation on Alpha channel.</li>
-<li><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Scale in color space.</li>
+<li><strong>path_imglist</strong> (<em>string, optional, default=''</em>) – Dataset Param: Path to image list.</li>
+<li><strong>path_imgrec</strong> (<em>string, optional, default='./data/imgrec.rec'</em>) – Dataset Param: Path to image record file.</li>
+<li><strong>aug_seq</strong> (<em>string, optional, default='det_aug_default'</em>) – Augmentation Param: the augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters. Make sure you don’t use normal augmenters for detection tasks.</li>
+<li><strong>label_width</strong> (<em>int, optional, default='-1'</em>) – Dataset Param: How many labels for an image, -1 for variable label size.</li>
+<li><strong>data_shape</strong> (<em>Shape(tuple), required</em>) – Dataset Param: Shape of each instance generated by the DataIter.</li>
+<li><strong>preprocess_threads</strong> (<em>int, optional, default='4'</em>) – Backend Param: Number of thread to do preprocessing.</li>
+<li><strong>verbose</strong> (<em>boolean, optional, default=True</em>) – Auxiliary Param: Whether to output parser information.</li>
+<li><strong>num_parts</strong> (<em>int, optional, default='1'</em>) – partition the data into multiple parts</li>
+<li><strong>part_index</strong> (<em>int, optional, default='0'</em>) – the index of the part will read</li>
+<li><strong>shuffle_chunk_size</strong> (<em>long (non-negative), optional, default=0</em>) – the size(MB) of the shuffle chunk, used with shuffle=True, it can enable global shuffling</li>
+<li><strong>shuffle_chunk_seed</strong> (<em>int, optional, default='0'</em>) – the seed for chunk shuffling</li>
+<li><strong>label_pad_width</strong> (<em>int, optional, default='0'</em>) – pad output label width if set larger than 0, -1 for auto estimate</li>
+<li><strong>label_pad_value</strong> (<em>float, optional, default=-1</em>) – label padding value if enabled</li>
+<li><strong>shuffle</strong> (<em>boolean, optional, default=False</em>) – Augmentation Param: Whether to shuffle data.</li>
+<li><strong>seed</strong> (<em>int, optional, default='0'</em>) – Augmentation Param: Random Seed.</li>
+<li><strong>batch_size</strong> (<em>int (non-negative), required</em>) – Batch size.</li>
+<li><strong>round_batch</strong> (<em>boolean, optional, default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
+<li><strong>prefetch_buffer</strong> (<em>long (non-negative), optional, default=4</em>) – Maximum number of batches to prefetch.</li>
+<li><strong>dtype</strong> (<em>{None, 'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
+<li><strong>resize</strong> (<em>int, optional, default='-1'</em>) – Augmentation Param: scale shorter edge to size before applying other augmentations, -1 to disable.</li>
+<li><strong>rand_crop_prob</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Probability of random cropping, <= 0 to disable</li>
+<li><strong>min_crop_scales</strong> (<em>, optional, default=(0,)</em>) – Augmentation Param: Min crop scales.</li>
+<li><strong>max_crop_scales</strong> (<em>, optional, default=(1,)</em>) – Augmentation Param: Max crop scales.</li>
+<li><strong>min_crop_aspect_ratios</strong> (<em>, optional, default=(1,)</em>) – Augmentation Param: Min crop aspect ratios.</li>
+<li><strong>max_crop_aspect_ratios</strong> (<em>, optional, default=(1,)</em>) – Augmentation Param: Max crop aspect ratios.</li>
+<li><strong>min_crop_overlaps</strong> (<em>, optional, default=(0,)</em>) – Augmentation Param: Minimum crop IOU between crop_box and ground-truths.</li>
+<li><strong>max_crop_overlaps</strong> (<em>, optional, default=(1,)</em>) – Augmentation Param: Maximum crop IOU between crop_box and ground-truth.</li>
+<li><strong>min_crop_sample_coverages</strong> (<em>, optional, default=(0,)</em>) – Augmentation Param: Minimum ratio of intersect/crop_area between crop box and ground-truths.</li>
+<li><strong>max_crop_sample_coverages</strong> (<em>, optional, default=(1,)</em>) – Augmentation Param: Maximum ratio of intersect/crop_area between crop box and ground-truths.</li>
+<li><strong>min_crop_object_coverages</strong> (<em>, optional, default=(0,)</em>) – Augmentation Param: Minimum ratio of intersect/gt_area between crop box and ground-truths.</li>
+<li><strong>max_crop_object_coverages</strong> (<em>, optional, default=(1,)</em>) – Augmentation Param: Maximum ratio of intersect/gt_area between crop box and ground-truths.</li>
+<li><strong>num_crop_sampler</strong> (<em>int, optional, default='1'</em>) – Augmentation Param: Number of crop samplers.</li>
+<li><strong>crop_emit_mode</strong> (<em>{'center', 'overlap'},optional, default='center'</em>) – Augmentation Param: Emition mode for invalid ground-truths after crop. center: emit if centroid of object is out of crop region; overlap: emit if overlap is less than emit_overlap_thresh.</li>
+<li><strong>emit_overlap_thresh</strong> (<em>float, optional, default=0.3</em>) – Augmentation Param: Emit overlap thresh for emit mode overlap only.</li>
+<li><strong>max_crop_trials</strong> (<em>Shape(tuple), optional, default=(25,)</em>) – Augmentation Param: Skip cropping if fail crop trail count exceeds this number.</li>
+<li><strong>rand_pad_prob</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Probability for random padding.</li>
+<li><strong>max_pad_scale</strong> (<em>float, optional, default=1</em>) – Augmentation Param: Maximum padding scale.</li>
+<li><strong>max_random_hue</strong> (<em>int, optional, default='0'</em>) – Augmentation Param: Maximum random value of H channel in HSL color space.</li>
+<li><strong>random_hue_prob</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Probability to apply random hue.</li>
+<li><strong>max_random_saturation</strong> (<em>int, optional, default='0'</em>) – Augmentation Param: Maximum random value of S channel in HSL color space.</li>
+<li><strong>random_saturation_prob</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Probability to apply random saturation.</li>
+<li><strong>max_random_illumination</strong> (<em>int, optional, default='0'</em>) – Augmentation Param: Maximum random value of L channel in HSL color space.</li>
+<li><strong>random_illumination_prob</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Probability to apply random illumination.</li>
+<li><strong>max_random_contrast</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Maximum random value of delta contrast.</li>
+<li><strong>random_contrast_prob</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Probability to apply random contrast.</li>
+<li><strong>rand_mirror_prob</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Probability to apply horizontal flip aka. mirror.</li>
+<li><strong>fill_value</strong> (<em>int, optional, default='127'</em>) – Augmentation Param: Filled color value while padding.</li>
+<li><strong>inter_method</strong> (<em>int, optional, default='1'</em>) – Augmentation Param: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
+<li><strong>resize_mode</strong> (<em>{'fit', 'force', 'shrink'},optional, default='force'</em>) – Augmentation Param: How image data fit in data_shape. force: force reshape to data_shape regardless of aspect ratio; shrink: ensure each side fit in data_shape, preserve aspect ratio; fit: fit image to data_shape, preserve ratio, will upscale if applicable.</li>
+<li><strong>mean_img</strong> (<em>string, optional, default=''</em>) – Augmentation Param: Mean Image to be subtracted.</li>
+<li><strong>mean_r</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Mean value on R channel.</li>
+<li><strong>mean_g</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Mean value on G channel.</li>
+<li><strong>mean_b</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Mean value on B channel.</li>
+<li><strong>mean_a</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Mean value on Alpha channel.</li>
+<li><strong>std_r</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Standard deviation on R channel.</li>
+<li><strong>std_g</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Standard deviation on G channel.</li>
+<li><strong>std_b</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Standard deviation on B channel.</li>
+<li><strong>std_a</strong> (<em>float, optional, default=0</em>) – Augmentation Param: Standard deviation on Alpha channel.</li>
+<li><strong>scale</strong> (<em>float, optional, default=1</em>) – Augmentation Param: Scale in color space.</li>
</ul>
</td>
</tr>
@@ -1048,7 +1049,7 @@ batch_size = 3, round_batch=False)
flexible to customization but is fast and has lot of language bindings. To
iterate over raw images directly use <code class="docutils literal"><span class="pre">ImageIter</span></code> instead (in Python).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">data_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ImageRecordIter</span><span class="p">(</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ImageRecordIter</span><span class="p">(</span>
<span class="n">path_imgrec</span><span class="o">=</span><span class="s2">"./sample.rec"</span><span class="p">,</span> <span class="c1"># The target record file.</span>
<span class="n">data_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">227</span><span class="p">,</span> <span class="mi">227</span><span class="p">),</span> <span class="c1"># Output data shape; 227x227 region will be cropped from the original image.</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="c1"># Number of items per batch.</span>
@@ -1065,55 +1066,55 @@ iterate over raw images directly use <code class="docutils literal"><span class=
</div>
<p>Defined in src/io/iter_image_recordio_2.cc:L583</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of="" record=""> <one labels="" more="" or=""> <relative folder="" from="" path="" root="">.</relative></one></index></li>
-<li><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</li>
-<li><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</li>
-<li><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The number of labels per image.</li>
-<li><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The shape of one output image in (channels, height, width) format.</li>
-<li><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – The number of threads to do preprocessing.</li>
-<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – If or not output verbose information.</li>
-<li><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Virtually partition the data into these many parts.</li>
-<li><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The <em>i</em>-th virtual partition to be read.</li>
-<li><strong>shuffle_chunk_size</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</li>
-<li><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed for shuffling</li>
-<li><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to shuffle data randomly or not.</li>
-<li><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed.</li>
-<li><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Batch size.</li>
-<li><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
-<li><strong>prefetch_buffer</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</li>
-<li><strong>dtype</strong> (<em>{None</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
-<li><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</li>
-<li><strong>rand_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If or not randomly crop the image</li>
-<li><strong>max_rotate_angle</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Rotate by a random degree in <code class="docutils literal"><span class="pre">[-v,</span> <span class="pre">v]</span></code></li>
-<li><strong>max_aspect_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal"><span class="pre">[1</span> <span class="pre">-</span> <span class="pre">max_aspect_ratio,</span> <span class="pre">1</span> <span class="pre">+</span> <span class="pre">max_aspect_ratio]</span></code></li>
-<li><strong>max_shear_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Apply a shear transformation (namely <code class="docutils literal"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal"><span class="pre">m</span></code> randomly chose from <code class="docutils literal"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></li>
-<li><strong>max_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
-<li><strong>min_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
-<li><strong>max_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
-<li><strong>min_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
-<li><strong>max_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</li>
-<li><strong>min_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</li>
-<li><strong>random_h</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</li>
-<li><strong>random_s</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</li>
-<li><strong>random_l</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</li>
-<li><strong>rotate</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal"><span class="pre">max_rotate_angle</span></code> option.</li>
-<li><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – Set the padding pixes value into <code class="docutils literal"><span class="pre">fill_value</span></code>.</li>
-<li><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
-<li><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Change size from <code class="docutils literal"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> < [...]
-<li><strong>mirror</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to mirror the image or not. If true, images are flipped along the horizontal axis.</li>
-<li><strong>rand_mirror</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to randomly mirror images or not. If true, 50% of the images will be randomly mirrored (flipped along the horizontal axis)</li>
-<li><strong>mean_img</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Filename of the mean image.</li>
-<li><strong>mean_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the R channel</li>
-<li><strong>mean_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the G channel</li>
-<li><strong>mean_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the B channel</li>
-<li><strong>mean_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the alpha channel</li>
-<li><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Multiply the image with a scale value.</li>
-<li><strong>max_random_contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the contrast with a value randomly chosen from <code class="docutils literal"><span class="pre">[-max_random_contrast,</span> <span class="pre">max_random_contrast]</span></code></li>
-<li><strong>max_random_illumination</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the illumination with a value randomly chosen from <code class="docutils literal"><span class="pre">[-max_random_illumination,</span> <span class="pre">max_random_illumination]</span></code></li>
+<li><strong>path_imglist</strong> (<em>string, optional, default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.</li>
+<li><strong>path_imgrec</strong> (<em>string, optional, default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</li>
+<li><strong>aug_seq</strong> (<em>string, optional, default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</li>
+<li><strong>label_width</strong> (<em>int, optional, default='1'</em>) – The number of labels per image.</li>
+<li><strong>data_shape</strong> (<em>Shape(tuple), required</em>) – The shape of one output image in (channels, height, width) format.</li>
+<li><strong>preprocess_threads</strong> (<em>int, optional, default='4'</em>) – The number of threads to do preprocessing.</li>
+<li><strong>verbose</strong> (<em>boolean, optional, default=True</em>) – If or not output verbose information.</li>
+<li><strong>num_parts</strong> (<em>int, optional, default='1'</em>) – Virtually partition the data into these many parts.</li>
+<li><strong>part_index</strong> (<em>int, optional, default='0'</em>) – The <em>i</em>-th virtual partition to be read.</li>
+<li><strong>shuffle_chunk_size</strong> (<em>long (non-negative), optional, default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</li>
+<li><strong>shuffle_chunk_seed</strong> (<em>int, optional, default='0'</em>) – The random seed for shuffling</li>
+<li><strong>shuffle</strong> (<em>boolean, optional, default=False</em>) – Whether to shuffle data randomly or not.</li>
+<li><strong>seed</strong> (<em>int, optional, default='0'</em>) – The random seed.</li>
+<li><strong>batch_size</strong> (<em>int (non-negative), required</em>) – Batch size.</li>
+<li><strong>round_batch</strong> (<em>boolean, optional, default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
+<li><strong>prefetch_buffer</strong> (<em>long (non-negative), optional, default=4</em>) – Maximum number of batches to prefetch.</li>
+<li><strong>dtype</strong> (<em>{None, 'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
+<li><strong>resize</strong> (<em>int, optional, default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</li>
+<li><strong>rand_crop</strong> (<em>boolean, optional, default=False</em>) – If or not randomly crop the image</li>
+<li><strong>max_rotate_angle</strong> (<em>int, optional, default='0'</em>) – Rotate by a random degree in <code class="docutils literal"><span class="pre">[-v,</span> <span class="pre">v]</span></code></li>
+<li><strong>max_aspect_ratio</strong> (<em>float, optional, default=0</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal"><span class="pre">[1</span> <span class="pre">-</span> <span class="pre">max_aspect_ratio,</span> <span class="pre">1</span> <span class="pre">+</span> <span class="pre">max_aspect_ratio]</span></code></li>
+<li><strong>max_shear_ratio</strong> (<em>float, optional, default=0</em>) – Apply a shear transformation (namely <code class="docutils literal"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal"><span class="pre">m</span></code> randomly chose from <code class="docutils literal"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></li>
+<li><strong>max_crop_size</strong> (<em>int, optional, default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
+<li><strong>min_crop_size</strong> (<em>int, optional, default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
+<li><strong>max_random_scale</strong> (<em>float, optional, default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
+<li><strong>min_random_scale</strong> (<em>float, optional, default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
+<li><strong>max_img_size</strong> (<em>float, optional, default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</li>
+<li><strong>min_img_size</strong> (<em>float, optional, default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</li>
+<li><strong>random_h</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</li>
+<li><strong>random_s</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</li>
+<li><strong>random_l</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</li>
+<li><strong>rotate</strong> (<em>int, optional, default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal"><span class="pre">max_rotate_angle</span></code> option.</li>
+<li><strong>fill_value</strong> (<em>int, optional, default='255'</em>) – Set the padding pixes value into <code class="docutils literal"><span class="pre">fill_value</span></code>.</li>
+<li><strong>inter_method</strong> (<em>int, optional, default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
+<li><strong>pad</strong> (<em>int, optional, default='0'</em>) – Change size from <code class="docutils literal"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> <span class="pre">+</span> <span clas [...]
+<li><strong>mirror</strong> (<em>boolean, optional, default=False</em>) – Whether to mirror the image or not. If true, images are flipped along the horizontal axis.</li>
+<li><strong>rand_mirror</strong> (<em>boolean, optional, default=False</em>) – Whether to randomly mirror images or not. If true, 50% of the images will be randomly mirrored (flipped along the horizontal axis)</li>
+<li><strong>mean_img</strong> (<em>string, optional, default=''</em>) – Filename of the mean image.</li>
+<li><strong>mean_r</strong> (<em>float, optional, default=0</em>) – The mean value to be subtracted on the R channel</li>
+<li><strong>mean_g</strong> (<em>float, optional, default=0</em>) – The mean value to be subtracted on the G channel</li>
+<li><strong>mean_b</strong> (<em>float, optional, default=0</em>) – The mean value to be subtracted on the B channel</li>
+<li><strong>mean_a</strong> (<em>float, optional, default=0</em>) – The mean value to be subtracted on the alpha channel</li>
+<li><strong>scale</strong> (<em>float, optional, default=1</em>) – Multiply the image with a scale value.</li>
+<li><strong>max_random_contrast</strong> (<em>float, optional, default=0</em>) – Change the contrast with a value randomly chosen from <code class="docutils literal"><span class="pre">[-max_random_contrast,</span> <span class="pre">max_random_contrast]</span></code></li>
+<li><strong>max_random_illumination</strong> (<em>float, optional, default=0</em>) – Change the illumination with a value randomly chosen from <code class="docutils literal"><span class="pre">[-max_random_illumination,</span> <span class="pre">max_random_illumination]</span></code></li>
</ul>
</td>
</tr>
@@ -1136,55 +1137,55 @@ options.</p>
files.</p>
<p>Defined in src/io/iter_image_recordio.cc:L328</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of="" record=""> <one labels="" more="" or=""> <relative folder="" from="" path="" root="">.</relative></one></index></li>
-<li><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</li>
-<li><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</li>
-<li><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The number of labels per image.</li>
-<li><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The shape of one output image in (channels, height, width) format.</li>
-<li><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – The number of threads to do preprocessing.</li>
-<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – If or not output verbose information.</li>
-<li><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Virtually partition the data into these many parts.</li>
-<li><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The <em>i</em>-th virtual partition to be read.</li>
-<li><strong>shuffle_chunk_size</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</li>
-<li><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed for shuffling</li>
-<li><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to shuffle data randomly or not.</li>
-<li><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed.</li>
-<li><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Batch size.</li>
-<li><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
-<li><strong>prefetch_buffer</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</li>
-<li><strong>dtype</strong> (<em>{None</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
-<li><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</li>
-<li><strong>rand_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If or not randomly crop the image</li>
-<li><strong>max_rotate_angle</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Rotate by a random degree in <code class="docutils literal"><span class="pre">[-v,</span> <span class="pre">v]</span></code></li>
-<li><strong>max_aspect_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal"><span class="pre">[1</span> <span class="pre">-</span> <span class="pre">max_aspect_ratio,</span> <span class="pre">1</span> <span class="pre">+</span> <span class="pre">max_aspect_ratio]</span></code></li>
-<li><strong>max_shear_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Apply a shear transformation (namely <code class="docutils literal"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal"><span class="pre">m</span></code> randomly chose from <code class="docutils literal"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></li>
-<li><strong>max_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
-<li><strong>min_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
-<li><strong>max_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
-<li><strong>min_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
-<li><strong>max_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</li>
-<li><strong>min_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</li>
-<li><strong>random_h</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</li>
-<li><strong>random_s</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</li>
-<li><strong>random_l</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</li>
-<li><strong>rotate</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal"><span class="pre">max_rotate_angle</span></code> option.</li>
-<li><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – Set the padding pixes value into <code class="docutils literal"><span class="pre">fill_value</span></code>.</li>
-<li><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
-<li><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Change size from <code class="docutils literal"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> < [...]
-<li><strong>mirror</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to mirror the image or not. If true, images are flipped along the horizontal axis.</li>
-<li><strong>rand_mirror</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to randomly mirror images or not. If true, 50% of the images will be randomly mirrored (flipped along the horizontal axis)</li>
-<li><strong>mean_img</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Filename of the mean image.</li>
-<li><strong>mean_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the R channel</li>
-<li><strong>mean_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the G channel</li>
-<li><strong>mean_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the B channel</li>
-<li><strong>mean_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the alpha channel</li>
-<li><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Multiply the image with a scale value.</li>
-<li><strong>max_random_contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the contrast with a value randomly chosen from <code class="docutils literal"><span class="pre">[-max_random_contrast,</span> <span class="pre">max_random_contrast]</span></code></li>
-<li><strong>max_random_illumination</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the illumination with a value randomly chosen from <code class="docutils literal"><span class="pre">[-max_random_illumination,</span> <span class="pre">max_random_illumination]</span></code></li>
+<li><strong>path_imglist</strong> (<em>string, optional, default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.</li>
+<li><strong>path_imgrec</strong> (<em>string, optional, default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</li>
+<li><strong>aug_seq</strong> (<em>string, optional, default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</li>
+<li><strong>label_width</strong> (<em>int, optional, default='1'</em>) – The number of labels per image.</li>
+<li><strong>data_shape</strong> (<em>Shape(tuple), required</em>) – The shape of one output image in (channels, height, width) format.</li>
+<li><strong>preprocess_threads</strong> (<em>int, optional, default='4'</em>) – The number of threads to do preprocessing.</li>
+<li><strong>verbose</strong> (<em>boolean, optional, default=True</em>) – If or not output verbose information.</li>
+<li><strong>num_parts</strong> (<em>int, optional, default='1'</em>) – Virtually partition the data into these many parts.</li>
+<li><strong>part_index</strong> (<em>int, optional, default='0'</em>) – The <em>i</em>-th virtual partition to be read.</li>
+<li><strong>shuffle_chunk_size</strong> (<em>long (non-negative), optional, default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</li>
+<li><strong>shuffle_chunk_seed</strong> (<em>int, optional, default='0'</em>) – The random seed for shuffling</li>
+<li><strong>shuffle</strong> (<em>boolean, optional, default=False</em>) – Whether to shuffle data randomly or not.</li>
+<li><strong>seed</strong> (<em>int, optional, default='0'</em>) – The random seed.</li>
+<li><strong>batch_size</strong> (<em>int (non-negative), required</em>) – Batch size.</li>
+<li><strong>round_batch</strong> (<em>boolean, optional, default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
+<li><strong>prefetch_buffer</strong> (<em>long (non-negative), optional, default=4</em>) – Maximum number of batches to prefetch.</li>
+<li><strong>dtype</strong> (<em>{None, 'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
+<li><strong>resize</strong> (<em>int, optional, default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</li>
+<li><strong>rand_crop</strong> (<em>boolean, optional, default=False</em>) – If or not randomly crop the image</li>
+<li><strong>max_rotate_angle</strong> (<em>int, optional, default='0'</em>) – Rotate by a random degree in <code class="docutils literal"><span class="pre">[-v,</span> <span class="pre">v]</span></code></li>
+<li><strong>max_aspect_ratio</strong> (<em>float, optional, default=0</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal"><span class="pre">[1</span> <span class="pre">-</span> <span class="pre">max_aspect_ratio,</span> <span class="pre">1</span> <span class="pre">+</span> <span class="pre">max_aspect_ratio]</span></code></li>
+<li><strong>max_shear_ratio</strong> (<em>float, optional, default=0</em>) – Apply a shear transformation (namely <code class="docutils literal"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal"><span class="pre">m</span></code> randomly chose from <code class="docutils literal"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></li>
+<li><strong>max_crop_size</strong> (<em>int, optional, default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
+<li><strong>min_crop_size</strong> (<em>int, optional, default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
+<li><strong>max_random_scale</strong> (<em>float, optional, default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
+<li><strong>min_random_scale</strong> (<em>float, optional, default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
+<li><strong>max_img_size</strong> (<em>float, optional, default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</li>
+<li><strong>min_img_size</strong> (<em>float, optional, default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</li>
+<li><strong>random_h</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</li>
+<li><strong>random_s</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</li>
+<li><strong>random_l</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</li>
+<li><strong>rotate</strong> (<em>int, optional, default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal"><span class="pre">max_rotate_angle</span></code> option.</li>
+<li><strong>fill_value</strong> (<em>int, optional, default='255'</em>) – Set the padding pixes value into <code class="docutils literal"><span class="pre">fill_value</span></code>.</li>
+<li><strong>inter_method</strong> (<em>int, optional, default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
+<li><strong>pad</strong> (<em>int, optional, default='0'</em>) – Change size from <code class="docutils literal"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> <span class="pre">+</span> <span clas [...]
+<li><strong>mirror</strong> (<em>boolean, optional, default=False</em>) – Whether to mirror the image or not. If true, images are flipped along the horizontal axis.</li>
+<li><strong>rand_mirror</strong> (<em>boolean, optional, default=False</em>) – Whether to randomly mirror images or not. If true, 50% of the images will be randomly mirrored (flipped along the horizontal axis)</li>
+<li><strong>mean_img</strong> (<em>string, optional, default=''</em>) – Filename of the mean image.</li>
+<li><strong>mean_r</strong> (<em>float, optional, default=0</em>) – The mean value to be subtracted on the R channel</li>
+<li><strong>mean_g</strong> (<em>float, optional, default=0</em>) – The mean value to be subtracted on the G channel</li>
+<li><strong>mean_b</strong> (<em>float, optional, default=0</em>) – The mean value to be subtracted on the B channel</li>
+<li><strong>mean_a</strong> (<em>float, optional, default=0</em>) – The mean value to be subtracted on the alpha channel</li>
+<li><strong>scale</strong> (<em>float, optional, default=1</em>) – Multiply the image with a scale value.</li>
+<li><strong>max_random_contrast</strong> (<em>float, optional, default=0</em>) – Change the contrast with a value randomly chosen from <code class="docutils literal"><span class="pre">[-max_random_contrast,</span> <span class="pre">max_random_contrast]</span></code></li>
+<li><strong>max_random_illumination</strong> (<em>float, optional, default=0</em>) – Change the illumination with a value randomly chosen from <code class="docutils literal"><span class="pre">[-max_random_illumination,</span> <span class="pre">max_random_illumination]</span></code></li>
</ul>
</td>
</tr>
@@ -1205,45 +1206,45 @@ files.</p>
the data type instead of <code class="docutils literal"><span class="pre">float</span></code>.</p>
<p>Defined in src/io/iter_image_recordio_2.cc:L600</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of="" record=""> <one labels="" more="" or=""> <relative folder="" from="" path="" root="">.</relative></one></index></li>
-<li><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</li>
-<li><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</li>
-<li><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The number of labels per image.</li>
-<li><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The shape of one output image in (channels, height, width) format.</li>
-<li><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – The number of threads to do preprocessing.</li>
-<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – If or not output verbose information.</li>
-<li><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Virtually partition the data into these many parts.</li>
-<li><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The <em>i</em>-th virtual partition to be read.</li>
-<li><strong>shuffle_chunk_size</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</li>
-<li><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed for shuffling</li>
-<li><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to shuffle data randomly or not.</li>
-<li><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed.</li>
-<li><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Batch size.</li>
-<li><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
-<li><strong>prefetch_buffer</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</li>
-<li><strong>dtype</strong> (<em>{None</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
-<li><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</li>
-<li><strong>rand_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If or not randomly crop the image</li>
-<li><strong>max_rotate_angle</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Rotate by a random degree in <code class="docutils literal"><span class="pre">[-v,</span> <span class="pre">v]</span></code></li>
-<li><strong>max_aspect_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal"><span class="pre">[1</span> <span class="pre">-</span> <span class="pre">max_aspect_ratio,</span> <span class="pre">1</span> <span class="pre">+</span> <span class="pre">max_aspect_ratio]</span></code></li>
-<li><strong>max_shear_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Apply a shear transformation (namely <code class="docutils literal"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal"><span class="pre">m</span></code> randomly chose from <code class="docutils literal"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></li>
-<li><strong>max_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
-<li><strong>min_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
-<li><strong>max_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
-<li><strong>min_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
-<li><strong>max_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</li>
-<li><strong>min_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</li>
-<li><strong>random_h</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</li>
-<li><strong>random_s</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</li>
-<li><strong>random_l</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</li>
-<li><strong>rotate</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal"><span class="pre">max_rotate_angle</span></code> option.</li>
-<li><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – Set the padding pixes value into <code class="docutils literal"><span class="pre">fill_value</span></code>.</li>
-<li><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
-<li><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Change size from <code class="docutils literal"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> < [...]
+<li><strong>path_imglist</strong> (<em>string, optional, default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.</li>
+<li><strong>path_imgrec</strong> (<em>string, optional, default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</li>
+<li><strong>aug_seq</strong> (<em>string, optional, default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</li>
+<li><strong>label_width</strong> (<em>int, optional, default='1'</em>) – The number of labels per image.</li>
+<li><strong>data_shape</strong> (<em>Shape(tuple), required</em>) – The shape of one output image in (channels, height, width) format.</li>
+<li><strong>preprocess_threads</strong> (<em>int, optional, default='4'</em>) – The number of threads to do preprocessing.</li>
+<li><strong>verbose</strong> (<em>boolean, optional, default=True</em>) – If or not output verbose information.</li>
+<li><strong>num_parts</strong> (<em>int, optional, default='1'</em>) – Virtually partition the data into these many parts.</li>
+<li><strong>part_index</strong> (<em>int, optional, default='0'</em>) – The <em>i</em>-th virtual partition to be read.</li>
+<li><strong>shuffle_chunk_size</strong> (<em>long (non-negative), optional, default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</li>
+<li><strong>shuffle_chunk_seed</strong> (<em>int, optional, default='0'</em>) – The random seed for shuffling</li>
+<li><strong>shuffle</strong> (<em>boolean, optional, default=False</em>) – Whether to shuffle data randomly or not.</li>
+<li><strong>seed</strong> (<em>int, optional, default='0'</em>) – The random seed.</li>
+<li><strong>batch_size</strong> (<em>int (non-negative), required</em>) – Batch size.</li>
+<li><strong>round_batch</strong> (<em>boolean, optional, default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
+<li><strong>prefetch_buffer</strong> (<em>long (non-negative), optional, default=4</em>) – Maximum number of batches to prefetch.</li>
+<li><strong>dtype</strong> (<em>{None, 'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
+<li><strong>resize</strong> (<em>int, optional, default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</li>
+<li><strong>rand_crop</strong> (<em>boolean, optional, default=False</em>) – If or not randomly crop the image</li>
+<li><strong>max_rotate_angle</strong> (<em>int, optional, default='0'</em>) – Rotate by a random degree in <code class="docutils literal"><span class="pre">[-v,</span> <span class="pre">v]</span></code></li>
+<li><strong>max_aspect_ratio</strong> (<em>float, optional, default=0</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal"><span class="pre">[1</span> <span class="pre">-</span> <span class="pre">max_aspect_ratio,</span> <span class="pre">1</span> <span class="pre">+</span> <span class="pre">max_aspect_ratio]</span></code></li>
+<li><strong>max_shear_ratio</strong> (<em>float, optional, default=0</em>) – Apply a shear transformation (namely <code class="docutils literal"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal"><span class="pre">m</span></code> randomly chose from <code class="docutils literal"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></li>
+<li><strong>max_crop_size</strong> (<em>int, optional, default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
+<li><strong>min_crop_size</strong> (<em>int, optional, default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
+<li><strong>max_random_scale</strong> (<em>float, optional, default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
+<li><strong>min_random_scale</strong> (<em>float, optional, default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
+<li><strong>max_img_size</strong> (<em>float, optional, default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</li>
+<li><strong>min_img_size</strong> (<em>float, optional, default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</li>
+<li><strong>random_h</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</li>
+<li><strong>random_s</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</li>
+<li><strong>random_l</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</li>
+<li><strong>rotate</strong> (<em>int, optional, default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal"><span class="pre">max_rotate_angle</span></code> option.</li>
+<li><strong>fill_value</strong> (<em>int, optional, default='255'</em>) – Set the padding pixes value into <code class="docutils literal"><span class="pre">fill_value</span></code>.</li>
+<li><strong>inter_method</strong> (<em>int, optional, default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
+<li><strong>pad</strong> (<em>int, optional, default='0'</em>) – Change size from <code class="docutils literal"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> <span class="pre">+</span> <span clas [...]
</ul>
</td>
</tr>
@@ -1264,45 +1265,45 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
the data type instead of <code class="docutils literal"><span class="pre">float</span></code>.</p>
<p>Defined in src/io/iter_image_recordio.cc:L349</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of="" record=""> <one labels="" more="" or=""> <relative folder="" from="" path="" root="">.</relative></one></index></li>
-<li><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</li>
-<li><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</li>
-<li><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The number of labels per image.</li>
-<li><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The shape of one output image in (channels, height, width) format.</li>
-<li><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – The number of threads to do preprocessing.</li>
-<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – If or not output verbose information.</li>
-<li><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Virtually partition the data into these many parts.</li>
-<li><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The <em>i</em>-th virtual partition to be read.</li>
-<li><strong>shuffle_chunk_size</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</li>
-<li><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed for shuffling</li>
-<li><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to shuffle data randomly or not.</li>
-<li><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed.</li>
-<li><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Batch size.</li>
-<li><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
-<li><strong>prefetch_buffer</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</li>
-<li><strong>dtype</strong> (<em>{None</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
-<li><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</li>
-<li><strong>rand_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If or not randomly crop the image</li>
-<li><strong>max_rotate_angle</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Rotate by a random degree in <code class="docutils literal"><span class="pre">[-v,</span> <span class="pre">v]</span></code></li>
-<li><strong>max_aspect_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal"><span class="pre">[1</span> <span class="pre">-</span> <span class="pre">max_aspect_ratio,</span> <span class="pre">1</span> <span class="pre">+</span> <span class="pre">max_aspect_ratio]</span></code></li>
-<li><strong>max_shear_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Apply a shear transformation (namely <code class="docutils literal"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal"><span class="pre">m</span></code> randomly chose from <code class="docutils literal"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></li>
-<li><strong>max_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
-<li><strong>min_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
-<li><strong>max_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
-<li><strong>min_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
-<li><strong>max_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</li>
-<li><strong>min_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</li>
-<li><strong>random_h</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</li>
-<li><strong>random_s</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</li>
-<li><strong>random_l</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</li>
-<li><strong>rotate</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal"><span class="pre">max_rotate_angle</span></code> option.</li>
-<li><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – Set the padding pixes value into <code class="docutils literal"><span class="pre">fill_value</span></code>.</li>
-<li><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
-<li><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Change size from <code class="docutils literal"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> < [...]
+<li><strong>path_imglist</strong> (<em>string, optional, default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.</li>
+<li><strong>path_imgrec</strong> (<em>string, optional, default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</li>
+<li><strong>aug_seq</strong> (<em>string, optional, default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</li>
+<li><strong>label_width</strong> (<em>int, optional, default='1'</em>) – The number of labels per image.</li>
+<li><strong>data_shape</strong> (<em>Shape(tuple), required</em>) – The shape of one output image in (channels, height, width) format.</li>
+<li><strong>preprocess_threads</strong> (<em>int, optional, default='4'</em>) – The number of threads to do preprocessing.</li>
+<li><strong>verbose</strong> (<em>boolean, optional, default=True</em>) – If or not output verbose information.</li>
+<li><strong>num_parts</strong> (<em>int, optional, default='1'</em>) – Virtually partition the data into these many parts.</li>
+<li><strong>part_index</strong> (<em>int, optional, default='0'</em>) – The <em>i</em>-th virtual partition to be read.</li>
+<li><strong>shuffle_chunk_size</strong> (<em>long (non-negative), optional, default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</li>
+<li><strong>shuffle_chunk_seed</strong> (<em>int, optional, default='0'</em>) – The random seed for shuffling</li>
+<li><strong>shuffle</strong> (<em>boolean, optional, default=False</em>) – Whether to shuffle data randomly or not.</li>
+<li><strong>seed</strong> (<em>int, optional, default='0'</em>) – The random seed.</li>
+<li><strong>batch_size</strong> (<em>int (non-negative), required</em>) – Batch size.</li>
+<li><strong>round_batch</strong> (<em>boolean, optional, default=True</em>) – Whether to use round robin to handle overflow batch or not.</li>
+<li><strong>prefetch_buffer</strong> (<em>long (non-negative), optional, default=4</em>) – Maximum number of batches to prefetch.</li>
+<li><strong>dtype</strong> (<em>{None, 'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
+<li><strong>resize</strong> (<em>int, optional, default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</li>
+<li><strong>rand_crop</strong> (<em>boolean, optional, default=False</em>) – If or not randomly crop the image</li>
+<li><strong>max_rotate_angle</strong> (<em>int, optional, default='0'</em>) – Rotate by a random degree in <code class="docutils literal"><span class="pre">[-v,</span> <span class="pre">v]</span></code></li>
+<li><strong>max_aspect_ratio</strong> (<em>float, optional, default=0</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal"><span class="pre">[1</span> <span class="pre">-</span> <span class="pre">max_aspect_ratio,</span> <span class="pre">1</span> <span class="pre">+</span> <span class="pre">max_aspect_ratio]</span></code></li>
+<li><strong>max_shear_ratio</strong> (<em>float, optional, default=0</em>) – Apply a shear transformation (namely <code class="docutils literal"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal"><span class="pre">m</span></code> randomly chose from <code class="docutils literal"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></li>
+<li><strong>max_crop_size</strong> (<em>int, optional, default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
+<li><strong>min_crop_size</strong> (<em>int, optional, default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size]</span></code></li>
+<li><strong>max_random_scale</strong> (<em>float, optional, default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
+<li><strong>min_random_scale</strong> (<em>float, optional, default=1</em>) – Resize into <code class="docutils literal"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code></li>
+<li><strong>max_img_size</strong> (<em>float, optional, default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</li>
+<li><strong>min_img_size</strong> (<em>float, optional, default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</li>
+<li><strong>random_h</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</li>
+<li><strong>random_s</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</li>
+<li><strong>random_l</strong> (<em>int, optional, default='0'</em>) – Add a random value in <code class="docutils literal"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</li>
+<li><strong>rotate</strong> (<em>int, optional, default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal"><span class="pre">max_rotate_angle</span></code> option.</li>
+<li><strong>fill_value</strong> (<em>int, optional, default='255'</em>) – Set the padding pixes value into <code class="docutils literal"><span class="pre">fill_value</span></code>.</li>
+<li><strong>inter_method</strong> (<em>int, optional, default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</li>
+<li><strong>pad</strong> (<em>int, optional, default='0'</em>) – Change size from <code class="docutils literal"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> <span class="pre">+</span> <span clas [...]
</ul>
</td>
</tr>
@@ -1322,21 +1323,21 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
<p>One can download the dataset from <a class="reference external" href="http://yann.lecun.com/exdb/mnist/">http://yann.lecun.com/exdb/mnist/</a></p>
<p>Defined in src/io/iter_mnist.cc:L246</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>image</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='./train-images-idx3-ubyte'</em>) – Dataset Param: Mnist image path.</li>
-<li><strong>label</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='./train-labels-idx1-ubyte'</em>) – Dataset Param: Mnist label path.</li>
-<li><strong>batch_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='128'</em>) – Batch Param: Batch Size.</li>
-<li><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Augmentation Param: Whether to shuffle data.</li>
-<li><strong>flat</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Augmentation Param: Whether to flat the data into 1D.</li>
-<li><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Random Seed.</li>
-<li><strong>silent</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Auxiliary Param: Whether to print out data info.</li>
-<li><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – partition the data into multiple parts</li>
-<li><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – the index of the part will read</li>
-<li><strong>prefetch_buffer</strong> (<em></em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</li>
-<li><strong>dtype</strong> (<em>{None</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
+<li><strong>image</strong> (<em>string, optional, default='./train-images-idx3-ubyte'</em>) – Dataset Param: Mnist image path.</li>
+<li><strong>label</strong> (<em>string, optional, default='./train-labels-idx1-ubyte'</em>) – Dataset Param: Mnist label path.</li>
+<li><strong>batch_size</strong> (<em>int, optional, default='128'</em>) – Batch Param: Batch Size.</li>
+<li><strong>shuffle</strong> (<em>boolean, optional, default=True</em>) – Augmentation Param: Whether to shuffle data.</li>
+<li><strong>flat</strong> (<em>boolean, optional, default=False</em>) – Augmentation Param: Whether to flat the data into 1D.</li>
+<li><strong>seed</strong> (<em>int, optional, default='0'</em>) – Augmentation Param: Random Seed.</li>
+<li><strong>silent</strong> (<em>boolean, optional, default=False</em>) – Auxiliary Param: Whether to print out data info.</li>
+<li><strong>num_parts</strong> (<em>int, optional, default='1'</em>) – partition the data into multiple parts</li>
+<li><strong>part_index</strong> (<em>int, optional, default='0'</em>) – the index of the part will read</li>
+<li><strong>prefetch_buffer</strong> (<em>long (non-negative), optional, default=4</em>) – Maximum number of batches to prefetch.</li>
+<li><strong>dtype</strong> (<em>{None, 'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='None'</em>) – Output data type. <code class="docutils literal"><span class="pre">None</span></code> means no change.</li>
</ul>
</td>
</tr>
@@ -1354,15 +1355,15 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
<dt id="mxnet.recordio.MXRecordIO">
<em class="property">class </em><code class="descclassname">mxnet.recordio.</code><code class="descname">MXRecordIO</code><span class="sig-paren">(</span><em>uri</em>, <em>flag</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.MXRecordIO" title="Permalink to this definition">¶</a></dt>
<dd><p>Reads/writes <cite>RecordIO</cite> data format, supporting sequential read and write.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span>
-<span class="go"><mxnet.recordio.mxrecordio 0x10ef40ed0="" at="" object=""></mxnet.recordio.mxrecordio></span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span>
+<span class="go"><mxnet.recordio.MXRecordIO object at 0x10ef40ed0></span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">record</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'record_</span><span class="si">%d</span><span class="s1">'</span><span class="o">%</span><span class="n">i</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">record</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">item</span> <span class="o">=</span> <span class="n">record</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
<span class="go">record_0</span>
<span class="go">record_1</span>
<span class="go">record_2</span>
@@ -1372,8 +1373,8 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>uri</strong> (<em>string</em>) – Path to the record file.</li>
@@ -1398,14 +1399,14 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
<code class="descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.MXRecordIO.reset" title="Permalink to this definition">¶</a></dt>
<dd><p>Resets the pointer to first item.</p>
<p>If the record is opened with ‘w’, this function will truncate the file to empty.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">item</span> <span class="o">=</span> <span class="n">record</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
<span class="go">record_0</span>
<span class="go">record_1</span>
<span class="gp">>>> </span><span class="n">record</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> <span class="c1"># Pointer is reset.</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">record</span><span class="o">.</span><span class="n">read</span><span class="p">())</span> <span class="c1"># Started reading from start again.</span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">record</span><span class="o">.</span><span class="n">read</span><span class="p">())</span> <span class="c1"># Started reading from start again.</span>
<span class="go">record_0</span>
<span class="gp">>>> </span><span class="n">record</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
@@ -1415,17 +1416,17 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
<dt id="mxnet.recordio.MXRecordIO.write">
<code class="descname">write</code><span class="sig-paren">(</span><em>buf</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.MXRecordIO.write" title="Permalink to this definition">¶</a></dt>
<dd><p>Inserts a string buffer as a record.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">record</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'record_</span><span class="si">%d</span><span class="s1">'</span><span class="o">%</span><span class="n">i</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">record</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>buf</strong> (<em>string</em><em> (</em><em>python2</em><em>)</em><em></em><em>, </em><em>bytes</em><em> (</em><em>python3</em><em>)</em><em></em>) – Buffer to write.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>buf</strong> (<em>string (python2), bytes (python3)</em>) – Buffer to write.</td>
</tr>
</tbody>
</table>
@@ -1434,10 +1435,10 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
<dt id="mxnet.recordio.MXRecordIO.read">
<code class="descname">read</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.MXRecordIO.read" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns record as a string.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">item</span> <span class="o">=</span> <span class="n">record</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
<span class="go">record_0</span>
<span class="go">record_1</span>
<span class="go">record_2</span>
@@ -1447,10 +1448,11 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>buf</strong> – Buffer read.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>buf</strong> –
+Buffer read.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">string</td>
</tr>
@@ -1460,9 +1462,9 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
</dd></dl>
<dl class="class">
<dt id="mxnet.recordio.MXIndexedRecordIO">
-<em class="property">class </em><code class="descclassname">mxnet.recordio.</code><code class="descname">MXIndexedRecordIO</code><span class="sig-paren">(</span><em>idx_path</em>, <em>uri</em>, <em>flag</em>, <em>key_type=<type 'int'=""></type></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.MXIndexedRecordIO" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">mxnet.recordio.</code><code class="descname">MXIndexedRecordIO</code><span class="sig-paren">(</span><em>idx_path</em>, <em>uri</em>, <em>flag</em>, <em>key_type=<type 'int'></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.MXIndexedRecordIO" title="Permalink to this definition">¶</a></dt>
<dd><p>Reads/writes <cite>RecordIO</cite> data format, supporting random access.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">record</span><span class="o">.</span><span class="n">write_idx</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="s1">'record_</span><span class="si">%d</span><span class="s1">'</span><span class="o">%</span><span class="n">i</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">record</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXIndexedRecordIO</span><span class="p">(</span><span class="s1">'tmp.idx'</span><span class="p">,</span> <span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
@@ -1471,8 +1473,8 @@ the data type instead of <code class="docutils literal"><span class="pre">float<
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>idx_path</strong> (<em>str</em>) – Path to the index file.</li>
@@ -1500,12 +1502,12 @@ reader pointer position. It doesn’t return anything.</p>
<dt id="mxnet.recordio.MXIndexedRecordIO.tell">
<code class="descname">tell</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.MXIndexedRecordIO.tell" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current position of write head.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXIndexedRecordIO</span><span class="p">(</span><span class="s1">'tmp.idx'</span><span class="p">,</span> <span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">record</span><span class="o">.</span><span class="n">tell</span><span class="p">())</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXIndexedRecordIO</span><span class="p">(</span><span class="s1">'tmp.idx'</span><span class="p">,</span> <span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">record</span><span class="o">.</span><span class="n">tell</span><span class="p">())</span>
<span class="go">0</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">record</span><span class="o">.</span><span class="n">write_idx</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="s1">'record_</span><span class="si">%d</span><span class="s1">'</span><span class="o">%</span><span class="n">i</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">record</span><span class="o">.</span><span class="n">tell</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">record</span><span class="o">.</span><span class="n">tell</span><span class="p">())</span>
<span class="go">16</span>
<span class="go">32</span>
<span class="go">48</span>
@@ -1518,7 +1520,7 @@ reader pointer position. It doesn’t return anything.</p>
<dt id="mxnet.recordio.MXIndexedRecordIO.read_idx">
<code class="descname">read_idx</code><span class="sig-paren">(</span><em>idx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.MXIndexedRecordIO.read_idx" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the record at given index.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXIndexedRecordIO</span><span class="p">(</span><span class="s1">'tmp.idx'</span><span class="p">,</span> <span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXIndexedRecordIO</span><span class="p">(</span><span class="s1">'tmp.idx'</span><span class="p">,</span> <span class="s1">'tmp.rec'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">record</span><span class="o">.</span><span class="n">write_idx</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="s1">'record_</span><span class="si">%d</span><span class="s1">'</span><span class="o">%</span><span class="n">i</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">record</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
@@ -1532,14 +1534,14 @@ reader pointer position. It doesn’t return anything.</p>
<dt id="mxnet.recordio.MXIndexedRecordIO.write_idx">
<code class="descname">write_idx</code><span class="sig-paren">(</span><em>idx</em>, <em>buf</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.MXIndexedRecordIO.write_idx" title="Permalink to this definition">¶</a></dt>
<dd><p>Inserts input record at given index.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">record</span><span class="o">.</span><span class="n">write_idx</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="s1">'record_</span><span class="si">%d</span><span class="s1">'</span><span class="o">%</span><span class="n">i</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">record</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>idx</strong> (<em>int</em>) – Index of a file.</li>
@@ -1557,12 +1559,12 @@ reader pointer position. It doesn’t return anything.</p>
<dd><p>An alias for HEADER. Used to store metadata (e.g. labels) accompanying a record.
See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>flag</strong> (<em>int</em>) – Available for convenience, can be set arbitrarily.</li>
-<li><strong>label</strong> (<em>float</em><em> or </em><em>an array of float</em>) – Typically used to store label(s) for a record.</li>
+<li><strong>label</strong> (<em>float or an array of float</em>) – Typically used to store label(s) for a record.</li>
<li><strong>id</strong> (<em>int</em>) – Usually a unique id representing record.</li>
<li><strong>id2</strong> (<em>int</em>) – Higher order bits of the unique id, should be set to 0 (in most cases).</li>
</ul>
@@ -1577,8 +1579,8 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
<code class="descclassname">mxnet.recordio.</code><code class="descname">pack</code><span class="sig-paren">(</span><em>header</em>, <em>s</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.pack" title="Permalink to this definition">¶</a></dt>
<dd><p>Pack a string into MXImageRecord.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>header</strong> (<a class="reference internal" href="#mxnet.recordio.IRHeader" title="mxnet.recordio.IRHeader"><em>IRHeader</em></a>) – Header of the image record.
@@ -1587,7 +1589,8 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>s</strong> – The packed string.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>s</strong> –
+The packed string.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">str</p>
@@ -1596,11 +1599,11 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">label</span> <span class="o">=</span> <span class="mi">4</span> <span class="c1"># label can also be a 1-D array, for example: label = [1,2,3]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">label</span> <span class="o">=</span> <span class="mi">4</span> <span class="c1"># label can also be a 1-D array, for example: label = [1,2,3]</span>
<span class="gp">>>> </span><span class="nb">id</span> <span class="o">=</span> <span class="mi">2574</span>
<span class="gp">>>> </span><span class="n">header</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">IRHeader</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="nb">id</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
-<span class="gp">... </span> <span class="n">s</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="nb">file</span><span class="p">:</span>
+<span class="gp">... </span> <span class="n">s</span> <span class="o">=</span> <span class="nb">file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">packed_s</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">pack</span><span class="p">(</span><span class="n">header</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
</pre></div>
</div>
@@ -1610,21 +1613,23 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
<code class="descclassname">mxnet.recordio.</code><code class="descname">unpack</code><span class="sig-paren">(</span><em>s</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.unpack" title="Permalink to this definition">¶</a></dt>
<dd><p>Unpack a MXImageRecord to string.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>s</strong> (<em>str</em>) – String buffer from <code class="docutils literal"><span class="pre">MXRecordIO.read</span></code>.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>header</strong> (<em>IRHeader</em>) – Header of the image record.</li>
-<li><strong>s</strong> (<em>str</em>) – Unpacked string.</li>
+<li><strong>header</strong> (<em>IRHeader</em>) –
+Header of the image record.</li>
+<li><strong>s</strong> (<em>str</em>) –
+Unpacked string.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'test.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'test.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">item</span> <span class="o">=</span> <span class="n">record</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">header</span><span class="p">,</span> <span class="n">s</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">unpack</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">header</span>
@@ -1637,8 +1642,8 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
<code class="descclassname">mxnet.recordio.</code><code class="descname">unpack_img</code><span class="sig-paren">(</span><em>s</em>, <em>iscolor=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.unpack_img" title="Permalink to this definition">¶</a></dt>
<dd><p>Unpack a MXImageRecord to image.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>s</strong> (<em>str</em>) – String buffer from <code class="docutils literal"><span class="pre">MXRecordIO.read</span></code>.</li>
@@ -1647,8 +1652,10 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>header</strong> (<em>IRHeader</em>) – Header of the image record.</li>
-<li><strong>img</strong> (<em>numpy.ndarray</em>) – Unpacked image.</li>
+<li><strong>header</strong> (<em>IRHeader</em>) –
+Header of the image record.</li>
+<li><strong>img</strong> (<em>numpy.ndarray</em>) –
+Unpacked image.</li>
</ul>
</p>
</td>
@@ -1656,7 +1663,7 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'test.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">record</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="s1">'test.rec'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">item</span> <span class="o">=</span> <span class="n">record</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">header</span><span class="p">,</span> <span class="n">img</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">unpack_img</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">header</span>
@@ -1681,8 +1688,8 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
<code class="descclassname">mxnet.recordio.</code><code class="descname">pack_img</code><span class="sig-paren">(</span><em>header</em>, <em>img</em>, <em>quality=95</em>, <em>img_fmt='.jpg'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.recordio.pack_img" title="Permalink to this definition">¶</a></dt>
<dd><p>Pack an image into <code class="docutils literal"><span class="pre">MXImageRecord</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>header</strong> (<a class="reference internal" href="#mxnet.recordio.IRHeader" title="mxnet.recordio.IRHeader"><em>IRHeader</em></a>) – Header of the image record.
@@ -1693,7 +1700,8 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>s</strong> – The packed string.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>s</strong> –
+The packed string.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">str</p>
@@ -1702,7 +1710,7 @@ See mxnet.recordio.pack and mxnet.recordio.pack_img for example uses.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">label</span> <span class="o">=</span> <span class="mi">4</span> <span class="c1"># label can also be a 1-D array, for example: label = [1,2,3]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">label</span> <span class="o">=</span> <span class="mi">4</span> <span class="c1"># label can also be a 1-D array, for example: label = [1,2,3]</span>
<span class="gp">>>> </span><span class="nb">id</span> <span class="o">=</span> <span class="mi">2574</span>
<span class="gp">>>> </span><span class="n">header</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">recordio</span><span class="o">.</span><span class="n">IRHeader</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="nb">id</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">'test.jpg'</span><span class="p">)</span>
diff --git a/versions/master/api/python/kvstore.html b/versions/master/api/python/kvstore.html
index 3520e30..823d512 100644
--- a/versions/master/api/python/kvstore.html
+++ b/versions/master/api/python/kvstore.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>KVStore API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="index.html" rel="up" title="MXNet - Python API"/>
+<link href="index.html" rel="up" title="MXNet - Python API">
<link href="io.html" rel="next" title="Data Loading API"/>
<link href="rnn.html" rel="prev" title="RNN Cell API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -229,9 +227,10 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">KVStore API</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">KVStore API</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#basic-push-and-pull">Basic Push and Pull</a></li>
<li class="toctree-l4"><a class="reference internal" href="#list-key-value-pairs">List Key-Value Pairs</a></li>
<li class="toctree-l4"><a class="reference internal" href="#api-reference">API Reference</a></li>
@@ -379,30 +378,30 @@ When multiple workers invoke <cite>init</cite> for the same key, only
the value supplied by worker with rank <cite>0</cite> is used. This function returns
after data has been initialized successfully.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>key</strong> (<em>str</em><em> or </em><em>sequence of str</em>) – The keys.</li>
-<li><strong>value</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>sequence of NDArray</em>) – Values corresponding to the keys.</li>
+<li><strong>key</strong> (<em>str or sequence of str</em>) – The keys.</li>
+<li><strong>value</strong> (<em>NDArray or sequence of NDArray</em>) – Values corresponding to the keys.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># init a single key-value pair</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># init a single key-value pair</span>
<span class="gp">>>> </span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">kv</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'local'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 2. 2. 2.]</span>
<span class="go">[ 2. 2. 2.]]</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># init a list of key-value pairs</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># init a list of key-value pairs</span>
<span class="gp">>>> </span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'5'</span><span class="p">,</span> <span class="s1">'7'</span><span class="p">,</span> <span class="s1">'9'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class [...]
</pre></div>
@@ -418,13 +417,13 @@ and <cite>pull</cite> calls for the same input key(s) are finished.
There is no synchronization between workers. One can use <code class="docutils literal"><span class="pre">_barrier()</span></code>
to sync all workers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>key</strong> (<em>str</em><em> or </em><em>list of str</em>) – Keys.</li>
-<li><strong>value</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em> or </em><em>list of list of NDArray</em>) – Values corresponding to the keys.</li>
-<li><strong>priority</strong> (<em>int</em><em>, </em><em>optional</em>) – The priority of the push operation.
+<li><strong>key</strong> (<em>str or list of str</em>) – Keys.</li>
+<li><strong>value</strong> (<em>NDArray or list of NDArray or list of list of NDArray</em>) – Values corresponding to the keys.</li>
+<li><strong>priority</strong> (<em>int, optional</em>) – The priority of the push operation.
Higher priority push operations are likely to be executed before
other push actions.</li>
</ul>
@@ -433,39 +432,39 @@ other push actions.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># push a single key-value pair</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># push a single key-value pair</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="mi">8</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span> <span class="c1"># pull out the value</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 8. 8. 8.]</span>
<span class="go">[ 8. 8. 8.]]</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># aggregate the value and the push</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># aggregate the value and the push</span>
<span class="gp">>>> </span><span class="n">gpus</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)]</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 4. 4. 4.]</span>
<span class="go">[ 4. 4. 4.]]</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># push a list of keys.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># push a list of keys.</span>
<span class="gp">>>> </span><span class="c1"># single device</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class [...]
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 1. 1. 1.]</span>
<span class="go">[ 1. 1. 1.]]</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># multiple devices:</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># multiple devices:</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]]</span> <s [...]
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 4. 4. 4.]</span>
<span class="go">[ 4. 4. 4.]]</span>
</pre></div>
@@ -482,13 +481,13 @@ pull operation completes.</p>
for the same input key(s) are finished.</p>
<p>The returned values are gauranteed to be the latest values in the store.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>key</strong> (<em>int</em><em> or </em><em>list of int</em>) – Keys.</li>
-<li><strong>out</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em> or </em><em>list of list of NDArray</em>) – Values corresponding to the keys.</li>
-<li><strong>priority</strong> (<em>int</em><em>, </em><em>optional</em>) – The priority of the pull operation.
+<li><strong>key</strong> (<em>int or list of int</em>) – Keys.</li>
+<li><strong>out</strong> (<em>NDArray or list of NDArray or list of list of NDArray</em>) – Values corresponding to the keys.</li>
+<li><strong>priority</strong> (<em>int, optional</em>) – The priority of the pull operation.
Higher priority pull operations are likely to be executed before
other pull actions.</li>
</ul>
@@ -497,34 +496,34 @@ other pull actions.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pull a single key-value pair</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pull a single key-value pair</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 2. 2. 2.]</span>
<span class="go">[ 2. 2. 2.]]</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pull into multiple devices</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pull into multiple devices</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 2. 2. 2.]</span>
<span class="go">[ 2. 2. 2.]]</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pull a list of key-value pairs.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pull a list of key-value pairs.</span>
<span class="gp">>>> </span><span class="c1"># On single device</span>
<span class="gp">>>> </span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'5'</span><span class="p">,</span> <span class="s1">'7'</span><span class="p">,</span> <span class="s1">'9'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 2. 2. 2.]</span>
<span class="go">[ 2. 2. 2.]]</span>
<span class="gp">>>> </span><span class="c1"># On multiple devices</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]]</span> <s [...]
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 2. 2. 2.]</span>
<span class="go">[ 2. 2. 2.]]</span>
</pre></div>
@@ -539,15 +538,15 @@ If using multiple machines and this operation is invoked from a worker node,
it will serialized the optimizer with pickle and send it to all servers.
The function returns after all servers have been updated.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>optimizer</strong> (<a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – The new optimizer for the store</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">kv</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">kv</span><span class="o">.</span><span class="n">create</span><span class="p">()</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">kv</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">kv</span><span class="o">.</span><span class="n">create</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">weight</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span>
@@ -568,10 +567,11 @@ The function returns after all servers have been updated.</p>
<code class="descname">type</code><a class="headerlink" href="#mxnet.kvstore.KVStore.type" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the type of this kvstore.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>type</strong> – the string type</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>type</strong> –
+the string type</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">str</td>
</tr>
@@ -583,10 +583,11 @@ The function returns after all servers have been updated.</p>
<code class="descname">rank</code><a class="headerlink" href="#mxnet.kvstore.KVStore.rank" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the rank of this worker node.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>rank</strong> – The rank of this node, which is in range [0, num_workers())</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>rank</strong> –
+The rank of this node, which is in range [0, num_workers())</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">int</td>
</tr>
@@ -598,10 +599,11 @@ The function returns after all servers have been updated.</p>
<code class="descname">num_workers</code><a class="headerlink" href="#mxnet.kvstore.KVStore.num_workers" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the number of worker nodes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>size</strong> – The number of worker nodes.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>size</strong> –
+The number of worker nodes.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">int</td>
</tr>
@@ -614,8 +616,8 @@ The function returns after all servers have been updated.</p>
<dd><p>Saves the optimizer (updater) state to a file. This is often used when checkpointing
the model during training.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fname</strong> (<em>str</em>) – Path to the output states file.</td>
</tr>
@@ -627,8 +629,8 @@ the model during training.</p>
<code class="descname">load_optimizer_states</code><span class="sig-paren">(</span><em>fname</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.kvstore.KVStore.load_optimizer_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Loads the optimizer (updater) state from the file.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fname</strong> (<em>str</em>) – Path to input states file.</td>
</tr>
@@ -657,12 +659,13 @@ The weights are updated whenever gradients are received from any machine.
No two updates happen on the same weight at the same time. However, the order is not
guaranteed.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>name</strong> (<em>{'local'</em><em>, </em><em>'device'</em><em>, </em><em>'dist_sync'</em><em>, </em><em>'dist_device_sync'</em><em>, </em><em>'dist_async'}</em>) – The type of KVStore.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>name</strong> (<em>{'local', 'device', 'dist_sync', 'dist_device_sync', 'dist_async'}</em>) – The type of KVStore.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>kv</strong> – The created KVStore.</td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>kv</strong> –
+The created KVStore.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore">KVStore</a></td>
</tr>
diff --git a/versions/master/api/python/metric.html b/versions/master/api/python/metric.html
index df7c939..39683dd 100644
--- a/versions/master/api/python/metric.html
+++ b/versions/master/api/python/metric.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Evaluation Metric API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="index.html" rel="up" title="MXNet - Python API"/>
+<link href="index.html" rel="up" title="MXNet - Python API">
<link href="../r/index.html" rel="next" title="MXNet - R API"/>
<link href="callback.html" rel="prev" title="Callback API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -229,6 +227,7 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
<li class="toctree-l3"><a class="reference internal" href="kvstore.html">KVStore API</a></li>
@@ -236,7 +235,7 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference internal" href="image.html">Image API</a></li>
<li class="toctree-l3"><a class="reference internal" href="optimization.html">Optimization: initialize and update weights</a></li>
<li class="toctree-l3"><a class="reference internal" href="callback.html">Callback API</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Evaluation Metric API</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Evaluation Metric API</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
<li class="toctree-l4"><a class="reference internal" href="#api-reference">API Reference</a></li>
</ul>
@@ -265,8 +264,8 @@ Previous Navbar Layout End -->
the performance of a learned model.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="model.html#module-mxnet.metric" title="mxnet.metric"><code class="xref py py-obj docutils literal"><span class="pre">mxnet.metric</span></code></a></td>
@@ -289,14 +288,14 @@ One should not use this class directly, but instead create new metric
classes that extend it.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -314,8 +313,8 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<code class="descname">update_dict</code><span class="sig-paren">(</span><em>label</em>, <em>pred</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.EvalMetric.update_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Update the internal evaluation with named label and pred</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (<em>OrderedDict of str -> NDArray</em>) – name to array mapping for labels.</li>
@@ -331,8 +330,8 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.EvalMetric.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -353,12 +352,14 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<code class="descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.EvalMetric.get" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets the current evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>names</strong> (<em>list of str</em>) – Name of the metrics.</li>
-<li><strong>values</strong> (<em>list of float</em>) – Value of the evaluations.</li>
+<li><strong>names</strong> (<em>list of str</em>) –
+Name of the metrics.</li>
+<li><strong>values</strong> (<em>list of float</em>) –
+Value of the evaluations.</li>
</ul>
</td>
</tr>
@@ -370,8 +371,8 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<code class="descname">get_name_value</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.EvalMetric.get_name_value" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns zipped name and value pairs.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A (name, value) tuple list.</td>
</tr>
@@ -387,11 +388,11 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<dd><p>Creates evaluation metric from metric names or instances of EvalMetric
or a custom metric function.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>metric</strong> (<em>str</em><em> or </em><em>callable</em>) – <p>Specifies the metric to create.
+<li><strong>metric</strong> (<em>str or callable</em>) – <p>Specifies the metric to create.
This argument must be one of the below:</p>
<ul>
<li>Name of a metric.</li>
@@ -401,17 +402,19 @@ This argument must be one of the below:</p>
labels and predictions.</li>
</ul>
</li>
-<li><strong>*args</strong> (<em>list</em>) – Additional arguments to metric constructor.
-Only used when metric is str.</li>
-<li><strong>**kwargs</strong> (<em>dict</em>) – Additional arguments to metric constructor.
-Only used when metric is str</li>
+<li><strong>*args</strong> – <p>Additional arguments to metric constructor.
+Only used when metric is str.</p>
+</li>
+<li><strong>**kwargs</strong> – <p>Additional arguments to metric constructor.
+Only used when metric is str</p>
+</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">label</span> <span class="o">-</span> <span class="n">pred</span><span class="p">))</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">metric1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'acc'</span><span class="p">)</span>
@@ -425,15 +428,15 @@ Only used when metric is str</li>
<em class="property">class </em><code class="descclassname">mxnet.metric.</code><code class="descname">CompositeEvalMetric</code><span class="sig-paren">(</span><em>metrics=None</em>, <em>name='composite'</em>, <em>output_names=None</em>, <em>label_names=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric" title="Permalink to this definition">¶</a></dt>
<dd><p>Manages multiple evaluation metrics.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>metrics</strong> (<em>list of EvalMetric</em>) – List of child metrics.</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -441,7 +444,7 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span>
<span class="gp">>>> </span><span class="n">eval_metrics_1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">eval_metrics_2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">F1</span><span class="p">()</span>
@@ -449,7 +452,7 @@ By default include all labels.</li>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">child_metric</span> <span class="ow">in</span> <span class="p">[</span><span class="n">eval_metrics_1</span><span class="p">,</span> <span class="n">eval_metrics_2</span><span class="p">]:</span>
<span class="gp">>>> </span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">child_metric</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">eval_metrics</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">(['accuracy', 'f1'], [0.6666666666666666, 0.8])</span>
</pre></div>
</div>
@@ -458,8 +461,8 @@ By default include all labels.</li>
<code class="descname">add</code><span class="sig-paren">(</span><em>metric</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.add" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds a child metric.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>metric</strong> – A metric instance.</td>
</tr>
@@ -471,8 +474,8 @@ By default include all labels.</li>
<code class="descname">get_metric</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.get_metric" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a child metric.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>index</strong> (<em>int</em>) – Index of child metric in the list of metrics.</td>
</tr>
@@ -484,8 +487,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -506,12 +509,14 @@ By default include all labels.</li>
<code class="descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.get" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>names</strong> (<em>list of str</em>) – Name of the metrics.</li>
-<li><strong>values</strong> (<em>list of float</em>) – Value of the evaluations.</li>
+<li><strong>names</strong> (<em>list of str</em>) –
+Name of the metrics.</li>
+<li><strong>values</strong> (<em>list of float</em>) –
+Value of the evaluations.</li>
</ul>
</td>
</tr>
@@ -528,15 +533,15 @@ By default include all labels.</li>
\[\text{accuracy}(y, \hat{y}) = \frac{1}{n} \sum_{i=0}^{n-1}
\text{1}(\hat{y_i} == y_i)\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>default=1</em>) – The axis that represents classes</li>
+<li><strong>axis</strong> (<em>int, default=1</em>) – The axis that represents classes</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -544,11 +549,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span>
<span class="gp">>>> </span><span class="n">acc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('accuracy', 0.6666666666666666)</span>
</pre></div>
</div>
@@ -557,8 +562,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.Accuracy.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -579,15 +584,15 @@ to be <code class="docutils literal"><span class="pre">True</span></code> as lon
predicated labels.</p>
<p>If <cite>top_k</cite> = <code class="docutils literal"><span class="pre">1</span></code>, then <cite>TopKAccuracy</cite> is identical to <cite>Accuracy</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>top_k</strong> (<em>int</em>) – Whether targets are in top k predictions.</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -595,13 +600,13 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">999</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">999</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">top_k</span> <span class="o">=</span> <span class="mi">3</span>
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span cla [...]
<span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><sp [...]
<span class="gp">>>> </span><span class="n">acc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">TopKAccuracy</span><span class="p">(</span><span class="n">top_k</span><span class="o">=</span><span class="n">top_k</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('top_k_accuracy', 0.3)</span>
</pre></div>
</div>
@@ -610,8 +615,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.TopKAccuracy.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -629,11 +634,11 @@ By default include all labels.</li>
<dd><p>Computes the F1 score of a binary classification problem.</p>
<p>The F1 score is equivalent to weighted average of the precision and recall,
where the best value is 1.0 and the worst value is 0.0. The formula for F1 score is:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">F1</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">precision</span> <span class="o">*</span> <span class="n">recall</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">precision</span> <span class="o">+</span> <span class="n">recall</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">F1</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">precision</span> <span class="o">*</span> <span class="n">recall</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">precision</span> <span class="o">+</span> <span class="n">recall</span><span class="p">)</span>
</pre></div>
</div>
<p>The formula for precision and recall is:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">precision</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_positives</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">precision</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_positives</span><span class="p">)</span>
<span class="n">recall</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_negatives</span><span class="p">)</span>
</pre></div>
</div>
@@ -642,14 +647,14 @@ where the best value is 1.0 and the worst value is 0.0. The formula for F1 score
<p class="last">This F1 score only supports binary classification.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -657,11 +662,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">])]</span>
<span class="gp">>>> </span><span class="n">acc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">F1</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('f1', 0.8)</span>
</pre></div>
</div>
@@ -670,8 +675,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.F1.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -704,20 +709,20 @@ and <span class="math">\(q(x_2) = 1.0\)</span>,
<span class="math">\(q(x_3) = 0.6\)</span>. The perplexity of model q is
<span class="math">\(exp\big(-(\log 0.3 + \log 1.0 + \log 0.6) / 3\big) = 1.77109762852\)</span>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>ignore_label</strong> (<em>int</em><em> or </em><em>None</em>) – Index of invalid label to ignore when
+<li><strong>ignore_label</strong> (<em>int or None</em>) – Index of invalid label to ignore when
counting. By default, sets to -1.
If set to <cite>None</cite>, it will include all entries.</li>
-<li><strong>axis</strong> (<em>int</em><em> (</em><em>default -1</em><em>)</em><em></em>) – The axis from prediction that was used to
+<li><strong>axis</strong> (<em>int (default -1)</em>) – The axis from prediction that was used to
compute softmax. By default use the last
axis.</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -725,11 +730,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span>
-<span class="gp">>>> </span><span class="n">perp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Perplexity</span><span class="p">(</span><span class="n">ignore_label</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">perp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Perplexity</span><span class="p">(</span><span class="n">ignore_label</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">perp</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">perp</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">perp</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('Perplexity', 1.7710976285155853)</span>
</pre></div>
</div>
@@ -738,8 +743,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.Perplexity.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -755,8 +760,8 @@ By default include all labels.</li>
<code class="descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.Perplexity.get" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Representing name of the metric and evaluation result.</td>
</tr>
@@ -774,14 +779,14 @@ By default include all labels.</li>
<div class="math">
\[\frac{\sum_i^n |y_i - \hat{y}_i|}{n}\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -789,11 +794,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span clas [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><spa [...]
<span class="gp">>>> </span><span class="n">mean_absolute_error</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">MAE</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">mean_absolute_error</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mean_absolute_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mean_absolute_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('mae', 0.5)</span>
</pre></div>
</div>
@@ -802,8 +807,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.MAE.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -823,14 +828,14 @@ By default include all labels.</li>
<div class="math">
\[\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -838,11 +843,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span clas [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><spa [...]
<span class="gp">>>> </span><span class="n">mean_squared_error</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">MSE</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">mean_squared_error</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('mse', 0.375)</span>
</pre></div>
</div>
@@ -851,8 +856,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.MSE.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -872,14 +877,14 @@ By default include all labels.</li>
<div class="math">
\[\sqrt{\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}}\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -887,11 +892,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span clas [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><spa [...]
<span class="gp">>>> </span><span class="n">root_mean_squared_error</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">RMSE</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">root_mean_squared_error</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">root_mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">root_mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('rmse', 0.612372457981)</span>
</pre></div>
</div>
@@ -900,8 +905,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.RMSE.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -921,16 +926,16 @@ By default include all labels.</li>
<div class="math">
\[-y\log \hat{y} + (1-y)\log (1-\hat{y})\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>eps</strong> (<em>float</em>) – Cross Entropy loss is undefined for predicted value is 0 or 1,
so predicted values are added with the small constant.</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -938,11 +943,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span>
<span class="gp">>>> </span><span class="n">ce</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">CrossEntropy</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">ce</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">ce</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">ce</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('cross-entropy', 0.57159948348999023)</span>
</pre></div>
</div>
@@ -951,8 +956,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CrossEntropy.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -972,14 +977,14 @@ By default include all labels.</li>
<div class="math">
\[\frac{cov(y, \hat{y})}{\sigma{y}\sigma{\hat{y}}}\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -987,11 +992,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span [...]
<span class="gp">>>> </span><span class="n">pr</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">PearsonCorrelation</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">pr</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">pr</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">pr</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('pearson-correlation', 0.42163704544016178)</span>
</pre></div>
</div>
@@ -1000,8 +1005,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.PearsonCorrelation.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1018,14 +1023,14 @@ By default include all labels.</li>
<em class="property">class </em><code class="descclassname">mxnet.metric.</code><code class="descname">Loss</code><span class="sig-paren">(</span><em>name='loss'</em>, <em>output_names=None</em>, <em>label_names=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.Loss" title="Permalink to this definition">¶</a></dt>
<dd><p>Dummy metric for directly printing loss.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1050,19 +1055,19 @@ By default include all labels.</li>
<p>The <cite>feval</cite> function can return a <cite>tuple</cite> of (sum_metric, num_inst) or return
an <cite>int</cite> sum_metric.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>feval</strong> (<em>callable</em><em>(</em><em>label</em><em>, </em><em>pred</em><em>)</em><em></em>) – Customized evaluation function.</li>
+<li><strong>feval</strong> (<em>callable(label, pred)</em>) – Customized evaluation function.</li>
<li><strong>name</strong> (<em>str</em>) – The name of the metric. (the default is None).</li>
-<li><strong>allow_extra_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If true, the prediction outputs can have extra outputs.
+<li><strong>allow_extra_outputs</strong> (<em>bool, optional</em>) – If true, the prediction outputs can have extra outputs.
This is useful in RNN, where the states are also produced
in outputs for forwarding. (the default is False).</li>
<li><strong>name</strong> – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1070,13 +1075,13 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span clas [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><spa [...]
<span class="gp">>>> </span><span class="n">feval</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">CustomMetric</span><span class="p">(</span><span class="n">feval</span><span class="o">=</span><span class="n">feval</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">eval_metrics</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="go">('custom(<lambda>)', 6.0)</lambda></span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="go">('custom(<lambda>)', 6.0)</span>
</pre></div>
</div>
<dl class="method">
@@ -1084,8 +1089,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CustomMetric.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1102,14 +1107,14 @@ By default include all labels.</li>
<code class="descclassname">mxnet.metric.</code><code class="descname">np</code><span class="sig-paren">(</span><em>numpy_feval</em>, <em>name=None</em>, <em>allow_extra_outputs=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.np" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a custom evaluation metric that receives its inputs as numpy arrays.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>numpy_feval</strong> (<em>callable</em><em>(</em><em>label</em><em>, </em><em>pred</em><em>)</em><em></em>) – Custom evaluation function that receives labels and predictions for a minibatch
+<li><strong>numpy_feval</strong> (<em>callable(label, pred)</em>) – Custom evaluation function that receives labels and predictions for a minibatch
as numpy arrays and returns the corresponding custom metric as a floating point number.</li>
-<li><strong>name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of the custom metric.</li>
-<li><strong>allow_extra_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether prediction output is allowed to have extra outputs. This is useful in cases
+<li><strong>name</strong> (<em>str, optional</em>) – Name of the custom metric.</li>
+<li><strong>allow_extra_outputs</strong> (<em>bool, optional</em>) – Whether prediction output is allowed to have extra outputs. This is useful in cases
like RNN where states are also part of output which can then be fed back to the RNN
in the next step. By default, extra outputs are not allowed.</li>
</ul>
@@ -1124,7 +1129,7 @@ in the next step. By default, extra outputs are not allowed.</li>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">label</span><span class="o">-</span><span class="n">pred</span><span class="p">))</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">metric</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">np</span><span class="p">(</span><span class="n">custom_metric</span><span class="p">)</span>
diff --git a/versions/master/api/python/model.html b/versions/master/api/python/model.html
index 85e7cd0..0b37f1b 100644
--- a/versions/master/api/python/model.html
+++ b/versions/master/api/python/model.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Model API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -237,7 +235,7 @@ Previous Navbar Layout End -->
<div class="section" id="model-api">
<span id="model-api"></span><h1>Model API<a class="headerlink" href="#model-api" title="Permalink to this headline">¶</a></h1>
<p>The model API provides a simplified way to train neural networks using common best practices.
-It’s a thin wrapper built on top of the <a class="reference internal" href="ndarray.html"><span class="doc">ndarray</span></a> and <a class="reference internal" href="symbol.html"><span class="doc">symbolic</span></a>
+It’s a thin wrapper built on top of the <a class="reference internal" href="ndarray.html"><em>ndarray</em></a> and <a class="reference internal" href="symbol.html"><em>symbolic</em></a>
modules that make neural network training easy.</p>
<p>Topics:</p>
<ul class="simple">
@@ -337,12 +335,12 @@ each iteration.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">InitDesc</code><a class="headerlink" href="#mxnet.initializer.InitDesc" title="Permalink to this definition">¶</a></dt>
<dd><p>Descriptor for the initialization pattern.</p>
<dl class="docutils">
-<dt>name</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">str</span><dd>Name of variable.</dd>
-<dt>attrs</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">dict of str to str</span><dd>Attributes of this variable taken from <code class="docutils literal"><span class="pre">Symbol.attr_dict</span></code>.</dd>
-<dt>global_init</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">Initializer</span><dd>Global initializer to fallback to.</dd>
+<dt>name <span class="classifier-delimiter">:</span> <span class="classifier">str</span></dt>
+<dd>Name of variable.</dd>
+<dt>attrs <span class="classifier-delimiter">:</span> <span class="classifier">dict of str to str</span></dt>
+<dd>Attributes of this variable taken from <code class="docutils literal"><span class="pre">Symbol.attr_dict</span></code>.</dd>
+<dt>global_init <span class="classifier-delimiter">:</span> <span class="classifier">Initializer</span></dt>
+<dd>Global initializer to fallback to.</dd>
</dl>
</dd></dl>
<dl class="class">
@@ -354,14 +352,14 @@ each iteration.</p>
<code class="descname">set_verbosity</code><span class="sig-paren">(</span><em>verbose=False</em>, <em>print_func=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Initializer.set_verbosity" title="Permalink to this definition">¶</a></dt>
<dd><p>Switch on/off verbose mode</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>verbose</strong> (<em>bool</em>) – switch on/off verbose mode</li>
<li><strong>print_func</strong> (<em>function</em>) – A function that computes statistics of initialized arrays.
Takes an <cite>NDArray</cite> and returns an <cite>str</cite>. Defaults to mean
-absolute value str((<a href="#id3"><span class="problematic" id="id4">|x|</span></a>/size(x)).asscalar()).</li>
+absolute value str((<a href="#id11"><span class="problematic" id="id12">|x|</span></a>/size(x)).asscalar()).</li>
</ul>
</td>
</tr>
@@ -373,8 +371,8 @@ absolute value str((<a href="#id3"><span class="problematic" id="id4">|x|</span>
<code class="descname">dumps</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Initializer.dumps" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves the initializer to string</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">JSON formatted string that describes the initializer.</td>
</tr>
@@ -383,7 +381,7 @@ absolute value str((<a href="#id3"><span class="problematic" id="id4">|x|</span>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Create initializer and retrieve its parameters</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Create initializer and retrieve its parameters</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">init</span><span class="o">.</span><span class="n">dumps</span><span class="p">()</span>
@@ -394,6 +392,23 @@ absolute value str((<a href="#id3"><span class="problematic" id="id4">|x|</span>
</pre></div>
</div>
</dd></dl>
+<dl class="method">
+<dt id="mxnet.initializer.Initializer.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>desc</em>, <em>arr</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Initializer.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Initialize an array</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>desc</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.InitDesc" title="mxnet.initializer.InitDesc"><em>InitDesc</em></a>) – Initialization pattern descriptor.</li>
+<li><strong>arr</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The array to be initialized.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
</dd></dl>
<dl class="function">
<dt id="mxnet.initializer.register">
@@ -403,21 +418,21 @@ absolute value str((<a href="#id3"><span class="problematic" id="id4">|x|</span>
required functions like <cite>_init_weight</cite> and <cite>_init_bias</cite>. The created initializer must be
registered using <cite>mx.init.register</cite> before it can be called by name.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>klass</strong> (<em>class</em>) – A subclass of <cite>mx.init.Initializer</cite> that needs to be registered as a custom initializer.</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Create and register a custom initializer that</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Create and register a custom initializer that</span>
<span class="gp">... </span><span class="c1"># initializes weights to 0.1 and biases to 1.</span>
<span class="gp">...</span>
-<span class="gp">>>> </span><span class="nd">@mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">register</span>
+<span class="gp">>>> </span><span class="nd">@mx.init.register</span>
<span class="gp">... </span><span class="nd">@alias</span><span class="p">(</span><span class="s1">'myinit'</span><span class="p">)</span>
<span class="gp">... </span><span class="k">class</span> <span class="nc">CustomInit</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Initializer</span><span class="p">):</span>
-<span class="gp">... </span> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="gp">... </span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="gp">... </span> <span class="nb">super</span><span class="p">(</span><span class="n">CustomInit</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="gp">... </span> <span class="k">def</span> <span class="nf">_init_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">arr</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="mf">0.1</span>
@@ -439,11 +454,11 @@ registered using <cite>mx.init.register</cite> before it can be called by name.<
<p><strong>Note</strong> Load will drop <code class="docutils literal"><span class="pre">arg:</span></code> or <code class="docutils literal"><span class="pre">aux:</span></code> from name and
initialize the variables that match with the prefix dropped.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>param</strong> (<em>str</em><em> or </em><em>dict of str->`NDArray`</em>) – Parameter file or dict mapping name to NDArray.</li>
+<li><strong>param</strong> (<em>str or dict of str->`NDArray`</em>) – Parameter file or dict mapping name to NDArray.</li>
<li><strong>default_init</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Default initializer when name is not found in <cite>param</cite>.</li>
<li><strong>verbose</strong> (<em>bool</em>) – Flag for enabling logging of source when initializing.</li>
</ul>
@@ -457,8 +472,8 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">Mixed</code><span class="sig-paren">(</span><em>patterns</em>, <em>initializers</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Mixed" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize parameters using multiple initializers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>patterns</strong> (<em>list of str</em>) – List of regular expressions matching parameter names.</li>
@@ -469,7 +484,7 @@ initialize the variables that match with the prefix dropped.</p>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize biases to zero</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize biases to zero</span>
<span class="gp">... </span><span class="c1"># and every other parameter to random values with uniform distribution.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">initializer</span><span class="o">.</span><span class="n">Mixed</span><span class="p">([</span><span class="s1">'bias'</span><span class="p">,</span> <span class="s1">'.*'</span><span class="p">],</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Ze [...]
@@ -477,8 +492,8 @@ initialize the variables that match with the prefix dropped.</p>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected1_weight</span>
<span class="go">[[ 0.0097627 0.01856892 0.04303787]]</span>
@@ -492,14 +507,14 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">Zero</code><a class="headerlink" href="#mxnet.initializer.Zero" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes weights to zero.</p>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights to zero.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights to zero.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">initializer</span><span class="o">.</span><span class="n">Zero</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">init</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected0_weight</span>
<span class="go">[[ 0. 0. 0.]]</span>
@@ -511,14 +526,14 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">One</code><a class="headerlink" href="#mxnet.initializer.One" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes weights to one.</p>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights to one.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights to one.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">initializer</span><span class="o">.</span><span class="n">One</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">init</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected0_weight</span>
<span class="go">[[ 1. 1. 1.]]</span>
@@ -530,8 +545,8 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">Constant</code><span class="sig-paren">(</span><em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Constant" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes the weights to a scalar value.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>value</strong> (<em>float</em>) – Fill value.</td>
</tr>
@@ -543,25 +558,25 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">Uniform</code><span class="sig-paren">(</span><em>scale=0.07</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Uniform" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes weights with random values uniformly sampled from a given range.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em>) – The bound on the range of the generated random values.
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>scale</strong> (<em>float, optional</em>) – The bound on the range of the generated random values.
Values are generated from the range [-<cite>scale</cite>, <cite>scale</cite>].
Default scale is 0.07.</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights</span>
<span class="gp">>>> </span><span class="c1"># to random values uniformly sampled between -0.1 and 0.1.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Uniform</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">init</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected0_weight</span>
<span class="go">[[ 0.01360891 -0.02144304 0.08511933]]</span>
@@ -574,24 +589,24 @@ Default scale is 0.07.</td>
<dd><p>Initializes weights with random values sampled from a normal distribution
with a mean of zero and standard deviation of <cite>sigma</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>sigma</strong> (<em>float</em><em>, </em><em>optional</em>) – Standard deviation of the normal distribution.
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>sigma</strong> (<em>float, optional</em>) – Standard deviation of the normal distribution.
Default standard deviation is 0.01.</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights</span>
<span class="gp">>>> </span><span class="c1"># to random values sampled from a normal distribution.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">init</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected0_weight</span>
<span class="go">[[-0.3214761 -0.12660924 0.53789419]]</span>
@@ -606,8 +621,8 @@ Default standard deviation is 0.01.</td>
learning in deep linear neural networks</em>, available at
<a class="reference external" href="https://arxiv.org/abs/1312.6120">https://arxiv.org/abs/1312.6120</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>scale</strong> (<em>float optional</em>) – Scaling factor of weight.</li>
@@ -636,13 +651,13 @@ Similarly when <cite>factor_type</cite> is <code class="docutils literal"><span
the initializer fills the weights with numbers from normal distribution with
a standard deviation of <span class="math">\(\sqrt{\frac{3.}{0.5 * (n_{in} + n_{out})}}\)</span>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>rnd_type</strong> (<em>str</em><em>, </em><em>optional</em>) – Random generator type, can be <code class="docutils literal"><span class="pre">'gaussian'</span></code> or <code class="docutils literal"><span class="pre">'uniform'</span></code>.</li>
-<li><strong>factor_type</strong> (<em>str</em><em>, </em><em>optional</em>) – Can be <code class="docutils literal"><span class="pre">'avg'</span></code>, <code class="docutils literal"><span class="pre">'in'</span></code>, or <code class="docutils literal"><span class="pre">'out'</span></code>.</li>
-<li><strong>magnitude</strong> (<em>float</em><em>, </em><em>optional</em>) – Scale of random number.</li>
+<li><strong>rnd_type</strong> (<em>str, optional</em>) – Random generator type, can be <code class="docutils literal"><span class="pre">'gaussian'</span></code> or <code class="docutils literal"><span class="pre">'uniform'</span></code>.</li>
+<li><strong>factor_type</strong> (<em>str, optional</em>) – Can be <code class="docutils literal"><span class="pre">'avg'</span></code>, <code class="docutils literal"><span class="pre">'in'</span></code>, or <code class="docutils literal"><span class="pre">'out'</span></code>.</li>
+<li><strong>magnitude</strong> (<em>float, optional</em>) – Scale of random number.</li>
</ul>
</td>
</tr>
@@ -659,12 +674,12 @@ Human-Level Performance on ImageNet Classification</em>, available at
<p>This initializer is proposed for initialization related to ReLu activation,
it maked some changes on top of Xavier method.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>factor_type</strong> (<em>str</em><em>, </em><em>optional</em>) – Can be <code class="docutils literal"><span class="pre">'avg'</span></code>, <code class="docutils literal"><span class="pre">'in'</span></code>, or <code class="docutils literal"><span class="pre">'out'</span></code>.</li>
-<li><strong>slope</strong> (<em>float</em><em>, </em><em>optional</em>) – initial slope of any PReLU (or similar) nonlinearities.</li>
+<li><strong>factor_type</strong> (<em>str, optional</em>) – Can be <code class="docutils literal"><span class="pre">'avg'</span></code>, <code class="docutils literal"><span class="pre">'in'</span></code>, or <code class="docutils literal"><span class="pre">'out'</span></code>.</li>
+<li><strong>slope</strong> (<em>float, optional</em>) – initial slope of any PReLU (or similar) nonlinearities.</li>
</ul>
</td>
</tr>
@@ -682,10 +697,10 @@ it maked some changes on top of Xavier method.</p>
<dd><p>Initialize all bias of an LSTMCell to 0.0 except for
the forget gate whose bias is set to custom value.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>forget_bias</strong> (<em>float</em><em>, </em><em>default 1.0</em>) – bias for the forget gate. Jozefowicz et al. 2015 recommends
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>forget_bias</strong> (<em>float, default 1.0</em>) – bias for the forget gate. Jozefowicz et al. 2015 recommends
setting this to 1.0.</td>
</tr>
</tbody>
@@ -696,8 +711,8 @@ setting this to 1.0.</td>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">FusedRNN</code><span class="sig-paren">(</span><em>init</em>, <em>num_hidden</em>, <em>num_layers</em>, <em>mode</em>, <em>bidirectional=False</em>, <em>forget_bias=1.0</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.FusedRNN" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize parameters for fused rnn layers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>init</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – initializer applied to unpacked weights. Fall back to global
@@ -730,14 +745,14 @@ One should not use this class directly, but instead create new metric
classes that extend it.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -755,8 +770,8 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<code class="descname">update_dict</code><span class="sig-paren">(</span><em>label</em>, <em>pred</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.EvalMetric.update_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Update the internal evaluation with named label and pred</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (<em>OrderedDict of str -> NDArray</em>) – name to array mapping for labels.</li>
@@ -772,8 +787,8 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.EvalMetric.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -794,12 +809,14 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<code class="descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.EvalMetric.get" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets the current evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>names</strong> (<em>list of str</em>) – Name of the metrics.</li>
-<li><strong>values</strong> (<em>list of float</em>) – Value of the evaluations.</li>
+<li><strong>names</strong> (<em>list of str</em>) –
+Name of the metrics.</li>
+<li><strong>values</strong> (<em>list of float</em>) –
+Value of the evaluations.</li>
</ul>
</td>
</tr>
@@ -811,8 +828,8 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<code class="descname">get_name_value</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.EvalMetric.get_name_value" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns zipped name and value pairs.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A (name, value) tuple list.</td>
</tr>
@@ -828,11 +845,11 @@ from configs with metric.create(<a href="#id1"><span class="problematic" id="id2
<dd><p>Creates evaluation metric from metric names or instances of EvalMetric
or a custom metric function.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>metric</strong> (<em>str</em><em> or </em><em>callable</em>) – <p>Specifies the metric to create.
+<li><strong>metric</strong> (<em>str or callable</em>) – <p>Specifies the metric to create.
This argument must be one of the below:</p>
<ul>
<li>Name of a metric.</li>
@@ -842,17 +859,19 @@ This argument must be one of the below:</p>
labels and predictions.</li>
</ul>
</li>
-<li><strong>*args</strong> (<em>list</em>) – Additional arguments to metric constructor.
-Only used when metric is str.</li>
-<li><strong>**kwargs</strong> (<em>dict</em>) – Additional arguments to metric constructor.
-Only used when metric is str</li>
+<li><strong>*args</strong> – <p>Additional arguments to metric constructor.
+Only used when metric is str.</p>
+</li>
+<li><strong>**kwargs</strong> – <p>Additional arguments to metric constructor.
+Only used when metric is str</p>
+</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">label</span> <span class="o">-</span> <span class="n">pred</span><span class="p">))</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">metric1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'acc'</span><span class="p">)</span>
@@ -866,15 +885,15 @@ Only used when metric is str</li>
<em class="property">class </em><code class="descclassname">mxnet.metric.</code><code class="descname">CompositeEvalMetric</code><span class="sig-paren">(</span><em>metrics=None</em>, <em>name='composite'</em>, <em>output_names=None</em>, <em>label_names=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric" title="Permalink to this definition">¶</a></dt>
<dd><p>Manages multiple evaluation metrics.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>metrics</strong> (<em>list of EvalMetric</em>) – List of child metrics.</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -882,7 +901,7 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span>
<span class="gp">>>> </span><span class="n">eval_metrics_1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">eval_metrics_2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">F1</span><span class="p">()</span>
@@ -890,7 +909,7 @@ By default include all labels.</li>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">child_metric</span> <span class="ow">in</span> <span class="p">[</span><span class="n">eval_metrics_1</span><span class="p">,</span> <span class="n">eval_metrics_2</span><span class="p">]:</span>
<span class="gp">>>> </span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">child_metric</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">eval_metrics</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">(['accuracy', 'f1'], [0.6666666666666666, 0.8])</span>
</pre></div>
</div>
@@ -899,8 +918,8 @@ By default include all labels.</li>
<code class="descname">add</code><span class="sig-paren">(</span><em>metric</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.add" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds a child metric.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>metric</strong> – A metric instance.</td>
</tr>
@@ -912,8 +931,8 @@ By default include all labels.</li>
<code class="descname">get_metric</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.get_metric" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a child metric.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>index</strong> (<em>int</em>) – Index of child metric in the list of metrics.</td>
</tr>
@@ -925,8 +944,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -947,12 +966,14 @@ By default include all labels.</li>
<code class="descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CompositeEvalMetric.get" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
-<li><strong>names</strong> (<em>list of str</em>) – Name of the metrics.</li>
-<li><strong>values</strong> (<em>list of float</em>) – Value of the evaluations.</li>
+<li><strong>names</strong> (<em>list of str</em>) –
+Name of the metrics.</li>
+<li><strong>values</strong> (<em>list of float</em>) –
+Value of the evaluations.</li>
</ul>
</td>
</tr>
@@ -969,15 +990,15 @@ By default include all labels.</li>
\[\text{accuracy}(y, \hat{y}) = \frac{1}{n} \sum_{i=0}^{n-1}
\text{1}(\hat{y_i} == y_i)\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>default=1</em>) – The axis that represents classes</li>
+<li><strong>axis</strong> (<em>int, default=1</em>) – The axis that represents classes</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -985,11 +1006,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span>
<span class="gp">>>> </span><span class="n">acc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('accuracy', 0.6666666666666666)</span>
</pre></div>
</div>
@@ -998,8 +1019,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.Accuracy.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1020,15 +1041,15 @@ to be <code class="docutils literal"><span class="pre">True</span></code> as lon
predicated labels.</p>
<p>If <cite>top_k</cite> = <code class="docutils literal"><span class="pre">1</span></code>, then <cite>TopKAccuracy</cite> is identical to <cite>Accuracy</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>top_k</strong> (<em>int</em>) – Whether targets are in top k predictions.</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1036,13 +1057,13 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">999</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">999</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">top_k</span> <span class="o">=</span> <span class="mi">3</span>
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span cla [...]
<span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><sp [...]
<span class="gp">>>> </span><span class="n">acc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">TopKAccuracy</span><span class="p">(</span><span class="n">top_k</span><span class="o">=</span><span class="n">top_k</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('top_k_accuracy', 0.3)</span>
</pre></div>
</div>
@@ -1051,8 +1072,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.TopKAccuracy.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1070,11 +1091,11 @@ By default include all labels.</li>
<dd><p>Computes the F1 score of a binary classification problem.</p>
<p>The F1 score is equivalent to weighted average of the precision and recall,
where the best value is 1.0 and the worst value is 0.0. The formula for F1 score is:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">F1</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">precision</span> <span class="o">*</span> <span class="n">recall</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">precision</span> <span class="o">+</span> <span class="n">recall</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">F1</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">precision</span> <span class="o">*</span> <span class="n">recall</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">precision</span> <span class="o">+</span> <span class="n">recall</span><span class="p">)</span>
</pre></div>
</div>
<p>The formula for precision and recall is:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">precision</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_positives</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">precision</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_positives</span><span class="p">)</span>
<span class="n">recall</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_negatives</span><span class="p">)</span>
</pre></div>
</div>
@@ -1083,14 +1104,14 @@ where the best value is 1.0 and the worst value is 0.0. The formula for F1 score
<p class="last">This F1 score only supports binary classification.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1098,11 +1119,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">])]</span>
<span class="gp">>>> </span><span class="n">acc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">F1</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('f1', 0.8)</span>
</pre></div>
</div>
@@ -1111,8 +1132,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.F1.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1145,20 +1166,20 @@ and <span class="math">\(q(x_2) = 1.0\)</span>,
<span class="math">\(q(x_3) = 0.6\)</span>. The perplexity of model q is
<span class="math">\(exp\big(-(\log 0.3 + \log 1.0 + \log 0.6) / 3\big) = 1.77109762852\)</span>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>ignore_label</strong> (<em>int</em><em> or </em><em>None</em>) – Index of invalid label to ignore when
+<li><strong>ignore_label</strong> (<em>int or None</em>) – Index of invalid label to ignore when
counting. By default, sets to -1.
If set to <cite>None</cite>, it will include all entries.</li>
-<li><strong>axis</strong> (<em>int</em><em> (</em><em>default -1</em><em>)</em><em></em>) – The axis from prediction that was used to
+<li><strong>axis</strong> (<em>int (default -1)</em>) – The axis from prediction that was used to
compute softmax. By default use the last
axis.</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1166,11 +1187,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span>
-<span class="gp">>>> </span><span class="n">perp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Perplexity</span><span class="p">(</span><span class="n">ignore_label</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">perp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Perplexity</span><span class="p">(</span><span class="n">ignore_label</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">perp</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">perp</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">perp</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('Perplexity', 1.7710976285155853)</span>
</pre></div>
</div>
@@ -1179,8 +1200,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.Perplexity.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1196,8 +1217,8 @@ By default include all labels.</li>
<code class="descname">get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.Perplexity.get" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Representing name of the metric and evaluation result.</td>
</tr>
@@ -1215,14 +1236,14 @@ By default include all labels.</li>
<div class="math">
\[\frac{\sum_i^n |y_i - \hat{y}_i|}{n}\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1230,11 +1251,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span clas [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><spa [...]
<span class="gp">>>> </span><span class="n">mean_absolute_error</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">MAE</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">mean_absolute_error</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mean_absolute_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mean_absolute_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('mae', 0.5)</span>
</pre></div>
</div>
@@ -1243,8 +1264,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.MAE.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1264,14 +1285,14 @@ By default include all labels.</li>
<div class="math">
\[\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1279,11 +1300,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span clas [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><spa [...]
<span class="gp">>>> </span><span class="n">mean_squared_error</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">MSE</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">mean_squared_error</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('mse', 0.375)</span>
</pre></div>
</div>
@@ -1292,8 +1313,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.MSE.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1313,14 +1334,14 @@ By default include all labels.</li>
<div class="math">
\[\sqrt{\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}}\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1328,11 +1349,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span clas [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><spa [...]
<span class="gp">>>> </span><span class="n">root_mean_squared_error</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">RMSE</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">root_mean_squared_error</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">root_mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">root_mean_squared_error</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('rmse', 0.612372457981)</span>
</pre></div>
</div>
@@ -1341,8 +1362,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.RMSE.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1362,16 +1383,16 @@ By default include all labels.</li>
<div class="math">
\[-y\log \hat{y} + (1-y)\log (1-\hat{y})\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>eps</strong> (<em>float</em>) – Cross Entropy loss is undefined for predicted value is 0 or 1,
so predicted values are added with the small constant.</li>
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1379,11 +1400,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span>
<span class="gp">>>> </span><span class="n">ce</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">CrossEntropy</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">ce</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">ce</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">ce</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('cross-entropy', 0.57159948348999023)</span>
</pre></div>
</div>
@@ -1392,8 +1413,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CrossEntropy.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1413,14 +1434,14 @@ By default include all labels.</li>
<div class="math">
\[\frac{cov(y, \hat{y})}{\sigma{y}\sigma{\hat{y}}}\]</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1428,11 +1449,11 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span [...]
<span class="gp">>>> </span><span class="n">pr</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">PearsonCorrelation</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">pr</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">pr</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">pr</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="go">('pearson-correlation', 0.42163704544016178)</span>
</pre></div>
</div>
@@ -1441,8 +1462,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.PearsonCorrelation.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1459,14 +1480,14 @@ By default include all labels.</li>
<em class="property">class </em><code class="descclassname">mxnet.metric.</code><code class="descname">Loss</code><span class="sig-paren">(</span><em>name='loss'</em>, <em>output_names=None</em>, <em>label_names=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.Loss" title="Permalink to this definition">¶</a></dt>
<dd><p>Dummy metric for directly printing loss.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1491,19 +1512,19 @@ By default include all labels.</li>
<p>The <cite>feval</cite> function can return a <cite>tuple</cite> of (sum_metric, num_inst) or return
an <cite>int</cite> sum_metric.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>feval</strong> (<em>callable</em><em>(</em><em>label</em><em>, </em><em>pred</em><em>)</em><em></em>) – Customized evaluation function.</li>
+<li><strong>feval</strong> (<em>callable(label, pred)</em>) – Customized evaluation function.</li>
<li><strong>name</strong> (<em>str</em>) – The name of the metric. (the default is None).</li>
-<li><strong>allow_extra_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If true, the prediction outputs can have extra outputs.
+<li><strong>allow_extra_outputs</strong> (<em>bool, optional</em>) – If true, the prediction outputs can have extra outputs.
This is useful in RNN, where the states are also produced
in outputs for forwarding. (the default is False).</li>
<li><strong>name</strong> – Name of this metric instance for display.</li>
-<li><strong>output_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of predictions that should be used when updating with update_dict.
+<li><strong>output_names</strong> (<em>list of str, or None</em>) – Name of predictions that should be used when updating with update_dict.
By default include all predictions.</li>
-<li><strong>label_names</strong> (<em>list of str</em><em>, or </em><em>None</em>) – Name of labels that should be used when updating with update_dict.
+<li><strong>label_names</strong> (<em>list of str, or None</em>) – Name of labels that should be used when updating with update_dict.
By default include all labels.</li>
</ul>
</td>
@@ -1511,13 +1532,13 @@ By default include all labels.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predicts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span clas [...]
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><spa [...]
<span class="gp">>>> </span><span class="n">feval</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">CustomMetric</span><span class="p">(</span><span class="n">feval</span><span class="o">=</span><span class="n">feval</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">eval_metrics</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
-<span class="go">('custom(<lambda>)', 6.0)</lambda></span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">eval_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="go">('custom(<lambda>)', 6.0)</span>
</pre></div>
</div>
<dl class="method">
@@ -1525,8 +1546,8 @@ By default include all labels.</li>
<code class="descname">update</code><span class="sig-paren">(</span><em>labels</em>, <em>preds</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.CustomMetric.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the internal evaluation result.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>labels</strong> (list of <cite>NDArray</cite>) – The labels of the data.</li>
@@ -1543,14 +1564,14 @@ By default include all labels.</li>
<code class="descclassname">mxnet.metric.</code><code class="descname">np</code><span class="sig-paren">(</span><em>numpy_feval</em>, <em>name=None</em>, <em>allow_extra_outputs=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.metric.np" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a custom evaluation metric that receives its inputs as numpy arrays.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>numpy_feval</strong> (<em>callable</em><em>(</em><em>label</em><em>, </em><em>pred</em><em>)</em><em></em>) – Custom evaluation function that receives labels and predictions for a minibatch
+<li><strong>numpy_feval</strong> (<em>callable(label, pred)</em>) – Custom evaluation function that receives labels and predictions for a minibatch
as numpy arrays and returns the corresponding custom metric as a floating point number.</li>
-<li><strong>name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of the custom metric.</li>
-<li><strong>allow_extra_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether prediction output is allowed to have extra outputs. This is useful in cases
+<li><strong>name</strong> (<em>str, optional</em>) – Name of the custom metric.</li>
+<li><strong>allow_extra_outputs</strong> (<em>bool, optional</em>) – Whether prediction output is allowed to have extra outputs. This is useful in cases
like RNN where states are also part of output which can then be fed back to the RNN
in the next step. By default, extra outputs are not allowed.</li>
</ul>
@@ -1565,7 +1586,7 @@ in the next step. By default, extra outputs are not allowed.</li>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">label</span><span class="o">-</span><span class="n">pred</span><span class="p">))</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">metric</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">np</span><span class="p">(</span><span class="n">custom_metric</span><span class="p">)</span>
@@ -1580,23 +1601,23 @@ in the next step. By default, extra outputs are not allowed.</li>
<div><span class="target" id="module-mxnet.optimizer"></span><p>Weight updating functions.</p>
<dl class="class">
<dt id="mxnet.optimizer.Optimizer">
-<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">Optimizer</code><span class="sig-paren">(</span><em>rescale_grad=1.0</em>, <em>param_idx2name=None</em>, <em>wd=0.0</em>, <em>clip_gradient=None</em>, <em>learning_rate=0.01</em>, <em>lr_scheduler=None</em>, <em>sym=None</em>, <em>begin_num_update=0</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Optimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">Optimizer</code><span class="sig-paren">(</span><em>rescale_grad=1.0</em>, <em>param_idx2name=None</em>, <em>wd=0.0</em>, <em>clip_gradient=None</em>, <em>learning_rate=0.01</em>, <em>lr_scheduler=None</em>, <em>sym=None</em>, <em>begin_num_update=0</em>, <em>param_dict=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Optimizer" title="Permalink t [...]
<dd><p>The base class inherited by all optimizers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em>) – Multiply the gradient with <cite>rescale_grad</cite> before updating. Often
+<li><strong>rescale_grad</strong> (<em>float, optional</em>) – Multiply the gradient with <cite>rescale_grad</cite> before updating. Often
choose to be <code class="docutils literal"><span class="pre">1.0/batch_size</span></code>.</li>
-<li><strong>param_idx2name</strong> (<em>dict from int to string</em><em>, </em><em>optional</em>) – A dictionary that maps int index to string name.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em>) – Clip the gradient by projecting onto the box <code class="docutils literal"><span class="pre">[-clip_gradient,</span> <span class="pre">clip_gradient]</span></code>.</li>
-<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>optional</em>) – The initial learning rate.</li>
-<li><strong>lr_scheduler</strong> (<a class="reference internal" href="optimization.html#mxnet.lr_scheduler.LRScheduler" title="mxnet.lr_scheduler.LRScheduler"><em>LRScheduler</em></a><em>, </em><em>optional</em>) – The learning rate scheduler.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em>) – The weight decay (or L2 regularization) coefficient. Modifies objective
+<li><strong>param_idx2name</strong> (<em>dict from int to string, optional</em>) – A dictionary that maps int index to string name.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional</em>) – Clip the gradient by projecting onto the box <code class="docutils literal"><span class="pre">[-clip_gradient,</span> <span class="pre">clip_gradient]</span></code>.</li>
+<li><strong>learning_rate</strong> (<em>float, optional</em>) – The initial learning rate.</li>
+<li><strong>lr_scheduler</strong> (<em>LRScheduler, optional</em>) – The learning rate scheduler.</li>
+<li><strong>wd</strong> (<em>float, optional</em>) – The weight decay (or L2 regularization) coefficient. Modifies objective
by adding a penalty for having large weights.</li>
-<li><strong>sym</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>optional</em>) – The Symbol this optimizer is applying to.</li>
-<li><strong>begin_num_update</strong> (<em>int</em><em>, </em><em>optional</em>) – The initial number of updates.</li>
+<li><strong>sym</strong> (<em>Symbol, optional</em>) – The Symbol this optimizer is applying to.</li>
+<li><strong>begin_num_update</strong> (<em>int, optional</em>) – The initial number of updates.</li>
</ul>
</td>
</tr>
@@ -1609,12 +1630,12 @@ by adding a penalty for having large weights.</li>
<p>Once an optimizer is registered, we can create an instance of this
optimizer with <cite>create_optimizer</cite> later.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nd">@mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">register</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nd">@mx.optimizer.Optimizer.register</span>
<span class="gp">... </span><span class="k">class</span> <span class="nc">MyOptimizer</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">pass</span>
<span class="gp">>>> </span><span class="n">optim</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'MyOptimizer'</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">optim</span><span class="p">))</span>
-<span class="go"><class '__main__.myoptimizer'=""></class></span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">optim</span><span class="p">))</span>
+<span class="go"><class '__main__.MyOptimizer'></span>
</pre></div>
</div>
</dd></dl>
@@ -1627,8 +1648,8 @@ optimizer with <cite>create_optimizer</cite> later.</p>
<p class="last">We can use the alias <cite>create</cite> for <code class="docutils literal"><span class="pre">Optimizer.create_optimizer</span></code>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>str</em>) – Name of the optimizer. Should be the name
@@ -1646,12 +1667,12 @@ of a subclass of Optimizer. Case insensitive.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sgd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'sgd'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sgd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'sgd'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">sgd</span><span class="p">)</span>
-<span class="go"><class 'mxnet.optimizer.sgd'=""></class></span>
+<span class="go"><class 'mxnet.optimizer.SGD'></span>
<span class="gp">>>> </span><span class="n">adam</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'adam'</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=.</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">adam</span><span class="p">)</span>
-<span class="go"><class 'mxnet.optimizer.adam'=""></class></span>
+<span class="go"><class 'mxnet.optimizer.Adam'></span>
</pre></div>
</div>
</dd></dl>
@@ -1664,8 +1685,8 @@ to gradients in order to update weights. This function creates state
for a given weight which will be used in <cite>update</cite>. This function is
called only once for each weight.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>index</strong> (<em>int</em>) – An unique index to identify the weight.</li>
@@ -1673,7 +1694,8 @@ called only once for each weight.</p>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>state</strong> – The state associated with the weight.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>state</strong> –
+The state associated with the weight.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">any obj</p>
@@ -1687,8 +1709,8 @@ called only once for each weight.</p>
<code class="descname">update</code><span class="sig-paren">(</span><em>index</em>, <em>weight</em>, <em>grad</em>, <em>state</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Optimizer.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the given parameter using the corresponding gradient and state.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>index</strong> (<em>int</em>) – The unique index of the parameter into the individual learning
@@ -1721,8 +1743,8 @@ the global learning rate <cite>self.lr</cite> and its multiplier.</p>
can be set with <cite>lr_mult</cite> argument in the constructor.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>args_lr_mult</strong> (<em>dict of str/int to float</em>) – <p>For each of its key-value entries, the learning rate multipler for the
parameter specified in the key will be set as the given value.</p>
@@ -1752,8 +1774,8 @@ parameters whose name don’t end with <code class="docutils literal"><span clas
can be set with its <cite>wd_mult</cite> argument in the constructor.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>args_wd_mult</strong> (<em>dict of string/int to float</em>) – <p>For each of its key-value entries, the weight decay multipler for the
parameter specified in the key will be set as the given value.</p>
@@ -1777,12 +1799,12 @@ compatibility, and we recommend to use the name instead.</p>
<p>Once an optimizer is registered, we can create an instance of this
optimizer with <cite>create_optimizer</cite> later.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nd">@mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">register</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nd">@mx.optimizer.Optimizer.register</span>
<span class="gp">... </span><span class="k">class</span> <span class="nc">MyOptimizer</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">pass</span>
<span class="gp">>>> </span><span class="n">optim</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'MyOptimizer'</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">optim</span><span class="p">))</span>
-<span class="go"><class '__main__.myoptimizer'=""></class></span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">optim</span><span class="p">))</span>
+<span class="go"><class '__main__.MyOptimizer'></span>
</pre></div>
</div>
</dd></dl>
@@ -1791,7 +1813,7 @@ optimizer with <cite>create_optimizer</cite> later.</p>
<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>momentum=0.0</em>, <em>multi_precision=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.SGD" title="Permalink to this definition">¶</a></dt>
<dd><p>The SGD optimizer with momentum and weight decay.</p>
<p>The optimizer updates the weight by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span> <span class="o">+</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">rescale_grad</span> <span class="o">*</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">clip_gradient</span><spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span> <span class="o">+</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">rescale_grad</span> <span class="o">*</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">clip_gradient</span><span [...]
<span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">-</span> <span class="n">state</span>
</pre></div>
</div>
@@ -1800,12 +1822,12 @@ optimizer with <cite>create_optimizer</cite> later.</p>
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em>) – The momentum value.</li>
-<li><strong>multi_precision</strong> (<em>bool</em><em>, </em><em>optional</em>) – <p>Flag to control the internal precision of the optimizer.
+<li><strong>momentum</strong> (<em>float, optional</em>) – The momentum value.</li>
+<li><strong>multi_precision</strong> (<em>bool, optional</em>) – <p>Flag to control the internal precision of the optimizer.
<code class="docutils literal"><span class="pre">False</span></code> results in using the same precision as the weights (default),
<code class="docutils literal"><span class="pre">True</span></code> makes internal 32-bit copy of the weights and applies gradients</p>
<blockquote>
@@ -1828,12 +1850,12 @@ available at <a class="reference external" href="https://arxiv.org/abs/1609.0832
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em>) – The momentum value.</li>
-<li><strong>lamda</strong> (<em>float</em><em>, </em><em>optional</em>) – Scale DC value.</li>
+<li><strong>momentum</strong> (<em>float, optional</em>) – The momentum value.</li>
+<li><strong>lamda</strong> (<em>float, optional</em>) – Scale DC value.</li>
</ul>
</td>
</tr>
@@ -1845,7 +1867,7 @@ by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimiz
<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">NAG</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.NAG" title="Permalink to this definition">¶</a></dt>
<dd><p>Nesterov accelerated SGD.</p>
<p>This optimizer updates each weight by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span> <span class="o">+</span> <span class="n">grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span> <span class="o">+</span> <span class="n">grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span>
<span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">-</span> <span class="p">(</span><span class="n">lr</span> <span class="o">*</span> <span class="p">(</span><span class="n">grad</span> <span class="o">+</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span><span class="p">))</span>
</pre></div>
</div>
@@ -1874,13 +1896,13 @@ Stochastic Optimization</em>, available at <a class="reference external" href="h
by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<p>For details of the update algorithm, see <code class="xref py py-class docutils literal"><span class="pre">ndarray.adam_update</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>beta1</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the first moment estimates.</li>
-<li><strong>beta2</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the second moment estimates.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – Small value to avoid division by 0.</li>
+<li><strong>beta1</strong> (<em>float, optional</em>) – Exponential decay rate for the first moment estimates.</li>
+<li><strong>beta2</strong> (<em>float, optional</em>) – Exponential decay rate for the second moment estimates.</li>
+<li><strong>epsilon</strong> (<em>float, optional</em>) – Small value to avoid division by 0.</li>
</ul>
</td>
</tr>
@@ -1897,10 +1919,10 @@ Methods for Online Learning and Stochastic Optimization</em>, and available at
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em>) – Small value to avoid division by 0.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>eps</strong> (<em>float, optional</em>) – Small value to avoid division by 0.</td>
</tr>
</tbody>
</table>
@@ -1920,17 +1942,17 @@ For details of the update algorithm see <a class="reference internal" href="ndar
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>gamma1</strong> (<em>float</em><em>, </em><em>optional</em>) – A decay factor of moving average over past squared gradient.</li>
-<li><strong>gamma2</strong> (<em>float</em><em>, </em><em>optional</em>) – A “momentum” factor. Only used if <cite>centered`=``True`</cite>.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – Small value to avoid division by 0.</li>
-<li><strong>centered</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flag to control which version of RMSProp to use.
+<li><strong>gamma1</strong> (<em>float, optional</em>) – A decay factor of moving average over past squared gradient.</li>
+<li><strong>gamma2</strong> (<em>float, optional</em>) – A “momentum” factor. Only used if <cite>centered`=``True`</cite>.</li>
+<li><strong>epsilon</strong> (<em>float, optional</em>) – Small value to avoid division by 0.</li>
+<li><strong>centered</strong> (<em>bool, optional</em>) – Flag to control which version of RMSProp to use.
<code class="docutils literal"><span class="pre">True</span></code> will use Graves’s version of <cite>RMSProp</cite>,
<code class="docutils literal"><span class="pre">False</span></code> will use Tieleman & Hinton’s version of <cite>RMSProp</cite>.</li>
-<li><strong>clip_weights</strong> (<em>float</em><em>, </em><em>optional</em>) – Clips weights into range <code class="docutils literal"><span class="pre">[-clip_weights,</span> <span class="pre">clip_weights]</span></code>.</li>
+<li><strong>clip_weights</strong> (<em>float, optional</em>) – Clips weights into range <code class="docutils literal"><span class="pre">[-clip_weights,</span> <span class="pre">clip_weights]</span></code>.</li>
</ul>
</td>
</tr>
@@ -1946,8 +1968,8 @@ learning rate method</em>, available at <a class="reference external" href="http
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>rho</strong> (<em>float</em>) – Decay rate for both squared gradients and delta.</li>
@@ -1965,15 +1987,16 @@ by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimiz
<p>Referenced from <em>Ad Click Prediction: a View from the Trenches</em>, available at
<a class="reference external" href="http://dl.acm.org/citation.cfm?id=2488200">http://dl.acm.org/citation.cfm?id=2488200</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>lamda1</strong> (<em>float</em><em>, </em><em>optional</em>) – L1 regularization coefficient.</li>
-<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>optional</em>) – The initial learning rate.</li>
-<li><strong>beta</strong> (<em>float</em><em>, </em><em>optional</em>) – Per-coordinate learning rate correlation parameter.</li>
+<li><strong>lamda1</strong> (<em>float, optional</em>) – L1 regularization coefficient.</li>
+<li><strong>learning_rate</strong> (<em>float, optional</em>) – The initial learning rate.</li>
+<li><strong>beta</strong> (<em>float, optional</em>) – Per-coordinate learning rate correlation parameter.</li>
<li><strong>eta</strong> – <div class="math">
-\[\eta_{t,i} = \frac{learningrate}{\beta+\sqrt{\sum_{s=1}^tg_{s,i}^t}}\]</div>
+</div>
+<p>eta_{t,i} = frac{learningrate}{beta+sqrt{sum_{s=1}^tg_{s,i}^t}}</p>
</li>
</ul>
</td>
@@ -1990,12 +2013,12 @@ available at <a class="reference external" href="http://arxiv.org/abs/1412.6980"
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>beta1</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the first moment estimates.</li>
-<li><strong>beta2</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the second moment estimates.</li>
+<li><strong>beta1</strong> (<em>float, optional</em>) – Exponential decay rate for the first moment estimates.</li>
+<li><strong>beta2</strong> (<em>float, optional</em>) – Exponential decay rate for the second moment estimates.</li>
</ul>
</td>
</tr>
@@ -2012,14 +2035,14 @@ at <a class="reference external" href="http://cs229.stanford.edu/proj2015/054_re
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>beta1</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the first moment estimates.</li>
-<li><strong>beta2</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the second moment estimates.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – Small value to avoid division by 0.</li>
-<li><strong>schedule_decay</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the momentum schedule</li>
+<li><strong>beta1</strong> (<em>float, optional</em>) – Exponential decay rate for the first moment estimates.</li>
+<li><strong>beta2</strong> (<em>float, optional</em>) – Exponential decay rate for the second moment estimates.</li>
+<li><strong>epsilon</strong> (<em>float, optional</em>) – Small value to avoid division by 0.</li>
+<li><strong>schedule_decay</strong> (<em>float, optional</em>) – Exponential decay rate for the momentum schedule</li>
</ul>
</td>
</tr>
@@ -2050,8 +2073,8 @@ by <a class="reference internal" href="optimization.html#mxnet.optimizer.Optimiz
<p class="last">We can use the alias <cite>create</cite> for <code class="docutils literal"><span class="pre">Optimizer.create_optimizer</span></code>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>str</em>) – Name of the optimizer. Should be the name
@@ -2069,12 +2092,12 @@ of a subclass of Optimizer. Case insensitive.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sgd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'sgd'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sgd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'sgd'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">sgd</span><span class="p">)</span>
-<span class="go"><class 'mxnet.optimizer.sgd'=""></class></span>
+<span class="go"><class 'mxnet.optimizer.SGD'></span>
<span class="gp">>>> </span><span class="n">adam</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'adam'</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=.</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">adam</span><span class="p">)</span>
-<span class="go"><class 'mxnet.optimizer.adam'=""></class></span>
+<span class="go"><class 'mxnet.optimizer.Adam'></span>
</pre></div>
</div>
</dd></dl>
@@ -2083,6 +2106,11 @@ of a subclass of Optimizer. Case insensitive.</li>
<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">Updater</code><span class="sig-paren">(</span><em>optimizer</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Updater" title="Permalink to this definition">¶</a></dt>
<dd><p>Updater for kvstore.</p>
<dl class="method">
+<dt id="mxnet.optimizer.Updater.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>index</em>, <em>grad</em>, <em>weight</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Updater.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Updates weight given gradient and index.</p>
+</dd></dl>
+<dl class="method">
<dt id="mxnet.optimizer.Updater.set_states">
<code class="descname">set_states</code><span class="sig-paren">(</span><em>states</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Updater.set_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets updater states.</p>
@@ -2098,12 +2126,13 @@ of a subclass of Optimizer. Case insensitive.</li>
<code class="descclassname">mxnet.optimizer.</code><code class="descname">get_updater</code><span class="sig-paren">(</span><em>optimizer</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.get_updater" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a closure of the updater needed for kvstore.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>optimizer</strong> (<a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – The optimizer.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>updater</strong> – The closure of the updater.</td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>updater</strong> –
+The closure of the updater.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">function</td>
</tr>
@@ -2126,8 +2155,8 @@ of a subclass of Optimizer. Case insensitive.</li>
<code class="descclassname">mxnet.model.</code><code class="descname">save_checkpoint</code><span class="sig-paren">(</span><em>prefix</em>, <em>epoch</em>, <em>symbol</em>, <em>arg_params</em>, <em>aux_params</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.model.save_checkpoint" title="Permalink to this definition">¶</a></dt>
<dd><p>Checkpoint the model data into file.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</li>
@@ -2151,8 +2180,8 @@ of a subclass of Optimizer. Case insensitive.</li>
<code class="descclassname">mxnet.model.</code><code class="descname">load_checkpoint</code><span class="sig-paren">(</span><em>prefix</em>, <em>epoch</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.model.load_checkpoint" title="Permalink to this definition">¶</a></dt>
<dd><p>Load model checkpoint from file.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</li>
@@ -2161,9 +2190,12 @@ of a subclass of Optimizer. Case insensitive.</li>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>symbol</strong> (<em>Symbol</em>) – The symbol configuration of computation network.</li>
-<li><strong>arg_params</strong> (<em>dict of str to NDArray</em>) – Model parameter, dict of name to NDArray of net’s weights.</li>
-<li><strong>aux_params</strong> (<em>dict of str to NDArray</em>) – Model parameter, dict of name to NDArray of net’s auxiliary states.</li>
+<li><strong>symbol</strong> (<em>Symbol</em>) –
+The symbol configuration of computation network.</li>
+<li><strong>arg_params</strong> (<em>dict of str to NDArray</em>) –
+Model parameter, dict of name to NDArray of net’s weights.</li>
+<li><strong>aux_params</strong> (<em>dict of str to NDArray</em>) –
+Model parameter, dict of name to NDArray of net’s auxiliary states.</li>
</ul>
</p>
</td>
@@ -2178,31 +2210,31 @@ of a subclass of Optimizer. Case insensitive.</li>
</dd></dl>
<dl class="class">
<dt id="mxnet.model.FeedForward">
-<em class="property">class </em><code class="descclassname">mxnet.model.</code><code class="descname">FeedForward</code><span class="sig-paren">(</span><em>symbol</em>, <em>ctx=None</em>, <em>num_epoch=None</em>, <em>epoch_size=None</em>, <em>optimizer='sgd'</em>, <em>initializer=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, <em>numpy_batch_size=128</em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_extra_params=False</em>, <em>begin_epoch=0</em [...]
+<em class="property">class </em><code class="descclassname">mxnet.model.</code><code class="descname">FeedForward</code><span class="sig-paren">(</span><em>symbol</em>, <em>ctx=None</em>, <em>num_epoch=None</em>, <em>epoch_size=None</em>, <em>optimizer='sgd'</em>, <em>initializer=<mxnet.initializer.Uniform object></em>, <em>numpy_batch_size=128</em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_extra_params=False</em>, <em>begin_epoch=0</em>, <em>**kwargs</em><span class [...]
<dd><p>Model class of MXNet for training and predicting feedforward nets.
This class is designed for a single-data single output supervised network.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>symbol</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The symbol configuration of computation network.</li>
-<li><strong>ctx</strong> (<em>Context</em><em> or </em><em>list of Context</em><em>, </em><em>optional</em>) – The device context of training and prediction.
+<li><strong>ctx</strong> (<em>Context or list of Context, optional</em>) – The device context of training and prediction.
To use multi GPU training, pass in a list of gpu contexts.</li>
-<li><strong>num_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – Training parameter, number of training epochs(epochs).</li>
-<li><strong>epoch_size</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of batches in a epoch. In default, it is set to
+<li><strong>num_epoch</strong> (<em>int, optional</em>) – Training parameter, number of training epochs(epochs).</li>
+<li><strong>epoch_size</strong> (<em>int, optional</em>) – Number of batches in a epoch. In default, it is set to
<code class="docutils literal"><span class="pre">ceil(num_train_examples</span> <span class="pre">/</span> <span class="pre">batch_size)</span></code>.</li>
-<li><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a><em>, </em><em>optional</em>) – Training parameter, name or optimizer object for training.</li>
-<li><strong>initializer</strong> (<em>initializer function</em><em>, </em><em>optional</em>) – Training parameter, the initialization scheme used.</li>
-<li><strong>numpy_batch_size</strong> (<em>int</em><em>, </em><em>optional</em>) – The batch size of training data.
+<li><strong>optimizer</strong> (<em>str or Optimizer, optional</em>) – Training parameter, name or optimizer object for training.</li>
+<li><strong>initializer</strong> (<em>initializer function, optional</em>) – Training parameter, the initialization scheme used.</li>
+<li><strong>numpy_batch_size</strong> (<em>int, optional</em>) – The batch size of training data.
Only needed when input array is numpy.</li>
-<li><strong>arg_params</strong> (<em>dict of str to NDArray</em><em>, </em><em>optional</em>) – Model parameter, dict of name to NDArray of net’s weights.</li>
-<li><strong>aux_params</strong> (<em>dict of str to NDArray</em><em>, </em><em>optional</em>) – Model parameter, dict of name to NDArray of net’s auxiliary states.</li>
-<li><strong>allow_extra_params</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol
+<li><strong>arg_params</strong> (<em>dict of str to NDArray, optional</em>) – Model parameter, dict of name to NDArray of net’s weights.</li>
+<li><strong>aux_params</strong> (<em>dict of str to NDArray, optional</em>) – Model parameter, dict of name to NDArray of net’s auxiliary states.</li>
+<li><strong>allow_extra_params</strong> (<em>boolean, optional</em>) – Whether allow extra parameters that are not needed by symbol
to be passed by aux_params and <code class="docutils literal"><span class="pre">arg_params</span></code>.
If this is True, no error will be thrown when <code class="docutils literal"><span class="pre">aux_params</span></code> and <code class="docutils literal"><span class="pre">arg_params</span></code>
contain more parameters than needed.</li>
-<li><strong>begin_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – The begining training epoch.</li>
+<li><strong>begin_epoch</strong> (<em>int, optional</em>) – The begining training epoch.</li>
<li><strong>kwargs</strong> (<em>dict</em>) – The additional keyword arguments passed to optimizer.</li>
</ul>
</td>
@@ -2214,16 +2246,17 @@ contain more parameters than needed.</li>
<code class="descname">predict</code><span class="sig-paren">(</span><em>X</em>, <em>num_batch=None</em>, <em>return_data=False</em>, <em>reset=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.model.FeedForward.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Run the prediction, always only use one device.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>X</strong> (<em>mxnet.DataIter</em>) – </li>
-<li><strong>num_batch</strong> (<em>int</em><em> or </em><em>None</em>) – The number of batch to run. Go though all batches if <code class="docutils literal"><span class="pre">None</span></code>.</li>
+<li><strong>num_batch</strong> (<em>int or None</em>) – The number of batch to run. Go though all batches if <code class="docutils literal"><span class="pre">None</span></code>.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>y</strong> – The predicted value of the output.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>y</strong> –
+The predicted value of the output.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">numpy.ndarray or a list of numpy.ndarray if the network has multiple outputs.</p>
@@ -2238,17 +2271,18 @@ contain more parameters than needed.</li>
<dd><p>Run the model given an input and calculate the score
as assessed by an evaluation metric.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>X</strong> (<em>mxnet.DataIter</em>) – </li>
<li><strong>eval_metric</strong> (<em>metric.metric</em>) – The metric for calculating score.</li>
-<li><strong>num_batch</strong> (<em>int</em><em> or </em><em>None</em>) – The number of batches to run. Go though all batches if <code class="docutils literal"><span class="pre">None</span></code>.</li>
+<li><strong>num_batch</strong> (<em>int or None</em>) – The number of batches to run. Go though all batches if <code class="docutils literal"><span class="pre">None</span></code>.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>s</strong> – The final score.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>s</strong> –
+The final score.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
@@ -2259,32 +2293,32 @@ as assessed by an evaluation metric.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.model.FeedForward.fit">
-<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>eval_data=None</em>, <em>eval_metric='acc'</em>, <em>epoch_end_callback=None</em>, <em>batch_end_callback=None</em>, <em>kvstore='local'</em>, <em>logger=None</em>, <em>work_load_list=None</em>, <em>monitor=None</em>, <em>eval_end_callback=<mxnet.callback.logvalidationmetricscallback object=""></mxnet.callback.logvalidationmetricscallback></em>, <em>eval_batch_end_callback=None</em><span cla [...]
+<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>eval_data=None</em>, <em>eval_metric='acc'</em>, <em>epoch_end_callback=None</em>, <em>batch_end_callback=None</em>, <em>kvstore='local'</em>, <em>logger=None</em>, <em>work_load_list=None</em>, <em>monitor=None</em>, <em>eval_end_callback=<mxnet.callback.LogValidationMetricsCallback object></em>, <em>eval_batch_end_callback=None</em><span class="sig-paren">)</span><a class="headerlink" href [...]
<dd><p>Fit the model.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>X</strong> (<a class="reference internal" href="io.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a><em>, or </em><em>numpy.ndarray/NDArray</em>) – Training data. If <cite>X</cite> is a <cite>DataIter</cite>, the name or (if name not available)
+<li><strong>X</strong> (<em>DataIter, or numpy.ndarray/NDArray</em>) – Training data. If <cite>X</cite> is a <cite>DataIter</cite>, the name or (if name not available)
the position of its outputs should match the corresponding variable
names defined in the symbolic graph.</li>
-<li><strong>y</strong> (<em>numpy.ndarray/NDArray</em><em>, </em><em>optional</em>) – Training set label.
+<li><strong>y</strong> (<em>numpy.ndarray/NDArray, optional</em>) – Training set label.
If X is <code class="docutils literal"><span class="pre">numpy.ndarray</span></code> or <cite>NDArray</cite>, <cite>y</cite> is required to be set.
While y can be 1D or 2D (with 2nd dimension as 1), its first dimension must be
the same as <cite>X</cite>, i.e. the number of data points and labels should be equal.</li>
-<li><strong>eval_data</strong> (<a class="reference internal" href="io.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a><em> or </em><em>numpy.ndarray/list/NDArray pair</em>) – If eval_data is numpy.ndarray/list/NDArray pair,
+<li><strong>eval_data</strong> (<em>DataIter or numpy.ndarray/list/NDArray pair</em>) – If eval_data is numpy.ndarray/list/NDArray pair,
it should be <code class="docutils literal"><span class="pre">(valid_data,</span> <span class="pre">valid_label)</span></code>.</li>
-<li><strong>eval_metric</strong> (<a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>metric.EvalMetric</em></a><em> or </em><em>str</em><em> or </em><em>callable</em>) – The evaluation metric. This could be the name of evaluation metric
+<li><strong>eval_metric</strong> (<em>metric.EvalMetric or str or callable</em>) – The evaluation metric. This could be the name of evaluation metric
or a custom evaluation function that returns statistics
based on a minibatch.</li>
-<li><strong>epoch_end_callback</strong> (<em>callable</em><em>(</em><em>epoch</em><em>, </em><a class="reference internal" href="symbol.html#module-mxnet.symbol" title="mxnet.symbol"><em>symbol</em></a><em>, </em><em>arg_params</em><em>, </em><em>aux_states</em><em>)</em><em></em>) – A callback that is invoked at end of each epoch.
+<li><strong>epoch_end_callback</strong> (<em>callable(epoch, symbol, arg_params, aux_states)</em>) – A callback that is invoked at end of each epoch.
This can be used to checkpoint model each epoch.</li>
-<li><strong>batch_end_callback</strong> (<em>callable</em><em>(</em><em>epoch</em><em>)</em><em></em>) – A callback that is invoked at end of each batch for purposes of printing.</li>
-<li><strong>kvstore</strong> (<a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><em>KVStore</em></a><em> or </em><em>str</em><em>, </em><em>optional</em>) – The KVStore or a string kvstore type: ‘local’, ‘dist_sync’, ‘dist_async’
+<li><strong>batch_end_callback</strong> (<em>callable(epoch)</em>) – A callback that is invoked at end of each batch for purposes of printing.</li>
+<li><strong>kvstore</strong> (<em>KVStore or str, optional</em>) – The KVStore or a string kvstore type: ‘local’, ‘dist_sync’, ‘dist_async’
In default uses ‘local’, often no need to change for single machiine.</li>
-<li><strong>logger</strong> (<em>logging logger</em><em>, </em><em>optional</em>) – When not specified, default logger will be used.</li>
-<li><strong>work_load_list</strong> (<em>float</em><em> or </em><em>int</em><em>, </em><em>optional</em>) – The list of work load for different devices,
+<li><strong>logger</strong> (<em>logging logger, optional</em>) – When not specified, default logger will be used.</li>
+<li><strong>work_load_list</strong> (<em>float or int, optional</em>) – The list of work load for different devices,
in the same order as <cite>ctx</cite>.</li>
</ul>
</td>
@@ -2308,8 +2342,8 @@ The advantage of <cite>load</cite> and <cite>save</cite> (as compared to <cite>p
the resulting file can be loaded from other MXNet language bindings.
One can also directly <cite>load</cite>/<cite>save</cite> from/to cloud storage(S3, HDFS)</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</td>
</tr>
@@ -2326,18 +2360,19 @@ One can also directly <cite>load</cite>/<cite>save</cite> from/to cloud storage(
<em class="property">static </em><code class="descname">load</code><span class="sig-paren">(</span><em>prefix</em>, <em>epoch</em>, <em>ctx=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.model.FeedForward.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Load model checkpoint from file.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</li>
<li><strong>epoch</strong> (<em>int</em>) – epoch number of model we would like to load.</li>
-<li><strong>ctx</strong> (<em>Context</em><em> or </em><em>list of Context</em><em>, </em><em>optional</em>) – The device context of training and prediction.</li>
+<li><strong>ctx</strong> (<em>Context or list of Context, optional</em>) – The device context of training and prediction.</li>
<li><strong>kwargs</strong> (<em>dict</em>) – Other parameters for model, including <cite>num_epoch</cite>, optimizer and <cite>numpy_batch_size</cite>.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>model</strong> – The loaded model that can be used for prediction.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>model</strong> –
+The loaded model that can be used for prediction.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.model.FeedForward" title="mxnet.model.FeedForward">FeedForward</a></p>
@@ -2353,37 +2388,37 @@ One can also directly <cite>load</cite>/<cite>save</cite> from/to cloud storage(
</dd></dl>
<dl class="staticmethod">
<dt id="mxnet.model.FeedForward.create">
-<em class="property">static </em><code class="descname">create</code><span class="sig-paren">(</span><em>symbol</em>, <em>X</em>, <em>y=None</em>, <em>ctx=None</em>, <em>num_epoch=None</em>, <em>epoch_size=None</em>, <em>optimizer='sgd'</em>, <em>initializer=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, <em>eval_data=None</em>, <em>eval_metric='acc'</em>, <em>epoch_end_callback=None</em>, <em>batch_end_callback=None</em>, <em>kvstore='local'</em>, <em>logger=Non [...]
+<em class="property">static </em><code class="descname">create</code><span class="sig-paren">(</span><em>symbol</em>, <em>X</em>, <em>y=None</em>, <em>ctx=None</em>, <em>num_epoch=None</em>, <em>epoch_size=None</em>, <em>optimizer='sgd'</em>, <em>initializer=<mxnet.initializer.Uniform object></em>, <em>eval_data=None</em>, <em>eval_metric='acc'</em>, <em>epoch_end_callback=None</em>, <em>batch_end_callback=None</em>, <em>kvstore='local'</em>, <em>logger=None</em>, <em>work_load_list=None [...]
<dd><p>Functional style to create a model.
This function is more consistent with functional
languages such as R, where mutation is not allowed.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>symbol</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The symbol configuration of a computation network.</li>
<li><strong>X</strong> (<a class="reference internal" href="io.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a>) – Training data.</li>
-<li><strong>y</strong> (<em>numpy.ndarray</em><em>, </em><em>optional</em>) – If <cite>X</cite> is a <code class="docutils literal"><span class="pre">numpy.ndarray</span></code>, <cite>y</cite> must be set.</li>
-<li><strong>ctx</strong> (<em>Context</em><em> or </em><em>list of Context</em><em>, </em><em>optional</em>) – The device context of training and prediction.
+<li><strong>y</strong> (<em>numpy.ndarray, optional</em>) – If <cite>X</cite> is a <code class="docutils literal"><span class="pre">numpy.ndarray</span></code>, <cite>y</cite> must be set.</li>
+<li><strong>ctx</strong> (<em>Context or list of Context, optional</em>) – The device context of training and prediction.
To use multi-GPU training, pass in a list of GPU contexts.</li>
-<li><strong>num_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of training epochs(epochs).</li>
-<li><strong>epoch_size</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of batches in a epoch. In default, it is set to
+<li><strong>num_epoch</strong> (<em>int, optional</em>) – The number of training epochs(epochs).</li>
+<li><strong>epoch_size</strong> (<em>int, optional</em>) – Number of batches in a epoch. In default, it is set to
<code class="docutils literal"><span class="pre">ceil(num_train_examples</span> <span class="pre">/</span> <span class="pre">batch_size)</span></code>.</li>
-<li><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a><em>, </em><em>optional</em>) – The name of the chosen optimizer, or an optimizer object, used for training.</li>
-<li><strong>initializer</strong> (<em>initializer function</em><em>, </em><em>optional</em>) – The initialization scheme used.</li>
-<li><strong>eval_data</strong> (<a class="reference internal" href="io.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a><em> or </em><em>numpy.ndarray pair</em>) – If <cite>eval_set</cite> is <code class="docutils literal"><span class="pre">numpy.ndarray</span></code> pair, it should
+<li><strong>optimizer</strong> (<em>str or Optimizer, optional</em>) – The name of the chosen optimizer, or an optimizer object, used for training.</li>
+<li><strong>initializer</strong> (<em>initializer function, optional</em>) – The initialization scheme used.</li>
+<li><strong>eval_data</strong> (<em>DataIter or numpy.ndarray pair</em>) – If <cite>eval_set</cite> is <code class="docutils literal"><span class="pre">numpy.ndarray</span></code> pair, it should
be (<cite>valid_data</cite>, <cite>valid_label</cite>).</li>
-<li><strong>eval_metric</strong> (<a class="reference internal" href="#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>metric.EvalMetric</em></a><em> or </em><em>str</em><em> or </em><em>callable</em>) – The evaluation metric. Can be the name of an evaluation metric
+<li><strong>eval_metric</strong> (<em>metric.EvalMetric or str or callable</em>) – The evaluation metric. Can be the name of an evaluation metric
or a custom evaluation function that returns statistics
based on a minibatch.</li>
-<li><strong>epoch_end_callback</strong> (<em>callable</em><em>(</em><em>epoch</em><em>, </em><a class="reference internal" href="symbol.html#module-mxnet.symbol" title="mxnet.symbol"><em>symbol</em></a><em>, </em><em>arg_params</em><em>, </em><em>aux_states</em><em>)</em><em></em>) – A callback that is invoked at end of each epoch.
+<li><strong>epoch_end_callback</strong> (<em>callable(epoch, symbol, arg_params, aux_states)</em>) – A callback that is invoked at end of each epoch.
This can be used to checkpoint model each epoch.</li>
-<li><strong>batch_end_callback</strong> (<em>callable</em><em>(</em><em>epoch</em><em>)</em><em></em>) – A callback that is invoked at end of each batch for print purposes.</li>
-<li><strong>kvstore</strong> (<a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><em>KVStore</em></a><em> or </em><em>str</em><em>, </em><em>optional</em>) – The KVStore or a string kvstore type: ‘local’, ‘dist_sync’, ‘dis_async’.
+<li><strong>batch_end_callback</strong> (<em>callable(epoch)</em>) – A callback that is invoked at end of each batch for print purposes.</li>
+<li><strong>kvstore</strong> (<em>KVStore or str, optional</em>) – The KVStore or a string kvstore type: ‘local’, ‘dist_sync’, ‘dis_async’.
Defaults to ‘local’, often no need to change for single machine.</li>
-<li><strong>logger</strong> (<em>logging logger</em><em>, </em><em>optional</em>) – When not specified, default logger will be used.</li>
-<li><strong>work_load_list</strong> (<em>list of float</em><em> or </em><em>int</em><em>, </em><em>optional</em>) – The list of work load for different devices,
+<li><strong>logger</strong> (<em>logging logger, optional</em>) – When not specified, default logger will be used.</li>
+<li><strong>work_load_list</strong> (<em>list of float or int, optional</em>) – The list of work load for different devices,
in the same order as <cite>ctx</cite>.</li>
</ul>
</td>
@@ -2397,10 +2432,10 @@ in the same order as <cite>ctx</cite>.</li>
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
-<li>See <a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a> for operations on NDArrays that assemble neural networks from layers.</li>
-<li>See <a class="reference internal" href="io.html"><span class="doc">IO Data Loading API</span></a> for parsing and loading data.</li>
-<li>See <a class="reference internal" href="ndarray.html"><span class="doc">NDArray API</span></a> for vector/matrix/tensor operations.</li>
-<li>See <a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> for multi-GPU and multi-host distributed training.</li>
+<li>See <a class="reference internal" href="symbol.html"><em>Symbolic API</em></a> for operations on NDArrays that assemble neural networks from layers.</li>
+<li>See <a class="reference internal" href="io.html"><em>IO Data Loading API</em></a> for parsing and loading data.</li>
+<li>See <a class="reference internal" href="ndarray.html"><em>NDArray API</em></a> for vector/matrix/tensor operations.</li>
+<li>See <a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> for multi-GPU and multi-host distributed training.</li>
</ul>
</div>
</div>
diff --git a/versions/master/api/python/module.html b/versions/master/api/python/module.html
index d130196..a422aa9 100644
--- a/versions/master/api/python/module.html
+++ b/versions/master/api/python/module.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Module API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
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<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="index.html" rel="up" title="MXNet - Python API"/>
-<link href="gluon.html" rel="next" title="Gluon Package"/>
+<link href="index.html" rel="up" title="MXNet - Python API">
+<link href="autograd.html" rel="next" title="Autograd Package"/>
<link href="symbol.html" rel="prev" title="Symbol API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
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@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
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-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
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@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
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+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
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<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
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@@ -228,13 +226,14 @@ Previous Navbar Layout End -->
<li class="toctree-l2 current"><a class="reference internal" href="index.html#table-of-contents">Table of contents</a><ul class="current">
<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Module API</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Module API</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
<li class="toctree-l4"><a class="reference internal" href="#the-basemodule-class">The <code class="docutils literal"><span class="pre">BaseModule</span></code> class</a></li>
<li class="toctree-l4"><a class="reference internal" href="#other-build-in-modules">Other build-in modules</a></li>
<li class="toctree-l4"><a class="reference internal" href="#api-reference">API Reference</a></li>
</ul>
</li>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
<li class="toctree-l3"><a class="reference internal" href="kvstore.html">KVStore API</a></li>
@@ -300,8 +299,8 @@ predict:</p>
<p>The <code class="docutils literal"><span class="pre">module</span></code> package provides several modules:</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.BaseModule" title="mxnet.module.BaseModule"><code class="xref py py-obj docutils literal"><span class="pre">BaseModule</span></code></a></td>
@@ -334,8 +333,8 @@ interface each module class should provide.</p>
<span id="initialize-memory"></span><h3>Initialize memory<a class="headerlink" href="#initialize-memory" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.BaseModule.bind" title="mxnet.module.BaseModule.bind"><code class="xref py py-obj docutils literal"><span class="pre">BaseModule.bind</span></code></a></td>
@@ -348,8 +347,8 @@ interface each module class should provide.</p>
<span id="get-and-set-parameters"></span><h3>Get and set parameters<a class="headerlink" href="#get-and-set-parameters" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
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<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.BaseModule.init_params" title="mxnet.module.BaseModule.init_params"><code class="xref py py-obj docutils literal"><span class="pre">BaseModule.init_params</span></code></a></td>
@@ -374,8 +373,8 @@ interface each module class should provide.</p>
<span id="train-and-predict"></span><h3>Train and predict<a class="headerlink" href="#train-and-predict" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.BaseModule.fit" title="mxnet.module.BaseModule.fit"><code class="xref py py-obj docutils literal"><span class="pre">BaseModule.fit</span></code></a></td>
@@ -397,8 +396,8 @@ interface each module class should provide.</p>
<span id="forward-and-backward"></span><h3>Forward and backward<a class="headerlink" href="#forward-and-backward" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
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<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.BaseModule.forward" title="mxnet.module.BaseModule.forward"><code class="xref py py-obj docutils literal"><span class="pre">BaseModule.forward</span></code></a></td>
@@ -417,8 +416,8 @@ interface each module class should provide.</p>
<span id="update-parameters"></span><h3>Update parameters<a class="headerlink" href="#update-parameters" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
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<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.BaseModule.init_optimizer" title="mxnet.module.BaseModule.init_optimizer"><code class="xref py py-obj docutils literal"><span class="pre">BaseModule.init_optimizer</span></code></a></td>
@@ -437,8 +436,8 @@ interface each module class should provide.</p>
<span id="input-and-output"></span><h3>Input and output<a class="headerlink" href="#input-and-output" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
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<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.BaseModule.data_names" title="mxnet.module.BaseModule.data_names"><code class="xref py py-obj docutils literal"><span class="pre">BaseModule.data_names</span></code></a></td>
@@ -469,8 +468,8 @@ interface each module class should provide.</p>
<span id="others"></span><h3>Others<a class="headerlink" href="#others" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
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<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.BaseModule.get_states" title="mxnet.module.BaseModule.get_states"><code class="xref py py-obj docutils literal"><span class="pre">BaseModule.get_states</span></code></a></td>
@@ -497,8 +496,8 @@ additional functionality. We summarize them in this section.</p>
<span id="class-module"></span><h3>Class <code class="docutils literal"><span class="pre">Module</span></code><a class="headerlink" href="#class-module" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.Module.load" title="mxnet.module.Module.load"><code class="xref py py-obj docutils literal"><span class="pre">Module.load</span></code></a></td>
@@ -526,8 +525,8 @@ additional functionality. We summarize them in this section.</p>
<span id="class-bucketmodule"></span><h3>Class <code class="docutils literal"><span class="pre">BucketModule</span></code><a class="headerlink" href="#class-bucketmodule" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">BucketModule.switch_bucket</span></code></td>
@@ -540,8 +539,8 @@ additional functionality. We summarize them in this section.</p>
<span id="class-sequentialmodule"></span><h3>Class <code class="docutils literal"><span class="pre">SequentialModule</span></code><a class="headerlink" href="#class-sequentialmodule" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.module.SequentialModule.add" title="mxnet.module.SequentialModule.add"><code class="xref py py-obj docutils literal"><span class="pre">SequentialModule.add</span></code></a></td>
@@ -555,7 +554,7 @@ additional functionality. We summarize them in this section.</p>
<span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline">¶</a></h2>
<script src="../../_static/js/auto_module_index.js" type="text/javascript"></script><dl class="class">
<dt id="mxnet.module.BaseModule">
-<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">BaseModule</code><span class="sig-paren">(</span><em>logger=<module '="" 'logging'="" __init__.pyc'="" anaconda="" from="" lib="" logging="" python2.7="" users="" wayao=""></module></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">BaseModule</code><span class="sig-paren">(</span><em>logger=<module 'logging' from '/usr/lib/python2.7/logging/__init__.pyc'></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule" title="Permalink to this definition">¶</a></dt>
<dd><p>The base class of a module.</p>
<p>A module represents a computation component. One can think of module as a computation machine.
A module can execute forward and backward passes and update parameters in a model.
@@ -668,8 +667,8 @@ high-level API will be automatically available for a module:</p>
<li><cite>score</cite>: run prediction on a data set and evaluate performance.</li>
</ul>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of creating a mxnet module.</span>
-<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of creating a mxnet module.</span>
+<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span>
<span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">fc1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc1'</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">128</span><span c [...]
<span class="gp">>>> </span><span class="n">act1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'relu1'</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s2">"relu"</span><span cla [...]
@@ -693,12 +692,12 @@ the given <code class="docutils literal"><span class="pre">eval_metric</span></c
<p>Checkout <a class="reference external" href="http://mxnet.io/tutorials/basic/module.html">Module Tutorial</a> to see
a end-to-end use-case.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>eval_data</strong> (<a class="reference internal" href="io.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a>) – Evaluation data to run prediction on.</li>
-<li><strong>eval_metric</strong> (<a class="reference internal" href="model.html#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>EvalMetric</em></a><em> or </em><em>list of EvalMetrics</em>) – Evaluation metric to use.</li>
+<li><strong>eval_metric</strong> (<em>EvalMetric or list of EvalMetrics</em>) – Evaluation metric to use.</li>
<li><strong>num_batch</strong> (<em>int</em>) – Number of batches to run. Defaults to <code class="docutils literal"><span class="pre">None</span></code>, indicating run until the <cite>DataIter</cite>
finishes.</li>
<li><strong>batch_end_callback</strong> (<em>function</em>) – Could also be a list of functions.</li>
@@ -712,7 +711,7 @@ During training, this will correspond to the training epoch number.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of using score for prediction.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of using score for prediction.</span>
<span class="gp">>>> </span><span class="c1"># Evaluate accuracy on val_dataiter</span>
<span class="gp">>>> </span><span class="n">metric</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">val_dataiter</span><span class="p">,</span> <span class="n">metric</span><span class="p">)</span>
@@ -724,15 +723,15 @@ During training, this will correspond to the training epoch number.</li>
<dt id="mxnet.module.BaseModule.iter_predict">
<code class="descname">iter_predict</code><span class="sig-paren">(</span><em>eval_data</em>, <em>num_batch=None</em>, <em>reset=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule.iter_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Iterates over predictions.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="n">pred</span><span class="p">,</span> <span class="n">i_batch</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">iter_predict</span><span class="p">(</span><span class="n">eval_data</span><span class="p">):</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="n">pred</span><span class="p">,</span> <span class="n">i_batch</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">iter_predict</span><span class="p">(</span><span class="n">eval_data</span><span class="p">):</span>
<span class="gp">... </span> <span class="c1"># pred is a list of outputs from the module</span>
<span class="gp">... </span> <span class="c1"># i_batch is a integer</span>
<span class="gp">... </span> <span class="c1"># batch is the data batch from the data iterator</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>eval_data</strong> (<a class="reference internal" href="io.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a>) – Evaluation data to run prediction on.</li>
@@ -760,8 +759,8 @@ number of outputs.</p>
<p>The objects in the results have type <cite>NDArray</cite>. If you need to work with a numpy array,
just call <code class="docutils literal"><span class="pre">.asnumpy()</span></code> on each <cite>NDArray</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>eval_data</strong> (<a class="reference internal" href="io.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a>) – Evaluation data to run prediction on.</li>
@@ -782,7 +781,7 @@ doing prediction.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of using `predict` for prediction.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of using `predict` for prediction.</span>
<span class="gp">>>> </span><span class="c1"># Predict on the first 10 batches of val_dataiter</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">eval_data</span><span class="o">=</span><span class="n">val_dataiter</span><span class="p">,</span> <span class="n">num_batch</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
@@ -790,33 +789,33 @@ doing prediction.</li>
</dd></dl>
<dl class="method">
<dt id="mxnet.module.BaseModule.fit">
-<code class="descname">fit</code><span class="sig-paren">(</span><em>train_data</em>, <em>eval_data=None</em>, <em>eval_metric='acc'</em>, <em>epoch_end_callback=None</em>, <em>batch_end_callback=None</em>, <em>kvstore='local'</em>, <em>optimizer='sgd'</em>, <em>optimizer_params=(('learning_rate'</em>, <em>0.01)</em>, <em>)</em>, <em>eval_end_callback=None</em>, <em>eval_batch_end_callback=None</em>, <em>initializer=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, [...]
+<code class="descname">fit</code><span class="sig-paren">(</span><em>train_data</em>, <em>eval_data=None</em>, <em>eval_metric='acc'</em>, <em>epoch_end_callback=None</em>, <em>batch_end_callback=None</em>, <em>kvstore='local'</em>, <em>optimizer='sgd'</em>, <em>optimizer_params=(('learning_rate'</em>, <em>0.01)</em>, <em>)</em>, <em>eval_end_callback=None</em>, <em>eval_batch_end_callback=None</em>, <em>initializer=<mxnet.initializer.Uniform object></em>, <em>arg_params=None</em>, <em>a [...]
<dd><p>Trains the module parameters.</p>
<p>Checkout <a class="reference external" href="http://mxnet.io/tutorials/basic/module.html">Module Tutorial</a> to see
a end-to-end use-case.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>train_data</strong> (<a class="reference internal" href="io.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a>) – Train DataIter.</li>
<li><strong>eval_data</strong> (<a class="reference internal" href="io.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a>) – If not <code class="docutils literal"><span class="pre">None</span></code>, will be used as validation set and the performance
after each epoch will be evaluated.</li>
-<li><strong>eval_metric</strong> (<em>str</em><em> or </em><a class="reference internal" href="model.html#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>EvalMetric</em></a>) – Defaults to ‘accuracy’. The performance measure used to display during training.
+<li><strong>eval_metric</strong> (<em>str or EvalMetric</em>) – Defaults to ‘accuracy’. The performance measure used to display during training.
Other possible predefined metrics are:
‘ce’ (CrossEntropy), ‘f1’, ‘mae’, ‘mse’, ‘rmse’, ‘top_k_accuracy’.</li>
-<li><strong>epoch_end_callback</strong> (<em>function</em><em> or </em><em>list of functions</em>) – Each callback will be called with the current <cite>epoch</cite>, <cite>symbol</cite>, <cite>arg_params</cite>
+<li><strong>epoch_end_callback</strong> (<em>function or list of functions</em>) – Each callback will be called with the current <cite>epoch</cite>, <cite>symbol</cite>, <cite>arg_params</cite>
and <cite>aux_params</cite>.</li>
-<li><strong>batch_end_callback</strong> (<em>function</em><em> or </em><em>list of function</em>) – Each callback will be called with a <cite>BatchEndParam</cite>.</li>
-<li><strong>kvstore</strong> (<em>str</em><em> or </em><a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><em>KVStore</em></a>) – Defaults to ‘local’.</li>
-<li><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – Defaults to ‘sgd’.</li>
+<li><strong>batch_end_callback</strong> (<em>function or list of function</em>) – Each callback will be called with a <cite>BatchEndParam</cite>.</li>
+<li><strong>kvstore</strong> (<em>str or KVStore</em>) – Defaults to ‘local’.</li>
+<li><strong>optimizer</strong> (<em>str or Optimizer</em>) – Defaults to ‘sgd’.</li>
<li><strong>optimizer_params</strong> (<em>dict</em>) – Defaults to <code class="docutils literal"><span class="pre">(('learning_rate',</span> <span class="pre">0.01),)</span></code>. The parameters for
the optimizer constructor.
The default value is not a dict, just to avoid pylint warning on dangerous
default values.</li>
-<li><strong>eval_end_callback</strong> (<em>function</em><em> or </em><em>list of function</em>) – These will be called at the end of each full evaluation, with the metrics over
+<li><strong>eval_end_callback</strong> (<em>function or list of function</em>) – These will be called at the end of each full evaluation, with the metrics over
the entire evaluation set.</li>
-<li><strong>eval_batch_end_callback</strong> (<em>function</em><em> or </em><em>list of function</em>) – These will be called at the end of each mini-batch during evaluation.</li>
+<li><strong>eval_batch_end_callback</strong> (<em>function or list of function</em>) – These will be called at the end of each mini-batch during evaluation.</li>
<li><strong>initializer</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – The initializer is called to initialize the module parameters when they are
not already initialized.</li>
<li><strong>arg_params</strong> (<em>dict</em>) – Defaults to <code class="docutils literal"><span class="pre">None</span></code>, if not <code class="docutils literal"><span class="pre">None</span></code>, should be existing parameters from a trained
@@ -841,7 +840,7 @@ N+1.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of using fit for training.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of using fit for training.</span>
<span class="gp">>>> </span><span class="c1"># Assume training dataIter and validation dataIter are ready</span>
<span class="gp">>>> </span><span class="c1"># Assume loading a previously checkpointed model</span>
<span class="gp">>>> </span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="n">model_prefix</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
@@ -886,8 +885,8 @@ list <code class="docutils literal"><span class="pre">[]</span></code>.</p>
<dd><p>Gets parameters, those are potentially copies of the the actual parameters used
to do computation on the device.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A pair of dictionaries each mapping parameter names to NDArray values.</td>
</tr>
@@ -896,21 +895,21 @@ to do computation on the device.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of getting module parameters.</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
-<span class="go">({'fc2_weight': <ndarray 64x128="" @cpu(0)="">, 'fc1_weight': <ndarray 128x100="" @cpu(0)="">,</ndarray></ndarray></span>
-<span class="go">'fc3_bias': <ndarray 10="" @cpu(0)="">, 'fc3_weight': <ndarray 10x64="" @cpu(0)="">,</ndarray></ndarray></span>
-<span class="go">'fc2_bias': <ndarray 64="" @cpu(0)="">, 'fc1_bias': <ndarray 128="" @cpu(0)="">}, {})</ndarray></ndarray></span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of getting module parameters.</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
+<span class="go">({'fc2_weight': <NDArray 64x128 @cpu(0)>, 'fc1_weight': <NDArray 128x100 @cpu(0)>,</span>
+<span class="go">'fc3_bias': <NDArray 10 @cpu(0)>, 'fc3_weight': <NDArray 10x64 @cpu(0)>,</span>
+<span class="go">'fc2_bias': <NDArray 64 @cpu(0)>, 'fc1_bias': <NDArray 128 @cpu(0)>}, {})</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="mxnet.module.BaseModule.init_params">
-<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule.init_params" title="Permalink to this definition">¶</a></dt>
+<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.Uniform object></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule.init_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes the parameters and auxiliary states.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>initializer</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Called to initialize parameters if needed.</li>
@@ -921,7 +920,7 @@ will be copied from that.</li>
<li><strong>allow_missing</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, params could contain missing values, and the initializer will be
called to fill those missing params.</li>
<li><strong>force_init</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, <cite>force_init</cite> will force re-initialize even if already initialized.</li>
-<li><strong>allow_extra</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol.
+<li><strong>allow_extra</strong> (<em>boolean, optional</em>) – Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.</li>
</ul>
@@ -930,7 +929,7 @@ contain extra parameters that is not needed by the executor.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of initializing module parameters.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of initializing module parameters.</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">init_params</span><span class="p">()</span>
</pre></div>
</div>
@@ -940,8 +939,8 @@ contain extra parameters that is not needed by the executor.</li>
<code class="descname">set_params</code><span class="sig-paren">(</span><em>arg_params</em>, <em>aux_params</em>, <em>allow_missing=False</em>, <em>force_init=True</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Assigns parameter and aux state values.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>arg_params</strong> (<em>dict</em>) – Dictionary of name to value (<cite>NDArray</cite>) mapping.</li>
@@ -949,7 +948,7 @@ contain extra parameters that is not needed by the executor.</li>
<li><strong>allow_missing</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, params could contain missing values, and the initializer will be
called to fill those missing params.</li>
<li><strong>force_init</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, will force re-initialize even if already initialized.</li>
-<li><strong>allow_extra</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol.
+<li><strong>allow_extra</strong> (<em>boolean, optional</em>) – Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.</li>
</ul>
@@ -958,7 +957,7 @@ contain extra parameters that is not needed by the executor.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of setting module parameters.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of setting module parameters.</span>
<span class="gp">>>> </span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="n">model_prefix</span><span class="p">,</span> <span class="n">n_epoch_load</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="o">=</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="n">aux_params</span><span class="p">)</span>
</pre></div>
@@ -969,15 +968,15 @@ contain extra parameters that is not needed by the executor.</li>
<code class="descname">save_params</code><span class="sig-paren">(</span><em>fname</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule.save_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves model parameters to file.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fname</strong> (<em>str</em>) – Path to output param file.</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of saving module parameters.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of saving module parameters.</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">save_params</span><span class="p">(</span><span class="s1">'myfile'</span><span class="p">)</span>
</pre></div>
</div>
@@ -987,15 +986,15 @@ contain extra parameters that is not needed by the executor.</li>
<code class="descname">load_params</code><span class="sig-paren">(</span><em>fname</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule.load_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Loads model parameters from file.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fname</strong> (<em>str</em>) – Path to input param file.</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of loading module parameters.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of loading module parameters.</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">load_params</span><span class="p">(</span><span class="s1">'myfile'</span><span class="p">)</span>
</pre></div>
</div>
@@ -1009,8 +1008,8 @@ Otherwise, it returns output of the form
<code class="docutils literal"><span class="pre">[[out1_dev1,</span> <span class="pre">out1_dev2],</span> <span class="pre">[out2_dev1,</span> <span class="pre">out2_dev2]]</span></code>.
All output elements are <cite>NDArray</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Defaults to <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the states
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicates that we
@@ -1029,8 +1028,8 @@ executor.</td>
<code class="descname">set_states</code><span class="sig-paren">(</span><em>states=None</em>, <em>value=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule.set_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets value for states. Only one of states & value can be specified.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>states</strong> (<em>list of list of NDArray</em>) – Source states arrays formatted like
@@ -1053,8 +1052,8 @@ executor.</td>
<dd><p>Prepares the module for processing a data batch.</p>
<p>Usually involves switching bucket and reshaping.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data_batch</strong> (<a class="reference internal" href="io.html#mxnet.io.DataBatch" title="mxnet.io.DataBatch"><em>DataBatch</em></a>) – </td>
</tr>
@@ -1070,8 +1069,8 @@ If reshaping of data batch relates to modification of symbol or module, such as
changing image layout ordering or switching from training to predicting, module
rebinding is required.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_batch</strong> (<a class="reference internal" href="io.html#mxnet.io.DataBatch" title="mxnet.io.DataBatch"><em>DataBatch</em></a>) – Could be anything with similar API implemented.</li>
@@ -1082,22 +1081,22 @@ rebinding is required.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
-<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">namedtuple</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span>
+<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
<span class="gp">>>> </span><span class="n">Batch</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s1">'Batch'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'data'</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">data</span> <span class="o">*</span> <span class="mi">2</span>
-<span class="gp">>>> </span><span class="n">mod</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">mod</span><span class="o">.</span><span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">mod</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">mod</span><span class="o">.</span><span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">data_shapes</span><span class="o">=</span><span class="p">[(</span><span class="s1">'data'</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))])</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">init_params</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">data1</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))]</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Batch</span><span class="p">(</span><span class="n">data1</span><span class="p">))</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]]</span>
<span class="gp">>>> </span><span class="c1"># Forward with data batch of different shape</span>
<span class="gp">>>> </span><span class="n">data2</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))]</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Batch</span><span class="p">(</span><span class="n">data2</span><span class="p">))</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 2. 2. 2. 2. 2.]</span>
<span class="go"> [ 2. 2. 2. 2. 2.]</span>
<span class="go"> [ 2. 2. 2. 2. 2.]]</span>
@@ -1109,19 +1108,19 @@ rebinding is required.</p>
<code class="descname">backward</code><span class="sig-paren">(</span><em>out_grads=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Backward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>out_grads</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em>, </em><em>optional</em>) – Gradient on the outputs to be propagated back.
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>out_grads</strong> (<em>NDArray or list of NDArray, optional</em>) – Gradient on the outputs to be propagated back.
This parameter is only needed when bind is called
on outputs that are not a loss function.</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of backward computation.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of backward computation.</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_input_grads</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_input_grads</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[[ 1.10182791e-05 5.12257748e-06 4.01927764e-06 8.32566820e-06</span>
<span class="go"> -1.59775993e-06 7.24269375e-06 7.28067835e-06 -1.65902311e-05</span>
<span class="go"> 5.46342608e-06 8.44196393e-07]</span>
@@ -1138,8 +1137,8 @@ it returns out put of form <code class="docutils literal"><span class="pre">[[ou
All the output elements have type <cite>NDArray</cite>. When <cite>merge_multi_context</cite> is <code class="docutils literal"><span class="pre">False</span></code>,
those <cite>NDArray</cite> instances might live on different devices.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Defaults to <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicates that we
@@ -1153,8 +1152,8 @@ executor.</td>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of getting forward output.</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of getting forward output.</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 0.09999977 0.10000153 0.10000716 0.10000195 0.09999853 0.09999743</span>
<span class="go"> 0.10000272 0.10000113 0.09999088 0.09999888]]</span>
</pre></div>
@@ -1169,8 +1168,8 @@ is like <code class="docutils literal"><span class="pre">[[grad1_dev1,</span> <s
elements have type <cite>NDArray</cite>. When <cite>merge_multi_context</cite> is <code class="docutils literal"><span class="pre">False</span></code>, those <cite>NDArray</cite>
instances might live on different devices.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Defaults to <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the gradients
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicates that we
@@ -1184,8 +1183,8 @@ executor.</td>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of getting input gradients.</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_input_grads</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of getting input gradients.</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_input_grads</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[[ 1.10182791e-05 5.12257748e-06 4.01927764e-06 8.32566820e-06</span>
<span class="go"> -1.59775993e-06 7.24269375e-06 7.28067835e-06 -1.65902311e-05</span>
<span class="go"> 5.46342608e-06 8.44196393e-07]</span>
@@ -1199,12 +1198,12 @@ executor.</td>
<dd><p>Updates parameters according to the installed optimizer and the gradients computed
in the previous forward-backward batch.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of updating module parameters.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of updating module parameters.</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">init_optimizer</span><span class="p">(</span><span class="n">kvstore</span><span class="o">=</span><span class="s1">'local'</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">'sgd'</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">optimizer_params</span><span class="o">=</span><span class="p">((</span><span class="s1">'learning_rate'</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">),</span> <span class="p">))</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">update</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_params</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="s1">'fc3_weight'</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_params</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="s1">'fc3_weight'</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 5.86930104e-03 5.28078526e-03 -8.88729654e-03 -1.08308345e-03</span>
<span class="go"> 6.13054074e-03 4.27560415e-03 1.53817423e-03 4.62131854e-03</span>
<span class="go"> 4.69872449e-03 -2.42400169e-03 9.94111411e-04 1.12386420e-03</span>
@@ -1218,8 +1217,8 @@ in the previous forward-backward batch.</p>
<dd><p>Evaluates and accumulates evaluation metric on outputs of the last forward
computation.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>eval_metric</strong> (<a class="reference internal" href="model.html#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>EvalMetric</em></a>) – Evaluation metric to use.</li>
@@ -1230,7 +1229,7 @@ computation.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of updating evaluation metric.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of updating evaluation metric.</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">data_batch</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">update_metric</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">label</span><span class="p">)</span>
</pre></div>
@@ -1242,13 +1241,13 @@ computation.</p>
<dd><p>Binds the symbols to construct executors. This is necessary before one
can perform computation with the module.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>) or </em><em>DataDesc objects</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_data</span></code>. Can also be a list of
+<li><strong>data_shapes</strong> (<em>list of (str, tuple) or DataDesc objects</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_data</span></code>. Can also be a list of
(data name, data shape).</li>
-<li><strong>label_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>) or </em><em>DataDesc objects</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_label</span></code>. Can also be a list of
+<li><strong>label_shapes</strong> (<em>list of (str, tuple) or DataDesc objects</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_label</span></code>. Can also be a list of
(label name, label shape).</li>
<li><strong>for_training</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>. Whether the executors should be bind for training.</li>
<li><strong>inputs_need_grad</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">False</span></code>. Whether the gradients to the input data need to be computed.
@@ -1259,7 +1258,7 @@ bound. But with this <code class="docutils literal"><span class="pre">True</span
<li><strong>shared_module</strong> (<a class="reference internal" href="#mxnet.module.Module" title="mxnet.module.Module"><em>Module</em></a>) – Default is <code class="docutils literal"><span class="pre">None</span></code>. This is used in bucketing. When not <code class="docutils literal"><span class="pre">None</span></code>, the shared module
essentially corresponds to a different bucket – a module with different symbol
but with the same sets of parameters (e.g. unrolled RNNs with different lengths).</li>
-<li><strong>grad_req</strong> (<em>str</em><em>, </em><em>list of str</em><em>, </em><em>dict of str to str</em>) – Requirement for gradient accumulation. Can be ‘write’, ‘add’, or ‘null’
+<li><strong>grad_req</strong> (<em>str, list of str, dict of str to str</em>) – Requirement for gradient accumulation. Can be ‘write’, ‘add’, or ‘null’
(default to ‘write’).
Can be specified globally (str) or for each argument (list, dict).</li>
</ul>
@@ -1268,7 +1267,7 @@ Can be specified globally (str) or for each argument (list, dict).</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of binding symbols.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of binding symbols.</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">data_shapes</span><span class="o">=</span><span class="p">[(</span><span class="s1">'data'</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))])</span>
<span class="gp">>>> </span><span class="c1"># Assume train_iter is already created.</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">data_shapes</span><span class="o">=</span><span class="n">train_iter</span><span class="o">.</span><span class="n">provide_data</span><span class="p">,</span> <span class="n">label_shapes</span><span class="o">=</span><span class="n">train_iter</span><span class="o">.</span><span class="n">provide_label</span><span class="p">)</span>
@@ -1280,12 +1279,12 @@ Can be specified globally (str) or for each argument (list, dict).</li>
<code class="descname">init_optimizer</code><span class="sig-paren">(</span><em>kvstore='local'</em>, <em>optimizer='sgd'</em>, <em>optimizer_params=(('learning_rate'</em>, <em>0.01)</em>, <em>)</em>, <em>force_init=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BaseModule.init_optimizer" title="Permalink to this definition">¶</a></dt>
<dd><p>Installs and initializes optimizers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>kvstore</strong> (<em>str</em><em> or </em><a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><em>KVStore</em></a>) – Defaults to <cite>‘local’</cite>.</li>
-<li><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – Defaults to <cite>‘sgd’</cite>.</li>
+<li><strong>kvstore</strong> (<em>str or KVStore</em>) – Defaults to <cite>‘local’</cite>.</li>
+<li><strong>optimizer</strong> (<em>str or Optimizer</em>) – Defaults to <cite>‘sgd’</cite>.</li>
<li><strong>optimizer_params</strong> (<em>dict</em>) – Defaults to <code class="docutils literal"><span class="pre">(('learning_rate',</span> <span class="pre">0.01),)</span></code>. The default value is not a dictionary,
just to avoid pylint warning of dangerous default values.</li>
<li><strong>force_init</strong> (<em>bool</em>) – Defaults to <code class="docutils literal"><span class="pre">False</span></code>, indicates whether to force re-initializing an optimizer
@@ -1296,7 +1295,7 @@ if it is already installed.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of initializing optimizer.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of initializing optimizer.</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">init_optimizer</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">'sgd'</span><span class="p">,</span> <span class="n">optimizer_params</span><span class="o">=</span><span class="p">((</span><span class="s1">'learning_rate'</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">),))</span>
</pre></div>
</div>
@@ -1312,12 +1311,12 @@ not even be associated with any symbols.</p>
</dd></dl>
<dl class="class">
<dt id="mxnet.module.Module">
-<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">Module</code><span class="sig-paren">(</span><em>symbol</em>, <em>data_names=('data'</em>, <em>)</em>, <em>label_names=('softmax_label'</em>, <em>)</em>, <em>logger=<module '="" 'logging'="" __init__.pyc'="" anaconda="" from="" lib="" logging="" python2.7="" users="" wayao=""></module></em>, <em>context=cpu(0)</em>, <em>work_load_list=None</em>, <em>fixed_param_names=None</em>, <em>stat [...]
+<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">Module</code><span class="sig-paren">(</span><em>symbol</em>, <em>data_names=('data'</em>, <em>)</em>, <em>label_names=('softmax_label'</em>, <em>)</em>, <em>logger=<module 'logging' from '/usr/lib/python2.7/logging/__init__.pyc'></em>, <em>context=cpu(0)</em>, <em>work_load_list=None</em>, <em>fixed_param_names=None</em>, <em>state_names=None</em><span class="sig-paren">)</span><a clas [...]
<dd><p>Module is a basic module that wrap a <cite>Symbol</cite>. It is functionally the same
as the <cite>FeedForward</cite> model, except under the module API.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>symbol</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – </li>
@@ -1325,7 +1324,7 @@ as the <cite>FeedForward</cite> model, except under the module API.</p>
<li><strong>label_names</strong> (<em>list of str</em>) – Defaults to <cite>(‘softmax_label’)</cite> for a typical model used in image
classification.</li>
<li><strong>logger</strong> (<em>Logger</em>) – Defaults to <cite>logging</cite>.</li>
-<li><strong>context</strong> (<em>Context</em><em> or </em><em>list of Context</em>) – Defaults to <code class="docutils literal"><span class="pre">mx.cpu()</span></code>.</li>
+<li><strong>context</strong> (<em>Context or list of Context</em>) – Defaults to <code class="docutils literal"><span class="pre">mx.cpu()</span></code>.</li>
<li><strong>work_load_list</strong> (<em>list of number</em>) – Default <code class="docutils literal"><span class="pre">None</span></code>, indicating uniform workload.</li>
<li><strong>fixed_param_names</strong> (<em>list of str</em>) – Default <code class="docutils literal"><span class="pre">None</span></code>, indicating no network parameters are fixed.</li>
<li><strong>state_names</strong> (<em>list of str</em>) – states are similar to data and label, but not provided by data iterator.
@@ -1340,8 +1339,8 @@ Instead they are initialized to 0 and can be set by <cite>set_states()</cite>.</
<em class="property">static </em><code class="descname">load</code><span class="sig-paren">(</span><em>prefix</em>, <em>epoch</em>, <em>load_optimizer_states=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a model from previously saved checkpoint.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>prefix</strong> (<em>str</em>) – path prefix of saved model files. You should have
@@ -1355,7 +1354,7 @@ to have been made with save_optimizer_states=True.</li>
<li><strong>label_names</strong> (<em>list of str</em>) – Default is <cite>(‘softmax_label’)</cite> for a typical model used in image
classification.</li>
<li><strong>logger</strong> (<em>Logger</em>) – Default is <cite>logging</cite>.</li>
-<li><strong>context</strong> (<em>Context</em><em> or </em><em>list of Context</em>) – Default is <code class="docutils literal"><span class="pre">cpu()</span></code>.</li>
+<li><strong>context</strong> (<em>Context or list of Context</em>) – Default is <code class="docutils literal"><span class="pre">cpu()</span></code>.</li>
<li><strong>work_load_list</strong> (<em>list of number</em>) – Default <code class="docutils literal"><span class="pre">None</span></code>, indicating uniform workload.</li>
<li><strong>fixed_param_names</strong> (<em>list of str</em>) – Default <code class="docutils literal"><span class="pre">None</span></code>, indicating no network parameters are fixed.</li>
</ul>
@@ -1370,8 +1369,8 @@ classification.</li>
<dd><p>Saves current progress to checkpoint.
Use <cite>mx.callback.module_checkpoint</cite> as <cite>epoch_end_callback</cite> to save during training.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>prefix</strong> (<em>str</em>) – The file prefix to checkpoint to.</li>
@@ -1403,8 +1402,8 @@ Use <cite>mx.callback.module_checkpoint</cite> as <cite>epoch_end_callback</cite
<code class="descname">data_shapes</code><a class="headerlink" href="#mxnet.module.Module.data_shapes" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets data shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
@@ -1418,8 +1417,8 @@ Use <cite>mx.callback.module_checkpoint</cite> as <cite>epoch_end_callback</cite
<code class="descname">label_shapes</code><a class="headerlink" href="#mxnet.module.Module.label_shapes" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets label shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The return value could be <code class="docutils literal"><span class="pre">None</span></code> if
the module does not need labels, or if the module is not bound for
@@ -1435,8 +1434,8 @@ training (in this case, label information is not available).</td>
<code class="descname">output_shapes</code><a class="headerlink" href="#mxnet.module.Module.output_shapes" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets output shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
@@ -1450,8 +1449,8 @@ training (in this case, label information is not available).</td>
<code class="descname">get_params</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets current parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A pair of dictionaries each mapping parameter names to NDArray values.</td>
</tr>
@@ -1462,11 +1461,11 @@ training (in this case, label information is not available).</td>
</dd></dl>
<dl class="method">
<dt id="mxnet.module.Module.init_params">
-<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.init_params" title="Permalink to this definition">¶</a></dt>
+<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.Uniform object></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.init_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes the parameters and auxiliary states.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>initializer</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Called to initialize parameters if needed.</li>
@@ -1477,7 +1476,7 @@ will be copied from that.</li>
<li><strong>allow_missing</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, params could contain missing values, and the initializer will be
called to fill those missing params.</li>
<li><strong>force_init</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, will force re-initialize even if already initialized.</li>
-<li><strong>allow_extra</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol.
+<li><strong>allow_extra</strong> (<em>boolean, optional</em>) – Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.</li>
</ul>
@@ -1491,8 +1490,8 @@ contain extra parameters that is not needed by the executor.</li>
<code class="descname">set_params</code><span class="sig-paren">(</span><em>arg_params</em>, <em>aux_params</em>, <em>allow_missing=False</em>, <em>force_init=True</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Assigns parameter and aux state values.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>arg_params</strong> (<em>dict</em>) – Dictionary of name to <cite>NDArray</cite>.</li>
@@ -1500,7 +1499,7 @@ contain extra parameters that is not needed by the executor.</li>
<li><strong>allow_missing</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, params could contain missing values, and the initializer will be
called to fill those missing params.</li>
<li><strong>force_init</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, will force re-initialize even if already initialized.</li>
-<li><strong>allow_extra</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol.
+<li><strong>allow_extra</strong> (<em>boolean, optional</em>) – Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.</li>
</ul>
@@ -1509,7 +1508,7 @@ contain extra parameters that is not needed by the executor.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of setting module parameters.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of setting module parameters.</span>
<span class="gp">>>> </span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="n">model_prefix</span><span class="p">,</span> <span class="n">n_epoch_load</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="o">=</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="n">aux_params</span><span class="p">)</span>
</pre></div>
@@ -1521,12 +1520,12 @@ contain extra parameters that is not needed by the executor.</li>
<dd><p>Binds the symbols to construct executors. This is necessary before one
can perform computation with the module.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_data</span></code>.</li>
-<li><strong>label_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_label</span></code>.</li>
+<li><strong>data_shapes</strong> (<em>list of (str, tuple)</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_data</span></code>.</li>
+<li><strong>label_shapes</strong> (<em>list of (str, tuple)</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_label</span></code>.</li>
<li><strong>for_training</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>. Whether the executors should be bound for training.</li>
<li><strong>inputs_need_grad</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">False</span></code>. Whether the gradients to the input data need to be computed.
Typically this is not needed. But this might be needed when implementing composition
@@ -1547,12 +1546,12 @@ but with the same sets of parameters (e.g. unrolled RNNs with different lengths)
<code class="descname">reshape</code><span class="sig-paren">(</span><em>data_shapes</em>, <em>label_shapes=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.reshape" title="Permalink to this definition">¶</a></dt>
<dd><p>Reshapes the module for new input shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
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+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_data</span></code>.</li>
-<li><strong>label_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_label</span></code>.</li>
+<li><strong>data_shapes</strong> (<em>list of (str, tuple)</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_data</span></code>.</li>
+<li><strong>label_shapes</strong> (<em>list of (str, tuple)</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_label</span></code>.</li>
</ul>
</td>
</tr>
@@ -1564,12 +1563,12 @@ but with the same sets of parameters (e.g. unrolled RNNs with different lengths)
<code class="descname">init_optimizer</code><span class="sig-paren">(</span><em>kvstore='local'</em>, <em>optimizer='sgd'</em>, <em>optimizer_params=(('learning_rate'</em>, <em>0.01)</em>, <em>)</em>, <em>force_init=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.init_optimizer" title="Permalink to this definition">¶</a></dt>
<dd><p>Installs and initializes optimizers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>kvstore</strong> (<em>str</em><em> or </em><a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><em>KVStore</em></a>) – Default <cite>‘local’</cite>.</li>
-<li><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – Default <cite>‘sgd’</cite></li>
+<li><strong>kvstore</strong> (<em>str or KVStore</em>) – Default <cite>‘local’</cite>.</li>
+<li><strong>optimizer</strong> (<em>str or Optimizer</em>) – Default <cite>‘sgd’</cite></li>
<li><strong>optimizer_params</strong> (<em>dict</em>) – Default <cite>((‘learning_rate’, 0.01),)</cite>. The default value is not a dictionary,
just to avoid pylint warning of dangerous default values.</li>
<li><strong>force_init</strong> (<em>bool</em>) – Default <code class="docutils literal"><span class="pre">False</span></code>, indicating whether we should force re-initializing the
@@ -1586,8 +1585,8 @@ optimizer in the case an optimizer is already installed.</li>
<dd><p>Borrows optimizer from a shared module. Used in bucketing, where exactly the same
optimizer (esp. kvstore) is used.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>shared_module</strong> (<a class="reference internal" href="#mxnet.module.Module" title="mxnet.module.Module"><em>Module</em></a>) – </td>
</tr>
@@ -1607,8 +1606,8 @@ rebinding is required.</p>
<p class="last"><a class="reference internal" href="#mxnet.module.BaseModule.forward" title="mxnet.module.BaseModule.forward"><code class="xref py py-meth docutils literal"><span class="pre">BaseModule.forward()</span></code></a></p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_batch</strong> (<a class="reference internal" href="io.html#mxnet.io.DataBatch" title="mxnet.io.DataBatch"><em>DataBatch</em></a>) – Could be anything with similar API implemented.</li>
@@ -1628,10 +1627,10 @@ rebinding is required.</p>
<p class="last"><a class="reference internal" href="#mxnet.module.BaseModule.backward" title="mxnet.module.BaseModule.backward"><code class="xref py py-meth docutils literal"><span class="pre">BaseModule.backward()</span></code></a></p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>out_grads</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em>, </em><em>optional</em>) – Gradient on the outputs to be propagated back.
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>out_grads</strong> (<em>NDArray or list of NDArray, optional</em>) – Gradient on the outputs to be propagated back.
This parameter is only needed when bind is called
on outputs that are not a loss function.</td>
</tr>
@@ -1657,8 +1656,8 @@ is like <code class="docutils literal"><span class="pre">[[out1_dev1,</span> <sp
elements are <cite>NDArray</cite>. When <cite>merge_multi_context</cite> is <cite>False</cite>, those <cite>NDArray</cite>
might live on different devices.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicate that we
@@ -1680,8 +1679,8 @@ executor.</td>
is like <code class="docutils literal"><span class="pre">[[grad1_dev1,</span> <span class="pre">grad1_dev2],</span> <span class="pre">[grad2_dev1,</span> <span class="pre">grad2_dev2]]</span></code>. All the output
elements are <cite>NDArray</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicate that we
@@ -1703,8 +1702,8 @@ executor.</td>
is like <code class="docutils literal"><span class="pre">[[out1_dev1,</span> <span class="pre">out1_dev2],</span> <span class="pre">[out2_dev1,</span> <span class="pre">out2_dev2]]</span></code>. All the output
elements are <cite>NDArray</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the states
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicate that we
@@ -1723,8 +1722,8 @@ executor.</td>
<code class="descname">set_states</code><span class="sig-paren">(</span><em>states=None</em>, <em>value=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.set_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets value for states. Only one of the states & value can be specified.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>states</strong> (<em>list of list of NDArrays</em>) – source states arrays formatted like <code class="docutils literal"><span class="pre">[[state1_dev1,</span> <span class="pre">state1_dev2],</span>
@@ -1745,8 +1744,8 @@ executor.</td>
<p class="last"><a class="reference internal" href="#mxnet.module.BaseModule.update_metric" title="mxnet.module.BaseModule.update_metric"><code class="xref py py-meth docutils literal"><span class="pre">BaseModule.update_metric()</span></code></a></p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>eval_metric</strong> (<a class="reference internal" href="model.html#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>EvalMetric</em></a>) – </li>
@@ -1762,8 +1761,8 @@ executor.</td>
<code class="descname">save_optimizer_states</code><span class="sig-paren">(</span><em>fname</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.save_optimizer_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves optimizer (updater) state to a file.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fname</strong> (<em>str</em>) – Path to output states file.</td>
</tr>
@@ -1775,8 +1774,8 @@ executor.</td>
<code class="descname">load_optimizer_states</code><span class="sig-paren">(</span><em>fname</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.Module.load_optimizer_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Loads optimizer (updater) state from a file.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fname</strong> (<em>str</em>) – Path to input states file.</td>
</tr>
@@ -1791,18 +1790,18 @@ executor.</td>
</dd></dl>
<dl class="class">
<dt id="mxnet.module.BucketingModule">
-<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">BucketingModule</code><span class="sig-paren">(</span><em>sym_gen</em>, <em>default_bucket_key=None</em>, <em>logger=<module '="" 'logging'="" __init__.pyc'="" anaconda="" from="" lib="" logging="" python2.7="" users="" wayao=""></module></em>, <em>context=cpu(0)</em>, <em>work_load_list=None</em>, <em>fixed_param_names=None</em>, <em>state_names=None</em><span class="sig-paren">)</span [...]
+<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">BucketingModule</code><span class="sig-paren">(</span><em>sym_gen</em>, <em>default_bucket_key=None</em>, <em>logger=<module 'logging' from '/usr/lib/python2.7/logging/__init__.pyc'></em>, <em>context=cpu(0)</em>, <em>work_load_list=None</em>, <em>fixed_param_names=None</em>, <em>state_names=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModu [...]
<dd><p>This module helps to deal efficiently with varying-length inputs.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>sym_gen</strong> (<em>function</em>) – A function when called with a bucket key, returns a triple
<code class="docutils literal"><span class="pre">(symbol,</span> <span class="pre">data_names,</span> <span class="pre">label_names)</span></code>.</li>
-<li><strong>default_bucket_key</strong> (<em>str</em><em> (or </em><em>any python object</em><em>)</em><em></em>) – The key for the default bucket.</li>
+<li><strong>default_bucket_key</strong> (<em>str (or any python object)</em>) – The key for the default bucket.</li>
<li><strong>logger</strong> (<em>Logger</em>) – </li>
-<li><strong>context</strong> (<em>Context</em><em> or </em><em>list of Context</em>) – Defaults to <code class="docutils literal"><span class="pre">mx.cpu()</span></code></li>
+<li><strong>context</strong> (<em>Context or list of Context</em>) – Defaults to <code class="docutils literal"><span class="pre">mx.cpu()</span></code></li>
<li><strong>work_load_list</strong> (<em>list of number</em>) – Defaults to <code class="docutils literal"><span class="pre">None</span></code>, indicating uniform workload.</li>
<li><strong>fixed_param_names</strong> (<em>list of str</em>) – Defaults to <code class="docutils literal"><span class="pre">None</span></code>, indicating no network parameters are fixed.</li>
<li><strong>state_names</strong> (<em>list of str</em>) – States are similar to data and label, but not provided by data iterator.
@@ -1827,8 +1826,8 @@ Instead they are initialized to 0 and can be set by set_states()</li>
<code class="descname">data_shapes</code><a class="headerlink" href="#mxnet.module.BucketingModule.data_shapes" title="Permalink to this definition">¶</a></dt>
<dd><p>Get data shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
@@ -1842,8 +1841,8 @@ Instead they are initialized to 0 and can be set by set_states()</li>
<code class="descname">label_shapes</code><a class="headerlink" href="#mxnet.module.BucketingModule.label_shapes" title="Permalink to this definition">¶</a></dt>
<dd><p>Get label shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The return value could be <code class="docutils literal"><span class="pre">None</span></code> if the module does not need labels,
or if the module is not bound for training (in this case, label information
@@ -1859,8 +1858,8 @@ is not available).</td>
<code class="descname">output_shapes</code><a class="headerlink" href="#mxnet.module.BucketingModule.output_shapes" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets output shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
@@ -1874,8 +1873,8 @@ is not available).</td>
<code class="descname">get_params</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets current parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A pair of dictionaries each mapping parameter names to NDArray values.</td>
</tr>
@@ -1889,8 +1888,8 @@ is not available).</td>
<code class="descname">set_params</code><span class="sig-paren">(</span><em>arg_params</em>, <em>aux_params</em>, <em>allow_missing=False</em>, <em>force_init=True</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Assigns parameters and aux state values.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>arg_params</strong> (<em>dict</em>) – Dictionary of name to value (<cite>NDArray</cite>) mapping.</li>
@@ -1898,7 +1897,7 @@ is not available).</td>
<li><strong>allow_missing</strong> (<em>bool</em>) – If true, params could contain missing values, and the initializer will be
called to fill those missing params.</li>
<li><strong>force_init</strong> (<em>bool</em>) – If true, will force re-initialize even if already initialized.</li>
-<li><strong>allow_extra</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol.
+<li><strong>allow_extra</strong> (<em>boolean, optional</em>) – Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.</li>
</ul>
@@ -1907,7 +1906,7 @@ contain extra parameters that is not needed by the executor.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of setting module parameters.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of setting module parameters.</span>
<span class="gp">>>> </span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="n">model_prefix</span><span class="p">,</span> <span class="n">n_epoch_load</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mod</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="o">=</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="n">aux_params</span><span class="p">)</span>
</pre></div>
@@ -1915,11 +1914,11 @@ contain extra parameters that is not needed by the executor.</li>
</dd></dl>
<dl class="method">
<dt id="mxnet.module.BucketingModule.init_params">
-<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.init_params" title="Permalink to this definition">¶</a></dt>
+<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.Uniform object></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.init_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>initializer</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – </li>
@@ -1930,7 +1929,7 @@ than <cite>initializer</cite>.</li>
<li><strong>allow_missing</strong> (<em>bool</em>) – Allow missing values in <cite>arg_params</cite> and <cite>aux_params</cite> (if not <code class="docutils literal"><span class="pre">None</span></code>).
In this case, missing values will be filled with <cite>initializer</cite>.</li>
<li><strong>force_init</strong> (<em>bool</em>) – Defaults to <code class="docutils literal"><span class="pre">False</span></code>.</li>
-<li><strong>allow_extra</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol.
+<li><strong>allow_extra</strong> (<em>boolean, optional</em>) – Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.</li>
</ul>
@@ -1944,8 +1943,8 @@ contain extra parameters that is not needed by the executor.</li>
<code class="descname">get_states</code><span class="sig-paren">(</span><em>merge_multi_context=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.get_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets states from all devices.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Default is <cite>True</cite>. In the case when data-parallelism is used, the states
will be collected from multiple devices. A <cite>True</cite> value indicate that we
@@ -1966,8 +1965,8 @@ elements are <cite>NDArray</cite>.</td>
<code class="descname">set_states</code><span class="sig-paren">(</span><em>states=None</em>, <em>value=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.set_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets value for states. Only one of states & values can be specified.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>states</strong> (<em>list of list of NDArrays</em>) – Source states arrays formatted like <code class="docutils literal"><span class="pre">[[state1_dev1,</span> <span class="pre">state1_dev2],</span>
@@ -1986,20 +1985,20 @@ elements are <cite>NDArray</cite>.</td>
executor for the default bucket key. Executors corresponding to other keys are
bound afterwards with <cite>switch_bucket</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – This should correspond to the symbol for the default bucket.</li>
-<li><strong>label_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – This should correspond to the symbol for the default bucket.</li>
+<li><strong>data_shapes</strong> (<em>list of (str, tuple)</em>) – This should correspond to the symbol for the default bucket.</li>
+<li><strong>label_shapes</strong> (<em>list of (str, tuple)</em>) – This should correspond to the symbol for the default bucket.</li>
<li><strong>for_training</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>.</li>
<li><strong>inputs_need_grad</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">False</span></code>.</li>
<li><strong>force_rebind</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">False</span></code>.</li>
<li><strong>shared_module</strong> (<a class="reference internal" href="#mxnet.module.BucketingModule" title="mxnet.module.BucketingModule"><em>BucketingModule</em></a>) – Default is <code class="docutils literal"><span class="pre">None</span></code>. This value is currently not used.</li>
-<li><strong>grad_req</strong> (<em>str</em><em>, </em><em>list of str</em><em>, </em><em>dict of str to str</em>) – Requirement for gradient accumulation. Can be ‘write’, ‘add’, or ‘null’
+<li><strong>grad_req</strong> (<em>str, list of str, dict of str to str</em>) – Requirement for gradient accumulation. Can be ‘write’, ‘add’, or ‘null’
(default to ‘write’).
Can be specified globally (str) or for each argument (list, dict).</li>
-<li><strong>bucket_key</strong> (<em>str</em><em> (or </em><em>any python object</em><em>)</em><em></em>) – bucket key for binding. by default use the default_bucket_key</li>
+<li><strong>bucket_key</strong> (<em>str (or any python object)</em>) – bucket key for binding. by default use the default_bucket_key</li>
</ul>
</td>
</tr>
@@ -2011,13 +2010,13 @@ Can be specified globally (str) or for each argument (list, dict).</li>
<code class="descname">switch_bucket</code><span class="sig-paren">(</span><em>bucket_key</em>, <em>data_shapes</em>, <em>label_shapes=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.switch_bucket" title="Permalink to this definition">¶</a></dt>
<dd><p>Switches to a different bucket. This will change <code class="docutils literal"><span class="pre">self.curr_module</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
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+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>bucket_key</strong> (<em>str</em><em> (or </em><em>any python object</em><em>)</em><em></em>) – The key of the target bucket.</li>
-<li><strong>data_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically <code class="docutils literal"><span class="pre">data_batch.provide_data</span></code>.</li>
-<li><strong>label_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically <code class="docutils literal"><span class="pre">data_batch.provide_label</span></code>.</li>
+<li><strong>bucket_key</strong> (<em>str (or any python object)</em>) – The key of the target bucket.</li>
+<li><strong>data_shapes</strong> (<em>list of (str, tuple)</em>) – Typically <code class="docutils literal"><span class="pre">data_batch.provide_data</span></code>.</li>
+<li><strong>label_shapes</strong> (<em>list of (str, tuple)</em>) – Typically <code class="docutils literal"><span class="pre">data_batch.provide_label</span></code>.</li>
</ul>
</td>
</tr>
@@ -2029,12 +2028,12 @@ Can be specified globally (str) or for each argument (list, dict).</li>
<code class="descname">init_optimizer</code><span class="sig-paren">(</span><em>kvstore='local'</em>, <em>optimizer='sgd'</em>, <em>optimizer_params=(('learning_rate'</em>, <em>0.01)</em>, <em>)</em>, <em>force_init=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.init_optimizer" title="Permalink to this definition">¶</a></dt>
<dd><p>Installs and initializes optimizers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>kvstore</strong> (<em>str</em><em> or </em><a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><em>KVStore</em></a>) – Defaults to <cite>‘local’</cite>.</li>
-<li><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – Defaults to <cite>‘sgd’</cite></li>
+<li><strong>kvstore</strong> (<em>str or KVStore</em>) – Defaults to <cite>‘local’</cite>.</li>
+<li><strong>optimizer</strong> (<em>str or Optimizer</em>) – Defaults to <cite>‘sgd’</cite></li>
<li><strong>optimizer_params</strong> (<em>dict</em>) – Defaults to <cite>((‘learning_rate’, 0.01),)</cite>. The default value is not a dictionary,
just to avoid pylint warning of dangerous default values.</li>
<li><strong>force_init</strong> (<em>bool</em>) – Defaults to <code class="docutils literal"><span class="pre">False</span></code>, indicating whether we should force re-initializing the
@@ -2050,8 +2049,8 @@ optimizer in the case an optimizer is already installed.</li>
<code class="descname">prepare</code><span class="sig-paren">(</span><em>data_batch</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.prepare" title="Permalink to this definition">¶</a></dt>
<dd><p>Prepares a data batch for forward.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data_batch</strong> (<a class="reference internal" href="io.html#mxnet.io.DataBatch" title="mxnet.io.DataBatch"><em>DataBatch</em></a>) – </td>
</tr>
@@ -2063,8 +2062,8 @@ optimizer in the case an optimizer is already installed.</li>
<code class="descname">forward</code><span class="sig-paren">(</span><em>data_batch</em>, <em>is_train=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Forward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
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+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_batch</strong> (<a class="reference internal" href="io.html#mxnet.io.DataBatch" title="mxnet.io.DataBatch"><em>DataBatch</em></a>) – </li>
@@ -2091,8 +2090,8 @@ in the previous forward-backward cycle.</p>
<code class="descname">get_outputs</code><span class="sig-paren">(</span><em>merge_multi_context=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.get_outputs" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets outputs from a previous forward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
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+<col class="field-body"/>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Defaults to <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicate that we
@@ -2113,8 +2112,8 @@ elements are numpy arrays.</td>
<code class="descname">get_input_grads</code><span class="sig-paren">(</span><em>merge_multi_context=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.get_input_grads" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets the gradients with respect to the inputs of the module.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Defaults to <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicate that we
@@ -2135,8 +2134,8 @@ elements are <cite>NDArray</cite>.</td>
<code class="descname">update_metric</code><span class="sig-paren">(</span><em>eval_metric</em>, <em>labels</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.BucketingModule.update_metric" title="Permalink to this definition">¶</a></dt>
<dd><p>Evaluates and accumulates evaluation metric on outputs of the last forward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>eval_metric</strong> (<a class="reference internal" href="model.html#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>EvalMetric</em></a>) – </li>
@@ -2160,7 +2159,7 @@ elements are <cite>NDArray</cite>.</td>
</dd></dl>
<dl class="class">
<dt id="mxnet.module.SequentialModule">
-<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">SequentialModule</code><span class="sig-paren">(</span><em>logger=<module '="" 'logging'="" __init__.pyc'="" anaconda="" from="" lib="" logging="" python2.7="" users="" wayao=""></module></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">SequentialModule</code><span class="sig-paren">(</span><em>logger=<module 'logging' from '/usr/lib/python2.7/logging/__init__.pyc'></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule" title="Permalink to this definition">¶</a></dt>
<dd><p>A SequentialModule is a container module that can chain multiple modules together.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
@@ -2173,8 +2172,8 @@ handy utility.</p>
<code class="descname">add</code><span class="sig-paren">(</span><em>module</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule.add" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds a module to the chain.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>module</strong> (<a class="reference internal" href="#mxnet.module.BaseModule" title="mxnet.module.BaseModule"><em>BaseModule</em></a>) – The new module to add.</li>
@@ -2201,7 +2200,7 @@ series of <cite>add</cite> calls.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of addinging two modules to a chain.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># An example of addinging two modules to a chain.</span>
<span class="gp">>>> </span><span class="n">seq_mod</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">mod</span><span class="o">.</span><span class="n">SequentialModule</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">seq_mod</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">mod1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">seq_mod</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">mod2</span><span class="p">)</span>
@@ -2223,8 +2222,8 @@ series of <cite>add</cite> calls.</p>
<code class="descname">data_shapes</code><a class="headerlink" href="#mxnet.module.SequentialModule.data_shapes" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets data shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A list of <cite>(name, shape)</cite> pairs. The data shapes of the first module
is the data shape of a <cite>SequentialModule</cite>.</td>
@@ -2239,8 +2238,8 @@ is the data shape of a <cite>SequentialModule</cite>.</td>
<code class="descname">label_shapes</code><a class="headerlink" href="#mxnet.module.SequentialModule.label_shapes" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets label shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
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<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A list of <cite>(name, shape)</cite> pairs. The return value could be <cite>None</cite> if
the module does not need labels, or if the module is not bound for
@@ -2256,8 +2255,8 @@ training (in this case, label information is not available).</td>
<code class="descname">output_shapes</code><a class="headerlink" href="#mxnet.module.SequentialModule.output_shapes" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets output shapes.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A list of <cite>(name, shape)</cite> pairs. The output shapes of the last
module is the output shape of a <cite>SequentialModule</cite>.</td>
@@ -2272,8 +2271,8 @@ module is the output shape of a <cite>SequentialModule</cite>.</td>
<code class="descname">get_params</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets current parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
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<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A pair of dictionaries each mapping parameter names to NDArray values. This
is a merged dictionary of all the parameters in the modules.</td>
@@ -2285,11 +2284,11 @@ is a merged dictionary of all the parameters in the modules.</td>
</dd></dl>
<dl class="method">
<dt id="mxnet.module.SequentialModule.init_params">
-<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule.init_params" title="Permalink to this definition">¶</a></dt>
+<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.Uniform object></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule.init_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>initializer</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – </li>
@@ -2300,7 +2299,7 @@ than <cite>initializer</cite>.</li>
<li><strong>allow_missing</strong> (<em>bool</em>) – Allow missing values in <cite>arg_params</cite> and <cite>aux_params</cite> (if not <code class="docutils literal"><span class="pre">None</span></code>).
In this case, missing values will be filled with <cite>initializer</cite>.</li>
<li><strong>force_init</strong> (<em>bool</em>) – Default <code class="docutils literal"><span class="pre">False</span></code>.</li>
-<li><strong>allow_extra</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol.
+<li><strong>allow_extra</strong> (<em>boolean, optional</em>) – Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.</li>
</ul>
@@ -2315,12 +2314,12 @@ contain extra parameters that is not needed by the executor.</li>
<dd><p>Binds the symbols to construct executors. This is necessary before one
can perform computation with the module.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically is <cite>data_iter.provide_data</cite>.</li>
-<li><strong>label_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically is <cite>data_iter.provide_label</cite>.</li>
+<li><strong>data_shapes</strong> (<em>list of (str, tuple)</em>) – Typically is <cite>data_iter.provide_data</cite>.</li>
+<li><strong>label_shapes</strong> (<em>list of (str, tuple)</em>) – Typically is <cite>data_iter.provide_label</cite>.</li>
<li><strong>for_training</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>. Whether the executors should be bind for training.</li>
<li><strong>inputs_need_grad</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">False</span></code>. Whether the gradients to the input data need to be computed.
Typically this is not needed. But this might be needed when implementing composition
@@ -2328,7 +2327,7 @@ of modules.</li>
<li><strong>force_rebind</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">False</span></code>. This function does nothing if the executors are already
bound. But with this <code class="docutils literal"><span class="pre">True</span></code>, the executors will be forced to rebind.</li>
<li><strong>shared_module</strong> (<a class="reference internal" href="#mxnet.module.Module" title="mxnet.module.Module"><em>Module</em></a>) – Default is <code class="docutils literal"><span class="pre">None</span></code>. Currently shared module is not supported for <cite>SequentialModule</cite>.</li>
-<li><strong>grad_req</strong> (<em>str</em><em>, </em><em>list of str</em><em>, </em><em>dict of str to str</em>) – Requirement for gradient accumulation. Can be ‘write’, ‘add’, or ‘null’
+<li><strong>grad_req</strong> (<em>str, list of str, dict of str to str</em>) – Requirement for gradient accumulation. Can be ‘write’, ‘add’, or ‘null’
(default to ‘write’).
Can be specified globally (str) or for each argument (list, dict).</li>
</ul>
@@ -2342,12 +2341,12 @@ Can be specified globally (str) or for each argument (list, dict).</li>
<code class="descname">init_optimizer</code><span class="sig-paren">(</span><em>kvstore='local'</em>, <em>optimizer='sgd'</em>, <em>optimizer_params=(('learning_rate'</em>, <em>0.01)</em>, <em>)</em>, <em>force_init=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule.init_optimizer" title="Permalink to this definition">¶</a></dt>
<dd><p>Installs and initializes optimizers.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>kvstore</strong> (<em>str</em><em> or </em><a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><em>KVStore</em></a>) – Default <cite>‘local’</cite>.</li>
-<li><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – Default <cite>‘sgd’</cite></li>
+<li><strong>kvstore</strong> (<em>str or KVStore</em>) – Default <cite>‘local’</cite>.</li>
+<li><strong>optimizer</strong> (<em>str or Optimizer</em>) – Default <cite>‘sgd’</cite></li>
<li><strong>optimizer_params</strong> (<em>dict</em>) – Default <code class="docutils literal"><span class="pre">(('learning_rate',</span> <span class="pre">0.01),)</span></code>. The default value is not a dictionary,
just to avoid pylint warning of dangerous default values.</li>
<li><strong>force_init</strong> (<em>bool</em>) – Default <code class="docutils literal"><span class="pre">False</span></code>, indicating whether we should force re-initializing the
@@ -2363,8 +2362,8 @@ optimizer in the case an optimizer is already installed.</li>
<code class="descname">forward</code><span class="sig-paren">(</span><em>data_batch</em>, <em>is_train=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Forward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_batch</strong> (<a class="reference internal" href="io.html#mxnet.io.DataBatch" title="mxnet.io.DataBatch"><em>DataBatch</em></a>) – </li>
@@ -2391,8 +2390,8 @@ in the previous forward-backward cycle.</p>
<code class="descname">get_outputs</code><span class="sig-paren">(</span><em>merge_multi_context=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule.get_outputs" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets outputs from a previous forward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
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+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicate that we
@@ -2413,8 +2412,8 @@ executor.</td>
<code class="descname">get_input_grads</code><span class="sig-paren">(</span><em>merge_multi_context=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule.get_input_grads" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets the gradients with respect to the inputs of the module.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A <code class="docutils literal"><span class="pre">True</span></code> value indicate that we
@@ -2435,8 +2434,8 @@ elements are <cite>NDArray</cite>.</td>
<code class="descname">update_metric</code><span class="sig-paren">(</span><em>eval_metric</em>, <em>labels</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.SequentialModule.update_metric" title="Permalink to this definition">¶</a></dt>
<dd><p>Evaluates and accumulates evaluation metric on outputs of the last forward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>eval_metric</strong> (<a class="reference internal" href="model.html#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>EvalMetric</em></a>) – </li>
@@ -2455,12 +2454,12 @@ elements are <cite>NDArray</cite>.</td>
</dd></dl>
<dl class="class">
<dt id="mxnet.module.PythonModule">
-<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">PythonModule</code><span class="sig-paren">(</span><em>data_names</em>, <em>label_names</em>, <em>output_names</em>, <em>logger=<module '="" 'logging'="" __init__.pyc'="" anaconda="" from="" lib="" logging="" python2.7="" users="" wayao=""></module></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.PythonModule" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">PythonModule</code><span class="sig-paren">(</span><em>data_names</em>, <em>label_names</em>, <em>output_names</em>, <em>logger=<module 'logging' from '/usr/lib/python2.7/logging/__init__.pyc'></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.PythonModule" title="Permalink to this definition">¶</a></dt>
<dd><p>A convenient module class that implements many of the module APIs as
empty functions.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_names</strong> (<em>list of str</em>) – Names of the data expected by the module.</li>
@@ -2507,8 +2506,8 @@ list <code class="docutils literal"><span class="pre">[]`</span></code>.</p>
to do computation on the device. Subclass should override this method if contains
parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
@@ -2519,12 +2518,12 @@ parameters.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.module.PythonModule.init_params">
-<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.uniform object=""></mxnet.initializer.uniform></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.PythonModule.init_params" title="Permalink to this definition">¶</a></dt>
+<code class="descname">init_params</code><span class="sig-paren">(</span><em>initializer=<mxnet.initializer.Uniform object></em>, <em>arg_params=None</em>, <em>aux_params=None</em>, <em>allow_missing=False</em>, <em>force_init=False</em>, <em>allow_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.PythonModule.init_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes the parameters and auxiliary states. By default this function
does nothing. Subclass should override this method if contains parameters.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>initializer</strong> (<a class="reference internal" href="optimization.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Called to initialize parameters if needed.</li>
@@ -2535,7 +2534,7 @@ will be copied from that.</li>
<li><strong>allow_missing</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, params could contain missing values, and the initializer will be
called to fill those missing params.</li>
<li><strong>force_init</strong> (<em>bool</em>) – If <code class="docutils literal"><span class="pre">True</span></code>, will force re-initialize even if already initialized.</li>
-<li><strong>allow_extra</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol.
+<li><strong>allow_extra</strong> (<em>boolean, optional</em>) – Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.</li>
</ul>
@@ -2557,8 +2556,8 @@ override this method if contains parameters.</p>
<dd><p>Evaluates and accumulates evaluation metric on outputs of the last forward computation.
Subclass should override this method if needed.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>eval_metric</strong> (<a class="reference internal" href="model.html#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>EvalMetric</em></a>) – </li>
@@ -2575,12 +2574,12 @@ Subclass should override this method if needed.</p>
<dd><p>Binds the symbols to construct executors. This is necessary before one
can perform computation with the module.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>data_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_data</span></code>.</li>
-<li><strong>label_shapes</strong> (<em>list of</em><em> (</em><em>str</em><em>, </em><em>tuple</em><em>)</em><em></em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_label</span></code>.</li>
+<li><strong>data_shapes</strong> (<em>list of (str, tuple)</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_data</span></code>.</li>
+<li><strong>label_shapes</strong> (<em>list of (str, tuple)</em>) – Typically is <code class="docutils literal"><span class="pre">data_iter.provide_label</span></code>.</li>
<li><strong>for_training</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">True</span></code>. Whether the executors should be bind for training.</li>
<li><strong>inputs_need_grad</strong> (<em>bool</em>) – Default is <code class="docutils literal"><span class="pre">False</span></code>. Whether the gradients to the input data need to be computed.
Typically this is not needed. But this might be needed when implementing composition
@@ -2590,7 +2589,7 @@ bound. But with this <code class="docutils literal"><span class="pre">True</span
<li><strong>shared_module</strong> (<a class="reference internal" href="#mxnet.module.Module" title="mxnet.module.Module"><em>Module</em></a>) – Default is <code class="docutils literal"><span class="pre">None</span></code>. This is used in bucketing. When not <code class="docutils literal"><span class="pre">None</span></code>, the shared module
essentially corresponds to a different bucket – a module with different symbol
but with the same sets of parameters (e.g. unrolled RNNs with different lengths).</li>
-<li><strong>grad_req</strong> (<em>str</em><em>, </em><em>list of str</em><em>, </em><em>dict of str to str</em>) – Requirement for gradient accumulation. Can be ‘write’, ‘add’, or ‘null’
+<li><strong>grad_req</strong> (<em>str, list of str, dict of str to str</em>) – Requirement for gradient accumulation. Can be ‘write’, ‘add’, or ‘null’
(default to ‘write’).
Can be specified globally (str) or for each argument (list, dict).</li>
</ul>
@@ -2605,12 +2604,12 @@ Can be specified globally (str) or for each argument (list, dict).</li>
<dd><p>Installs and initializes optimizers. By default we do nothing. Subclass should
override this method if needed.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>kvstore</strong> (<em>str</em><em> or </em><a class="reference internal" href="kvstore.html#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><em>KVStore</em></a>) – Default <cite>‘local’</cite>.</li>
-<li><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="optimization.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – Default <cite>‘sgd’</cite></li>
+<li><strong>kvstore</strong> (<em>str or KVStore</em>) – Default <cite>‘local’</cite>.</li>
+<li><strong>optimizer</strong> (<em>str or Optimizer</em>) – Default <cite>‘sgd’</cite></li>
<li><strong>optimizer_params</strong> (<em>dict</em>) – Default <cite>((‘learning_rate’, 0.01),)</cite>. The default value is not a dictionary,
just to avoid pylint warning of dangerous default values.</li>
<li><strong>force_init</strong> (<em>bool</em>) – Default <cite>False</cite>, indicating whether we should force re-initializing the
@@ -2624,12 +2623,12 @@ optimizer in the case an optimizer is already installed.</li>
</dd></dl>
<dl class="class">
<dt id="mxnet.module.PythonLossModule">
-<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">PythonLossModule</code><span class="sig-paren">(</span><em>name='pyloss'</em>, <em>data_names=('data'</em>, <em>)</em>, <em>label_names=('softmax_label'</em>, <em>)</em>, <em>logger=<module '="" 'logging'="" __init__.pyc'="" anaconda="" from="" lib="" logging="" python2.7="" users="" wayao=""></module></em>, <em>grad_func=None</em><span class="sig-paren">)</span><a class="headerlink" hr [...]
+<em class="property">class </em><code class="descclassname">mxnet.module.</code><code class="descname">PythonLossModule</code><span class="sig-paren">(</span><em>name='pyloss'</em>, <em>data_names=('data'</em>, <em>)</em>, <em>label_names=('softmax_label'</em>, <em>)</em>, <em>logger=<module 'logging' from '/usr/lib/python2.7/logging/__init__.pyc'></em>, <em>grad_func=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.PythonLossModule" title="Permalink to [...]
<dd><p>A convenient module class that implements many of the module APIs as
empty functions.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – Names of the module. The outputs will be named <cite>[name + ‘_output’]</cite>.</li>
@@ -2652,8 +2651,8 @@ value could be a numpy array or an <cite>NDArray</cite>.</li>
<dd><p>Forward computation. Here we do nothing but to keep a reference to
the scores and the labels so that we can do backward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data_batch</strong> (<a class="reference internal" href="io.html#mxnet.io.DataBatch" title="mxnet.io.DataBatch"><em>DataBatch</em></a>) – Could be anything with similar API implemented.</li>
@@ -2670,8 +2669,8 @@ the scores and the labels so that we can do backward computation.</p>
<dd><p>Gets outputs of the previous forward computation. As a output loss module,
we treat the inputs to this module as scores, and simply return them.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Should always be <code class="docutils literal"><span class="pre">True</span></code>, because we do not use multiple contexts for computing.</td>
</tr>
@@ -2683,10 +2682,10 @@ we treat the inputs to this module as scores, and simply return them.</p>
<code class="descname">backward</code><span class="sig-paren">(</span><em>out_grads=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.PythonLossModule.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Backward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>out_grads</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em>, </em><em>optional</em>) – Gradient on the outputs to be propagated back.
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>out_grads</strong> (<em>NDArray or list of NDArray, optional</em>) – Gradient on the outputs to be propagated back.
This parameter is only needed when bind is called
on outputs that are not a loss function.</td>
</tr>
@@ -2698,8 +2697,8 @@ on outputs that are not a loss function.</td>
<code class="descname">get_input_grads</code><span class="sig-paren">(</span><em>merge_multi_context=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.module.PythonLossModule.get_input_grads" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets the gradients to the inputs, computed in the previous backward computation.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>merge_multi_context</strong> (<em>bool</em>) – Should always be <code class="docutils literal"><span class="pre">True</span></code> because we do not use multiple context for computation.</td>
</tr>
diff --git a/versions/master/api/python/ndarray.html b/versions/master/api/python/ndarray.html
index aa6d5a1..524d55d 100644
--- a/versions/master/api/python/ndarray.html
+++ b/versions/master/api/python/ndarray.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>NDArray API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="index.html" rel="up" title="MXNet - Python API"/>
+<link href="index.html" rel="up" title="MXNet - Python API">
<link href="symbol.html" rel="next" title="Symbol API"/>
<link href="index.html" rel="prev" title="MXNet - Python API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
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<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
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@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
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<div class="form-group inner-addon left-addon">
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-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
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@@ -226,18 +224,20 @@ Previous Navbar Layout End -->
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<li class="toctree-l1 current"><a class="reference internal" href="index.html">Python Documents</a><ul class="current">
<li class="toctree-l2 current"><a class="reference internal" href="index.html#table-of-contents">Table of contents</a><ul class="current">
-<li class="toctree-l3 current"><a class="current reference internal" href="#">NDArray API</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">NDArray API</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
<li class="toctree-l4"><a class="reference internal" href="#the-ndarray-class">The <code class="docutils literal"><span class="pre">NDArray</span></code> class</a></li>
<li class="toctree-l4"><a class="reference internal" href="#array-creation-routines">Array creation routines</a></li>
<li class="toctree-l4"><a class="reference internal" href="#array-manipulation-routines">Array manipulation routines</a></li>
<li class="toctree-l4"><a class="reference internal" href="#mathematical-functions">Mathematical functions</a></li>
<li class="toctree-l4"><a class="reference internal" href="#neural-network">Neural network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#contrib">Contrib</a></li>
<li class="toctree-l4"><a class="reference internal" href="#api-reference">API Reference</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
<li class="toctree-l3"><a class="reference internal" href="kvstore.html">KVStore API</a></li>
@@ -269,8 +269,8 @@ Previous Navbar Layout End -->
<p>This document lists the routines of the <em>n</em>-dimensional array package:</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#module-mxnet.ndarray" title="mxnet.ndarray"><code class="xref py py-obj docutils literal"><span class="pre">mxnet.ndarray</span></code></a></td>
@@ -283,7 +283,7 @@ imperative tensor operations on CPU/GPU.
An <code class="docutils literal"><span class="pre">NDArray</span></code> represents a multi-dimensional, fixed-size homogenous array.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi" [...]
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="go"><class 'mxnet.ndarray.ndarray'=""></class></span>
+<span class="go"><class 'mxnet.ndarray.NDArray'></span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(2, 3)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="mi">3</span>
@@ -292,12 +292,12 @@ An <code class="docutils literal"><span class="pre">NDArray</span></code> repres
<span class="go"> [ 7. 8. 9.]]</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
-<span class="go"><ndarray 2x3="" @gpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2x3 @gpu(0)></span>
</pre></div>
</div>
<p>A detailed tutorial is available at
<a class="reference external" href="http://mxnet.io/tutorials/basic/ndarray.html">NDArray - Imperative tensor operations on CPU/GPU</a>.
-<br><br/></br></p>
+<br/><br/></p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p><code class="docutils literal"><span class="pre">mxnet.ndarray</span></code> is similar to <code class="docutils literal"><span class="pre">numpy.ndarray</span></code> in some aspects. But the differences are not negligible. For instance:</p>
@@ -334,8 +334,8 @@ broadcasting enabled by default.</li>
<span id="array-attributes"></span><h3>Array attributes<a class="headerlink" href="#array-attributes" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.shape" title="mxnet.ndarray.NDArray.shape"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.shape</span></code></a></td>
@@ -357,8 +357,8 @@ broadcasting enabled by default.</li>
<span id="array-conversion"></span><h3>Array conversion<a class="headerlink" href="#array-conversion" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.copy" title="mxnet.ndarray.NDArray.copy"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.copy</span></code></a></td>
@@ -386,8 +386,8 @@ broadcasting enabled by default.</li>
<span id="array-change-shape"></span><h3>Array change shape<a class="headerlink" href="#array-change-shape" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.T" title="mxnet.ndarray.NDArray.T"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.T</span></code></a></td>
@@ -406,38 +406,38 @@ broadcasting enabled by default.</li>
<span id="arithmetic-operations"></span><h3>Arithmetic operations<a class="headerlink" href="#arithmetic-operations" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__add__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__add__" title="mxnet.ndarray.NDArray.__add__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__add__</span></code></a></td>
<td>x.__add__(y) <=> x+y <=> mx.nd.add(x, y)</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__sub__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__sub__" title="mxnet.ndarray.NDArray.__sub__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__sub__</span></code></a></td>
<td>x.__sub__(y) <=> x-y <=> mx.nd.subtract(x, y)</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__rsub__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__rsub__" title="mxnet.ndarray.NDArray.__rsub__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__rsub__</span></code></a></td>
<td>x.__rsub__(y) <=> y-x <=> mx.nd.subtract(y, x)</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__neg__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__neg__" title="mxnet.ndarray.NDArray.__neg__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__neg__</span></code></a></td>
<td>x.__neg__(y) <=> -x</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__mul__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__mul__" title="mxnet.ndarray.NDArray.__mul__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__mul__</span></code></a></td>
<td>x.__mul__(y) <=> x*y <=> mx.nd.multiply(x, y)</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__div__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__div__" title="mxnet.ndarray.NDArray.__div__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__div__</span></code></a></td>
<td>x.__div__(y) <=> x/y <=> mx.nd.divide(x, y)</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__rdiv__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__rdiv__" title="mxnet.ndarray.NDArray.__rdiv__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__rdiv__</span></code></a></td>
<td>x.__rdiv__(y) <=> y/x <=> mx.nd.divide(y, x)</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__mod__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__mod__" title="mxnet.ndarray.NDArray.__mod__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__mod__</span></code></a></td>
<td>x.__mod__(y) <=> x%y <=> mx.nd.modulo(x, y)</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__rmod__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__rmod__" title="mxnet.ndarray.NDArray.__rmod__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__rmod__</span></code></a></td>
<td>x.__rmod__(y) <=> y%x <=> mx.nd.modulo(y, x)</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__pow__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__pow__" title="mxnet.ndarray.NDArray.__pow__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__pow__</span></code></a></td>
<td>x.__pow__(y) <=> x**y <=> mx.nd.power(x,y)</td>
</tr>
</tbody>
@@ -447,23 +447,23 @@ broadcasting enabled by default.</li>
<span id="in-place-arithmetic-operations"></span><h3>In-place arithmetic operations<a class="headerlink" href="#in-place-arithmetic-operations" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__iadd__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__iadd__" title="mxnet.ndarray.NDArray.__iadd__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__iadd__</span></code></a></td>
<td>x.__iadd__(y) <=> x+=y</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__isub__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__isub__" title="mxnet.ndarray.NDArray.__isub__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__isub__</span></code></a></td>
<td>x.__isub__(y) <=> x-=y</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__imul__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__imul__" title="mxnet.ndarray.NDArray.__imul__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__imul__</span></code></a></td>
<td>x.__imul__(y) <=> x*=y</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__idiv__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__idiv__" title="mxnet.ndarray.NDArray.__idiv__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__idiv__</span></code></a></td>
<td>x.__rdiv__(y) <=> x/=y</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__imod__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__imod__" title="mxnet.ndarray.NDArray.__imod__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__imod__</span></code></a></td>
<td>x.__rmod__(y) <=> x%=y</td>
</tr>
</tbody>
@@ -473,26 +473,26 @@ broadcasting enabled by default.</li>
<span id="comparison-operators"></span><h3>Comparison operators<a class="headerlink" href="#comparison-operators" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__lt__</span></code></td>
-<td>x.__lt__(y) <=> x<y <=""> mx.nd.lesser(x, y)</y></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__lt__" title="mxnet.ndarray.NDArray.__lt__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__lt__</span></code></a></td>
+<td>x.__lt__(y) <=> x<y <=> mx.nd.lesser(x, y)</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__le__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__le__" title="mxnet.ndarray.NDArray.__le__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__le__</span></code></a></td>
<td>x.__le__(y) <=> x<=y <=> mx.nd.less_equal(x, y)</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__gt__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__gt__" title="mxnet.ndarray.NDArray.__gt__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__gt__</span></code></a></td>
<td>x.__gt__(y) <=> x>y <=> mx.nd.greater(x, y)</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__ge__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__ge__" title="mxnet.ndarray.NDArray.__ge__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__ge__</span></code></a></td>
<td>x.__ge__(y) <=> x>=y <=> mx.nd.greater_equal(x, y)</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__eq__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__eq__" title="mxnet.ndarray.NDArray.__eq__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__eq__</span></code></a></td>
<td>x.__eq__(y) <=> x==y <=> mx.nd.equal(x, y)</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__ne__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__ne__" title="mxnet.ndarray.NDArray.__ne__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__ne__</span></code></a></td>
<td>x.__ne__(y) <=> x!=y <=> mx.nd.not_equal(x, y)</td>
</tr>
</tbody>
@@ -502,14 +502,14 @@ broadcasting enabled by default.</li>
<span id="indexing"></span><h3>Indexing<a class="headerlink" href="#indexing" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__getitem__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__getitem__" title="mxnet.ndarray.NDArray.__getitem__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__getitem__</span></code></a></td>
<td>x.__getitem__(i) <=> x[i]</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__setitem__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.__setitem__" title="mxnet.ndarray.NDArray.__setitem__"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.__setitem__</span></code></a></td>
<td>x.__setitem__(i, y) <=> x[i]=y</td>
</tr>
</tbody>
@@ -519,8 +519,8 @@ broadcasting enabled by default.</li>
<span id="lazy-evaluation"></span><h3>Lazy evaluation<a class="headerlink" href="#lazy-evaluation" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.NDArray.wait_to_read" title="mxnet.ndarray.NDArray.wait_to_read"><code class="xref py py-obj docutils literal"><span class="pre">NDArray.wait_to_read</span></code></a></td>
@@ -534,8 +534,8 @@ broadcasting enabled by default.</li>
<span id="array-creation-routines"></span><h2>Array creation routines<a class="headerlink" href="#array-creation-routines" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
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<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><code class="xref py py-obj docutils literal"><span class="pre">array</span></code></a></td>
@@ -571,8 +571,8 @@ broadcasting enabled by default.</li>
<span id="changing-array-shape-and-type"></span><h3>Changing array shape and type<a class="headerlink" href="#changing-array-shape-and-type" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.cast" title="mxnet.ndarray.cast"><code class="xref py py-obj docutils literal"><span class="pre">cast</span></code></a></td>
@@ -594,8 +594,8 @@ broadcasting enabled by default.</li>
<span id="expanding-array-elements"></span><h3>Expanding array elements<a class="headerlink" href="#expanding-array-elements" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.broadcast_to" title="mxnet.ndarray.broadcast_to"><code class="xref py py-obj docutils literal"><span class="pre">broadcast_to</span></code></a></td>
@@ -620,8 +620,8 @@ broadcasting enabled by default.</li>
<span id="rearranging-elements"></span><h3>Rearranging elements<a class="headerlink" href="#rearranging-elements" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.transpose" title="mxnet.ndarray.transpose"><code class="xref py py-obj docutils literal"><span class="pre">transpose</span></code></a></td>
@@ -640,8 +640,8 @@ broadcasting enabled by default.</li>
<span id="joining-and-splitting-arrays"></span><h3>Joining and splitting arrays<a class="headerlink" href="#joining-and-splitting-arrays" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.concat" title="mxnet.ndarray.concat"><code class="xref py py-obj docutils literal"><span class="pre">concat</span></code></a></td>
@@ -657,8 +657,8 @@ broadcasting enabled by default.</li>
<span id="indexing-routines"></span><h3>Indexing routines<a class="headerlink" href="#indexing-routines" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.slice" title="mxnet.ndarray.slice"><code class="xref py py-obj docutils literal"><span class="pre">slice</span></code></a></td>
@@ -689,8 +689,8 @@ broadcasting enabled by default.</li>
<span id="id1"></span><h3>Arithmetic operations<a class="headerlink" href="#arithmetic-operations" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.add" title="mxnet.ndarray.add"><code class="xref py py-obj docutils literal"><span class="pre">add</span></code></a></td>
@@ -727,8 +727,8 @@ broadcasting enabled by default.</li>
<span id="trigonometric-functions"></span><h3>Trigonometric functions<a class="headerlink" href="#trigonometric-functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.sin" title="mxnet.ndarray.sin"><code class="xref py py-obj docutils literal"><span class="pre">sin</span></code></a></td>
@@ -762,8 +762,8 @@ broadcasting enabled by default.</li>
<span id="hyperbolic-functions"></span><h3>Hyperbolic functions<a class="headerlink" href="#hyperbolic-functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.sinh" title="mxnet.ndarray.sinh"><code class="xref py py-obj docutils literal"><span class="pre">sinh</span></code></a></td>
@@ -791,8 +791,8 @@ broadcasting enabled by default.</li>
<span id="reduce-functions"></span><h3>Reduce functions<a class="headerlink" href="#reduce-functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.sum" title="mxnet.ndarray.sum"><code class="xref py py-obj docutils literal"><span class="pre">sum</span></code></a></td>
@@ -826,8 +826,8 @@ broadcasting enabled by default.</li>
<span id="rounding"></span><h3>Rounding<a class="headerlink" href="#rounding" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.round" title="mxnet.ndarray.round"><code class="xref py py-obj docutils literal"><span class="pre">round</span></code></a></td>
@@ -855,8 +855,8 @@ broadcasting enabled by default.</li>
<span id="exponents-and-logarithms"></span><h3>Exponents and logarithms<a class="headerlink" href="#exponents-and-logarithms" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.exp" title="mxnet.ndarray.exp"><code class="xref py py-obj docutils literal"><span class="pre">exp</span></code></a></td>
@@ -884,8 +884,8 @@ broadcasting enabled by default.</li>
<span id="powers"></span><h3>Powers<a class="headerlink" href="#powers" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.power" title="mxnet.ndarray.power"><code class="xref py py-obj docutils literal"><span class="pre">power</span></code></a></td>
@@ -907,8 +907,8 @@ broadcasting enabled by default.</li>
<span id="logic-functions"></span><h3>Logic functions<a class="headerlink" href="#logic-functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.equal" title="mxnet.ndarray.equal"><code class="xref py py-obj docutils literal"><span class="pre">equal</span></code></a></td>
@@ -936,8 +936,8 @@ broadcasting enabled by default.</li>
<span id="random-sampling"></span><h3>Random sampling<a class="headerlink" href="#random-sampling" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.random_uniform" title="mxnet.ndarray.random_uniform"><code class="xref py py-obj docutils literal"><span class="pre">random_uniform</span></code></a></td>
@@ -971,8 +971,8 @@ broadcasting enabled by default.</li>
<span id="sorting-and-searching"></span><h3>Sorting and searching<a class="headerlink" href="#sorting-and-searching" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.sort" title="mxnet.ndarray.sort"><code class="xref py py-obj docutils literal"><span class="pre">sort</span></code></a></td>
@@ -997,8 +997,8 @@ broadcasting enabled by default.</li>
<span id="miscellaneous"></span><h3>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.maximum" title="mxnet.ndarray.maximum"><code class="xref py py-obj docutils literal"><span class="pre">maximum</span></code></a></td>
@@ -1032,8 +1032,8 @@ broadcasting enabled by default.</li>
<span id="basic"></span><h3>Basic<a class="headerlink" href="#basic" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.FullyConnected" title="mxnet.ndarray.FullyConnected"><code class="xref py py-obj docutils literal"><span class="pre">FullyConnected</span></code></a></td>
@@ -1067,8 +1067,8 @@ broadcasting enabled by default.</li>
<span id="more"></span><h3>More<a class="headerlink" href="#more" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.Correlation" title="mxnet.ndarray.Correlation"><code class="xref py py-obj docutils literal"><span class="pre">Correlation</span></code></a></td>
@@ -1105,7 +1105,7 @@ broadcasting enabled by default.</li>
<td>Applies dropout operation to input array.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#mxnet.ndarray.BilinearSampler" title="mxnet.ndarray.BilinearSampler"><code class="xref py py-obj docutils literal"><span class="pre">BilinearSampler</span></code></a></td>
-<td>Applies bilinear sampling to input feature map, which is the key of “[NIPS2015] Spatial Transformer Networks” output[batch, channel, y_dst, x_dst] = G(data[batch, channel, y_src, x_src) x_dst, y_dst enumerate all spatial locations in output x_src = grid[batch, 0, y_dst, x_dst] y_src = grid[batch, 1, y_dst, x_dst] G() denotes the bilinear interpolation kernel The out-boundary points will be padded as zeros.</td>
+<td>Applies bilinear sampling to input feature map.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.ndarray.GridGenerator" title="mxnet.ndarray.GridGenerator"><code class="xref py py-obj docutils literal"><span class="pre">GridGenerator</span></code></a></td>
<td>Generates 2D sampling grid for bilinear sampling.</td>
@@ -1150,6 +1150,67 @@ broadcasting enabled by default.</li>
</table>
</div>
</div>
+<div class="section" id="contrib">
+<span id="contrib"></span><h2>Contrib<a class="headerlink" href="#contrib" title="Permalink to this headline">¶</a></h2>
+<div class="admonition warning">
+<p class="first admonition-title">Warning</p>
+<p class="last">This package contains experimental APIs and may change in the near future.</p>
+</div>
+<p>The <code class="docutils literal"><span class="pre">contrib.ndarray</span></code> module contains many useful experimental APIs for new features. This is a place for the community to try out the new features, so that feature contributors can receive feedback.</p>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.ndarray.CTCLoss" title="mxnet.contrib.ndarray.CTCLoss"><code class="xref py py-obj docutils literal"><span class="pre">CTCLoss</span></code></a></td>
+<td>Connectionist Temporal Classification Loss.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.ndarray.DeformableConvolution" title="mxnet.contrib.ndarray.DeformableConvolution"><code class="xref py py-obj docutils literal"><span class="pre">DeformableConvolution</span></code></a></td>
+<td>Compute 2-D deformable convolution on 4-D input.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.ndarray.DeformablePSROIPooling" title="mxnet.contrib.ndarray.DeformablePSROIPooling"><code class="xref py py-obj docutils literal"><span class="pre">DeformablePSROIPooling</span></code></a></td>
+<td>Performs deformable position-sensitive region-of-interest pooling on inputs.The DeformablePSROIPooling operation is described in <a class="reference external" href="https://arxiv.org/abs/1703.06211.batch_size">https://arxiv.org/abs/1703.06211.batch_size</a> will change to the number of region bounding boxes after DeformablePSROIPooling</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.ndarray.MultiBoxDetection" title="mxnet.contrib.ndarray.MultiBoxDetection"><code class="xref py py-obj docutils literal"><span class="pre">MultiBoxDetection</span></code></a></td>
+<td>Convert multibox detection predictions.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.ndarray.MultiBoxPrior" title="mxnet.contrib.ndarray.MultiBoxPrior"><code class="xref py py-obj docutils literal"><span class="pre">MultiBoxPrior</span></code></a></td>
+<td>Generate prior(anchor) boxes from data, sizes and ratios.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.ndarray.MultiBoxTarget" title="mxnet.contrib.ndarray.MultiBoxTarget"><code class="xref py py-obj docutils literal"><span class="pre">MultiBoxTarget</span></code></a></td>
+<td>Compute Multibox training targets</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.ndarray.MultiProposal" title="mxnet.contrib.ndarray.MultiProposal"><code class="xref py py-obj docutils literal"><span class="pre">MultiProposal</span></code></a></td>
+<td>Generate region proposals via RPN</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.ndarray.PSROIPooling" title="mxnet.contrib.ndarray.PSROIPooling"><code class="xref py py-obj docutils literal"><span class="pre">PSROIPooling</span></code></a></td>
+<td>Performs region-of-interest pooling on inputs.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.ndarray.Proposal" title="mxnet.contrib.ndarray.Proposal"><code class="xref py py-obj docutils literal"><span class="pre">Proposal</span></code></a></td>
+<td>Generate region proposals via RPN</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.ndarray.count_sketch" title="mxnet.contrib.ndarray.count_sketch"><code class="xref py py-obj docutils literal"><span class="pre">count_sketch</span></code></a></td>
+<td>Apply CountSketch to input: map a d-dimension data to k-dimension data”</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.ndarray.ctc_loss" title="mxnet.contrib.ndarray.ctc_loss"><code class="xref py py-obj docutils literal"><span class="pre">ctc_loss</span></code></a></td>
+<td>Connectionist Temporal Classification Loss.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.ndarray.dequantize" title="mxnet.contrib.ndarray.dequantize"><code class="xref py py-obj docutils literal"><span class="pre">dequantize</span></code></a></td>
+<td>Dequantize the input tensor into a float tensor.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.ndarray.fft" title="mxnet.contrib.ndarray.fft"><code class="xref py py-obj docutils literal"><span class="pre">fft</span></code></a></td>
+<td>Apply 1D FFT to input”</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.ndarray.ifft" title="mxnet.contrib.ndarray.ifft"><code class="xref py py-obj docutils literal"><span class="pre">ifft</span></code></a></td>
+<td>Apply 1D ifft to input”</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.ndarray.quantize" title="mxnet.contrib.ndarray.quantize"><code class="xref py py-obj docutils literal"><span class="pre">quantize</span></code></a></td>
+<td>Quantize a input tensor from float to <cite>out_type</cite>, with user-specified <cite>min_range</cite> and <cite>max_range</cite>.</td>
+</tr>
+</tbody>
+</table>
+</div>
<div class="section" id="api-reference">
<span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline">¶</a></h2>
<script src="../../_static/js/auto_module_index.js" type="text/javascript"></script><span class="target" id="module-mxnet.ndarray"></span><p>NDArray API of MXNet.</p>
@@ -1165,12 +1226,205 @@ broadcasting enabled by default.</li>
<dd><p>An array object representing a multidimensional, homogeneous array of
fixed-size items.</p>
<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__repr__">
+<code class="descname">__repr__</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__repr__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a string representation of the array.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__add__">
+<code class="descname">__add__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__add__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__add__(y) <=> x+y <=> mx.nd.add(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__iadd__">
+<code class="descname">__iadd__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__iadd__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__iadd__(y) <=> x+=y</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__sub__">
+<code class="descname">__sub__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__sub__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__sub__(y) <=> x-y <=> mx.nd.subtract(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__isub__">
+<code class="descname">__isub__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__isub__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__isub__(y) <=> x-=y</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__rsub__">
+<code class="descname">__rsub__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__rsub__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__rsub__(y) <=> y-x <=> mx.nd.subtract(y, x)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__mul__">
+<code class="descname">__mul__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__mul__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__mul__(y) <=> x*y <=> mx.nd.multiply(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__neg__">
+<code class="descname">__neg__</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__neg__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__neg__(y) <=> -x</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__imul__">
+<code class="descname">__imul__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__imul__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__imul__(y) <=> x*=y</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__div__">
+<code class="descname">__div__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__div__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__div__(y) <=> x/y <=> mx.nd.divide(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__rdiv__">
+<code class="descname">__rdiv__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__rdiv__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__rdiv__(y) <=> y/x <=> mx.nd.divide(y, x)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__idiv__">
+<code class="descname">__idiv__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__idiv__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__rdiv__(y) <=> x/=y</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__mod__">
+<code class="descname">__mod__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__mod__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__mod__(y) <=> x%y <=> mx.nd.modulo(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__rmod__">
+<code class="descname">__rmod__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__rmod__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__rmod__(y) <=> y%x <=> mx.nd.modulo(y, x)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__imod__">
+<code class="descname">__imod__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__imod__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__rmod__(y) <=> x%=y</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__pow__">
+<code class="descname">__pow__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__pow__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__pow__(y) <=> x**y <=> mx.nd.power(x,y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__rpow__">
+<code class="descname">__rpow__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__rpow__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__pow__(y) <=> y**x <=> mx.nd.power(y,x)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__eq__">
+<code class="descname">__eq__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__eq__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__eq__(y) <=> x==y <=> mx.nd.equal(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__ne__">
+<code class="descname">__ne__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__ne__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__ne__(y) <=> x!=y <=> mx.nd.not_equal(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__gt__">
+<code class="descname">__gt__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__gt__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__gt__(y) <=> x>y <=> mx.nd.greater(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__ge__">
+<code class="descname">__ge__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__ge__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__ge__(y) <=> x>=y <=> mx.nd.greater_equal(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__lt__">
+<code class="descname">__lt__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__lt__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__lt__(y) <=> x<y <=> mx.nd.lesser(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__le__">
+<code class="descname">__le__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__le__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__le__(y) <=> x<=y <=> mx.nd.less_equal(x, y)</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__len__">
+<code class="descname">__len__</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__len__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Number of element along the first axis.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__setitem__">
+<code class="descname">__setitem__</code><span class="sig-paren">(</span><em>key</em>, <em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__setitem__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__setitem__(i, y) <=> x[i]=y</p>
+<p>Set self[key] to value.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>key</strong> (<em>int, slice or tuple</em>) – The indexing key.</li>
+<li><strong>value</strong> (<em>scalar, NDArray or numpy.ndarray</em>) – The value to set.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="p">[:]</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 1., 1., 1.],</span>
+<span class="go"> [ 1., 1., 1.]], dtype=float32)</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 1., 1., 1.],</span>
+<span class="go"> [ 1., 1., 1.]], dtype=float32)</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 1., 2., 1.],</span>
+<span class="go"> [ 1., 2., 1.]], dtype=float32)</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">:]</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 1., 2., 1.],</span>
+<span class="go"> [ 1., 3., 3.]], dtype=float32)</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">) [...]
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 1., 2., 1.],</span>
+<span class="go"> [ 0., 0., 3.]], dtype=float32)</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 1., 2., 1.],</span>
+<span class="go"> [ 0., 0., 4.]], dtype=float32)</span>
+</pre></div>
+</div>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.ndarray.NDArray.__getitem__">
+<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>key</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.__getitem__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__getitem__(i) <=> x[i]</p>
+<p>Returns a sliced view of this array.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>key</strong> (<em>int or slice</em>) – Indexing key.</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi [...]
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 0., 1., 2.],</span>
+<span class="go"> [ 3., 4., 5.]], dtype=float32)</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([ 3., 4., 5.], dtype=float32)</span>
+<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
+<span class="gp">>>> </span><span class="n">y</span><span class="p">[:]</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 2., 2., 2.],</span>
+<span class="go"> [ 3., 4., 5.]], dtype=float32)</span>
+</pre></div>
+</div>
+</dd></dl>
+<dl class="method">
<dt id="mxnet.ndarray.NDArray.reshape">
<code class="descname">reshape</code><span class="sig-paren">(</span><em>shape</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.reshape" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a <strong>view</strong> of this array with a new shape without altering any data.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>shape</strong> (<em>tuple of int</em>) – <p>The new shape should not change the array size, namely
<code class="docutils literal"><span class="pre">np.prod(new_shape)</span></code> should be equal to <code class="docutils literal"><span class="pre">np.prod(self.shape)</span></code>.</p>
@@ -1187,7 +1441,7 @@ if x.shape == (3, 4, 5), x.reshape((0, 20)).shape will be (3, 20).</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="m [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi [...]
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 0., 1., 2.],</span>
<span class="go"> [ 3., 4., 5.]], dtype=float32)</span>
@@ -1217,8 +1471,8 @@ the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
shape (2, 3) to (2, 3, 3).</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>shape</strong> (<em>tuple of int</em>) – The shape of the desired array.</td>
</tr>
@@ -1230,7 +1484,7 @@ array, even if the new shape is the same as <code class="docutils literal"><span
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="m [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi [...]
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[[ 0.],</span>
<span class="go"> [ 1.],</span>
@@ -1254,14 +1508,14 @@ array, even if the new shape is the same as <code class="docutils literal"><span
<p>This method guarantees that all previous write operations that pushed
into the backend engine for execution are actually finished.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">time</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">time</span>
<span class="gp">>>> </span><span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1000</span><span class="p">,</span><span class="mi">1000</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span>
<span class="go">0.003854036331176758</span>
<span class="gp">>>> </span><span class="n">b</span><span class="o">.</span><span class="n">wait_to_read</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span>
<span class="go">0.0893700122833252</span>
</pre></div>
</div>
@@ -1271,7 +1525,7 @@ into the backend engine for execution are actually finished.</p>
<code class="descname">ndim</code><a class="headerlink" href="#mxnet.ndarray.NDArray.ndim" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the number of dimensions of this array</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p"> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">] [...]
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">ndim</span>
<span class="go">1</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
@@ -1285,7 +1539,7 @@ into the backend engine for execution are actually finished.</p>
<code class="descname">shape</code><a class="headerlink" href="#mxnet.ndarray.NDArray.shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Tuple of array dimensions.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p"> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">] [...]
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(4L,)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
@@ -1300,7 +1554,7 @@ into the backend engine for execution are actually finished.</p>
<dd><p>Number of elements in the array.</p>
<p>Equivalent to the product of the array’s dimensions.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">size</span>
<span class="go">30</span>
@@ -1314,11 +1568,11 @@ into the backend engine for execution are actually finished.</p>
<code class="descname">context</code><a class="headerlink" href="#mxnet.ndarray.NDArray.context" title="Permalink to this definition">¶</a></dt>
<dd><p>Device context of the array.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p"> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">] [...]
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">context</span>
<span class="go">cpu(0)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">context</span><span class="p">)</span>
-<span class="go"><class 'mxnet.context.context'=""></class></span>
+<span class="go"><class 'mxnet.context.Context'></span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">context</span>
<span class="go">gpu(0)</span>
@@ -1330,8 +1584,8 @@ into the backend engine for execution are actually finished.</p>
<code class="descname">dtype</code><a class="headerlink" href="#mxnet.ndarray.NDArray.dtype" title="Permalink to this definition">¶</a></dt>
<dd><p>Data-type of the array’s elements.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">This NDArray’s data type.</td>
</tr>
@@ -1340,12 +1594,12 @@ into the backend engine for execution are actually finished.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span>
-<span class="go"><type 'numpy.float32'=""></type></span>
+<span class="go"><type 'numpy.float32'></span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">dtype</span>
-<span class="go"><type 'numpy.int32'=""></type></span>
+<span class="go"><type 'numpy.int32'></span>
</pre></div>
</div>
</dd></dl>
@@ -1358,7 +1612,7 @@ self is returned if <code class="docutils literal"><span class="pre">self.ndim</
<p>Unlike <code class="docutils literal"><span class="pre">numpy.ndarray.T</span></code>, this function returns a copy
rather than a view of the array unless <code class="docutils literal"><span class="pre">self.ndim</span> <span class="pre"><</span> <span class="pre">2</span></code>.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="m [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi [...]
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 0., 1., 2.],</span>
<span class="go"> [ 3., 4., 5.]], dtype=float32)</span>
@@ -1374,10 +1628,10 @@ rather than a view of the array unless <code class="docutils literal"><span clas
<code class="descname">asnumpy</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.asnumpy" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a <code class="docutils literal"><span class="pre">numpy.ndarray</span></code> object with value copied from this array.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-<span class="go"><type 'numpy.ndarray'=""></type></span>
+<span class="go"><type 'numpy.ndarray'></span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">array([[ 1., 1., 1.],</span>
<span class="go"> [ 1., 1., 1.]], dtype=float32)</span>
@@ -1394,11 +1648,11 @@ rather than a view of the array unless <code class="docutils literal"><span clas
<dd><p>Returns a scalar whose value is copied from this array.</p>
<p>This function is equivalent to <code class="docutils literal"><span class="pre">self.asnumpy()[0]</span></code>. This NDArray must have shape (1,).</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span>
<span class="go">1</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">asscalar</span><span class="p">())</span>
-<span class="go"><type 'numpy.int32'=""></type></span>
+<span class="go"><type 'numpy.int32'></span>
</pre></div>
</div>
</dd></dl>
@@ -1407,18 +1661,18 @@ rather than a view of the array unless <code class="docutils literal"><span clas
<code class="descname">astype</code><span class="sig-paren">(</span><em>dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.astype" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a copy of the array after casting to a specified type.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dtype</strong> (<em>numpy.dtype</em><em> or </em><em>str</em>) – The type of the returned array.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dtype</strong> (<em>numpy.dtype or str</em>) – The type of the returned array.</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span c [...]
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">dtype</span>
-<span class="go"><type 'numpy.int32'=""></type></span>
+<span class="go"><type 'numpy.int32'></span>
</pre></div>
</div>
</dd></dl>
@@ -1432,10 +1686,10 @@ rather than a view of the array unless <code class="docutils literal"><span clas
<p>If <code class="docutils literal"><span class="pre">other</span></code> is a context, a new <code class="docutils literal"><span class="pre">NDArray</span></code> will be first created on
the target context, and the value of <code class="docutils literal"><span class="pre">self</span></code> is copied.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>other</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>Context</em>) – The destination array or context.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>other</strong> (<em>NDArray or Context</em>) – The destination array or context.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">The copied array. If <code class="docutils literal"><span class="pre">other</span></code> is an <code class="docutils literal"><span class="pre">NDArray</span></code>, then the return value
and <code class="docutils literal"><span class="pre">other</span></code> will point to the same <code class="docutils literal"><span class="pre">NDArray</span></code>.</td>
@@ -1445,7 +1699,7 @@ and <code class="docutils literal"><span class="pre">other</span></code> will po
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="ow">is</span> <span class="n">y</span>
@@ -1454,7 +1708,7 @@ and <code class="docutils literal"><span class="pre">other</span></code> will po
<span class="go">array([[ 1., 1., 1.],</span>
<span class="go"> [ 1., 1., 1.]], dtype=float32)</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
-<span class="go"><ndarray 2x3="" @gpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2x3 @gpu(0)></span>
</pre></div>
</div>
</dd></dl>
@@ -1463,8 +1717,8 @@ and <code class="docutils literal"><span class="pre">other</span></code> will po
<code class="descname">copy</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.copy" title="Permalink to this definition">¶</a></dt>
<dd><p>Makes a copy of this <code class="docutils literal"><span class="pre">NDArray</span></code>, keeping the same context.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The copied array</td>
</tr>
@@ -1473,7 +1727,7 @@ and <code class="docutils literal"><span class="pre">other</span></code> will po
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 1., 1., 1.],</span>
@@ -1488,8 +1742,8 @@ and <code class="docutils literal"><span class="pre">other</span></code> will po
<p>If the target context is the same as <code class="docutils literal"><span class="pre">self.context</span></code>, then <code class="docutils literal"><span class="pre">self</span></code> is
returned. Otherwise, a copy is made.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>context</strong> (<em>Context</em>) – The target context.</td>
</tr>
@@ -1500,7 +1754,7 @@ returned. Otherwise, a copy is made.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="ow">is</span> <span class="n">x</span>
<span class="go">True</span>
@@ -1516,10 +1770,10 @@ returned. Otherwise, a copy is made.</p>
<dd><p>Attach a gradient buffer to this NDArray, so that <cite>backward</cite>
can compute gradient with respect to it.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>grad_req</strong> (<em>{'write'</em><em>, </em><em>'add'</em><em>, </em><em>'null'}</em>) – How gradient will be accumulated.
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>grad_req</strong> (<em>{'write', 'add', 'null'}</em>) – How gradient will be accumulated.
- ‘write’: gradient will be overwritten on every backward.
- ‘add’: gradient will be added to existing value on every backward.
- ‘null’: do not compute gradient for this NDArray.</td>
@@ -1539,13 +1793,20 @@ can compute gradient with respect to it.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.ndarray.NDArray.backward">
-<code class="descname">backward</code><span class="sig-paren">(</span><em>out_grad=None</em>, <em>retain_graph=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.backward" title="Permalink to this definition">¶</a></dt>
+<code class="descname">backward</code><span class="sig-paren">(</span><em>out_grad=None</em>, <em>retain_graph=False</em>, <em>is_train=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.NDArray.backward" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the gradients of this NDArray w.r.t variables.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>out_grad</strong> (<em>list of NDArray</em><em> or </em><em>None</em>) – </td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>out_grad</strong> (<em>NDArray, optional</em>) – Gradient with respect to head.</li>
+<li><strong>retain_graph</strong> (<em>bool, optional</em>) – Whether to retain the computaion graph for another backward
+pass on the same graph. By default the computaion history
+is cleared.</li>
+<li><strong>is_train</strong> (<em>bool, optional</em>) – Whether to compute gradient for training or inference.</li>
+</ul>
+</td>
</tr>
</tbody>
</table>
@@ -1562,16 +1823,16 @@ can compute gradient with respect to it.</p>
</dd></dl>
<dl class="function">
<dt id="mxnet.ndarray.empty">
-<code class="descclassname">mxnet.ndarray.</code><code class="descname">empty</code><span class="sig-paren">(</span><em>shape</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'=""></type></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.empty" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">mxnet.ndarray.</code><code class="descname">empty</code><span class="sig-paren">(</span><em>shape</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.empty" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new array of given shape and type, without initializing entries.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – The shape of the empty array.</li>
-<li><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – An optional device context (default is the current default context).</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em><em>, </em><em>optional</em>) – An optional value type (default is <cite>float32</cite>).</li>
+<li><strong>shape</strong> (<em>int or tuple of int</em>) – The shape of the empty array.</li>
+<li><strong>ctx</strong> (<em>Context, optional</em>) – An optional device context (default is the current default context).</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype, optional</em>) – An optional value type (default is <cite>float32</cite>).</li>
</ul>
</td>
</tr>
@@ -1584,28 +1845,28 @@ can compute gradient with respect to it.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="go"><ndarray 1="" @cpu(0)=""></ndarray></span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="go"><NDArray 1 @cpu(0)></span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
-<span class="go"><ndarray 1x2="" @gpu(0)=""></ndarray></span>
+<span class="go"><NDArray 1x2 @gpu(0)></span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'float16'</span><span class="p">)</span>
-<span class="go"><ndarray 1x2="" @gpu(0)=""></ndarray></span>
+<span class="go"><NDArray 1x2 @gpu(0)></span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="mxnet.ndarray.zeros">
-<code class="descclassname">mxnet.ndarray.</code><code class="descname">zeros</code><span class="sig-paren">(</span><em>shape</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'=""></type></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.zeros" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">mxnet.ndarray.</code><code class="descname">zeros</code><span class="sig-paren">(</span><em>shape</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.zeros" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new array filled with all zeros, with the given shape and type.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – The shape of the empty array.</li>
-<li><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – An optional device context (default is the current default context).</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em><em>, </em><em>optional</em>) – An optional value type (default is <cite>float32</cite>).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray (default is <cite>None</cite>).</li>
+<li><strong>shape</strong> (<em>int or tuple of int</em>) – The shape of the empty array.</li>
+<li><strong>ctx</strong> (<em>Context, optional</em>) – An optional device context (default is the current default context).</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype, optional</em>) – An optional value type (default is <cite>float32</cite>).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray (default is <cite>None</cite>).</li>
</ul>
</td>
</tr>
@@ -1618,10 +1879,10 @@ can compute gradient with respect to it.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([ 0.], dtype=float32)</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
-<span class="go"><ndarray 1x2="" @gpu(0)=""></ndarray></span>
+<span class="go"><NDArray 1x2 @gpu(0)></span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'float16'</span><span class="p">)</span><span [...]
<span class="go">array([[ 0., 0.]], dtype=float16)</span>
</pre></div>
@@ -1629,18 +1890,18 @@ can compute gradient with respect to it.</p>
</dd></dl>
<dl class="function">
<dt id="mxnet.ndarray.ones">
-<code class="descclassname">mxnet.ndarray.</code><code class="descname">ones</code><span class="sig-paren">(</span><em>shape</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'=""></type></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.ones" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">mxnet.ndarray.</code><code class="descname">ones</code><span class="sig-paren">(</span><em>shape</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.ones" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new array filled with all ones, with the given shape and type.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of int</em><em> or </em><em>list of int</em>) – The shape of the empty array.</li>
-<li><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – An optional device context.
+<li><strong>shape</strong> (<em>int or tuple of int or list of int</em>) – The shape of the empty array.</li>
+<li><strong>ctx</strong> (<em>Context, optional</em>) – An optional device context.
Defaults to the current default context (<code class="docutils literal"><span class="pre">mxnet.Context.default_ctx</span></code>).</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em><em>, </em><em>optional</em>) – An optional value type (default is <cite>float32</cite>).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray (default is <cite>None</cite>).</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype, optional</em>) – An optional value type (default is <cite>float32</cite>).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray (default is <cite>None</cite>).</li>
</ul>
</td>
</tr>
@@ -1653,10 +1914,10 @@ Defaults to the current default context (<code class="docutils literal"><span cl
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([ 1.], dtype=float32)</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
-<span class="go"><ndarray 1x2="" @gpu(0)=""></ndarray></span>
+<span class="go"><NDArray 1x2 @gpu(0)></span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float16'</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 1., 1.]], dtype=float16)</span>
</pre></div>
@@ -1664,18 +1925,18 @@ Defaults to the current default context (<code class="docutils literal"><span cl
</dd></dl>
<dl class="function">
<dt id="mxnet.ndarray.full">
-<code class="descclassname">mxnet.ndarray.</code><code class="descname">full</code><span class="sig-paren">(</span><em>shape</em>, <em>val</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'=""></type></em>, <em>out=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.full" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">mxnet.ndarray.</code><code class="descname">full</code><span class="sig-paren">(</span><em>shape</em>, <em>val</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'></em>, <em>out=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.full" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new array of given shape and type, filled with the given value <cite>val</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of int</em>) – The shape of the new array.</li>
+<li><strong>shape</strong> (<em>int or tuple of int</em>) – The shape of the new array.</li>
<li><strong>val</strong> (<em>scalar</em>) – Fill value.</li>
-<li><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context (default is the current default context).</li>
+<li><strong>ctx</strong> (<em>Context, optional</em>) – Device context (default is the current default context).</li>
<li><strong>dtype</strong> (<cite>str</cite> or <cite>numpy.dtype</cite>, optional) – The data type of the returned <cite>NDArray</cite>. The default datatype is <cite>float32</cite>.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray (default is <cite>None</cite>).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray (default is <cite>None</cite>).</li>
</ul>
</td>
</tr>
@@ -1688,10 +1949,10 @@ Defaults to the current default context (<code class="docutils literal"><span cl
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([ 2.], dtype=float32)</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
-<span class="go"><ndarray 1x2="" @gpu(0)=""></ndarray></span>
+<span class="go"><NDArray 1x2 @gpu(0)></span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float16'</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</spa [...]
<span class="go">array([[ 2., 2.]], dtype=float16)</span>
</pre></div>
@@ -1702,14 +1963,14 @@ Defaults to the current default context (<code class="docutils literal"><span cl
<code class="descclassname">mxnet.ndarray.</code><code class="descname">array</code><span class="sig-paren">(</span><em>source_array</em>, <em>ctx=None</em>, <em>dtype=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.array" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates an array from any object exposing the array interface.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>source_array</strong> (<em>array_like</em>) – An object exposing the array interface, an object whose <cite>__array__</cite>
method returns an array, or any (nested) sequence.</li>
-<li><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context (default is the current default context).</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em><em>, </em><em>optional</em>) – The data type of the output array. The default dtype is <code class="docutils literal"><span class="pre">source_array.dtype</span></code>
+<li><strong>ctx</strong> (<em>Context, optional</em>) – Device context (default is the current default context).</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype, optional</em>) – The data type of the output array. The default dtype is <code class="docutils literal"><span class="pre">source_array.dtype</span></code>
if <cite>source_array</cite> is an <cite>NDArray</cite>, <cite>float32</cite> otherwise.</li>
</ul>
</td>
@@ -1723,15 +1984,15 @@ if <cite>source_array</cite> is an <cite>NDArray</cite>, <cite>float32</cite> ot
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
-<span class="go"><ndarray 3="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 3 @cpu(0)></span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-<span class="go"><ndarray 2x2="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2x2 @cpu(0)></span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
-<span class="go"><ndarray 3x2="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 3x2 @cpu(0)></span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class [...]
-<span class="go"><ndarray 3x2="" @gpu(0)=""></ndarray></span>
+<span class="go"><NDArray 3x2 @gpu(0)></span>
</pre></div>
</div>
</dd></dl>
@@ -1741,8 +2002,8 @@ if <cite>source_array</cite> is an <cite>NDArray</cite>, <cite>float32</cite> ot
<dd><p>Moves the <cite>source</cite> axis into the <cite>destination</cite> position
while leaving the other axes in their original order</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>tensor</strong> (<em>mx.nd.array</em>) – The array which axes should be reordered</li>
@@ -1751,7 +2012,8 @@ while leaving the other axes in their original order</p>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>result</strong> – Array with moved axes.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>result</strong> –
+Array with moved axes.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">mx.nd.array</p>
@@ -1760,7 +2022,7 @@ while leaving the other axes in their original order</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi" [...]
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(3L, 2L)</span>
</pre></div>
@@ -1768,23 +2030,23 @@ while leaving the other axes in their original order</p>
</dd></dl>
<dl class="function">
<dt id="mxnet.ndarray.arange">
-<code class="descclassname">mxnet.ndarray.</code><code class="descname">arange</code><span class="sig-paren">(</span><em>start</em>, <em>stop=None</em>, <em>step=1.0</em>, <em>repeat=1</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'=""></type></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.arange" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">mxnet.ndarray.</code><code class="descname">arange</code><span class="sig-paren">(</span><em>start</em>, <em>stop=None</em>, <em>step=1.0</em>, <em>repeat=1</em>, <em>ctx=None</em>, <em>dtype=<type 'numpy.float32'></em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.arange" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns evenly spaced values within a given interval.</p>
<p>Values are generated within the half-open interval [<cite>start</cite>, <cite>stop</cite>). In other
words, the interval includes <cite>start</cite> but excludes <cite>stop</cite>. The function is
similar to the built-in Python function <cite>range</cite> and to <cite>numpy.arange</cite>,
but returns an <cite>NDArray</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>start</strong> (<em>float</em><em>, </em><em>optional</em>) – Start of interval. The default start value is 0.</li>
+<li><strong>start</strong> (<em>float, optional</em>) – Start of interval. The default start value is 0.</li>
<li><strong>stop</strong> (<em>float</em>) – End of interval.</li>
-<li><strong>step</strong> (<em>float</em><em>, </em><em>optional</em>) – Spacing between values. The default step size is 1.</li>
-<li><strong>repeat</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of times to repeat each element. The default repeat count is 1.</li>
-<li><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context. Default context is the current default context.</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em><em>, </em><em>optional</em>) – The data type of the <cite>NDArray</cite>. The default datatype is <cite>np.float32</cite>.</li>
+<li><strong>step</strong> (<em>float, optional</em>) – Spacing between values. The default step size is 1.</li>
+<li><strong>repeat</strong> (<em>int, optional</em>) – Number of times to repeat each element. The default repeat count is 1.</li>
+<li><strong>ctx</strong> (<em>Context, optional</em>) – Device context. Default context is the current default context.</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype, optional</em>) – The data type of the <cite>NDArray</cite>. The default datatype is <cite>np.float32</cite>.</li>
</ul>
</td>
</tr>
@@ -1797,7 +2059,7 @@ but returns an <cite>NDArray</cite>.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([ 0., 1., 2.], dtype=float32)</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([ 2., 3., 4., 5.], dtype=float32)</span>
@@ -1822,12 +2084,12 @@ but returns an <cite>NDArray</cite>.</p>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be added.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be added.
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be added.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be added.
If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
@@ -1842,7 +2104,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -1880,12 +2142,12 @@ broadcastable to a common shape.</li>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be subtracted.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be subtracted.
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be subtracted.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be subtracted.
If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
@@ -1900,7 +2162,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -1937,12 +2199,12 @@ broadcastable to a common shape.</li>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be multiplied.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be multiplied.
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be multiplied.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be multiplied.
If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
@@ -1957,7 +2219,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -1994,12 +2256,12 @@ broadcastable to a common shape.</li>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array in division.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array in division.
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array in division.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array in division.
The arrays to be divided. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
@@ -2014,7 +2276,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="mi">6</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="mi">6</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span><span class="o">*</span><span class="mi">2</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 6., 6., 6.],</span>
@@ -2023,7 +2285,7 @@ broadcastable to a common shape.</li>
<span class="go">array([[ 2.],</span>
<span class="go"> [ 2.]], dtype=float32)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">/</span><span class="mi">2</span>
-<span class="go"><ndarray 2x3="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2x3 @cpu(0)></span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 2., 2., 2.],</span>
<span class="go"> [ 2., 2., 2.]], dtype=float32)</span>
@@ -2047,12 +2309,12 @@ broadcastable to a common shape.</li>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array in modulo.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array in modulo.
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array in modulo.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array in modulo.
The arrays to be taken modulo. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
@@ -2067,7 +2329,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="mi">6</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="mi">6</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span><span class="o">*</span><span class="mi">4</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 6., 6., 6.],</span>
@@ -2076,7 +2338,7 @@ broadcastable to a common shape.</li>
<span class="go">array([[ 4.],</span>
<span class="go"> [ 4.]], dtype=float32)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">%</span><span class="mi">5</span>
-<span class="go"><ndarray 2x3="" @cpu(0)=""></ndarray></span>
+<span class="go"><NDArray 2x3 @cpu(0)></span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">x</span><span class="o">%</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 1., 1., 1.],</span>
<span class="go"> [ 1., 1., 1.]], dtype=float32)</span>
@@ -2101,12 +2363,12 @@ with broadcasting.</p>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>base</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The base array</li>
-<li><strong>exp</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The exponent array. If <code class="docutils literal"><span class="pre">base.shape</span> <span class="pre">!=</span> <span class="pre">exp.shape</span></code>, they must be
+<li><strong>base</strong> (<em>scalar or NDArray</em>) – The base array</li>
+<li><strong>exp</strong> (<em>scalar or NDArray</em>) – The exponent array. If <code class="docutils literal"><span class="pre">base.shape</span> <span class="pre">!=</span> <span class="pre">exp.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
</td>
@@ -2120,7 +2382,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="mi">2</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class= [...]
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class= [...]
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -2158,12 +2420,12 @@ broadcastable to a common shape.</li>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be compared.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be compared.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
</td>
@@ -2177,7 +2439,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -2211,12 +2473,12 @@ broadcastable to a common shape.</li>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be compared.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be compared.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
</td>
@@ -2230,7 +2492,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -2267,12 +2529,12 @@ otherwise return 0(false).</p>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be compared.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be compared.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
</td>
@@ -2286,7 +2548,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -2326,12 +2588,12 @@ otherwise return 0(false).</p>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be compared.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be compared.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
</td>
@@ -2345,7 +2607,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -2388,12 +2650,12 @@ otherwise return 0(false).</p>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be compared.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be compared.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
</td>
@@ -2407,7 +2669,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -2447,12 +2709,12 @@ otherwise return 0(false).</p>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be compared.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be compared.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
</td>
@@ -2466,7 +2728,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -2506,12 +2768,12 @@ otherwise return 0(false).</p>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be compared.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be compared.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
</td>
@@ -2525,7 +2787,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -2565,12 +2827,12 @@ lesser than equal to rhs, otherwise return 0(false).</p>
then the arrays are broadcastable to a common shape.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – First array to be compared.</li>
-<li><strong>rhs</strong> (<em>scalar</em><em> or </em><a class="reference internal" href="#mxnet.ndarray.array" title="mxnet.ndarray.array"><em>array</em></a>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
+<li><strong>lhs</strong> (<em>scalar or array</em>) – First array to be compared.</li>
+<li><strong>rhs</strong> (<em>scalar or array</em>) – Second array to be compared. If <code class="docutils literal"><span class="pre">lhs.shape</span> <span class="pre">!=</span> <span class="pre">rhs.shape</span></code>, they must be
broadcastable to a common shape.</li>
</ul>
</td>
@@ -2584,7 +2846,7 @@ broadcastable to a common shape.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
@@ -2621,19 +2883,20 @@ broadcastable to a common shape.</li>
<dd><p>Numerical negative of the argument, element-wise.</p>
<p>From:src/operator/tensor/elemwise_unary_op.cc:206</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -2645,8 +2908,8 @@ broadcastable to a common shape.</li>
<dd><p>Loads an array from file.</p>
<p>See more details in <code class="docutils literal"><span class="pre">save</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fname</strong> (<em>str</em>) – The filename.</td>
</tr>
@@ -2668,26 +2931,26 @@ broadcastable to a common shape.</li>
<li><code class="docutils literal"><span class="pre">hdfs://path/to/file</span></code> (if compiled with HDFS supports)</li>
</ul>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>fname</strong> (<em>str</em>) – The filename.</li>
-<li><strong>data</strong> (list of <code class="docutils literal"><span class="pre">NDArray`</span> <span class="pre">or</span> <span class="pre">dict</span> <span class="pre">of</span> <span class="pre">str</span> <span class="pre">to</span> <span class="pre">``NDArray</span></code>) – The data to save.</li>
+<li><strong>data</strong> (<code class="docutils literal"><span class="pre">NDArray</span></code>, list of <code class="docutils literal"><span class="pre">NDArray`</span> <span class="pre">or</span> <span class="pre">dict</span> <span class="pre">of</span> <span class="pre">str</span> <span class="pre">to</span> <span class="pre">``NDArray</span></code>) – The data to save.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'my_list'</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'my_dict'</span><span class="p">,</span> <span class="p">{</span><span class="s1">'x'</span><span class="p">:</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">:</span><span class="n">y</span><span class="p">})</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'my_list'</span><span class="p">)</span>
-<span class="go">[<ndarray 2x3="" @cpu(0)="">, <ndarray 1x4="" @cpu(0)="">]</ndarray></ndarray></span>
+<span class="go">[<NDArray 2x3 @cpu(0)>, <NDArray 1x4 @cpu(0)>]</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'my_dict'</span><span class="p">)</span>
-<span class="go">{'y': <ndarray 1x4="" @cpu(0)="">, 'x': <ndarray 2x3="" @cpu(0)="">}</ndarray></ndarray></span>
+<span class="go">{'y': <NDArray 1x4 @cpu(0)>, 'x': <NDArray 2x3 @cpu(0)>}</span>
</pre></div>
</div>
</dd></dl>
@@ -2696,8 +2959,8 @@ broadcastable to a common shape.</li>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">concatenate</code><span class="sig-paren">(</span><em>arrays</em>, <em>axis=0</em>, <em>always_copy=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.concatenate" title="Permalink to this definition">¶</a></dt>
<dd><p>DEPRECATED, use <code class="docutils literal"><span class="pre">concat</span></code> instead</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>arrays</strong> (list of <cite>NDArray</cite>) – Arrays to be concatenate. They must have identical shape except
@@ -2722,8 +2985,8 @@ the first dimension. They also must have the same data type.</li>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">imdecode</code><span class="sig-paren">(</span><em>str_img</em>, <em>clip_rect=(0</em>, <em>0</em>, <em>0</em>, <em>0)</em>, <em>out=None</em>, <em>index=0</em>, <em>channels=3</em>, <em>mean=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.imdecode" title="Permalink to this definition">¶</a></dt>
<dd><p>DEPRECATED, use mx.img instead</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>str_img</strong> (<em>str</em>) – Binary image data</li>
@@ -2751,20 +3014,21 @@ the first dimension. They also must have the same data type.</li>
</ul>
<p>Defined in src/operator/activation.cc:L73</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array to activation function.</li>
-<li><strong>act_type</strong> (<em>{'relu'</em><em>, </em><em>'sigmoid'</em><em>, </em><em>'softrelu'</em><em>, </em><em>'tanh'}</em><em>, </em><em>required</em>) – Activation function to be applied.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>act_type</strong> (<em>{'relu', 'sigmoid', 'softrelu', 'tanh'}, required</em>) – Activation function to be applied.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -2792,7 +3056,7 @@ have shape <em>(k,)</em>. If <code class="docutils literal"><span class="pre">ou
states, <code class="docutils literal"><span class="pre">moving_mean</span></code> and <code class="docutils literal"><span class="pre">moving_var</span></code>, which are <em>k</em>-length
vectors. They are global statistics for the whole dataset, which are updated
by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">moving_mean</span> <span class="o">=</span> <span class="n">moving_mean</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_mean</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">moving_mean</span> <span class="o">=</span> <span class="n">moving_mean</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_mean</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
<span class="n">moving_var</span> <span class="o">=</span> <span class="n">moving_var</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_var</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
</pre></div>
</div>
@@ -2806,8 +3070,8 @@ axis to be the last item in the input shape.</p>
then set <code class="docutils literal"><span class="pre">gamma</span></code> to 1 and its gradient to 0.</p>
<p>Defined in src/operator/batch_norm.cc:L381</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to batch normalization</li>
@@ -2815,21 +3079,22 @@ then set <code class="docutils literal"><span class="pre">gamma</span></code> to
<li><strong>beta</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – beta array</li>
<li><strong>moving_mean</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – running mean of input</li>
<li><strong>moving_var</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – running variance of input</li>
-<li><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.001</em>) – Epsilon to prevent div 0. Must be bigger than CUDNN_BN_MIN_EPSILON defined in cudnn.h when using cudnn (usually 1e-5)</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – Momentum for moving average</li>
-<li><strong>fix_gamma</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Fix gamma while training</li>
-<li><strong>use_global_stats</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</li>
-<li><strong>output_mean_var</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Output All,normal mean and var</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Specify which shape axis the channel is specified</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Do not select CUDNN operator, if available</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>eps</strong> (<em>double, optional, default=0.001</em>) – Epsilon to prevent div 0. Must be no less than CUDNN_BN_MIN_EPSILON defined in cudnn.h when using cudnn (usually 1e-5)</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0.9</em>) – Momentum for moving average</li>
+<li><strong>fix_gamma</strong> (<em>boolean, optional, default=True</em>) – Fix gamma while training</li>
+<li><strong>use_global_stats</strong> (<em>boolean, optional, default=False</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</li>
+<li><strong>output_mean_var</strong> (<em>boolean, optional, default=False</em>) – Output All,normal mean and var</li>
+<li><strong>axis</strong> (<em>int, optional, default='1'</em>) – Specify which shape axis the channel is specified</li>
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Do not select CUDNN operator, if available</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -2857,7 +3122,7 @@ have shape <em>(k,)</em>. If <code class="docutils literal"><span class="pre">ou
states, <code class="docutils literal"><span class="pre">moving_mean</span></code> and <code class="docutils literal"><span class="pre">moving_var</span></code>, which are <em>k</em>-length
vectors. They are global statistics for the whole dataset, which are updated
by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">moving_mean</span> <span class="o">=</span> <span class="n">moving_mean</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_mean</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">moving_mean</span> <span class="o">=</span> <span class="n">moving_mean</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_mean</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
<span class="n">moving_var</span> <span class="o">=</span> <span class="n">moving_var</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_var</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
</pre></div>
</div>
@@ -2868,26 +3133,27 @@ the output. It is often used during inference.</p>
then set <code class="docutils literal"><span class="pre">gamma</span></code> to 1 and its gradient to 0.</p>
<p>Defined in src/operator/batch_norm_v1.cc:L71</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to batch normalization</li>
<li><strong>gamma</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – gamma array</li>
<li><strong>beta</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – beta array</li>
-<li><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.001</em>) – Epsilon to prevent div 0</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – Momentum for moving average</li>
-<li><strong>fix_gamma</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Fix gamma while training</li>
-<li><strong>use_global_stats</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</li>
-<li><strong>output_mean_var</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Output All,normal mean and var</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>eps</strong> (<em>float, optional, default=0.001</em>) – Epsilon to prevent div 0</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0.9</em>) – Momentum for moving average</li>
+<li><strong>fix_gamma</strong> (<em>boolean, optional, default=True</em>) – Fix gamma while training</li>
+<li><strong>use_global_stats</strong> (<em>boolean, optional, default=False</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</li>
+<li><strong>output_mean_var</strong> (<em>boolean, optional, default=False</em>) – Output All,normal mean and var</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -2896,32 +3162,87 @@ then set <code class="docutils literal"><span class="pre">gamma</span></code> to
<dl class="function">
<dt id="mxnet.ndarray.BilinearSampler">
<code class="descclassname">mxnet.ndarray.</code><code class="descname">BilinearSampler</code><span class="sig-paren">(</span><em>data=None</em>, <em>grid=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.BilinearSampler" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="docutils">
-<dt>Applies bilinear sampling to input feature map, which is the key of “[NIPS2015] Spatial Transformer Networks”</dt>
-<dd>output[batch, channel, y_dst, x_dst] = G(data[batch, channel, y_src, x_src)
-x_dst, y_dst enumerate all spatial locations in output
-x_src = grid[batch, 0, y_dst, x_dst]
-y_src = grid[batch, 1, y_dst, x_dst]
-G() denotes the bilinear interpolation kernel</dd>
-</dl>
-<p>The out-boundary points will be padded as zeros. (The boundary is defined to be [-1, 1])
-The shape of output will be (data.shape[0], data.shape[1], grid.shape[2], grid.shape[3])
-The operator assumes that grid has been nomalized. If you want to design a CustomOp to manipulate grid, please refer to GridGeneratorOp.</p>
+<dd><p>Applies bilinear sampling to input feature map.</p>
+<p>Bilinear Sampling is the key of [NIPS2015] “Spatial Transformer Networks”. The usage of the operator is very similar to remap function in OpenCV,
+except that the operator has the backward pass.</p>
+<p>Given <span class="math">\(data\)</span> and <span class="math">\(grid\)</span>, then the output is computed by</p>
+<div class="math">
+\[\begin{split}x_{src} = grid[batch, 0, y_{dst}, x_{dst}] \\
+y_{src} = grid[batch, 1, y_{dst}, x_{dst}] \\
+output[batch, channel, y_{dst}, x_{dst}] = G(data[batch, channel, y_{src}, x_{src})\end{split}\]</div>
+<p><span class="math">\(x_{dst}\)</span>, <span class="math">\(y_{dst}\)</span> enumerate all spatial locations in <span class="math">\(output\)</span>, and <span class="math">\(G()\)</span> denotes the bilinear interpolation kernel.
+The out-boundary points will be padded with zeros.The shape of the output will be (data.shape[0], data.shape[1], grid.shape[2], grid.shape[3]).</p>
+<p>The operator assumes that <span class="math">\(data\)</span> has ‘NCHW’ layout and <span class="math">\(grid\)</span> has been normalized to [-1, 1].</p>
+<p>BilinearSampler often cooperates with GridGenerator which generates sampling grids for BilinearSampler.
+GridGenerator supports two kinds of transformation: <code class="docutils literal"><span class="pre">affine</span></code> and <code class="docutils literal"><span class="pre">warp</span></code>.
+If users want to design a CustomOp to manipulate <span class="math">\(grid\)</span>, please firstly refer to the code of GridGenerator.</p>
+<p>Example 1:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span>## Zoom out data two times
+data = array([[[[1, 4, 3, 6],
+ [1, 8, 8, 9],
+ [0, 4, 1, 5],
+ [1, 0, 1, 3]]]])
+
+affine_matrix = array([[2, 0, 0],
+ [0, 2, 0]])
+
+affine_matrix = reshape(affine_matrix, shape=(1, 6))
+
+grid = GridGenerator(data=affine_matrix, transform_type='affine', target_shape=(4, 4))
+
+out = BilinearSampler(data, grid)
+
+out
+[[[[ 0, 0, 0, 0],
+ [ 0, 3.5, 6.5, 0],
+ [ 0, 1.25, 2.5, 0],
+ [ 0, 0, 0, 0]]]
+</pre></div>
+</div>
+<p>Example 2:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span>## shift data horizontally by -1 pixel
+
+data = array([[[[1, 4, 3, 6],
+ [1, 8, 8, 9],
+ [0, 4, 1, 5],
+ [1, 0, 1, 3]]]])
+
+warp_maxtrix = array([[[[1, 1, 1, 1],
+ [1, 1, 1, 1],
+ [1, 1, 1, 1],
+ [1, 1, 1, 1]],
+ [[0, 0, 0, 0],
+ [0, 0, 0, 0],
+ [0, 0, 0, 0],
+ [0, 0, 0, 0]]]])
+
+grid = GridGenerator(data=warp_matrix, transform_type='warp')
+out = BilinearSampler(data, grid)
+
+out
+[[[[ 4, 3, 6, 0],
+ [ 8, 8, 9, 0],
+ [ 4, 1, 5, 0],
+ [ 0, 1, 3, 0]]]
+</pre></div>
+</div>
+<p>Defined in src/operator/bilinear_sampler.cc:L226</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the BilinearsamplerOp.</li>
<li><strong>grid</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input grid to the BilinearsamplerOp.grid has two channels: x_src, y_src</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -2935,39 +3256,40 @@ The operator assumes that grid has been nomalized. If you want to design a Custo
in the backward direction. In other words, this operator prevents the contribution
of its inputs to be taken into account for computing gradients.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">v1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-<span class="n">v2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
-<span class="n">b</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
-<span class="n">b_stop_grad</span> <span class="o">=</span> <span class="n">stop_gradient</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">b</span><span class="p">)</span>
-<span class="n">loss</span> <span class="o">=</span> <span class="n">MakeLoss</span><span class="p">(</span><span class="n">b_stop_grad</span> <span class="o">+</span> <span class="n">a</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>v1 = [1, 2]
+v2 = [0, 1]
+a = Variable('a')
+b = Variable('b')
+b_stop_grad = stop_gradient(3 * b)
+loss = MakeLoss(b_stop_grad + a)
-<span class="n">executor</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">simple_bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">a</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">b</span><span class="o">=</span><sp [...]
-<span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="n">v1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">v2</span><span class="p">)</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">outputs</span>
-<span class="p">[</span> <span class="mf">1.</span> <span class="mf">5.</span><span class="p">]</span>
+executor = loss.simple_bind(ctx=cpu(), a=(1,2), b=(1,2))
+executor.forward(is_train=True, a=v1, b=v2)
+executor.outputs
+[ 1. 5.]
-<span class="n">executor</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">grad_arrays</span>
-<span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
+executor.backward()
+executor.grad_arrays
+[ 0. 0.]
+[ 1. 1.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L99</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -2982,27 +3304,28 @@ of its inputs to be taken into account for computing gradients.</p>
<p class="last"><code class="docutils literal"><span class="pre">Cast</span></code> is deprecated. Use <code class="docutils literal"><span class="pre">cast</span></code> instead.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">cast</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cast</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">cast</span><span class="p">([</span><span class="mf">1e20</span><span class="p">,</span> <span class="mf">11.1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float16'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="n">inf</span><span class="p">,</span> <span class="mf">11.09375</span><span class="p">]</span>
<span class="n">cast</span><span class="p">([</span><span class="mi">300</span><span class="p">,</span> <span class="mf">11.1</span><span class="p">,</span> <span class="mf">10.9</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'uint8'</span><span class="p">)</span> <span class="o">=</span> [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L175</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input.</li>
-<li><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>, </em><em>required</em>) – Output data type.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>dtype</strong> (<em>{'float16', 'float32', 'float64', 'int32', 'uint8'}, required</em>) – Output data type.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3021,43 +3344,44 @@ which they will be concatenated.
The dimension of the output array along the concatenated axis will be equal
to the sum of the corresponding dimensions of the input arrays.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">]]</span>
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">]]</span>
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">6</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">],[</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[1,1],[2,2]]
+y = [[3,3],[4,4],[5,5]]
+z = [[6,6], [7,7],[8,8]]
-<span class="n">concat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+concat(x,y,z,dim=0) = [[ 1., 1.],
+ [ 2., 2.],
+ [ 3., 3.],
+ [ 4., 4.],
+ [ 5., 5.],
+ [ 6., 6.],
+ [ 7., 7.],
+ [ 8., 8.]]
-<span class="n">Note</span> <span class="n">that</span> <span class="n">you</span> <span class="n">cannot</span> <span class="n">concat</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span> <span class="n">along</span> <span class="n">dimension</span> <span class="mi">1</span> <span class="n">since</span> <span class="n">dimension</span>
-<span class="mi">0</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">the</span> <span class="n">same</span> <span class="k">for</span> <span class="nb">all</span> <span class="n">the</span> <span class="nb">input</span> <span class="n">arrays</span><span class="o">.</span>
+Note that you cannot concat x,y,z along dimension 1 since dimension
+0 is not the same for all the input arrays.
-<span class="n">concat</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span clas [...]
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+concat(y,z,dim=1) = [[ 3., 3., 6., 6.],
+ [ 4., 4., 7., 7.],
+ [ 5., 5., 8., 8.]]
</pre></div>
</div>
<p>Defined in src/operator/concat.cc:L80</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>[</em><em></em><em>]</em><em></em>) – List of arrays to concatenate</li>
-<li><strong>dim</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – the dimension to be concated.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>data</strong> (<em>NDArray[]</em>) – List of arrays to concatenate</li>
+<li><strong>dim</strong> (<em>int, optional, default='1'</em>) – the dimension to be concated.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3081,11 +3405,11 @@ weight[i,j,:,:]\]</div>
<li><strong>out</strong>: <em>(batch_size, num_filter, out_height, out_width)</em>.</li>
</ul>
<p>Define:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="n">p</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">d</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="n">p</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">d</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span [...]
</pre></div>
</div>
<p>then we have:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><sp [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><spa [...]
<span class="n">out_width</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dilate</sp [...]
</pre></div>
</div>
@@ -3131,33 +3455,34 @@ the performance.</li>
</ul>
<p>Defined in src/operator/convolution.cc:L151</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the ConvolutionOp.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight matrix.</li>
<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Bias parameter.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – convolution stride: (h, w) or (d, h, w)</li>
-<li><strong>dilate</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – convolution dilate: (h, w) or (d, h, w)</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – pad for convolution: (h, w) or (d, h, w)</li>
-<li><strong>num_filter</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – convolution filter(channel) number</li>
-<li><strong>num_group</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Number of group partitions.</li>
-<li><strong>workspace</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1024</em>) – Maximum temporary workspace allowed for convolution (MB).</li>
-<li><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to disable bias parameter.</li>
-<li><strong>cudnn_tune</strong> (<em>{None</em><em>, </em><em>'fastest'</em><em>, </em><em>'limited_workspace'</em><em>, </em><em>'off'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Whether to pick convolution algo by running performance test.</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Turn off cudnn for this layer.</li>
-<li><strong>layout</strong> (<em>{None</em><em>, </em><em>'NCDHW'</em><em>, </em><em>'NCHW'</em><em>, </em><em>'NCW'</em><em>, </em><em>'NDHWC'</em><em>, </em><em>'NHWC'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Set layout for input, output and weight. Empty for
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution stride: (h, w) or (d, h, w)</li>
+<li><strong>dilate</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution dilate: (h, w) or (d, h, w)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for convolution: (h, w) or (d, h, w)</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), required</em>) – convolution filter(channel) number</li>
+<li><strong>num_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of group partitions.</li>
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=1024</em>) – Maximum temporary workspace allowed for convolution (MB).</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=False</em>) – Whether to disable bias parameter.</li>
+<li><strong>cudnn_tune</strong> (<em>{None, 'fastest', 'limited_workspace', 'off'},optional, default='None'</em>) – Whether to pick convolution algo by running performance test.</li>
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Turn off cudnn for this layer.</li>
+<li><strong>layout</strong> (<em>{None, 'NCDHW', 'NCHW', 'NCW', 'NDHWC', 'NHWC'},optional, default='None'</em>) – Set layout for input, output and weight. Empty for
default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3168,23 +3493,23 @@ default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">Convolution_v1</code><span class="sig-paren">(</span><em>data=None</em>, <em>weight=None</em>, <em>bias=None</em>, <em>kernel=_Null</em>, <em>stride=_Null</em>, <em>dilate=_Null</em>, <em>pad=_Null</em>, <em>num_filter=_Null</em>, <em>num_group=_Null</em>, <em>workspace=_Null</em>, <em>no_bias=_Null</em>, <em>cudnn_tune=_Null</em>, <em>cudnn_off=_Null</em>, <em>layout=_Null</em>, <em>out=None</em>, <em>name=None</em> [...]
<dd><p>This operator is DEPRECATED. Apply convolution to input then add a bias.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the ConvolutionV1Op.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight matrix.</li>
<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Bias parameter.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – convolution stride: (h, w) or (d, h, w)</li>
-<li><strong>dilate</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – convolution dilate: (h, w) or (d, h, w)</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – pad for convolution: (h, w) or (d, h, w)</li>
-<li><strong>num_filter</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – convolution filter(channel) number</li>
-<li><strong>num_group</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Number of group partitions. Equivalent to slicing input into num_group
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution stride: (h, w) or (d, h, w)</li>
+<li><strong>dilate</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution dilate: (h, w) or (d, h, w)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for convolution: (h, w) or (d, h, w)</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), required</em>) – convolution filter(channel) number</li>
+<li><strong>num_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of group partitions. Equivalent to slicing input into num_group
partitions, apply convolution on each, then concatenate the results</li>
-<li><strong>workspace</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1024</em>) – Maximum tmp workspace allowed for convolution (MB).</li>
-<li><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to disable bias parameter.</li>
-<li><strong>cudnn_tune</strong> (<em>{None</em><em>, </em><em>'fastest'</em><em>, </em><em>'limited_workspace'</em><em>, </em><em>'off'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Whether to pick convolution algo by running performance test.
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=1024</em>) – Maximum tmp workspace allowed for convolution (MB).</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=False</em>) – Whether to disable bias parameter.</li>
+<li><strong>cudnn_tune</strong> (<em>{None, 'fastest', 'limited_workspace', 'off'},optional, default='None'</em>) – Whether to pick convolution algo by running performance test.
Leads to higher startup time but may give faster speed. Options are:
‘off’: no tuning
‘limited_workspace’: run test and pick the fastest algorithm that doesn’t exceed workspace limit.
@@ -3192,17 +3517,18 @@ Leads to higher startup time but may give faster speed. Options are:
If set to None (default), behavior is determined by environment
variable MXNET_CUDNN_AUTOTUNE_DEFAULT: 0 for off,
1 for limited workspace (default), 2 for fastest.</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Turn off cudnn for this layer.</li>
-<li><strong>layout</strong> (<em>{None</em><em>, </em><em>'NCDHW'</em><em>, </em><em>'NCHW'</em><em>, </em><em>'NDHWC'</em><em>, </em><em>'NHWC'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Set layout for input, output and weight. Empty for
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Turn off cudnn for this layer.</li>
+<li><strong>layout</strong> (<em>{None, 'NCDHW', 'NCHW', 'NDHWC', 'NHWC'},optional, default='None'</em>) – Set layout for input, output and weight. Empty for
default layout: NCHW for 2d and NCDHW for 3d.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3218,7 +3544,7 @@ the correlation layer lets the network compare each patch from <span class="math
<p>For now we consider only a single comparison of two patches. The ‘correlation’ of two patches centered at <span class="math">\(x_{1}\)</span> in the first map and
<span class="math">\(x_{2}\)</span> in the second map is then defined as:</p>
<div class="math">
-\[c(x_{1}, x_{2}) = \sum_{o \in [-k,k] \times [-k,k]} <f_{1}(x_{1} +="" f_{2}(x_{2}="" o)="" o),="">\]</f_{1}(x_{1}></div>
+\[\begin{split}c(x_{1}, x_{2}) = \sum_{o \in [-k,k] \times [-k,k]} <f_{1}(x_{1} + o), f_{2}(x_{2} + o)>\end{split}\]</div>
<p>for a square patch of size <span class="math">\(K:=2k+1\)</span>.</p>
<p>Note that the equation above is identical to one step of a convolution in neural networks, but instead of convolving data with a filter, it convolves data with other
data. For this reason, it has no training weights.</p>
@@ -3232,26 +3558,27 @@ centered around <span class="math">\(x_{1}\)</span>.</p>
<p>where <span class="math">\(i\)</span> and <span class="math">\(j\)</span> enumerate spatial locations in <span class="math">\(f_{1}\)</span>, and <span class="math">\(q\)</span> denotes the <span class="math">\(q^{th}\)</span> neighborhood of <span class="math">\(x_{i,j}\)</span>.</p>
<p>Defined in src/operator/correlation.cc:L173</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data1</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data1 to the correlation.</li>
<li><strong>data2</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data2 to the correlation.</li>
-<li><strong>kernel_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – kernel size for Correlation must be an odd number</li>
-<li><strong>max_displacement</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Max displacement of Correlation</li>
-<li><strong>stride1</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – stride1 quantize data1 globally</li>
-<li><strong>stride2</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – stride2 quantize data2 within the neighborhood centered around data1</li>
-<li><strong>pad_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – pad for Correlation</li>
-<li><strong>is_multiply</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – operation type is either multiplication or subduction</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>kernel_size</strong> (<em>int (non-negative), optional, default=1</em>) – kernel size for Correlation must be an odd number</li>
+<li><strong>max_displacement</strong> (<em>int (non-negative), optional, default=1</em>) – Max displacement of Correlation</li>
+<li><strong>stride1</strong> (<em>int (non-negative), optional, default=1</em>) – stride1 quantize data1 globally</li>
+<li><strong>stride2</strong> (<em>int (non-negative), optional, default=1</em>) – stride2 quantize data2 within the neighborhood centered around data1</li>
+<li><strong>pad_size</strong> (<em>int (non-negative), optional, default=0</em>) – pad for Correlation</li>
+<li><strong>is_multiply</strong> (<em>boolean, optional, default=True</em>) – operation type is either multiplication or subduction</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3269,22 +3596,23 @@ with width and height of the second input symbol, i.e., with one input, we need
specify the crop height and width, otherwise the second input symbol’s size will be used</p>
<p>Defined in src/operator/crop.cc:L31</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em></em><em>]</em><em></em>) – Tensor or List of Tensors, the second input will be used as crop_like shape reference</li>
-<li><strong>offset</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em>0</em><em>)</em><em></em>) – crop offset coordinate: (y, x)</li>
-<li><strong>h_w</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em>0</em><em>)</em><em></em>) – crop height and width: (h, w)</li>
-<li><strong>center_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to true, then it will use be the center_crop,or it will crop using the shape of crop_like</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>data</strong> (<em>Symbol or Symbol[]</em>) – Tensor or List of Tensors, the second input will be used as crop_like shape reference</li>
+<li><strong>offset</strong> (<em>Shape(tuple), optional, default=(0,0)</em>) – crop offset coordinate: (y, x)</li>
+<li><strong>h_w</strong> (<em>Shape(tuple), optional, default=(0,0)</em>) – crop height and width: (h, w)</li>
+<li><strong>center_crop</strong> (<em>boolean, optional, default=False</em>) – If set to true, then it will use be the center_crop,or it will crop using the shape of crop_like</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3299,27 +3627,28 @@ The custom operator must be registered before it can be used.
Please check the tutorial here: <a class="reference external" href="http://mxnet.io/how_to/new_op.html">http://mxnet.io/how_to/new_op.html</a>.</p>
<p>Defined in src/operator/custom/custom.cc:L333</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>[</em><em></em><em>]</em><em></em>) – Input data for the custom operator.</li>
+<li><strong>data</strong> (<em>NDArray[]</em>) – Input data for the custom operator.</li>
<li><strong>op_type</strong> (<em>string</em>) – Name of the custom operator. This is the name that is passed to <cite>mx.operator.register</cite> to register the operator.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
<p>Applies a custom operator named <cite>my_custom_operator</cite> to <cite>input</cite>.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Custom</span><span class="p">(</span><span class="n">op_type</span><span class="o">=</span><span class="s1">'my_custom_operator'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Custom</span><span class="p">(</span><span class="n">op_type</span><span class="o">=</span><span class="s1">'my_custom_operator'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span cla [...]
</pre></div>
</div>
</dd></dl>
@@ -3328,34 +3657,35 @@ Please check the tutorial here: <a class="reference external" href="http://mxnet
<code class="descclassname">mxnet.ndarray.</code><code class="descname">Deconvolution</code><span class="sig-paren">(</span><em>data=None</em>, <em>weight=None</em>, <em>bias=None</em>, <em>kernel=_Null</em>, <em>stride=_Null</em>, <em>dilate=_Null</em>, <em>pad=_Null</em>, <em>adj=_Null</em>, <em>target_shape=_Null</em>, <em>num_filter=_Null</em>, <em>num_group=_Null</em>, <em>workspace=_Null</em>, <em>no_bias=_Null</em>, <em>cudnn_tune=_Null</em>, <em>cudnn_off=_Null</em>, <em>layout=_ [...]
<dd><p>Computes 2D transposed convolution (aka fractionally strided convolution) of the input tensor. This operation can be seen as the gradient of Convolution operation with respect to its input. Convolution usually reduces the size of the input. Transposed convolution works the other way, going from a smaller input to a larger output while preserving the connectivity pattern.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input tensor to the deconvolution operation.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weights representing the kernel.</li>
<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Bias added to the result after the deconvolution operation.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – Deconvolution kernel size: (h, w) or (d, h, w). This is same as the kernel size used for the corresponding convolution</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The stride used for the corresponding convolution: (h, w) or (d, h, w).</li>
-<li><strong>dilate</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Dilation factor for each dimension of the input: (h, w) or (d, h, w).</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The amount of implicit zero padding added during convolution for each dimension of the input: (h, w) or (d, h, w). <code class="docutils literal"><span class="pre">(kernel-1)/2</span></code> is usually a good choice. If <cite>target_shape</cite> is set, <cite>pad</cite> will be ignored and a padding that will [...]
-<li><strong>adj</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Adjustment for output shape: (h, w) or (d, h, w). If <cite>target_shape</cite> is set, <cite>adj</cite> will be ignored and computed accordingly.</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output tensor: (h, w) or (d, h, w).</li>
-<li><strong>num_filter</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Number of output filters.</li>
-<li><strong>num_group</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Number of groups partition.</li>
-<li><strong>workspace</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=512</em>) – Maximum temporal workspace allowed for deconvolution (MB).</li>
-<li><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to disable bias parameter.</li>
-<li><strong>cudnn_tune</strong> (<em>{None</em><em>, </em><em>'fastest'</em><em>, </em><em>'limited_workspace'</em><em>, </em><em>'off'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Whether to pick convolution algorithm by running performance test.</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Turn off cudnn for this layer.</li>
-<li><strong>layout</strong> (<em>{None</em><em>, </em><em>'NCDHW'</em><em>, </em><em>'NCHW'</em><em>, </em><em>'NCW'</em><em>, </em><em>'NDHWC'</em><em>, </em><em>'NHWC'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Set layout for input, output and weight. Empty for default layout, NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – Deconvolution kernel size: (h, w) or (d, h, w). This is same as the kernel size used for the corresponding convolution</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – The stride used for the corresponding convolution: (h, w) or (d, h, w).</li>
+<li><strong>dilate</strong> (<em>Shape(tuple), optional, default=()</em>) – Dilation factor for each dimension of the input: (h, w) or (d, h, w).</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – The amount of implicit zero padding added during convolution for each dimension of the input: (h, w) or (d, h, w). <code class="docutils literal"><span class="pre">(kernel-1)/2</span></code> is usually a good choice. If <cite>target_shape</cite> is set, <cite>pad</cite> will be ignored and a padding that will generate the target shape will be used.</li>
+<li><strong>adj</strong> (<em>Shape(tuple), optional, default=()</em>) – Adjustment for output shape: (h, w) or (d, h, w). If <cite>target_shape</cite> is set, <cite>adj</cite> will be ignored and computed accordingly.</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output tensor: (h, w) or (d, h, w).</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), required</em>) – Number of output filters.</li>
+<li><strong>num_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of groups partition.</li>
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=512</em>) – Maximum temporal workspace allowed for deconvolution (MB).</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=True</em>) – Whether to disable bias parameter.</li>
+<li><strong>cudnn_tune</strong> (<em>{None, 'fastest', 'limited_workspace', 'off'},optional, default='None'</em>) – Whether to pick convolution algorithm by running performance test.</li>
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Turn off cudnn for this layer.</li>
+<li><strong>layout</strong> (<em>{None, 'NCDHW', 'NCHW', 'NCW', 'NDHWC', 'NHWC'},optional, default='None'</em>) – Set layout for input, output and weight. Empty for default layout, NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3363,51 +3693,54 @@ Please check the tutorial here: <a class="reference external" href="http://mxnet
</dd></dl>
<dl class="function">
<dt id="mxnet.ndarray.Dropout">
-<code class="descclassname">mxnet.ndarray.</code><code class="descname">Dropout</code><span class="sig-paren">(</span><em>data=None</em>, <em>p=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.Dropout" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">mxnet.ndarray.</code><code class="descname">Dropout</code><span class="sig-paren">(</span><em>data=None</em>, <em>p=_Null</em>, <em>mode=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.Dropout" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies dropout operation to input array.</p>
<ul class="simple">
<li>During training, each element of the input is set to zero with probability p.
The whole array is rescaled by <span class="math">\(1/(1-p)\)</span> to keep the expected
sum of the input unchanged.</li>
-<li>During testing, this operator does not change the input.</li>
+<li>During testing, this operator does not change the input if mode is ‘training’.
+If mode is ‘always’, the same computaion as during training will be applied.</li>
</ul>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">998</span><span class="p">)</span>
-<span class="n">input_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">([[</span><span class="mf">3.</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]])</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
-<span class="n">dropout</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">)</span>
-<span class="n">executor</span> <span class="o">=</span> <span class="n">dropout</span><span class="o">.</span><span class="n">simple_bind</span><span class="p">(</span><span class="n">a</span> <span class="o">=</span> <span class="n">input_array</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>random.seed(998)
+input_array = array([[3., 0.5, -0.5, 2., 7.],
+ [2., -0.4, 7., 3., 0.2]])
+a = symbol.Variable('a')
+dropout = symbol.Dropout(a, p = 0.2)
+executor = dropout.simple_bind(a = input_array.shape)
-<span class="c1">## If training</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">input_array</span><span class="p">)</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">outputs</span>
-<span class="p">[[</span> <span class="mf">3.75</span> <span class="mf">0.625</span> <span class="o">-</span><span class="mf">0.</span> <span class="mf">2.5</span> <span class="mf">8.75</span> <span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.5</span> <span class="o">-</span><span class="mf">0.5</span> <span class="mf">8.75</span> <span class="mf">3.75</span> <span class="mf">0.</span> <span class="p">]]</span>
+## If training
+executor.forward(is_train = True, a = input_array)
+executor.outputs
+[[ 3.75 0.625 -0. 2.5 8.75 ]
+ [ 2.5 -0.5 8.75 3.75 0. ]]
-<span class="c1">## If testing</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">input_array</span><span class="p">)</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">outputs</span>
-<span class="p">[[</span> <span class="mf">3.</span> <span class="mf">0.5</span> <span class="o">-</span><span class="mf">0.5</span> <span class="mf">2.</span> <span class="mf">7.</span> <span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span> <span class="o">-</span><span class="mf">0.4</span> <span class="mf">7.</span> <span class="mf">3.</span> <span class="mf">0.2</span> <span class="p">]]</span>
+## If testing
+executor.forward(is_train = False, a = input_array)
+executor.outputs
+[[ 3. 0.5 -0.5 2. 7. ]
+ [ 2. -0.4 7. 3. 0.2 ]]
</pre></div>
</div>
-<p>Defined in src/operator/dropout.cc:L58</p>
+<p>Defined in src/operator/dropout.cc:L59</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array to which dropout will be applied.</li>
-<li><strong>p</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.5</em>) – Fraction of the input that gets dropped out during training time.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>p</strong> (<em>float, optional, default=0.5</em>) – Fraction of the input that gets dropped out during training time.</li>
+<li><strong>mode</strong> (<em>{'always', 'training'},optional, default='training'</em>) – Whether to only turn on dropout during training or to also turn on for inference.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3422,19 +3755,20 @@ sum of the input unchanged.</li>
<p><code class="docutils literal"><span class="pre">add_n</span></code> is potentially more efficient than calling <code class="docutils literal"><span class="pre">add</span></code> by <cite>n</cite> times.</p>
<p>Defined in src/operator/tensor/elemwise_sum.cc:L47</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>args</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>[</em><em></em><em>]</em><em></em>) – Positional input arguments</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>args</strong> (<em>NDArray[]</em>) – Positional input arguments</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3456,46 +3790,47 @@ All the input values should be integers in the range [0, input_dim).</p>
<p>By default, if any index mentioned is too large, it is replaced by the index that addresses
the last vector in an embedding matrix.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">input_dim</span> <span class="o">=</span> <span class="mi">4</span>
-<span class="n">output_dim</span> <span class="o">=</span> <span class="mi">5</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>input_dim = 4
+output_dim = 5
-<span class="o">//</span> <span class="n">Each</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">weight</span> <span class="n">matrix</span> <span class="n">y</span> <span class="n">represents</span> <span class="n">a</span> <span class="n">word</span><span class="o">.</span> <span class="n">So</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="n">w0</span><span class="p">,</span><span class="n" [...]
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">,</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">,</span> <span class="mf">19.</span><span class="p">]]</span>
+// Each row in weight matrix y represents a word. So, y = (w0,w1,w2,w3)
+y = [[ 0., 1., 2., 3., 4.],
+ [ 5., 6., 7., 8., 9.],
+ [ 10., 11., 12., 13., 14.],
+ [ 15., 16., 17., 18., 19.]]
-<span class="o">//</span> <span class="n">Input</span> <span class="n">array</span> <span class="n">x</span> <span class="n">represents</span> <span class="n">n</span><span class="o">-</span><span class="n">grams</span><span class="p">(</span><span class="mi">2</span><span class="o">-</span><span class="n">gram</span><span class="p">)</span><span class="o">.</span> <span class="n">So</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[(</span [...]
-<span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]</span>
+// Input array x represents n-grams(2-gram). So, x = [(w1,w3), (w0,w2)]
+x = [[ 1., 3.],
+ [ 0., 2.]]
-<span class="o">//</span> <span class="n">Mapped</span> <span class="nb">input</span> <span class="n">x</span> <span class="n">to</span> <span class="n">its</span> <span class="n">vector</span> <span class="n">representation</span> <span class="n">y</span><span class="o">.</span>
-<span class="n">Embedding</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span> [...]
- <span class="p">[</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">,</span> <span class="mf">19.</span><span class="p">]],</span>
+// Mapped input x to its vector representation y.
+Embedding(x, y, 4, 5) = [[[ 5., 6., 7., 8., 9.],
+ [ 15., 16., 17., 18., 19.]],
- <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">,</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">]]]</span>
+ [[ 0., 1., 2., 3., 4.],
+ [ 10., 11., 12., 13., 14.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/indexing_op.cc:L55</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array to the embedding operator.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The embedding weight matrix.</li>
-<li><strong>input_dim</strong> (<em>int</em><em>, </em><em>required</em>) – Vocabulary size of the input indices.</li>
-<li><strong>output_dim</strong> (<em>int</em><em>, </em><em>required</em>) – Dimension of the embedding vectors.</li>
-<li><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='float32'</em>) – Data type of weight.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>input_dim</strong> (<em>int, required</em>) – Vocabulary size of the input indices.</li>
+<li><strong>output_dim</strong> (<em>int, required</em>) – Dimension of the embedding vectors.</li>
+<li><strong>dtype</strong> (<em>{'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='float32'</em>) – Data type of weight.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3512,7 +3847,7 @@ the last vector in an embedding matrix.</p>
<p>For an input array with shape <code class="docutils literal"><span class="pre">(d1,</span> <span class="pre">d2,</span> <span class="pre">...,</span> <span class="pre">dk)</span></code>, <cite>flatten</cite> operation reshapes
the input array into an output array of shape <code class="docutils literal"><span class="pre">(d1,</span> <span class="pre">d2*...*dk)</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span>
<span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span>
<span class="p">[</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">]</span>
@@ -3528,19 +3863,20 @@ the input array into an output array of shape <code class="docutils literal"><sp
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L132</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3561,23 +3897,24 @@ the input array into an output array of shape <code class="docutils literal"><sp
<p>If <code class="docutils literal"><span class="pre">no_bias</span></code> is set to be true, then the <code class="docutils literal"><span class="pre">bias</span></code> term is ignored.</p>
<p>Defined in src/operator/fully_connected.cc:L72</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight matrix.</li>
<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Bias parameter.</li>
-<li><strong>num_hidden</strong> (<em>int</em><em>, </em><em>required</em>) – Number of hidden nodes of the output.</li>
-<li><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to disable bias parameter.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>num_hidden</strong> (<em>int, required</em>) – Number of hidden nodes of the output.</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=False</em>) – Whether to disable bias parameter.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3588,21 +3925,22 @@ the input array into an output array of shape <code class="docutils literal"><sp
<code class="descclassname">mxnet.ndarray.</code><code class="descname">GridGenerator</code><span class="sig-paren">(</span><em>data=None</em>, <em>transform_type=_Null</em>, <em>target_shape=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.GridGenerator" title="Permalink to this definition">¶</a></dt>
<dd><p>Generates 2D sampling grid for bilinear sampling.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the function.</li>
-<li><strong>transform_type</strong> (<em>{'affine'</em><em>, </em><em>'warp'}</em><em>, </em><em>required</em>) – The type of transformation. For <cite>affine</cite>, input data should be an affine matrix of size (batch, 6). For <cite>warp</cite>, input data should be an optical flow of size (batch, 2, h, w).</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em>0</em><em>)</em><em></em>) – Specifies the output shape (H, W). This is required if transformation type is <cite>affine</cite>. If transformation type is <cite>warp</cite>, this parameter is ignored.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>transform_type</strong> (<em>{'affine', 'warp'}, required</em>) – The type of transformation. For <cite>affine</cite>, input data should be an affine matrix of size (batch, 6). For <cite>warp</cite>, input data should be an optical flow of size (batch, 2, h, w).</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=(0,0)</em>) – Specifies the output shape (H, W). This is required if transformation type is <cite>affine</cite>. If transformation type is <cite>warp</cite>, this parameter is ignored.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3613,22 +3951,23 @@ the input array into an output array of shape <code class="docutils literal"><sp
<code class="descclassname">mxnet.ndarray.</code><code class="descname">IdentityAttachKLSparseReg</code><span class="sig-paren">(</span><em>data=None</em>, <em>sparseness_target=_Null</em>, <em>penalty=_Null</em>, <em>momentum=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.IdentityAttachKLSparseReg" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply a sparse regularization to the output a sigmoid activation function.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data.</li>
-<li><strong>sparseness_target</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.1</em>) – The sparseness target</li>
-<li><strong>penalty</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.001</em>) – The tradeoff parameter for the sparseness penalty</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – The momentum for running average</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>sparseness_target</strong> (<em>float, optional, default=0.1</em>) – The sparseness target</li>
+<li><strong>penalty</strong> (<em>float, optional, default=0.001</em>) – The tradeoff parameter for the sparseness penalty</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0.9</em>) – The momentum for running average</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3651,46 +3990,47 @@ operation is also known as <cite>contrast normalization</cite>.</p>
<cite>gamma</cite> and <cite>beta</cite> parameters must be vectors of shape [channel].</p>
<p>This implementation is based on paper:</p>
<table class="docutils footnote" frame="void" id="id2" rules="none">
-<colgroup><col class="label"></col><col></col></colgroup>
+<colgroup><col class="label"/><col/></colgroup>
<tbody valign="top">
<tr><td class="label">[1]</td><td>Instance Normalization: The Missing Ingredient for Fast Stylization,
D. Ulyanov, A. Vedaldi, V. Lempitsky, 2016 (arXiv:1607.08022v2).</td></tr>
</tbody>
</table>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Input</span> <span class="n">of</span> <span class="n">shape</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.1</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mf">3.3</span><span class="p">,</span> <span class="mf">4.4</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Input of shape (2,1,2)
+x = [[[ 1.1, 2.2]],
+ [[ 3.3, 4.4]]]
-<span class="o">//</span> <span class="n">gamma</span> <span class="n">parameter</span> <span class="n">of</span> <span class="n">length</span> <span class="mi">1</span>
-<span class="n">gamma</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.5</span><span class="p">]</span>
+// gamma parameter of length 1
+gamma = [1.5]
-<span class="o">//</span> <span class="n">beta</span> <span class="n">parameter</span> <span class="n">of</span> <span class="n">length</span> <span class="mi">1</span>
-<span class="n">beta</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">]</span>
+// beta parameter of length 1
+beta = [0.5]
-<span class="o">//</span> <span class="n">Instance</span> <span class="n">normalization</span> <span class="ow">is</span> <span class="n">calculated</span> <span class="k">with</span> <span class="n">the</span> <span class="n">above</span> <span class="n">formula</span>
-<span class="n">InstanceNorm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">gamma</span><span class="p">,</span><span class="n">beta</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="o">-</span><span class="mf">0.997527</span> <span class="p">,</span> <span class="mf">1.99752665</span><span class="p">]],</span>
- <span class="p">[[</span><span class="o">-</span><span class="mf">0.99752653</span><span class="p">,</span> <span class="mf">1.99752724</span><span class="p">]]]</span>
+// Instance normalization is calculated with the above formula
+InstanceNorm(x,gamma,beta) = [[[-0.997527 , 1.99752665]],
+ [[-0.99752653, 1.99752724]]]
</pre></div>
</div>
<p>Defined in src/operator/instance_norm.cc:L76</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – An n-dimensional input array (n > 2) of the form [batch, channel, spatial_dim1, spatial_dim2, ...].</li>
<li><strong>gamma</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – A vector of length ‘channel’, which multiplies the normalized input.</li>
<li><strong>beta</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – A vector of length ‘channel’, which is added to the product of the normalized input and the weight.</li>
-<li><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.001</em>) – An <cite>epsilon</cite> parameter to prevent division by 0.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>eps</strong> (<em>float, optional, default=0.001</em>) – An <cite>epsilon</cite> parameter to prevent division by 0.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3701,71 +4041,72 @@ D. Ulyanov, A. Vedaldi, V. Lempitsky, 2016 (arXiv:1607.08022v2).</td></tr>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">L2Normalization</code><span class="sig-paren">(</span><em>data=None</em>, <em>eps=_Null</em>, <em>mode=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.L2Normalization" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize the input array using the L2 norm.</p>
<p>For 1-D NDArray, it computes:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out</span> <span class="o">=</span> <span class="n">data</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out</span> <span class="o">=</span> <span class="n">data</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span>
</pre></div>
</div>
<p>For N-D NDArray, if the input array has shape (N, N, ..., N),</p>
<p>with <code class="docutils literal"><span class="pre">mode</span></code> = <code class="docutils literal"><span class="pre">instance</span></code>, it normalizes each instance in the multidimensional
array by its L2 norm.:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="mf">0.</span><span class="o">..</span><span class="n">N</span>
- <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="o">...</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="o">...</span><span class="p">,:]</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>for i in 0...N
+ out[i,:,:,...,:] = data[i,:,:,...,:] / sqrt(sum(data[i,:,:,...,:] ** 2) + eps)
</pre></div>
</div>
<p>with <code class="docutils literal"><span class="pre">mode</span></code> = <code class="docutils literal"><span class="pre">channel</span></code>, it normalizes each channel in the array by its L2 norm.:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="mf">0.</span><span class="o">..</span><span class="n">N</span>
- <span class="n">out</span><span class="p">[:,</span><span class="n">i</span><span class="p">,:,</span><span class="o">...</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:,</span><span class="n">i</span><span class="p">,:,</span><span class="o">...</span><span class="p">,:]</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>for i in 0...N
+ out[:,i,:,...,:] = data[:,i,:,...,:] / sqrt(sum(data[:,i,:,...,:] ** 2) + eps)
</pre></div>
</div>
<p>with <code class="docutils literal"><span class="pre">mode</span></code> = <code class="docutils literal"><span class="pre">spatial</span></code>, it normalizes the cross channel norm for each position
in the array by its L2 norm.:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="mf">2.</span><span class="o">..</span><span class="n">N</span>
- <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="mf">0.</span><span class="o">..</span><span class="n">N</span>
- <span class="n">out</span><span class="p">[</span><span class="o">.....</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">take</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span [...]
- <span class="o">-</span><span class="n">dim</span><span class="o">-</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>for dim in 2...N
+ for i in 0...N
+ out[.....,i,...] = take(out, indices=i, axis=dim) / sqrt(sum(take(out, indices=i, axis=dim) ** 2) + eps)
+ -dim-
</pre></div>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]]]</span>
-
-<span class="n">L2Normalization</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'instance'</span><span class="p">)</span>
-<span class="o">=</span><span class="p">[[[</span> <span class="mf">0.18257418</span> <span class="mf">0.36514837</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.54772252</span> <span class="mf">0.73029673</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">0.24077171</span> <span class="mf">0.24077171</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.60192931</span> <span class="mf">0.72231513</span><span class="p">]]]</span>
-
-<span class="n">L2Normalization</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'channel'</span><span class="p">)</span>
-<span class="o">=</span><span class="p">[[[</span> <span class="mf">0.31622776</span> <span class="mf">0.44721359</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.94868326</span> <span class="mf">0.89442718</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">0.37139067</span> <span class="mf">0.31622776</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.92847669</span> <span class="mf">0.94868326</span><span class="p">]]]</span>
-
-<span class="n">L2Normalization</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'spatial'</span><span class="p">)</span>
-<span class="o">=</span><span class="p">[[[</span> <span class="mf">0.44721359</span> <span class="mf">0.89442718</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.60000002</span> <span class="mf">0.80000001</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">0.70710677</span> <span class="mf">0.70710677</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.6401844</span> <span class="mf">0.76822126</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[1,2],
+ [3,4]],
+ [[2,2],
+ [5,6]]]
+
+L2Normalization(x, mode='instance')
+=[[[ 0.18257418 0.36514837]
+ [ 0.54772252 0.73029673]]
+ [[ 0.24077171 0.24077171]
+ [ 0.60192931 0.72231513]]]
+
+L2Normalization(x, mode='channel')
+=[[[ 0.31622776 0.44721359]
+ [ 0.94868326 0.89442718]]
+ [[ 0.37139067 0.31622776]
+ [ 0.92847669 0.94868326]]]
+
+L2Normalization(x, mode='spatial')
+=[[[ 0.44721359 0.89442718]
+ [ 0.60000002 0.80000001]]
+ [[ 0.70710677 0.70710677]
+ [ 0.6401844 0.76822126]]]
</pre></div>
</div>
<p>Defined in src/operator/l2_normalization.cc:L74</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array to normalize.</li>
-<li><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e-10</em>) – A small constant for numerical stability.</li>
-<li><strong>mode</strong> (<em>{'channel'</em><em>, </em><em>'instance'</em><em>, </em><em>'spatial'}</em><em>,</em><em>optional</em><em>, </em><em>default='instance'</em>) – Specify the dimension along which to compute L2 norm.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>eps</strong> (<em>float, optional, default=1e-10</em>) – A small constant for numerical stability.</li>
+<li><strong>mode</strong> (<em>{'channel', 'instance', 'spatial'},optional, default='instance'</em>) – Specify the dimension along which to compute L2 norm.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3786,23 +4127,24 @@ activity <span class="math">\(b_{x,y}^{i}\)</span> is given by the expression:</
number of kernels in the layer.</p>
<p>Defined in src/operator/lrn.cc:L54</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data.</li>
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.0001</em>) – The variance scaling parameter <span class="math">\(lpha\)</span> in the LRN expression.</li>
-<li><strong>beta</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.75</em>) – The power parameter <span class="math">\(eta\)</span> in the LRN expression.</li>
-<li><strong>knorm</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – The parameter <span class="math">\(k\)</span> in the LRN expression.</li>
-<li><strong>nsize</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – normalization window width in elements.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>alpha</strong> (<em>float, optional, default=0.0001</em>) – The variance scaling parameter <span class="math">\(lpha\)</span> in the LRN expression.</li>
+<li><strong>beta</strong> (<em>float, optional, default=0.75</em>) – The power parameter <span class="math">\(eta\)</span> in the LRN expression.</li>
+<li><strong>knorm</strong> (<em>float, optional, default=2</em>) – The parameter <span class="math">\(k\)</span> in the LRN expression.</li>
+<li><strong>nsize</strong> (<em>int (non-negative), required</em>) – normalization window width in elements.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3825,23 +4167,24 @@ when the input is negative and has a slope of one when input is positive.</p>
</ul>
<p>Defined in src/operator/leaky_relu.cc:L39</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to activation function.</li>
-<li><strong>act_type</strong> (<em>{'elu'</em><em>, </em><em>'leaky'</em><em>, </em><em>'prelu'</em><em>, </em><em>'rrelu'}</em><em>,</em><em>optional</em><em>, </em><em>default='leaky'</em>) – Activation function to be applied.</li>
-<li><strong>slope</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.25</em>) – Init slope for the activation. (For leaky and elu only)</li>
-<li><strong>lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.125</em>) – Lower bound of random slope. (For rrelu only)</li>
-<li><strong>upper_bound</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.334</em>) – Upper bound of random slope. (For rrelu only)</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>act_type</strong> (<em>{'elu', 'leaky', 'prelu', 'rrelu'},optional, default='leaky'</em>) – Activation function to be applied.</li>
+<li><strong>slope</strong> (<em>float, optional, default=0.25</em>) – Init slope for the activation. (For leaky and elu only)</li>
+<li><strong>lower_bound</strong> (<em>float, optional, default=0.125</em>) – Lower bound of random slope. (For rrelu only)</li>
+<li><strong>upper_bound</strong> (<em>float, optional, default=0.334</em>) – Upper bound of random slope. (For rrelu only)</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3863,21 +4206,22 @@ then the squared loss estimated over <span class="math">\(n\)</span> samples is
The parameter <cite>grad_scale</cite> can be used to change this scale to <cite>grad_scale/n</cite>.</p>
<p>Defined in src/operator/regression_output.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the function.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input label to the function.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scale the gradient by a float factor</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scale the gradient by a float factor</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3900,21 +4244,22 @@ It is suitable for binary classification or probability prediction tasks.</p>
The parameter <cite>grad_scale</cite> can be used to change this scale to <cite>grad_scale/n</cite>.</p>
<p>Defined in src/operator/regression_output.cc:L93</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the function.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input label to the function.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scale the gradient by a float factor</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scale the gradient by a float factor</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3936,21 +4281,22 @@ then the mean absolute error (MAE) estimated over <span class="math">\(n\)</span
The parameter <cite>grad_scale</cite> can be used to change this scale to <cite>grad_scale/n</cite>.</p>
<p>Defined in src/operator/regression_output.cc:L72</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the function.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input label to the function.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scale the gradient by a float factor</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scale the gradient by a float factor</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -3965,7 +4311,7 @@ the symbol should be an operator with no backward dependency.
The output of this function is the gradient of loss with respect to the input data.</p>
<p>For example, if you are a making a cross entropy loss function. Assume <code class="docutils literal"><span class="pre">out</span></code> is the
predicted output and <code class="docutils literal"><span class="pre">label</span></code> is the true label, then the cross entropy can be defined as:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">cross_entropy</span> <span class="o">=</span> <span class="n">label</span> <span class="o">*</span> <span class="n">log</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">label</span><span class="p">)</span> <span class="o">*</span> <span class="n">log</span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cross_entropy</span> <span class="o">=</span> <span class="n">label</span> <span class="o">*</span> <span class="n">log</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">label</span><span class="p">)</span> <span class="o">*</span> <span class="n">log</span> [...]
<span class="n">loss</span> <span class="o">=</span> <span class="n">MakeLoss</span><span class="p">(</span><span class="n">cross_entropy</span><span class="p">)</span>
</pre></div>
</div>
@@ -3980,22 +4326,23 @@ so that the gradient of the loss will be rescaled in the backpropagation.</p>
</div>
<p>Defined in src/operator/make_loss.cc:L52</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Gradient scale as a supplement to unary and binary operators</li>
-<li><strong>valid_thresh</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – clip each element in the array to 0 when it is less than <code class="docutils literal"><span class="pre">valid_thresh</span></code>. This is used when <code class="docutils literal"><span class="pre">normalization</span></code> is set to <code class="docutils literal"><span class="pre">'valid'</span></code>.</li>
-<li><strong>normalization</strong> (<em>{'batch'</em><em>, </em><em>'null'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='null'</em>) – If this is set to null, the output gradient will not be normalized. If this is set to batch, the output gradient will be divided by the batch size. If this is set to valid, the output gradient will be divided by the number of valid input elements.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Gradient scale as a supplement to unary and binary operators</li>
+<li><strong>valid_thresh</strong> (<em>float, optional, default=0</em>) – clip each element in the array to 0 when it is less than <code class="docutils literal"><span class="pre">valid_thresh</span></code>. This is used when <code class="docutils literal"><span class="pre">normalization</span></code> is set to <code class="docutils literal"><span class="pre">'valid'</span></code>.</li>
+<li><strong>normalization</strong> (<em>{'batch', 'null', 'valid'},optional, default='null'</em>) – If this is set to null, the output gradient will not be normalized. If this is set to batch, the output gradient will be divided by the batch size. If this is set to valid, the output gradient will be divided by the number of valid input elements.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4024,84 +4371,85 @@ to add before and after the elements of the array along dimension <code class="d
The widths of the higher two dimensions <code class="docutils literal"><span class="pre">before_1</span></code>, <code class="docutils literal"><span class="pre">after_1</span></code>, <code class="docutils literal"><span class="pre">before_2</span></code>,
<code class="docutils literal"><span class="pre">after_2</span></code> must be 0.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[[</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[[ 1. 2. 3.]
+ [ 4. 5. 6.]]
- <span class="p">[[</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span><span class="p">]]]</span>
+ [[ 7. 8. 9.]
+ [ 10. 11. 12.]]]
- <span class="p">[[[</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span><span class="p">]]</span>
+ [[[ 11. 12. 13.]
+ [ 14. 15. 16.]]
- <span class="p">[[</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span><span class="p">]]]]</span>
+ [[ 17. 18. 19.]
+ [ 20. 21. 22.]]]]
-<span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">mode</span><span class="o">=</span><span class="s2">"edge"</span><span class="p">,</span> <span class="n">pad_width</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class= [...]
+pad(x,mode="edge", pad_width=(0,0,0,0,1,1,1,1)) =
- <span class="p">[[[[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">6.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">6.</span><span class="p">]]</span>
+ [[[[ 1. 1. 2. 3. 3.]
+ [ 1. 1. 2. 3. 3.]
+ [ 4. 4. 5. 6. 6.]
+ [ 4. 4. 5. 6. 6.]]
- <span class="p">[[</span> <span class="mf">7.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">7.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">12.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">12.</span><span class="p">]]]</span>
+ [[ 7. 7. 8. 9. 9.]
+ [ 7. 7. 8. 9. 9.]
+ [ 10. 10. 11. 12. 12.]
+ [ 10. 10. 11. 12. 12.]]]
- <span class="p">[[[</span> <span class="mf">11.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">11.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">16.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">16.</span><span class="p">]]</span>
+ [[[ 11. 11. 12. 13. 13.]
+ [ 11. 11. 12. 13. 13.]
+ [ 14. 14. 15. 16. 16.]
+ [ 14. 14. 15. 16. 16.]]
- <span class="p">[[</span> <span class="mf">17.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">17.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">22.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">22.</span><span class="p">]]]]</span>
+ [[ 17. 17. 18. 19. 19.]
+ [ 17. 17. 18. 19. 19.]
+ [ 20. 20. 21. 22. 22.]
+ [ 20. 20. 21. 22. 22.]]]]
-<span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"constant"</span><span class="p">,</span> <span class="n">constant_value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">pad_width</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,< [...]
+pad(x, mode="constant", constant_value=0, pad_width=(0,0,0,0,1,1,1,1)) =
- <span class="p">[[[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[[[ 0. 0. 0. 0. 0.]
+ [ 0. 1. 2. 3. 0.]
+ [ 0. 4. 5. 6. 0.]
+ [ 0. 0. 0. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]</span>
+ [[ 0. 0. 0. 0. 0.]
+ [ 0. 7. 8. 9. 0.]
+ [ 0. 10. 11. 12. 0.]
+ [ 0. 0. 0. 0. 0.]]]
- <span class="p">[[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[[ 0. 0. 0. 0. 0.]
+ [ 0. 11. 12. 13. 0.]
+ [ 0. 14. 15. 16. 0.]
+ [ 0. 0. 0. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]]</span>
+ [[ 0. 0. 0. 0. 0.]
+ [ 0. 17. 18. 19. 0.]
+ [ 0. 20. 21. 22. 0.]
+ [ 0. 0. 0. 0. 0.]]]]
</pre></div>
</div>
<p>Defined in src/operator/pad.cc:L747</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – An n-dimensional input array.</li>
-<li><strong>mode</strong> (<em>{'constant'</em><em>, </em><em>'edge'</em><em>, </em><em>'reflect'}</em><em>, </em><em>required</em>) – Padding type to use. “constant” pads with <cite>constant_value</cite> “edge” pads using the edge values of the input array “reflect” pads by reflecting values with respect to the edges.</li>
-<li><strong>pad_width</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – Widths of the padding regions applied to the edges of each axis. It is a tuple of integer padding widths for each axis of the format <code class="docutils literal"><span class="pre">(before_1,</span> <span class="pre">after_1,</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">before_N,</span> <span class="pre">after_N)</span></code>. It s [...]
-<li><strong>constant_value</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The value used for padding when <cite>mode</cite> is “constant”.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>mode</strong> (<em>{'constant', 'edge', 'reflect'}, required</em>) – Padding type to use. “constant” pads with <cite>constant_value</cite> “edge” pads using the edge values of the input array “reflect” pads by reflecting values with respect to the edges.</li>
+<li><strong>pad_width</strong> (<em>Shape(tuple), required</em>) – Widths of the padding regions applied to the edges of each axis. It is a tuple of integer padding widths for each axis of the format <code class="docutils literal"><span class="pre">(before_1,</span> <span class="pre">after_1,</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">before_N,</span> <span class="pre">after_N)</span></code>. It should be of length <code class="docutils literal"><spa [...]
+<li><strong>constant_value</strong> (<em>double, optional, default=0</em>) – The value used for padding when <cite>mode</cite> is “constant”.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4121,7 +4469,7 @@ The widths of the higher two dimensions <code class="docutils literal"><span cla
<li><p class="first"><strong>data</strong>: <em>(batch_size, channel, height, width)</em></p>
</li>
<li><p class="first"><strong>out</strong>: <em>(batch_size, num_filter, out_height, out_width)</em>, with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><s [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
</pre></div>
</div>
@@ -4130,12 +4478,12 @@ The widths of the higher two dimensions <code class="docutils literal"><span cla
<p>The definition of <em>f</em> depends on <code class="docutils literal"><span class="pre">pooling_convention</span></code>, which has two options:</p>
<ul>
<li><p class="first"><strong>valid</strong> (default):</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><s [...]
</pre></div>
</div>
</li>
<li><p class="first"><strong>full</strong>, which is compatible with Caffe:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><s [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><sp [...]
</pre></div>
</div>
</li>
@@ -4153,26 +4501,27 @@ The widths of the higher two dimensions <code class="docutils literal"><span cla
height, width)</em>.</p>
<p>Defined in src/operator/pooling.cc:L116</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the pooling operator.</li>
-<li><strong>global_pool</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Ignore kernel size, do global pooling based on current input feature map.</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Turn off cudnn pooling and use MXNet pooling operator.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – pooling kernel size: (y, x) or (d, y, x)</li>
-<li><strong>pool_type</strong> (<em>{'avg'</em><em>, </em><em>'max'</em><em>, </em><em>'sum'}</em><em>, </em><em>required</em>) – Pooling type to be applied.</li>
-<li><strong>pooling_convention</strong> (<em>{'full'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='valid'</em>) – Pooling convention to be applied.</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – stride: for pooling (y, x) or (d, y, x)</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – pad for pooling: (y, x) or (d, y, x)</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>global_pool</strong> (<em>boolean, optional, default=False</em>) – Ignore kernel size, do global pooling based on current input feature map.</li>
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Turn off cudnn pooling and use MXNet pooling operator.</li>
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – pooling kernel size: (y, x) or (d, y, x)</li>
+<li><strong>pool_type</strong> (<em>{'avg', 'max', 'sum'}, required</em>) – Pooling type to be applied.</li>
+<li><strong>pooling_convention</strong> (<em>{'full', 'valid'},optional, default='valid'</em>) – Pooling convention to be applied.</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – stride: for pooling (y, x) or (d, y, x)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for pooling: (y, x) or (d, y, x)</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4188,7 +4537,7 @@ Perform pooling on the input.</p>
<li><p class="first"><strong>data</strong>: <em>(batch_size, channel, height, width)</em></p>
</li>
<li><p class="first"><strong>out</strong>: <em>(batch_size, num_filter, out_height, out_width)</em>, with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><s [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
</pre></div>
</div>
@@ -4197,12 +4546,12 @@ Perform pooling on the input.</p>
<p>The definition of <em>f</em> depends on <code class="docutils literal"><span class="pre">pooling_convention</span></code>, which has two options:</p>
<ul>
<li><p class="first"><strong>valid</strong> (default):</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><s [...]
</pre></div>
</div>
</li>
<li><p class="first"><strong>full</strong>, which is compatible with Caffe:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><s [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><sp [...]
</pre></div>
</div>
</li>
@@ -4222,25 +4571,26 @@ Perform pooling on the input.</p>
height, width)</em>.</p>
<p>Defined in src/operator/pooling_v1.cc:L85</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the pooling operator.</li>
-<li><strong>global_pool</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Ignore kernel size, do global pooling based on current input feature map.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – pooling kernel size: (y, x) or (d, y, x)</li>
-<li><strong>pool_type</strong> (<em>{'avg'</em><em>, </em><em>'max'</em><em>, </em><em>'sum'}</em><em>, </em><em>required</em>) – Pooling type to be applied.</li>
-<li><strong>pooling_convention</strong> (<em>{'full'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='valid'</em>) – Pooling convention to be applied.</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – stride: for pooling (y, x) or (d, y, x)</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – pad for pooling: (y, x) or (d, y, x)</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>global_pool</strong> (<em>boolean, optional, default=False</em>) – Ignore kernel size, do global pooling based on current input feature map.</li>
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – pooling kernel size: (y, x) or (d, y, x)</li>
+<li><strong>pool_type</strong> (<em>{'avg', 'max', 'sum'}, required</em>) – Pooling type to be applied.</li>
+<li><strong>pooling_convention</strong> (<em>{'full', 'valid'},optional, default='valid'</em>) – Pooling convention to be applied.</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – stride: for pooling (y, x) or (d, y, x)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for pooling: (y, x) or (d, y, x)</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4251,28 +4601,29 @@ height, width)</em>.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">RNN</code><span class="sig-paren">(</span><em>data=None</em>, <em>parameters=None</em>, <em>state=None</em>, <em>state_cell=None</em>, <em>state_size=_Null</em>, <em>num_layers=_Null</em>, <em>bidirectional=_Null</em>, <em>mode=_Null</em>, <em>p=_Null</em>, <em>state_outputs=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.R [...]
<dd><p>Applies a recurrent layer to input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to RNN</li>
<li><strong>parameters</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Vector of all RNN trainable parameters concatenated</li>
<li><strong>state</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – initial hidden state of the RNN</li>
<li><strong>state_cell</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – initial cell state for LSTM networks (only for LSTM)</li>
-<li><strong>state_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – size of the state for each layer</li>
-<li><strong>num_layers</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – number of stacked layers</li>
-<li><strong>bidirectional</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – whether to use bidirectional recurrent layers</li>
-<li><strong>mode</strong> (<em>{'gru'</em><em>, </em><em>'lstm'</em><em>, </em><em>'rnn_relu'</em><em>, </em><em>'rnn_tanh'}</em><em>, </em><em>required</em>) – the type of RNN to compute</li>
-<li><strong>p</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Dropout probability, fraction of the input that gets dropped out at training time</li>
-<li><strong>state_outputs</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to have the states as symbol outputs.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>state_size</strong> (<em>int (non-negative), required</em>) – size of the state for each layer</li>
+<li><strong>num_layers</strong> (<em>int (non-negative), required</em>) – number of stacked layers</li>
+<li><strong>bidirectional</strong> (<em>boolean, optional, default=False</em>) – whether to use bidirectional recurrent layers</li>
+<li><strong>mode</strong> (<em>{'gru', 'lstm', 'rnn_relu', 'rnn_tanh'}, required</em>) – the type of RNN to compute</li>
+<li><strong>p</strong> (<em>float, optional, default=0</em>) – Dropout probability, fraction of the input that gets dropped out at training time</li>
+<li><strong>state_outputs</strong> (<em>boolean, optional, default=False</em>) – Whether to have the states as symbol outputs.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4297,7 +4648,7 @@ bounding boxes after <cite>ROIPooling</cite>.</p>
<p>The size of each region of interest doesn’t have to be perfectly divisible by
the number of pooling sections(<cite>pooled_size</cite>).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>x = [[[[ 0., 1., 2., 3., 4., 5.],
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[[ 0., 1., 2., 3., 4., 5.],
[ 6., 7., 8., 9., 10., 11.],
[ 12., 13., 14., 15., 16., 17.],
[ 18., 19., 20., 21., 22., 23.],
@@ -4320,22 +4671,23 @@ ROIPooling(x, y, (2,2), 0.7) = [[[[ 7., 9.],
</div>
<p>Defined in src/operator/roi_pooling.cc:L269</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array to the pooling operator, a 4D Feature maps</li>
<li><strong>rois</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]], where (x1, y1) and (x2, y2) are top left and bottom right corners of designated region of interest. <cite>batch_index</cite> indicates the index of corresponding image in the input array</li>
-<li><strong>pooled_size</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – ROI pooling output shape (h,w)</li>
-<li><strong>spatial_scale</strong> (<em>float</em><em>, </em><em>required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>pooled_size</strong> (<em>Shape(tuple), required</em>) – ROI pooling output shape (h,w)</li>
+<li><strong>spatial_scale</strong> (<em>float, required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4352,15 +4704,15 @@ ROIPooling(x, y, (2,2), 0.7) = [[[[ 7., 9.],
<p>Given an array and a shape, this function returns a copy of the array in the new shape.
The shape is a tuple of integers such as (2,3,4).The size of the new shape should be same as the size of the input array.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">reshape</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">) [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">reshape</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)) [...]
</pre></div>
</div>
<p>Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}. The significance of each is explained below:</p>
<ul>
<li><p class="first"><code class="docutils literal"><span class="pre">0</span></code> copy this dimension from the input to the output shape.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
+- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
</pre></div>
</div>
</li>
@@ -4368,33 +4720,33 @@ The shape is a tuple of integers such as (2,3,4).The size of the new shape shoul
keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span clas [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,),</span> <span class="n">output</span> <span class="n">shape</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
+- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
+- input shape = (2,3,4), shape=(-1,), output shape = (24,)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-2</span></code> copy all/remainder of the input dimensions to the output shape.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span> <span cl [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
+- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
+- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-3</span></code> use the product of two consecutive dimensions of the input shape as the output dimension.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span clas [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">),</span> <span cl [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
+- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
+- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
+- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-4</span></code> split one dimension of the input into two dimensions passed subsequent to -4 in shape (can contain -1).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
+- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
</pre></div>
</div>
</li>
@@ -4402,37 +4754,38 @@ At most one dimension of shape can be -1.</p>
<p>If the argument <cite>reverse</cite> is set to 1, then the special values are inferred from right to left.</p>
<blockquote>
<div><p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="n">without</span> <span class="n">reverse</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="k">for</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span>< [...]
-<span class="o">-</span> <span class="k">with</span> <span class="n">reverse</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">output</span> <span class="n">shape</span> <span class="n">will</span> <span class="n">be</span> <span class="p">(</span><span class="mi">50</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>- without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be (40,5)
+- with reverse=1, output shape will be (50,4).
</pre></div>
</div>
</div></blockquote>
<p>Defined in src/operator/tensor/matrix_op.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to reshape.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The target shape</li>
-<li><strong>reverse</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then the special values are inferred from right to left</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Target new shape. One and only one dim can be 0, in which case it will be inferred from the rest of dims</li>
-<li><strong>keep_highest</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Whether keep the highest dim unchanged.If set to true, then the first dim in target_shape is ignored,and always fixed as input</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – The target shape</li>
+<li><strong>reverse</strong> (<em>boolean, optional, default=False</em>) – If true then the special values are inferred from right to left</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=()</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Target new shape. One and only one dim can be 0, in which case it will be inferred from the rest of dims</li>
+<li><strong>keep_highest</strong> (<em>boolean, optional, default=False</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Whether keep the highest dim unchanged.If set to true, then the first dim in target_shape is ignored,and always fixed as input</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<p>Reshapes the input array into a new shape.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p"> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">] [...]
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(4L,)</span>
@@ -4445,7 +4798,7 @@ At most one dimension of shape can be -1.</p>
</div>
<p>You can use <code class="docutils literal"><span class="pre">0</span></code> to copy a particular dimension from the input to the output shape
and ‘-1’ to infer the dimensions of the output.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(2L, 3L, 4L)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class=" [...]
@@ -4461,23 +4814,24 @@ and ‘-1’ to infer the dimensions of the output.</p>
<p>This tutorial demonstrates using SVM as output layer for classification instead of softmax:
<a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example/svm_mnist">https://github.com/dmlc/mxnet/tree/master/example/svm_mnist</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data for SVM transformation.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Class label for the input data.</li>
-<li><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – The loss function penalizes outputs that lie outside this margin. Default margin is 1.</li>
-<li><strong>regularization_coefficient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Regularization parameter for the SVM. This balances the tradeoff between coefficient size and error.</li>
-<li><strong>use_linear</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to use L1-SVM objective. L2-SVM objective is used by default.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>margin</strong> (<em>float, optional, default=1</em>) – The loss function penalizes outputs that lie outside this margin. Default margin is 1.</li>
+<li><strong>regularization_coefficient</strong> (<em>float, optional, default=1</em>) – Regularization parameter for the SVM. This balances the tradeoff between coefficient size and error.</li>
+<li><strong>use_linear</strong> (<em>boolean, optional, default=False</em>) – Whether to use L1-SVM objective. L2-SVM objective is used by default.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4499,53 +4853,54 @@ to have the max sequence length.</p>
<p class="last">Alternatively, you can also use <cite>take</cite> operator.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">]],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1., 2., 3.],
+ [ 4., 5., 6.],
+ [ 7., 8., 9.]],
- <span class="p">[[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]],</span>
+ [[ 10., 11., 12.],
+ [ 13., 14., 15.],
+ [ 16., 17., 18.]],
- <span class="p">[[</span> <span class="mf">19.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">,</span> <span class="mf">21.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">22.</span><span class="p">,</span> <span class="mf">23.</span><span class="p">,</span> <span class="mf">24.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">,</span> <span class="mf">27.</span><span class="p">]]]</span>
+ [[ 19., 20., 21.],
+ [ 22., 23., 24.],
+ [ 25., 26., 27.]]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">last</span> <span class="n">sequence</span> <span class="n">when</span> <span class="n">sequence_length</span> <span class="n">parameter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">used</span>
-<span class="n">SequenceLast</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">19.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">,</span> <span class="mf">21.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">22.</span><span class="p">,</span> <span class="mf">23.</span><span class="p">,</span> <span class="mf">24.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">,</span> <span class="mf">27.</span><span class="p">]]</span>
+// returns last sequence when sequence_length parameter is not used
+SequenceLast(x) = [[ 19., 20., 21.],
+ [ 22., 23., 24.],
+ [ 25., 26., 27.]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="n">y</span> <span class="ow">is</span> <span class="n">used</span>
-<span class="n">SequenceLast</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">]]</span>
+// sequence_length y is used
+SequenceLast(x, y=[1,1,1], use_sequence_length=True) =
+ [[ 1., 2., 3.],
+ [ 4., 5., 6.],
+ [ 7., 8., 9.]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="n">y</span> <span class="ow">is</span> <span class="n">used</span>
-<span class="n">SequenceLast</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">,</span> <span class="mf">27.</span><span class="p">]]</span>
+// sequence_length y is used
+SequenceLast(x, y=[1,2,3], use_sequence_length=True) =
+ [[ 1., 2., 3.],
+ [ 13., 14., 15.],
+ [ 25., 26., 27.]]
</pre></div>
</div>
<p>Defined in src/operator/sequence_last.cc:L73</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – n-dimensional input array of the form [max_sequence_length, batch_size, other_feature_dims] where n>2</li>
<li><strong>sequence_length</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – vector of sequence lengths of the form [batch_size]</li>
-<li><strong>use_sequence_length</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>use_sequence_length</strong> (<em>boolean, optional, default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4563,78 +4918,79 @@ To use this parameter, set <cite>use_sequence_length</cite> to <cite>True</cite>
otherwise each example in the batch is assumed to have the max sequence length and
this operator works as the <cite>identity</cite> operator.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 13., 14., 15.],
+ [ 16., 17., 18.]]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="mi">1</span>
-<span class="n">B1</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">]]</span>
+// Batch 1
+B1 = [[ 1., 2., 3.],
+ [ 7., 8., 9.],
+ [ 13., 14., 15.]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="mi">2</span>
-<span class="n">B2</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]</span>
+// Batch 2
+B2 = [[ 4., 5., 6.],
+ [ 10., 11., 12.],
+ [ 16., 17., 18.]]
-<span class="o">//</span> <span class="n">works</span> <span class="k">as</span> <span class="n">identity</span> <span class="n">operator</span> <span class="n">when</span> <span class="n">sequence_length</span> <span class="n">parameter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">used</span>
-<span class="n">SequenceMask</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+// works as identity operator when sequence_length parameter is not used
+SequenceMask(x) = [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 13., 14., 15.],
+ [ 16., 17., 18.]]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="n">means</span> <span class="mi">1</span> <span class="n">of</span> <span class="n">each</span> <span class="n">batch</span> <span class="n">will</span> <span class="n">be</span> <span class="n">kept</span>
-<span class="o">//</span> <span class="ow">and</span> <span class="n">other</span> <span class="n">rows</span> <span class="n">are</span> <span class="n">masked</span> <span class="k">with</span> <span class="n">default</span> <span class="n">mask</span> <span class="n">value</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">SequenceMask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+// sequence_length [1,1] means 1 of each batch will be kept
+// and other rows are masked with default mask value = 0
+SequenceMask(x, y=[1,1], use_sequence_length=True) =
+ [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]],</span>
+ [[ 0., 0., 0.],
+ [ 0., 0., 0.]],
- <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]]]</span>
+ [[ 0., 0., 0.],
+ [ 0., 0., 0.]]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> <span class="n">means</span> <span class="mi">2</span> <span class="n">of</span> <span class="n">batch</span> <span class="n">B1</span> <span class="ow">and</span> <span class="mi">3</span> <span class="n">of</span> <span class="n">batch</span> <span class="n">B2</span> <span class="n">will</span> <span [...]
-<span class="o">//</span> <span class="ow">and</span> <span class="n">other</span> <span class="n">rows</span> <span class="n">are</span> <span class="n">masked</span> <span class="k">with</span> <span class="n">value</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">SequenceMask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p"> [...]
- <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+// sequence_length [2,3] means 2 of batch B1 and 3 of batch B2 will be kept
+// and other rows are masked with value = 1
+SequenceMask(x, y=[2,3], use_sequence_length=True, value=1) =
+ [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 1., 1., 1.],
+ [ 16., 17., 18.]]]
</pre></div>
</div>
<p>Defined in src/operator/sequence_mask.cc:L108</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – n-dimensional input array of the form [max_sequence_length, batch_size, other_feature_dims] where n>2</li>
<li><strong>sequence_length</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – vector of sequence lengths of the form [batch_size]</li>
-<li><strong>use_sequence_length</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
-<li><strong>value</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The value to be used as a mask.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>use_sequence_length</strong> (<em>boolean, optional, default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
+<li><strong>value</strong> (<em>float, optional, default=0</em>) – The value to be used as a mask.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4651,77 +5007,78 @@ and returns an array of the same shape.</p>
To use this parameter, set <cite>use_sequence_length</cite> to <cite>True</cite>,
otherwise each example in the batch is assumed to have the max sequence length.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 13., 14., 15.],
+ [ 16., 17., 18.]]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="mi">1</span>
-<span class="n">B1</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">]]</span>
+// Batch 1
+B1 = [[ 1., 2., 3.],
+ [ 7., 8., 9.],
+ [ 13., 14., 15.]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="mi">2</span>
-<span class="n">B2</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]</span>
+// Batch 2
+B2 = [[ 4., 5., 6.],
+ [ 10., 11., 12.],
+ [ 16., 17., 18.]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">reverse</span> <span class="n">sequence</span> <span class="n">when</span> <span class="n">sequence_length</span> <span class="n">parameter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">used</span>
-<span class="n">SequenceReverse</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]],</span>
+// returns reverse sequence when sequence_length parameter is not used
+SequenceReverse(x) = [[[ 13., 14., 15.],
+ [ 16., 17., 18.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[ 1., 2., 3.],
+ [ 4., 5., 6.]]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="n">means</span> <span class="mi">2</span> <span class="n">rows</span> <span class="n">of</span>
-<span class="o">//</span> <span class="n">both</span> <span class="n">batch</span> <span class="n">B1</span> <span class="ow">and</span> <span class="n">B2</span> <span class="n">will</span> <span class="n">be</span> <span class="nb">reversed</span><span class="o">.</span>
-<span class="n">SequenceReverse</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+// sequence_length [2,2] means 2 rows of
+// both batch B1 and B2 will be reversed.
+SequenceReverse(x, y=[2,2], use_sequence_length=True) =
+ [[[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+ [[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 13., 14., 15.],
+ [ 16., 17., 18.]]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> <span class="n">means</span> <span class="mi">2</span> <span class="n">of</span> <span class="n">batch</span> <span class="n">B2</span> <span class="ow">and</span> <span class="mi">3</span> <span class="n">of</span> <span class="n">batch</span> <span class="n">B3</span>
-<span class="o">//</span> <span class="n">will</span> <span class="n">be</span> <span class="nb">reversed</span><span class="o">.</span>
-<span class="n">SequenceReverse</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]],</span>
+// sequence_length [2,3] means 2 of batch B2 and 3 of batch B3
+// will be reversed.
+SequenceReverse(x, y=[2,3], use_sequence_length=True) =
+ [[[ 7., 8., 9.],
+ [ 16., 17., 18.]],
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 1., 2., 3.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[ 13., 14, 15.],
+ [ 4., 5., 6.]]]
</pre></div>
</div>
<p>Defined in src/operator/sequence_reverse.cc:L94</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – n-dimensional input array of the form [max_sequence_length, batch_size, other dims] where n>2</li>
<li><strong>sequence_length</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – vector of sequence lengths of the form [batch_size]</li>
-<li><strong>use_sequence_length</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>use_sequence_length</strong> (<em>boolean, optional, default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4738,36 +5095,36 @@ otherwise each example in the batch is assumed to have the max sequence length.<
<p><strong>Note</strong> that <cite>num_outputs</cite> should evenly divide the length of the axis
along which to split the array.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]]</span>
-<span class="n">x</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1.]
+ [ 2.]]
+ [[ 3.]
+ [ 4.]]
+ [[ 5.]
+ [ 6.]]]
+x.shape = (3, 2, 1)
-<span class="n">y</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="n">a</span> <span class="nb">list</span> <span class="n">of</span> <span class="mi">2</span> [...]
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]]]</span>
+y = split(x, axis=1, num_outputs=2) // a list of 2 arrays with shape (3, 1, 1)
+y = [[[ 1.]]
+ [[ 3.]]
+ [[ 5.]]]
- <span class="p">[[[</span> <span class="mf">2.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">4.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[[ 2.]]
+ [[ 4.]]
+ [[ 6.]]]
-<span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+y[0].shape = (3, 1, 1)
-<span class="n">z</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">//</span> <span class="n">a</span> <span class="nb">list</span> <span class="n">of</span> <span class="mi">3</span> [...]
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]]</span>
+z = split(x, axis=0, num_outputs=3) // a list of 3 arrays with shape (1, 2, 1)
+z = [[[ 1.]
+ [ 2.]]]
- <span class="p">[[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]]</span>
+ [[[ 3.]
+ [ 4.]]]
- <span class="p">[[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[[ 5.]
+ [ 6.]]]
-<span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+z[0].shape = (1, 2, 1)
</pre></div>
</div>
<p><cite>squeeze_axis=1</cite> removes the axis with length 1 from the shapes of the output arrays.
@@ -4775,36 +5132,37 @@ along which to split the array.</p>
along the <cite>axis</cite> which it is split.
Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils literal"><span class="pre">input.shape[axis]</span> <span class="pre">==</span> <span class="pre">num_outputs</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">squeeze_axis</span><span class="o">=</span>< [...]
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>z = split(x, axis=0, num_outputs=3, squeeze_axis=1) // a list of 3 arrays with shape (2, 1)
+z = [[ 1.]
+ [ 2.]]
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
+ [[ 3.]
+ [ 4.]]
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]</span>
-<span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="p">,</span><span class="mi">1</span> <span class="p">)</span>
+ [[ 5.]
+ [ 6.]]
+z[0].shape = (2 ,1 )
</pre></div>
</div>
<p>Defined in src/operator/slice_channel.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>num_outputs</strong> (<em>int</em><em>, </em><em>required</em>) – Number of splits. Note that this should evenly divide the length of the <cite>axis</cite>.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Axis along which to split.</li>
-<li><strong>squeeze_axis</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true, Removes the axis with length 1 from the shapes of the output arrays. <strong>Note</strong> that setting <cite>squeeze_axis</cite> to <code class="docutils literal"><span class="pre">true</span></code> removes axis with length 1 only along the <cite>axis</cite> which it is split. Also <cite>squeeze_axis</cite> can be set to <code class="docutils literal"><span class [...]
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>num_outputs</strong> (<em>int, required</em>) – Number of splits. Note that this should evenly divide the length of the <cite>axis</cite>.</li>
+<li><strong>axis</strong> (<em>int, optional, default='1'</em>) – Axis along which to split.</li>
+<li><strong>squeeze_axis</strong> (<em>boolean, optional, default=False</em>) – If true, Removes the axis with length 1 from the shapes of the output arrays. <strong>Note</strong> that setting <cite>squeeze_axis</cite> to <code class="docutils literal"><span class="pre">true</span></code> removes axis with length 1 only along the <cite>axis</cite> which it is split. Also <cite>squeeze_axis</cite> can be set to <code class="docutils literal"><span class="pre">true</span></code> only if <c [...]
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4822,26 +5180,27 @@ To just compute softmax output, use the <cite>softmax</cite> operator.</p>
</div>
<p>Defined in src/operator/softmax_output.cc:L119</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scales the gradient by a float factor.</li>
-<li><strong>ignore_label</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – The instances whose <cite>labels</cite> == <cite>ignore_label</cite> will be ignored during backward, if <cite>use_ignore</cite> is set to <code class="docutils literal"><span class="pre">true</span></code>).</li>
-<li><strong>multi_output</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along axis <code class="docutils literal"><span class="pre">1</span></code>. This is applied when the shape of input array differs from the shape of label array.</li>
-<li><strong>use_ignore</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the <cite>ignore_label</cite> value will not contribute to the backward gradient.</li>
-<li><strong>preserve_shape</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along the last axis (<code class="docutils literal"><span class="pre">-1</span></code>).</li>
-<li><strong>normalization</strong> (<em>{'batch'</em><em>, </em><em>'null'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='null'</em>) – Normalizes the gradient.</li>
-<li><strong>out_grad</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiplies gradient with output gradient element-wise.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scales the gradient by a float factor.</li>
+<li><strong>ignore_label</strong> (<em>float, optional, default=-1</em>) – The instances whose <cite>labels</cite> == <cite>ignore_label</cite> will be ignored during backward, if <cite>use_ignore</cite> is set to <code class="docutils literal"><span class="pre">true</span></code>).</li>
+<li><strong>multi_output</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along axis <code class="docutils literal"><span class="pre">1</span></code>. This is applied when the shape of input array differs from the shape of label array.</li>
+<li><strong>use_ignore</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the <cite>ignore_label</cite> value will not contribute to the backward gradient.</li>
+<li><strong>preserve_shape</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along the last axis (<code class="docutils literal"><span class="pre">-1</span></code>).</li>
+<li><strong>normalization</strong> (<em>{'batch', 'null', 'valid'},optional, default='null'</em>) – Normalizes the gradient.</li>
+<li><strong>out_grad</strong> (<em>boolean, optional, default=False</em>) – Multiplies gradient with output gradient element-wise.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4862,30 +5221,31 @@ of each instance, where <cite>k</cite> = <code class="docutils literal"><span cl
has at least 3 dimensions.
This can be used for <cite>fully convolutional network</cite>, <cite>image segmentation</cite>, etc.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">input_array</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">3.</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> < [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">input_array</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">3.</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <s [...]
<span class="gp">>>> </span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="o">-.</span><span class="mi">4</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">softmax_act</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">SoftmaxActivation</span><span class="p">(</span><span class="n">input_array</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">softmax_act</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">softmax_act</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 1.78322066e-02 1.46375655e-03 5.38485940e-04 6.56010211e-03 9.73605454e-01]</span>
<span class="go"> [ 6.56221947e-03 5.95310994e-04 9.73919690e-01 1.78379621e-02 1.08472735e-03]]</span>
</pre></div>
</div>
<p>Defined in src/operator/softmax_activation.cc:L48</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array to activation function.</li>
-<li><strong>mode</strong> (<em>{'channel'</em><em>, </em><em>'instance'}</em><em>,</em><em>optional</em><em>, </em><em>default='instance'</em>) – Specifies how to compute the softmax. If set to <code class="docutils literal"><span class="pre">instance</span></code>, it computes softmax for each instance. If set to <code class="docutils literal"><span class="pre">channel</span></code>, It computes cross channel softmax for each position of each instance.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>mode</strong> (<em>{'channel', 'instance'},optional, default='instance'</em>) – Specifies how to compute the softmax. If set to <code class="docutils literal"><span class="pre">instance</span></code>, it computes softmax for each instance. If set to <code class="docutils literal"><span class="pre">channel</span></code>, It computes cross channel softmax for each position of each instance.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -4941,23 +5301,23 @@ The provided label can be a one-hot label array or a probability label array.</p
with a particular label to be ignored during backward propagation. <strong>This has no effect when
softmax `output` has same shape as `label`</strong>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</sp [...]
-<span class="n">label</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-<span class="n">ignore_label</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">SoftmaxOutput</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="p">,</span>\
- <span class="n">multi_output</span><span class="o">=</span><span class="n">true</span><span class="p">,</span> <span class="n">use_ignore</span><span class="o">=</span><span class="n">true</span><span class="p">,</span>\
- <span class="n">ignore_label</span><span class="o">=</span><span class="n">ignore_label</span><span class="p">)</span>
-<span class="c1">## forward softmax output</span>
-<span class="p">[[</span> <span class="mf">0.0320586</span> <span class="mf">0.08714432</span> <span class="mf">0.23688284</span> <span class="mf">0.64391428</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="p">]]</span>
-<span class="c1">## backward gradient output</span>
-<span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="p">]</span>
- <span class="p">[</span><span class="o">-</span><span class="mf">0.75</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="o">-</span><span class="mf">0.75</span> <span class="mf">0.25</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="o">-</span><span class="mf">0.75</span><span class="p">]]</span>
-<span class="c1">## notice that the first row is all 0 because label[0] is 1, which is equal to ignore_label.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>data = [[1,2,3,4],[2,2,2,2],[3,3,3,3],[4,4,4,4]]
+label = [1,0,2,3]
+ignore_label = 1
+SoftmaxOutput(data=data, label = label,\
+ multi_output=true, use_ignore=true,\
+ ignore_label=ignore_label)
+## forward softmax output
+[[ 0.0320586 0.08714432 0.23688284 0.64391428]
+ [ 0.25 0.25 0.25 0.25 ]
+ [ 0.25 0.25 0.25 0.25 ]
+ [ 0.25 0.25 0.25 0.25 ]]
+## backward gradient output
+[[ 0. 0. 0. 0. ]
+ [-0.75 0.25 0.25 0.25]
+ [ 0.25 0.25 -0.75 0.25]
+ [ 0.25 0.25 0.25 -0.75]]
+## notice that the first row is all 0 because label[0] is 1, which is equal to ignore_label.
</pre></div>
</div>
</li>
@@ -4978,27 +5338,28 @@ The <cite>normalization</cite> mode can be set to the followings:</p>
</ul>
<p>Defined in src/operator/softmax_output.cc:L104</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Ground truth label.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scales the gradient by a float factor.</li>
-<li><strong>ignore_label</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – The instances whose <cite>labels</cite> == <cite>ignore_label</cite> will be ignored during backward, if <cite>use_ignore</cite> is set to <code class="docutils literal"><span class="pre">true</span></code>).</li>
-<li><strong>multi_output</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along axis <code class="docutils literal"><span class="pre">1</span></code>. This is applied when the shape of input array differs from the shape of label array.</li>
-<li><strong>use_ignore</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the <cite>ignore_label</cite> value will not contribute to the backward gradient.</li>
-<li><strong>preserve_shape</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along the last axis (<code class="docutils literal"><span class="pre">-1</span></code>).</li>
-<li><strong>normalization</strong> (<em>{'batch'</em><em>, </em><em>'null'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='null'</em>) – Normalizes the gradient.</li>
-<li><strong>out_grad</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiplies gradient with output gradient element-wise.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scales the gradient by a float factor.</li>
+<li><strong>ignore_label</strong> (<em>float, optional, default=-1</em>) – The instances whose <cite>labels</cite> == <cite>ignore_label</cite> will be ignored during backward, if <cite>use_ignore</cite> is set to <code class="docutils literal"><span class="pre">true</span></code>).</li>
+<li><strong>multi_output</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along axis <code class="docutils literal"><span class="pre">1</span></code>. This is applied when the shape of input array differs from the shape of label array.</li>
+<li><strong>use_ignore</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the <cite>ignore_label</cite> value will not contribute to the backward gradient.</li>
+<li><strong>preserve_shape</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along the last axis (<code class="docutils literal"><span class="pre">-1</span></code>).</li>
+<li><strong>normalization</strong> (<em>{'batch', 'null', 'valid'},optional, default='null'</em>) – Normalizes the gradient.</li>
+<li><strong>out_grad</strong> (<em>boolean, optional, default=False</em>) – Multiplies gradient with output gradient element-wise.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5009,23 +5370,24 @@ The <cite>normalization</cite> mode can be set to the followings:</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">SpatialTransformer</code><span class="sig-paren">(</span><em>data=None</em>, <em>loc=None</em>, <em>target_shape=_Null</em>, <em>transform_type=_Null</em>, <em>sampler_type=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.SpatialTransformer" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a spatial transformer to input feature map.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the SpatialTransformerOp.</li>
<li><strong>loc</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – localisation net, the output dim should be 6 when transform_type is affine. You shold initialize the weight and bias with identity tranform.</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em>0</em><em>)</em><em></em>) – output shape(h, w) of spatial transformer: (y, x)</li>
-<li><strong>transform_type</strong> (<em>{'affine'}</em><em>, </em><em>required</em>) – transformation type</li>
-<li><strong>sampler_type</strong> (<em>{'bilinear'}</em><em>, </em><em>required</em>) – sampling type</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=(0,0)</em>) – output shape(h, w) of spatial transformer: (y, x)</li>
+<li><strong>transform_type</strong> (<em>{'affine'}, required</em>) – transformation type</li>
+<li><strong>sampler_type</strong> (<em>{'bilinear'}, required</em>) – sampling type</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5036,39 +5398,40 @@ The <cite>normalization</cite> mode can be set to the followings:</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">SwapAxis</code><span class="sig-paren">(</span><em>data=None</em>, <em>dim1=_Null</em>, <em>dim2=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.SwapAxis" title="Permalink to this definition">¶</a></dt>
<dd><p>Interchanges two axes of an array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
- <span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> x = [[1, 2, 3]])
+ swapaxes(x, 0, 1) = [[ 1],
+ [ 2],
+ [ 3]]
- <span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]]</span> <span class="o">//</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="n">array</span>
+ x = [[[ 0, 1],
+ [ 2, 3]],
+ [[ 4, 5],
+ [ 6, 7]]] // (2,2,2) array
-<span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]]</span>
+swapaxes(x, 0, 2) = [[[ 0, 4],
+ [ 2, 6]],
+ [[ 1, 5],
+ [ 3, 7]]]
</pre></div>
</div>
<p>Defined in src/operator/swapaxis.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array.</li>
-<li><strong>dim1</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the first axis to be swapped.</li>
-<li><strong>dim2</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the second axis to be swapped.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>dim1</strong> (<em>int (non-negative), optional, default=0</em>) – the first axis to be swapped.</li>
+<li><strong>dim2</strong> (<em>int (non-negative), optional, default=0</em>) – the second axis to be swapped.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5079,24 +5442,25 @@ The <cite>normalization</cite> mode can be set to the followings:</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">UpSampling</code><span class="sig-paren">(</span><em>*data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.UpSampling" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs nearest neighbor/bilinear up sampling to inputs.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>[</em><em></em><em>]</em><em></em>) – Array of tensors to upsample</li>
-<li><strong>scale</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Up sampling scale</li>
-<li><strong>num_filter</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Input filter. Only used by bilinear sample_type.</li>
-<li><strong>sample_type</strong> (<em>{'bilinear'</em><em>, </em><em>'nearest'}</em><em>, </em><em>required</em>) – upsampling method</li>
-<li><strong>multi_input_mode</strong> (<em>{'concat'</em><em>, </em><em>'sum'}</em><em>,</em><em>optional</em><em>, </em><em>default='concat'</em>) – How to handle multiple input. concat means concatenate upsampled images along the channel dimension. sum means add all images together, only available for nearest neighbor upsampling.</li>
-<li><strong>workspace</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=512</em>) – Tmp workspace for deconvolution (MB)</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>data</strong> (<em>NDArray[]</em>) – Array of tensors to upsample</li>
+<li><strong>scale</strong> (<em>int (non-negative), required</em>) – Up sampling scale</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), optional, default=0</em>) – Input filter. Only used by bilinear sample_type.</li>
+<li><strong>sample_type</strong> (<em>{'bilinear', 'nearest'}, required</em>) – upsampling method</li>
+<li><strong>multi_input_mode</strong> (<em>{'concat', 'sum'},optional, default='concat'</em>) – How to handle multiple input. concat means concatenate upsampled images along the channel dimension. sum means add all images together, only available for nearest neighbor upsampling.</li>
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=512</em>) – Tmp workspace for deconvolution (MB)</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5107,24 +5471,25 @@ The <cite>normalization</cite> mode can be set to the followings:</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">abs</code><span class="sig-paren">(</span><em>data=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.abs" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise absolute value of the input.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="nb">abs</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">abs</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L236</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5143,36 +5508,37 @@ m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t\\
v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\
W_t = W_{t-1} - \alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon }\end{split}\]</div>
<p>It updates the weights using:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">beta1</span><span class="o">*</span><span class="n">m</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">beta1</span><span class="p">)</span><span class="o">*</span><span class="n">grad</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">beta1</span><span class="o">*</span><span class="n">m</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">beta1</span><span class="p">)</span><span class="o">*</span><span class="n">grad</span>
<span class="n">v</span> <span class="o">=</span> <span class="n">beta2</span><span class="o">*</span><span class="n">v</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">beta2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">grad</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="n">w</span> <span class="o">+=</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">m</span> <span class="o">/</span> <span class="p">(</span><span class="n">sqrt</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span>
</pre></div>
</div>
<p>Defined in src/operator/optimizer_op.cc:L126</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Gradient</li>
<li><strong>mean</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Moving mean</li>
<li><strong>var</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Moving variance</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>beta1</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – The decay rate for the 1st moment estimates.</li>
-<li><strong>beta2</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.999</em>) – The decay rate for the 2nd moment estimates.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e-08</em>) – A small constant for numerical stability.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>beta1</strong> (<em>float, optional, default=0.9</em>) – The decay rate for the 1st moment estimates.</li>
+<li><strong>beta2</strong> (<em>float, optional, default=0.999</em>) – The decay rate for the 2nd moment estimates.</li>
+<li><strong>epsilon</strong> (<em>float, optional, default=1e-08</em>) – A small constant for numerical stability.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5187,19 +5553,20 @@ W_t = W_{t-1} - \alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon }\end{split}\]</div
<p><code class="docutils literal"><span class="pre">add_n</span></code> is potentially more efficient than calling <code class="docutils literal"><span class="pre">add</span></code> by <cite>n</cite> times.</p>
<p>Defined in src/operator/tensor/elemwise_sum.cc:L47</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>args</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>[</em><em></em><em>]</em><em></em>) – Positional input arguments</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>args</strong> (<em>NDArray[]</em>) – Positional input arguments</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5215,19 +5582,20 @@ The output is in the closed interval <span class="math">\([0, \pi]\)</span></p>
\[arccos([-1, -.707, 0, .707, 1]) = [\pi, 3\pi/4, \pi/2, \pi/4, 0]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L541</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5239,19 +5607,20 @@ The output is in the closed interval <span class="math">\([0, \pi]\)</span></p>
<dd><p>Returns the element-wise inverse hyperbolic cosine of the input array, computed element-wise.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L647</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5267,19 +5636,20 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
\[arcsin([-1, -.707, 0, .707, 1]) = [-\pi/2, -\pi/4, 0, \pi/4, \pi/2]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L524</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5291,19 +5661,20 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
<dd><p>Returns the element-wise inverse hyperbolic sine of the input array, computed element-wise.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L637</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5318,19 +5689,20 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
\[arctan([-1, 0, 1]) = [-\pi/4, 0, \pi/4]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L557</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5342,19 +5714,20 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
<dd><p>Returns the element-wise inverse hyperbolic tangent of the input array, computed element-wise.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L657</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5367,37 +5740,38 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
<p>In the case of multiple occurrences of maximum values, the indices corresponding to the first occurrence
are returned.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 0., 1., 2.],
+ [ 3., 4., 5.]]
-<span class="o">//</span> <span class="n">argmax</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">argmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]</span>
+// argmax along axis 0
+argmax(x, axis=0) = [ 1., 1., 1.]
-<span class="o">//</span> <span class="n">argmax</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span>
-<span class="n">argmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]</span>
+// argmax along axis 1
+argmax(x, axis=1) = [ 2., 2.]
-<span class="o">//</span> <span class="n">argmax</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span> <span class="n">keeping</span> <span class="n">same</span> <span class="n">dims</span> <span class="k">as</span> <span class="n">an</span> <span class="nb">input</span> <span class="n">array</span>
-<span class="n">argmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
+// argmax along axis 1 keeping same dims as an input array
+argmax(x, axis=1, keepdims=True) = [[ 2.],
+ [ 2.]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_index.cc:L33</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because [...]
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because</span> <span class="pre">global</span> <span class="p [...]
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5411,7 +5785,7 @@ are returned.</p>
<p>In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence
are returned.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">]]</span>
<span class="n">argmax_channel</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]</span>
@@ -5419,19 +5793,20 @@ are returned.</p>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_index.cc:L78</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5444,37 +5819,38 @@ are returned.</p>
<p>In the case of multiple occurrences of minimum values, the indices corresponding to the first occurrence
are returned.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 0., 1., 2.],
+ [ 3., 4., 5.]]
-<span class="o">//</span> <span class="n">argmin</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">argmin</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]</span>
+// argmin along axis 0
+argmin(x, axis=0) = [ 0., 0., 0.]
-<span class="o">//</span> <span class="n">argmin</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span>
-<span class="n">argmin</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]</span>
+// argmin along axis 1
+argmin(x, axis=1) = [ 0., 0.]
-<span class="o">//</span> <span class="n">argmin</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span> <span class="n">keeping</span> <span class="n">same</span> <span class="n">dims</span> <span class="k">as</span> <span class="n">an</span> <span class="nb">input</span> <span class="n">array</span>
-<span class="n">argmin</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">]]</span>
+// argmin along axis 1 keeping same dims as an input array
+argmin(x, axis=1, keepdims=True) = [[ 0.],
+ [ 0.]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_index.cc:L58</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because [...]
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because</span> <span class="pre">global</span> <span class="p [...]
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5487,38 +5863,39 @@ are returned.</p>
<p>This function performs sorting along the given axis and returns an array of indices having same shape
as an input array that index data in sorted order.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 0.3, 0.2, 0.4],
+ [ 0.1, 0.3, 0.2]]
-<span class="o">//</span> <span class="n">sort</span> <span class="n">along</span> <span class="n">axis</span> <span class="o">-</span><span class="mi">1</span>
-<span class="n">argsort</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
+// sort along axis -1
+argsort(x) = [[ 1., 0., 2.],
+ [ 0., 2., 1.]]
-<span class="o">//</span> <span class="n">sort</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">argsort</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]]</span>
+// sort along axis 0
+argsort(x, axis=0) = [[ 1., 0., 1.]
+ [ 0., 1., 0.]]
-<span class="o">//</span> <span class="n">flatten</span> <span class="ow">and</span> <span class="n">then</span> <span class="n">sort</span>
-<span class="n">argsort</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]</span>
+// flatten and then sort
+argsort(x) = [ 3., 1., 5., 0., 4., 2.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/ordering_op.cc:L157</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Axis along which to sort the input tensor. If not given, the flattened array is used. Default is -1.</li>
-<li><strong>is_ascend</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to sort in ascending or descending order.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='-1'</em>) – Axis along which to sort the input tensor. If not given, the flattened array is used. Default is -1.</li>
+<li><strong>is_ascend</strong> (<em>boolean, optional, default=True</em>) – Whether to sort in ascending or descending order.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5533,27 +5910,28 @@ as an input array that index data in sorted order.</p>
<p>For example, given <code class="docutils literal"><span class="pre">x</span></code> with shape <cite>(batch_size, n, m)</cite> and <code class="docutils literal"><span class="pre">y</span></code> with shape
<cite>(batch_size, m, k)</cite>, the result array will have shape <cite>(batch_size, n, k)</cite>,
which is computed by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">batch_dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="n">i</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:],</span> <span class="n">y</span><span class=" [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">batch_dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="n">i</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:],</span> <span class="n">y</span><span class="p [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L412</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The second input</li>
-<li><strong>transpose_a</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then transpose the first input before dot.</li>
-<li><strong>transpose_b</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then transpose the second input before dot.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>transpose_a</strong> (<em>boolean, optional, default=False</em>) – If true then transpose the first input before dot.</li>
+<li><strong>transpose_b</strong> (<em>boolean, optional, default=False</em>) – If true then transpose the second input before dot.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5569,34 +5947,35 @@ which is computed by:</p>
</div>
<p>Given an input array of shape <code class="docutils literal"><span class="pre">(d0,</span> <span class="pre">d1)</span></code> and indices of shape <code class="docutils literal"><span class="pre">(i0,)</span></code>, the result will be
an output array of shape <code class="docutils literal"><span class="pre">(i0,)</span></code> with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
</pre></div>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1., 2.],
+ [ 3., 4.],
+ [ 5., 6.]]
-<span class="o">//</span> <span class="n">takes</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span>
-<span class="n">batch_take</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span> <span class="mf">4.</span> <span class="mf">5.</span><span class="p">]</span>
+// takes elements with specified indices
+batch_take(x, [0,1,0]) = [ 1. 4. 5.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/indexing_op.cc:L172</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>a</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array</li>
<li><strong>indices</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The index array</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5608,7 +5987,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<dd><p>Returns element-wise sum of the input arrays with broadcasting.</p>
<p><cite>broadcast_plus</cite> is an alias to the function <cite>broadcast_add</cite>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5623,20 +6002,21 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L32</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5649,37 +6029,38 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<p>Broadcasting is allowed on axes with size 1, such as from <cite>(2,1,3,1)</cite> to
<cite>(2,8,3,9)</cite>. Elements will be duplicated on the broadcasted axes.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">given</span> <span class="n">x</span> <span class="n">of</span> <span class="n">shape</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// given x of shape (1,2,1)
+x = [[[ 1.],
+ [ 2.]]]
-<span class="o">//</span> <span class="n">broadcast</span> <span class="n">x</span> <span class="n">on</span> <span class="n">on</span> <span class="n">axis</span> <span class="mi">2</span>
-<span class="n">broadcast_axis</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</spa [...]
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]]</span>
-<span class="o">//</span> <span class="n">broadcast</span> <span class="n">x</span> <span class="n">on</span> <span class="n">on</span> <span class="n">axes</span> <span class="mi">0</span> <span class="ow">and</span> <span class="mi">2</span>
-<span class="n">broadcast_axis</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span c [...]
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]]</span>
+// broadcast x on on axis 2
+broadcast_axis(x, axis=2, size=3) = [[[ 1., 1., 1.],
+ [ 2., 2., 2.]]]
+// broadcast x on on axes 0 and 2
+broadcast_axis(x, axis=(0,2), size=(2,3)) = [[[ 1., 1., 1.],
+ [ 2., 2., 2.]],
+ [[ 1., 1., 1.],
+ [ 2., 2., 2.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L168</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The axes to perform the broadcasting.</li>
-<li><strong>size</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Target sizes of the broadcasting axes.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – The axes to perform the broadcasting.</li>
+<li><strong>size</strong> (<em>Shape(tuple), optional, default=()</em>) – Target sizes of the broadcasting axes.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5692,37 +6073,38 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<p>Broadcasting is allowed on axes with size 1, such as from <cite>(2,1,3,1)</cite> to
<cite>(2,8,3,9)</cite>. Elements will be duplicated on the broadcasted axes.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">given</span> <span class="n">x</span> <span class="n">of</span> <span class="n">shape</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// given x of shape (1,2,1)
+x = [[[ 1.],
+ [ 2.]]]
-<span class="o">//</span> <span class="n">broadcast</span> <span class="n">x</span> <span class="n">on</span> <span class="n">on</span> <span class="n">axis</span> <span class="mi">2</span>
-<span class="n">broadcast_axis</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</spa [...]
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]]</span>
-<span class="o">//</span> <span class="n">broadcast</span> <span class="n">x</span> <span class="n">on</span> <span class="n">on</span> <span class="n">axes</span> <span class="mi">0</span> <span class="ow">and</span> <span class="mi">2</span>
-<span class="n">broadcast_axis</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span c [...]
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]]</span>
+// broadcast x on on axis 2
+broadcast_axis(x, axis=2, size=3) = [[[ 1., 1., 1.],
+ [ 2., 2., 2.]]]
+// broadcast x on on axes 0 and 2
+broadcast_axis(x, axis=(0,2), size=(2,3)) = [[[ 1., 1., 1.],
+ [ 2., 2., 2.]],
+ [[ 1., 1., 1.],
+ [ 2., 2., 2.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L168</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The axes to perform the broadcasting.</li>
-<li><strong>size</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Target sizes of the broadcasting axes.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – The axes to perform the broadcasting.</li>
+<li><strong>size</strong> (<em>Shape(tuple), optional, default=()</em>) – Target sizes of the broadcasting axes.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5733,7 +6115,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_div</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_div" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise division of the input arrays with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
@@ -5745,20 +6127,21 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L137</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5769,7 +6152,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_equal</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_equal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>equal to</strong> (==) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5781,20 +6164,21 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L27</p>
<table class="docutils field-list" frame="void" rules="none">
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+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5805,7 +6189,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_greater</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_greater" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>greater than</strong> (>) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5817,20 +6201,21 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L63</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
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+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5841,7 +6226,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_greater_equal</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_greater_equal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>greater than or equal to</strong> (>=) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5853,20 +6238,21 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L81</p>
<table class="docutils field-list" frame="void" rules="none">
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+<col class="field-body"/>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5879,7 +6265,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
with broadcasting.</p>
<p>It is equivalent to doing <span class="math">\(sqrt(x_1^2 + x_2^2)\)</span>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
@@ -5896,20 +6282,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_extended.cc:L137</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5920,7 +6307,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_lesser</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_lesser" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>lesser than</strong> (<) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5932,20 +6319,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L99</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5956,7 +6344,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_lesser_equal</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_lesser_equal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>lesser than or equal to</strong> (<=) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5968,20 +6356,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L117</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -5993,7 +6382,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise maximum of the input arrays with broadcasting.</p>
<p>This function compares two input arrays and returns a new array having the element-wise maxima.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -6005,20 +6394,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_extended.cc:L61</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6030,7 +6420,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise minimum of the input arrays with broadcasting.</p>
<p>This function compares two input arrays and returns a new array having the element-wise minima.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -6042,20 +6432,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_extended.cc:L96</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6067,7 +6458,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise difference of the input arrays with broadcasting.</p>
<p><cite>broadcast_minus</cite> is an alias to the function <cite>broadcast_sub</cite>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -6082,20 +6473,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L71</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6106,7 +6498,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_mod</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_mod" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise modulo of the input arrays with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
@@ -6118,20 +6510,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L170</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6142,7 +6535,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_mul</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_mul" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise product of the input arrays with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -6154,20 +6547,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L104</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6178,7 +6572,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_not_equal</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_not_equal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>not equal to</strong> (!=) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -6190,20 +6584,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L45</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6215,7 +6610,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise sum of the input arrays with broadcasting.</p>
<p><cite>broadcast_plus</cite> is an alias to the function <cite>broadcast_add</cite>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -6230,20 +6625,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L32</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6254,7 +6650,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">broadcast_power</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.broadcast_power" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns result of first array elements raised to powers from second array, element-wise with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -6266,20 +6662,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_extended.cc:L26</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6291,7 +6688,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise difference of the input arrays with broadcasting.</p>
<p><cite>broadcast_minus</cite> is an alias to the function <cite>broadcast_sub</cite>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -6306,20 +6703,21 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L71</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Second input to the function</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6335,28 +6733,29 @@ Also see, <a class="reference external" href="https://docs.scipy.org/doc/numpy/u
<p>Broadcasting is allowed on axes with size 1, such as from <cite>(2,1,3,1)</cite> to
<cite>(2,8,3,9)</cite>. Elements will be duplicated on the broadcasted axes.</p>
<p>For example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">broadcast_to</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span cl [...]
- <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]])</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>broadcast_to([[1,2,3]], shape=(2,3)) = [[ 1., 2., 3.],
+ [ 1., 2., 3.]])
</pre></div>
</div>
<p>The dimension which you do not want to change can also be kept as <cite>0</cite> which means copy the original value.
So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above example.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L192</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The shape of the desired array. We can set the dim to zero if it’s same as the original. E.g <cite>A = broadcast_to(B, shape=(10, 0, 0))</cite> has the same meaning as <cite>A = broadcast_axis(B, axis=0, size=10)</cite>.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – The shape of the desired array. We can set the dim to zero if it’s same as the original. E.g <cite>A = broadcast_to(B, shape=(10, 0, 0))</cite> has the same meaning as <cite>A = broadcast_axis(B, axis=0, size=10)</cite>.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6371,27 +6770,28 @@ So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above
<p class="last"><code class="docutils literal"><span class="pre">Cast</span></code> is deprecated. Use <code class="docutils literal"><span class="pre">cast</span></code> instead.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">cast</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cast</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">cast</span><span class="p">([</span><span class="mf">1e20</span><span class="p">,</span> <span class="mf">11.1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float16'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="n">inf</span><span class="p">,</span> <span class="mf">11.09375</span><span class="p">]</span>
<span class="n">cast</span><span class="p">([</span><span class="mi">300</span><span class="p">,</span> <span class="mf">11.1</span><span class="p">,</span> <span class="mf">10.9</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'uint8'</span><span class="p">)</span> <span class="o">=</span> [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L175</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input.</li>
-<li><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>, </em><em>required</em>) – Output data type.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>dtype</strong> (<em>{'float16', 'float32', 'float64', 'int32', 'uint8'}, required</em>) – Output data type.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6403,24 +6803,25 @@ So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above
<dd><p>Returns element-wise ceiling of the input.</p>
<p>The ceil of the scalar x is the smallest integer i, such that i >= x.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">ceil</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ceil</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L295</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6431,20 +6832,21 @@ So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above
<code class="descclassname">mxnet.ndarray.</code><code class="descname">choose_element_0index</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.choose_element_0index" title="Permalink to this definition">¶</a></dt>
<dd><p>Choose one element from each line(row for python, column for R/Julia) in lhs according to index indicated by rhs. This function assume rhs uses 0-based index.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Left operand to the function.</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Right operand to the function.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6456,32 +6858,33 @@ So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above
<dd><p>Clips (limits) the values in an array.</p>
<p>Given an interval, values outside the interval are clipped to the interval edges.
Clipping <code class="docutils literal"><span class="pre">x</span></code> between <cite>a_min</cite> and <cite>a_x</cite> would be:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">clip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">a_min</span><span class="p">,</span> <span class="n">a_max</span><span class="p">)</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">a_max</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>clip(x, a_min, a_max) = max(min(x, a_max), a_min))
</pre></div>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</sp [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</spa [...]
<span class="n">clip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span clas [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L457</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array.</li>
-<li><strong>a_min</strong> (<em>float</em><em>, </em><em>required</em>) – Minimum value</li>
-<li><strong>a_max</strong> (<em>float</em><em>, </em><em>required</em>) – Maximum value</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>a_min</strong> (<em>float, required</em>) – Minimum value</li>
+<li><strong>a_max</strong> (<em>float, required</em>) – Maximum value</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6500,43 +6903,44 @@ which they will be concatenated.
The dimension of the output array along the concatenated axis will be equal
to the sum of the corresponding dimensions of the input arrays.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">]]</span>
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">]]</span>
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">6</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">],[</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[1,1],[2,2]]
+y = [[3,3],[4,4],[5,5]]
+z = [[6,6], [7,7],[8,8]]
-<span class="n">concat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+concat(x,y,z,dim=0) = [[ 1., 1.],
+ [ 2., 2.],
+ [ 3., 3.],
+ [ 4., 4.],
+ [ 5., 5.],
+ [ 6., 6.],
+ [ 7., 7.],
+ [ 8., 8.]]
-<span class="n">Note</span> <span class="n">that</span> <span class="n">you</span> <span class="n">cannot</span> <span class="n">concat</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span> <span class="n">along</span> <span class="n">dimension</span> <span class="mi">1</span> <span class="n">since</span> <span class="n">dimension</span>
-<span class="mi">0</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">the</span> <span class="n">same</span> <span class="k">for</span> <span class="nb">all</span> <span class="n">the</span> <span class="nb">input</span> <span class="n">arrays</span><span class="o">.</span>
+Note that you cannot concat x,y,z along dimension 1 since dimension
+0 is not the same for all the input arrays.
-<span class="n">concat</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span clas [...]
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+concat(y,z,dim=1) = [[ 3., 3., 6., 6.],
+ [ 4., 4., 7., 7.],
+ [ 5., 5., 8., 8.]]
</pre></div>
</div>
<p>Defined in src/operator/concat.cc:L80</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>[</em><em></em><em>]</em><em></em>) – List of arrays to concatenate</li>
-<li><strong>dim</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – the dimension to be concated.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>data</strong> (<em>NDArray[]</em>) – List of arrays to concatenate</li>
+<li><strong>dim</strong> (<em>int, optional, default='1'</em>) – the dimension to be concated.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6551,19 +6955,20 @@ to the sum of the corresponding dimensions of the input arrays.</p>
\[cos([0, \pi/4, \pi/2]) = [1, 0.707, 0]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L491</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6577,19 +6982,20 @@ to the sum of the corresponding dimensions of the input arrays.</p>
\[cosh(x) = 0.5\times(exp(x) + exp(-x))\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L613</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6611,7 +7017,7 @@ and <code class="docutils literal"><span class="pre">end=(e_1,</span> <span clas
<p>The resulting array’s <em>k</em>-th dimension contains elements
from the <em>k</em>-th dimension of the input array with the open range <code class="docutils literal"><span class="pre">[b_k,</span> <span class="pre">e_k)</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">9.</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]]</span>
@@ -6621,21 +7027,22 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L257</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Source input</li>
-<li><strong>begin</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – starting indices for the slice operation, supports negative indices.</li>
-<li><strong>end</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – ending indices for the slice operation, supports negative indices.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>begin</strong> (<em>Shape(tuple), required</em>) – starting indices for the slice operation, supports negative indices.</li>
+<li><strong>end</strong> (<em>Shape(tuple), required</em>) – ending indices for the slice operation, supports negative indices.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6649,19 +7056,20 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
\[degrees([0, \pi/2, \pi, 3\pi/2, 2\pi]) = [0, 90, 180, 270, 360]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L571</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6681,11 +7089,11 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
axis of the second input</p>
<p>For example, given 3-D <code class="docutils literal"><span class="pre">x</span></code> with shape <cite>(n,m,k)</cite> and <code class="docutils literal"><span class="pre">y</span></code> with shape <cite>(k,r,s)</cite>, the
result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span [...]
</pre></div>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">reshape</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</s [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">reshape</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</sp [...]
<span class="n">y</span> <span class="o">=</span> <span class="n">reshape</span><span class="p">([</span><span class="mi">7</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],< [...]
<span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,:]</span><span class="o">*</span><span class="n">y</span><span class="p">[:,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span> <span class="o">=</span> <span class="mi">0</span>
@@ -6695,22 +7103,23 @@ result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
</ul>
<p>Defined in src/operator/tensor/matrix_op.cc:L376</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The second input</li>
-<li><strong>transpose_a</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then transpose the first input before dot.</li>
-<li><strong>transpose_b</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then transpose the second input before dot.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>transpose_a</strong> (<em>boolean, optional, default=False</em>) – If true then transpose the first input before dot.</li>
+<li><strong>transpose_b</strong> (<em>boolean, optional, default=False</em>) – If true then transpose the second input before dot.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6721,26 +7130,27 @@ result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">elemwise_add</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.elemwise_add" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds arguments element-wise.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – second input</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p"> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">] [...]
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.1</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">,</span> <span class="mf">3.1</span><span class="p">,</span> <span class="mf">4.1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([ 2.0999999 , 4.0999999 , 6.0999999 , 8.10000038], dtype=float32)</span>
@@ -6754,24 +7164,25 @@ result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
<div class="math">
\[exp(x) = e^x \approx 2.718^x\]</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">exp</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="n">inf</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.707</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">exp</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="n">inf</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.707</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L402</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6785,20 +7196,21 @@ result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
will return a new array with shape <code class="docutils literal"><span class="pre">(2,1,3,4)</span></code>.</p>
<p>Defined in src/operator/tensor/matrix_op.cc:L213</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Source input</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>required</em>) – Position where new axis is to be inserted. Suppose that the input <cite>NDArray</cite>‘s dimension is <cite>ndim</cite>, the range of the inserted axis is <cite>[-ndim, ndim]</cite></li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int, required</em>) – Position where new axis is to be inserted. Suppose that the input <cite>NDArray</cite>‘s dimension is <cite>ndim</cite>, the range of the inserted axis is <cite>[-ndim, ndim]</cite></li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6811,19 +7223,20 @@ will return a new array with shape <code class="docutils literal"><span class="p
<p>This function provides greater precision than <code class="docutils literal"><span class="pre">exp(x)</span> <span class="pre">-</span> <span class="pre">1</span></code> for small values of <code class="docutils literal"><span class="pre">x</span></code>.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L475</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6834,21 +7247,22 @@ will return a new array with shape <code class="docutils literal"><span class="p
<code class="descclassname">mxnet.ndarray.</code><code class="descname">fill_element_0index</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>mhs=None</em>, <em>rhs=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.fill_element_0index" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill one element of each line(row for python, column for R/Julia) in lhs according to index indicated by rhs and values indicated by mhs. This function assume rhs uses 0-based index.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Left operand to the function.</li>
<li><strong>mhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Middle operand to the function.</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Right operand to the function.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6859,24 +7273,25 @@ will return a new array with shape <code class="docutils literal"><span class="p
<code class="descclassname">mxnet.ndarray.</code><code class="descname">fix</code><span class="sig-paren">(</span><em>data=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.fix" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise rounded value to the nearest integer towards zero of the input.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">fix</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">2.</span><span class=" [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">fix</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">2.</span><span class="p [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L333</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6893,7 +7308,7 @@ will return a new array with shape <code class="docutils literal"><span class="p
<p>For an input array with shape <code class="docutils literal"><span class="pre">(d1,</span> <span class="pre">d2,</span> <span class="pre">...,</span> <span class="pre">dk)</span></code>, <cite>flatten</cite> operation reshapes
the input array into an output array of shape <code class="docutils literal"><span class="pre">(d1,</span> <span class="pre">d2*...*dk)</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span>
<span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span>
<span class="p">[</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">]</span>
@@ -6909,19 +7324,20 @@ the input array into an output array of shape <code class="docutils literal"><sp
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L132</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6933,7 +7349,7 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Reverses the order of elements along given axis while preserving array shape.</p>
<p>Note: reverse and flip are equivalent. We use reverse in the following examples.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">]]</span>
<span class="n">reverse</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><sp [...]
@@ -6945,20 +7361,21 @@ the input array into an output array of shape <code class="docutils literal"><sp
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L601</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data array</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The axis which to reverse elements.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>Shape(tuple), required</em>) – The axis which to reverse elements.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6970,24 +7387,25 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Returns element-wise floor of the input.</p>
<p>The floor of the scalar x is the largest integer i, such that i <= x.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">floor</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">floor</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span clas [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L308</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -6999,19 +7417,20 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Returns the gamma function (extension of the factorial function to the reals) , computed element-wise on the input array.</p>
<p>From:src/operator/tensor/elemwise_unary_op.cc:667</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7023,19 +7442,20 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Returns element-wise log of the absolute value of the gamma function of the input.</p>
<p>From:src/operator/tensor/elemwise_unary_op.cc:677</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7047,19 +7467,20 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Returns a copy of the input.</p>
<p>From:src/operator/tensor/elemwise_unary_op.cc:49</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7084,42 +7505,43 @@ is either the identity or the matrix transposition.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">multiply</span><span class="o">-</span><span class="n">add</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">C</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">linalg_gemm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">transpose_b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf"> [...]
- <span class="o">=</span> <span class="p">[[</span><span class="mf">14.0</span><span class="p">,</span> <span class="mf">14.0</span><span class="p">,</span> <span class="mf">14.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">14.0</span><span class="p">,</span> <span class="mf">14.0</span><span class="p">,</span> <span class="mf">14.0</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix multiply-add
+A = [[1.0, 1.0], [1.0, 1.0]]
+B = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]
+C = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
+linalg_gemm(A, B, C, transpose_b = 1, alpha = 2.0 , beta = 10.0)
+ = [[14.0, 14.0, 14.0], [14.0, 14.0, 14.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">multiply</span><span class="o">-</span><span class="n">add</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]]</span>
-<span class="n">C</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">10.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.01</span><span class="p">]]]</span>
-<span class="n">linalg_gemm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">transpose_b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf"> [...]
- <span class="o">=</span> <span class="p">[[[</span><span class="mf">104.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.14</span><span class="p">]]]</span>
+// Batch matrix multiply-add
+A = [[[1.0, 1.0]], [[0.1, 0.1]]]
+B = [[[1.0, 1.0]], [[0.1, 0.1]]]
+C = [[[10.0]], [[0.01]]]
+linalg_gemm(A, B, C, transpose_b = 1, alpha = 2.0 , beta = 10.0)
+ = [[[104.0]], [[0.14]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L48</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of input matrices</li>
<li><strong>B</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of input matrices</li>
<li><strong>C</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of input matrices</li>
-<li><strong>transpose_a</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply with transposed of first input (A).</li>
-<li><strong>transpose_b</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply with transposed of second input (B).</li>
-<li><strong>alpha</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor multiplied with A*B.</li>
-<li><strong>beta</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor multiplied with C.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>transpose_a</strong> (<em>boolean, optional, default=False</em>) – Multiply with transposed of first input (A).</li>
+<li><strong>transpose_b</strong> (<em>boolean, optional, default=False</em>) – Multiply with transposed of second input (B).</li>
+<li><strong>alpha</strong> (<em>double, optional, default=1</em>) – Scalar factor multiplied with A*B.</li>
+<li><strong>beta</strong> (<em>double, optional, default=1</em>) – Scalar factor multiplied with C.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7144,38 +7566,39 @@ the identity or the matrix transposition.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">multiply</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">linalg_gemm2</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">transpose_b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span>
- <span class="o">=</span> <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix multiply
+A = [[1.0, 1.0], [1.0, 1.0]]
+B = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]
+linalg_gemm2(A, B, transpose_b = 1, alpha = 2.0)
+ = [[4.0, 4.0, 4.0], [4.0, 4.0, 4.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">multiply</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]]</span>
-<span class="n">linalg_gemm2</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">transpose_b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="p">)</span>
- <span class="o">=</span> <span class="p">[[[</span><span class="mf">4.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.04</span> <span class="p">]]]</span>
+// Batch matrix multiply
+A = [[[1.0, 1.0]], [[0.1, 0.1]]]
+B = [[[1.0, 1.0]], [[0.1, 0.1]]]
+linalg_gemm2(A, B, transpose_b = 1, alpha = 2.0 )
+ = [[[4.0]], [[0.04 ]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L106</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of input matrices</li>
<li><strong>B</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of input matrices</li>
-<li><strong>transpose_a</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply with transposed of first input (A).</li>
-<li><strong>transpose_b</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply with transposed of second input (B).</li>
-<li><strong>alpha</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor multiplied with A*B.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>transpose_a</strong> (<em>boolean, optional, default=False</em>) – Multiply with transposed of first input (A).</li>
+<li><strong>transpose_b</strong> (<em>boolean, optional, default=False</em>) – Multiply with transposed of second input (B).</li>
+<li><strong>alpha</strong> (<em>double, optional, default=1</em>) – Scalar factor multiplied with A*B.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7202,30 +7625,31 @@ apart from the diagonal.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">factorization</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">4.25</span><span class="p">]]</span>
-<span class="n">linalg_potrf</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix factorization
+A = [[4.0, 1.0], [1.0, 4.25]]
+linalg_potrf(A) = [[2.0, 0], [0.5, 2.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">factorization</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">4.25</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">16.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</spa [...]
-<span class="n">linalg_potrf</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mi">0</spa [...]
+// Batch matrix factorization
+A = [[[4.0, 1.0], [1.0, 4.25]], [[16.0, 4.0], [4.0, 17.0]]]
+linalg_potrf(A) = [[[2.0, 0], [0.5, 2.0]], [[4.0, 0], [1.0, 4.0]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L159</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of input matrices to be decomposed</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7251,31 +7675,32 @@ inverse of each <em>B</em><sub>i</sub> from this decomposition, i.e</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">inverse</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]]</span>
-<span class="n">linalg_potri</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.26563</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0625</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.0625</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix inverse
+A = [[2.0, 0], [0.5, 2.0]]
+linalg_potri(A) = [[0.26563, -0.0625], [-0.0625, 0.25]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">inverse</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><spa [...]
-<span class="n">linalg_potri</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">0.26563</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0625</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.0625</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mf">0.06641</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.01562</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.01562</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0625</span><span class="p">]]]</span>
+// Batch matrix inverse
+A = [[[2.0, 0], [0.5, 2.0]], [[4.0, 0], [1.0, 4.0]]]
+linalg_potri(A) = [[[0.26563, -0.0625], [-0.0625, 0.25]],
+ [[0.06641, -0.01562], [-0.01562, 0,0625]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L211</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of lower triangular matrices</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7296,30 +7721,31 @@ of all diagonal elements. All matrices must be square and all diagonal elements
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">reduction</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">]]</span>
-<span class="n">linalg_sumlogdiag</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.9459</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix reduction
+A = [[1.0, 1.0], [1.0, 7.0]]
+linalg_sumlogdiag(A) = [1.9459]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">reduction</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><spa [...]
-<span class="n">linalg_sumlogdiag</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.9459</span><span class="p">,</span> <span class="mf">3.9318</span><span class="p">]</span>
+// Batch matrix reduction
+A = [[[1.0, 1.0], [1.0, 7.0]], [[3.0, 0], [0, 17.0]]]
+linalg_sumlogdiag(A) = [1.9459, 3.9318]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L379</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of square matrices</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7349,37 +7775,38 @@ All matrices <em>A</em><sub>i</sub> must be lower triangular.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">multiply</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">linalg_trmm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix multiply
+A = [[1.0, 0], [1.0, 1.0]]
+B = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
+linalg_trmm(A, B, alpha = 2.0) = [[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">multiply</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><spa [...]
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> [...]
-<span class="n">linalg_trmm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</sp [...]
- <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]]]</span>
+// Batch matrix multiply
+A = [[[1.0, 0], [1.0, 1.0]], [[1.0, 0], [1.0, 1.0]]]
+B = [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]]
+linalg_trmm(A, B, alpha = 2.0 ) = [[[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]],
+ [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L268</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of lower triangular matrices</li>
<li><strong>B</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of matrices</li>
-<li><strong>transpose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Use transposed of the triangular matrix</li>
-<li><strong>rightside</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply triangular matrix from the right to non-triangular one.</li>
-<li><strong>alpha</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor to be applied to the result.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>transpose</strong> (<em>boolean, optional, default=False</em>) – Use transposed of the triangular matrix</li>
+<li><strong>rightside</strong> (<em>boolean, optional, default=False</em>) – Multiply triangular matrix from the right to non-triangular one.</li>
+<li><strong>alpha</strong> (<em>double, optional, default=1</em>) – Scalar factor to be applied to the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7409,38 +7836,39 @@ All matrices <em>A</em><sub>i</sub> must be lower triangular.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">solve</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]]</span>
-<span class="n">linalg_trsm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix solve
+A = [[1.0, 0], [1.0, 1.0]]
+B = [[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]]
+linalg_trsm(A, B, alpha = 0.5) = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">solve</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><spa [...]
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">]]]</span>
-<span class="n">linalg_trsm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</sp [...]
- <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span> <span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]]]</span>
+// Batch matrix solve
+A = [[[1.0, 0], [1.0, 1.0]], [[1.0, 0], [1.0, 1.0]]]
+B = [[[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]],
+ [[4.0, 4.0, 4.0], [8.0, 8.0, 8.0]]]
+linalg_trsm(A, B, alpha = 0.5 ) = [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
+ [[2.0, 2.0, 2.0 ], [2.0, 2.0, 2.0]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L331</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of lower triangular matrices</li>
<li><strong>B</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Tensor of matrices</li>
-<li><strong>transpose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Use transposed of the triangular matrix</li>
-<li><strong>rightside</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply triangular matrix from the right to non-triangular one.</li>
-<li><strong>alpha</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor to be applied to the result.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>transpose</strong> (<em>boolean, optional, default=False</em>) – Use transposed of the triangular matrix</li>
+<li><strong>rightside</strong> (<em>boolean, optional, default=False</em>) – Multiply triangular matrix from the right to non-triangular one.</li>
+<li><strong>alpha</strong> (<em>double, optional, default=1</em>) – Scalar factor to be applied to the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7453,19 +7881,20 @@ proper backward gradients.</p>
<p>The natural logarithm is logarithm in base <em>e</em>, so that <code class="docutils literal"><span class="pre">log(exp(x))</span> <span class="pre">=</span> <span class="pre">x</span></code></p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L412</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7478,19 +7907,20 @@ proper backward gradients.</p>
<p><code class="docutils literal"><span class="pre">10**log10(x)</span> <span class="pre">=</span> <span class="pre">x</span></code></p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L422</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7504,19 +7934,20 @@ proper backward gradients.</p>
<span class="math">\(1+x\approx 1\)</span></p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L462</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7529,19 +7960,20 @@ proper backward gradients.</p>
<p><code class="docutils literal"><span class="pre">2**log2(x)</span> <span class="pre">=</span> <span class="pre">x</span></code></p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L432</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7553,7 +7985,7 @@ proper backward gradients.</p>
<dd><p>Computes the log softmax of the input.
This is equivalent to computing softmax followed by log.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">.</span><span class="mi">1</span><span class="p">])</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">.</span><span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([-1.41702998, -0.41702995, -2.31702995], dtype=float32)</span>
@@ -7564,20 +7996,21 @@ This is equivalent to computing softmax followed by log.</p>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – The axis along which to compute softmax.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int, optional, default='-1'</em>) – The axis along which to compute softmax.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7590,19 +8023,20 @@ This is equivalent to computing softmax followed by log.</p>
.. note:: <code class="docutils literal"><span class="pre">make_loss</span></code> is deprecated, use <code class="docutils literal"><span class="pre">MakeLoss</span></code>.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L110</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7614,32 +8048,32 @@ This is equivalent to computing softmax followed by log.</p>
<dd><p>Computes the max of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L121</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7651,32 +8085,32 @@ NOT in axis instead.</p>
<dd><p>Computes the max of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L121</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7688,32 +8122,32 @@ NOT in axis instead.</p>
<dd><p>Computes the mean of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L64</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7725,32 +8159,32 @@ NOT in axis instead.</p>
<dd><p>Computes the min of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L135</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7762,32 +8196,32 @@ NOT in axis instead.</p>
<dd><p>Computes the min of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L135</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7798,27 +8232,28 @@ NOT in axis instead.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">mp_sgd_mom_update</code><span class="sig-paren">(</span><em>weight=None</em>, <em>grad=None</em>, <em>mom=None</em>, <em>weight32=None</em>, <em>lr=_Null</em>, <em>momentum=_Null</em>, <em>wd=_Null</em>, <em>rescale_grad=_Null</em>, <em>clip_gradient=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.mp_sgd_mom_update" title=" [...]
<dd><p>Updater function for multi-precision sgd optimizer</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Gradient</li>
<li><strong>mom</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Momentum</li>
<li><strong>weight32</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight32</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The decay rate of momentum estimates at each epoch.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0</em>) – The decay rate of momentum estimates at each epoch.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7829,25 +8264,26 @@ NOT in axis instead.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">mp_sgd_update</code><span class="sig-paren">(</span><em>weight=None</em>, <em>grad=None</em>, <em>weight32=None</em>, <em>lr=_Null</em>, <em>wd=_Null</em>, <em>rescale_grad=_Null</em>, <em>clip_gradient=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.mp_sgd_update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updater function for multi-precision sgd optimizer</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – gradient</li>
<li><strong>weight32</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight32</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7859,32 +8295,32 @@ NOT in axis instead.</p>
<dd><p>Computes the product of array elements over given axes treating Not a Numbers (<code class="docutils literal"><span class="pre">NaN</span></code>) as one.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L107</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7896,32 +8332,32 @@ NOT in axis instead.</p>
<dd><p>Computes the sum of array elements over given axes treating Not a Numbers (<code class="docutils literal"><span class="pre">NaN</span></code>) as zero.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L92</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7932,7 +8368,7 @@ NOT in axis instead.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">norm</code><span class="sig-paren">(</span><em>data=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.norm" title="Permalink to this definition">¶</a></dt>
<dd><p>Flattens the input array and then computes the l2 norm.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
<span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mf">5.47722578</span><span class="p">]</span>
@@ -7940,19 +8376,20 @@ NOT in axis instead.</p>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L218</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Source input</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -7968,29 +8405,30 @@ NOT in axis instead.</p>
</div>
<p>Samples are distributed according to a normal distribution parametrized by <em>loc</em> (mean) and <em>scale</em> (standard deviation).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cla [...]
<span class="p">[</span><span class="o">-</span><span class="mf">1.23474145</span><span class="p">,</span> <span class="mf">1.55807114</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L62</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>loc</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Mean of the distribution.</li>
-<li><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Standard deviation of the distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>loc</strong> (<em>float, optional, default=0</em>) – Mean of the distribution.</li>
+<li><strong>scale</strong> (<em>float, optional, default=1</em>) – Standard deviation of the distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8004,51 +8442,52 @@ NOT in axis instead.</p>
other locations take value <cite>off_value</cite>.</p>
<p><cite>one_hot</cite> operation with <cite>indices</cite> of shape <code class="docutils literal"><span class="pre">(i0,</span> <span class="pre">i1)</span></code> and <cite>depth</cite> of <code class="docutils literal"><span class="pre">d</span></code> would result
in an output array of shape <code class="docutils literal"><span class="pre">(i0,</span> <span class="pre">i1,</span> <span class="pre">d)</span></code> with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">off_value</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">off_value</span>
<span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]]</span> <span class="o">=</span> <span class="n">on_value</span>
</pre></div>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">one_hot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">1.</span> <span clas [...]
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>one_hot([1,0,2,0], 3) = [[ 0. 1. 0.]
+ [ 1. 0. 0.]
+ [ 0. 0. 1.]
+ [ 1. 0. 0.]]
-<span class="n">one_hot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">,</span> <span class="n">on_value</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">off_value</span><span class="o">=</span><span class="mi">1</span><sp [...]
- <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span> <span class="mi">8</span> <span class="mi">1</span><span class="p">]</span>
- <span class="p">[</span><span class="mi">8</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]</span>
- <span class="p">[</span><span class="mi">1</span> <span class="mi">1</span> <span class="mi">8</span><span class="p">]</span>
- <span class="p">[</span><span class="mi">8</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]]</span>
+one_hot([1,0,2,0], 3, on_value=8, off_value=1,
+ dtype='int32') = [[1 8 1]
+ [8 1 1]
+ [1 1 8]
+ [8 1 1]]
-<span class="n">one_hot</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">]],</span> <span class="mi">3</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">0.</span> <span [...]
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+one_hot([[1,0],[1,0],[2,0]], 3) = [[[ 0. 1. 0.]
+ [ 1. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[ 0. 1. 0.]
+ [ 1. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]</span>
+ [[ 0. 0. 1.]
+ [ 1. 0. 0.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/indexing_op.cc:L218</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>indices</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – array of locations where to set on_value</li>
-<li><strong>depth</strong> (<em>int</em><em>, </em><em>required</em>) – Depth of the one hot dimension.</li>
-<li><strong>on_value</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – The value assigned to the locations represented by indices.</li>
-<li><strong>off_value</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The value assigned to the locations not represented by indices.</li>
-<li><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='float32'</em>) – DType of the output</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>depth</strong> (<em>int, required</em>) – Depth of the one hot dimension.</li>
+<li><strong>on_value</strong> (<em>double, optional, default=1</em>) – The value assigned to the locations represented by indices.</li>
+<li><strong>off_value</strong> (<em>double, optional, default=0</em>) – The value assigned to the locations not represented by indices.</li>
+<li><strong>dtype</strong> (<em>{'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='float32'</em>) – DType of the output</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8060,7 +8499,7 @@ in an output array of shape <code class="docutils literal"><span class="pre">(i0
<dd><p>Return an array of ones with the same shape and type
as the input array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]]</span>
<span class="n">ones_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
@@ -8068,19 +8507,20 @@ as the input array.</p>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8109,84 +8549,85 @@ to add before and after the elements of the array along dimension <code class="d
The widths of the higher two dimensions <code class="docutils literal"><span class="pre">before_1</span></code>, <code class="docutils literal"><span class="pre">after_1</span></code>, <code class="docutils literal"><span class="pre">before_2</span></code>,
<code class="docutils literal"><span class="pre">after_2</span></code> must be 0.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[[</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[[ 1. 2. 3.]
+ [ 4. 5. 6.]]
- <span class="p">[[</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span><span class="p">]]]</span>
+ [[ 7. 8. 9.]
+ [ 10. 11. 12.]]]
- <span class="p">[[[</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span><span class="p">]]</span>
+ [[[ 11. 12. 13.]
+ [ 14. 15. 16.]]
- <span class="p">[[</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span><span class="p">]]]]</span>
+ [[ 17. 18. 19.]
+ [ 20. 21. 22.]]]]
-<span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">mode</span><span class="o">=</span><span class="s2">"edge"</span><span class="p">,</span> <span class="n">pad_width</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class= [...]
+pad(x,mode="edge", pad_width=(0,0,0,0,1,1,1,1)) =
- <span class="p">[[[[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">6.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">6.</span><span class="p">]]</span>
+ [[[[ 1. 1. 2. 3. 3.]
+ [ 1. 1. 2. 3. 3.]
+ [ 4. 4. 5. 6. 6.]
+ [ 4. 4. 5. 6. 6.]]
- <span class="p">[[</span> <span class="mf">7.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">7.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">12.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">12.</span><span class="p">]]]</span>
+ [[ 7. 7. 8. 9. 9.]
+ [ 7. 7. 8. 9. 9.]
+ [ 10. 10. 11. 12. 12.]
+ [ 10. 10. 11. 12. 12.]]]
- <span class="p">[[[</span> <span class="mf">11.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">11.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">16.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">16.</span><span class="p">]]</span>
+ [[[ 11. 11. 12. 13. 13.]
+ [ 11. 11. 12. 13. 13.]
+ [ 14. 14. 15. 16. 16.]
+ [ 14. 14. 15. 16. 16.]]
- <span class="p">[[</span> <span class="mf">17.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">17.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">22.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">22.</span><span class="p">]]]]</span>
+ [[ 17. 17. 18. 19. 19.]
+ [ 17. 17. 18. 19. 19.]
+ [ 20. 20. 21. 22. 22.]
+ [ 20. 20. 21. 22. 22.]]]]
-<span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"constant"</span><span class="p">,</span> <span class="n">constant_value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">pad_width</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,< [...]
+pad(x, mode="constant", constant_value=0, pad_width=(0,0,0,0,1,1,1,1)) =
- <span class="p">[[[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[[[ 0. 0. 0. 0. 0.]
+ [ 0. 1. 2. 3. 0.]
+ [ 0. 4. 5. 6. 0.]
+ [ 0. 0. 0. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]</span>
+ [[ 0. 0. 0. 0. 0.]
+ [ 0. 7. 8. 9. 0.]
+ [ 0. 10. 11. 12. 0.]
+ [ 0. 0. 0. 0. 0.]]]
- <span class="p">[[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[[ 0. 0. 0. 0. 0.]
+ [ 0. 11. 12. 13. 0.]
+ [ 0. 14. 15. 16. 0.]
+ [ 0. 0. 0. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]]</span>
+ [[ 0. 0. 0. 0. 0.]
+ [ 0. 17. 18. 19. 0.]
+ [ 0. 20. 21. 22. 0.]
+ [ 0. 0. 0. 0. 0.]]]]
</pre></div>
</div>
<p>Defined in src/operator/pad.cc:L747</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – An n-dimensional input array.</li>
-<li><strong>mode</strong> (<em>{'constant'</em><em>, </em><em>'edge'</em><em>, </em><em>'reflect'}</em><em>, </em><em>required</em>) – Padding type to use. “constant” pads with <cite>constant_value</cite> “edge” pads using the edge values of the input array “reflect” pads by reflecting values with respect to the edges.</li>
-<li><strong>pad_width</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – Widths of the padding regions applied to the edges of each axis. It is a tuple of integer padding widths for each axis of the format <code class="docutils literal"><span class="pre">(before_1,</span> <span class="pre">after_1,</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">before_N,</span> <span class="pre">after_N)</span></code>. It s [...]
-<li><strong>constant_value</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The value used for padding when <cite>mode</cite> is “constant”.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>mode</strong> (<em>{'constant', 'edge', 'reflect'}, required</em>) – Padding type to use. “constant” pads with <cite>constant_value</cite> “edge” pads using the edge values of the input array “reflect” pads by reflecting values with respect to the edges.</li>
+<li><strong>pad_width</strong> (<em>Shape(tuple), required</em>) – Widths of the padding regions applied to the edges of each axis. It is a tuple of integer padding widths for each axis of the format <code class="docutils literal"><span class="pre">(before_1,</span> <span class="pre">after_1,</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">before_N,</span> <span class="pre">after_N)</span></code>. It should be of length <code class="docutils literal"><spa [...]
+<li><strong>constant_value</strong> (<em>double, optional, default=0</em>) – The value used for padding when <cite>mode</cite> is “constant”.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8198,51 +8639,52 @@ The widths of the higher two dimensions <code class="docutils literal"><span cla
<dd><p>Picks elements from an input array according to the input indices along the given axis.</p>
<p>Given an input array of shape <code class="docutils literal"><span class="pre">(d0,</span> <span class="pre">d1)</span></code> and indices of shape <code class="docutils literal"><span class="pre">(i0,)</span></code>, the result will be
an output array of shape <code class="docutils literal"><span class="pre">(i0,)</span></code> with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
</pre></div>
</div>
<p>By default, if any index mentioned is too large, it is replaced by the index that addresses
the last element along an axis (the <cite>clip</cite> mode).</p>
<p>This function supports n-dimensional input and (n-1)-dimensional indices arrays.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1., 2.],
+ [ 3., 4.],
+ [ 5., 6.]]
-<span class="o">//</span> <span class="n">picks</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">pick</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]</span>
+// picks elements with specified indices along axis 0
+pick(x, y=[0,1], 0) = [ 1., 4.]
-<span class="o">//</span> <span class="n">picks</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span>
-<span class="n">pick</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">4. [...]
+// picks elements with specified indices along axis 1
+pick(x, y=[0,1,0], 1) = [ 1., 4., 5.]
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
+y = [[ 1.],
+ [ 0.],
+ [ 2.]]
-<span class="o">//</span> <span class="n">picks</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">dims</span> <span class="n">are</span> <span class="n">maintained</span>
-<span class="n">pick</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]</span>
+// picks elements with specified indices along axis 1 and dims are maintained
+pick(x,y, 1, keepdims=True) = [[ 2.],
+ [ 3.],
+ [ 6.]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_index.cc:L126</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array</li>
<li><strong>index</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The index array</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because [...]
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because</span> <span class="pre">global</span> <span class="p [...]
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8254,32 +8696,32 @@ the last element along an axis (the <cite>clip</cite> mode).</p>
<dd><p>Computes the product of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L77</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8293,19 +8735,20 @@ NOT in axis instead.</p>
\[radians([0, 90, 180, 270, 360]) = [0, \pi/2, \pi, 3\pi/2, 2\pi]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L585</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8317,28 +8760,29 @@ NOT in axis instead.</p>
<dd><p>Draw random samples from an exponential distribution.</p>
<p>Samples are distributed according to an exponential distribution parametrized by <em>lambda</em> (rate).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_exponential</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.0097189</ [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_exponential</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.0097189</s [...]
<span class="p">[</span> <span class="mf">0.04146638</span><span class="p">,</span> <span class="mf">0.31715935</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lam</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Lambda parameter (rate) of the exponential distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>lam</strong> (<em>float, optional, default=1</em>) – Lambda parameter (rate) of the exponential distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8350,29 +8794,30 @@ NOT in axis instead.</p>
<dd><p>Draw random samples from a gamma distribution.</p>
<p>Samples are distributed according to a gamma distribution parametrized by <em>alpha</em> (shape) and <em>beta</em> (scale).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_gamma</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_gamma</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span c [...]
<span class="p">[</span> <span class="mf">3.91697288</span><span class="p">,</span> <span class="mf">3.65933681</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L75</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Alpha parameter (shape) of the gamma distribution.</li>
-<li><strong>beta</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Beta parameter (scale) of the gamma distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>alpha</strong> (<em>float, optional, default=1</em>) – Alpha parameter (shape) of the gamma distribution.</li>
+<li><strong>beta</strong> (<em>float, optional, default=1</em>) – Beta parameter (scale) of the gamma distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8387,29 +8832,30 @@ NOT in axis instead.</p>
number of unsuccessful experiments (generalized to real numbers).
Samples will always be returned as a floating point data type.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span c [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span cl [...]
<span class="p">[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L133</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>mu</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Mean of the negative binomial distribution.</li>
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Alpha (dispersion) parameter of the negative binomial distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>mu</strong> (<em>float, optional, default=1</em>) – Mean of the negative binomial distribution.</li>
+<li><strong>alpha</strong> (<em>float, optional, default=1</em>) – Alpha (dispersion) parameter of the negative binomial distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8423,29 +8869,30 @@ Samples will always be returned as a floating point data type.</p>
<em>k</em> (limit of unsuccessful experiments) and <em>p</em> (failure probability in each experiment).
Samples will always be returned as a floating point data type.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><s [...]
<span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L117</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>k</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Limit of unsuccessful experiments.</li>
-<li><strong>p</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Failure probability in each experiment.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>k</strong> (<em>int, optional, default='1'</em>) – Limit of unsuccessful experiments.</li>
+<li><strong>p</strong> (<em>float, optional, default=1</em>) – Failure probability in each experiment.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8461,29 +8908,30 @@ Samples will always be returned as a floating point data type.</p>
</div>
<p>Samples are distributed according to a normal distribution parametrized by <em>loc</em> (mean) and <em>scale</em> (standard deviation).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cla [...]
<span class="p">[</span><span class="o">-</span><span class="mf">1.23474145</span><span class="p">,</span> <span class="mf">1.55807114</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L62</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>loc</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Mean of the distribution.</li>
-<li><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Standard deviation of the distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>loc</strong> (<em>float, optional, default=0</em>) – Mean of the distribution.</li>
+<li><strong>scale</strong> (<em>float, optional, default=1</em>) – Standard deviation of the distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8496,28 +8944,29 @@ Samples will always be returned as a floating point data type.</p>
<p>Samples are distributed according to a Poisson distribution parametrized by <em>lambda</em> (rate).
Samples will always be returned as a floating point data type.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">5.</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">5.</span><span c [...]
<span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L102</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lam</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Lambda parameter (rate) of the Poisson distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>lam</strong> (<em>float, optional, default=1</em>) – Lambda parameter (rate) of the Poisson distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8534,29 +8983,30 @@ Samples will always be returned as a floating point data type.</p>
<p>Samples are uniformly distributed over the half-open interval <em>[low, high)</em>
(includes <em>low</em>, but excludes <em>high</em>).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cla [...]
<span class="p">[</span> <span class="mf">0.54488319</span><span class="p">,</span> <span class="mf">0.84725171</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L45</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>low</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Lower bound of the distribution.</li>
-<li><strong>high</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Upper bound of the distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>low</strong> (<em>float, optional, default=0</em>) – Lower bound of the distribution.</li>
+<li><strong>high</strong> (<em>float, optional, default=1</em>) – Upper bound of the distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8568,24 +9018,25 @@ Samples will always be returned as a floating point data type.</p>
<dd><p>Returns the reciprocal of the argument, element-wise.</p>
<p>Calculates 1/x.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">reciprocal</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mf">1.6</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">reciprocal</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mf">1.6</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L220</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8599,19 +9050,20 @@ Samples will always be returned as a floating point data type.</p>
\[max(features, 0)\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L18</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8623,14 +9075,14 @@ Samples will always be returned as a floating point data type.</p>
<dd><p>Repeats elements of an array.</p>
<p>By default, <code class="docutils literal"><span class="pre">repeat</span></code> flattens the input array into 1-D and then repeats the
elements:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
<span class="n">repeat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><s [...]
</pre></div>
</div>
<p>The parameter <code class="docutils literal"><span class="pre">axis</span></code> specifies the axis along which to perform repeat:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">repeat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">repeat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class [...]
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]]</span>
<span class="n">repeat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
@@ -8644,21 +9096,22 @@ elements:</p>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L499</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data array</li>
-<li><strong>repeats</strong> (<em>int</em><em>, </em><em>required</em>) – The number of repetitions for each element.</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – The axis along which to repeat values. The negative numbers are interpreted counting from the backward. By default, use the flattened input array, and return a flat output array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>repeats</strong> (<em>int, required</em>) – The number of repetitions for each element.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='None'</em>) – The axis along which to repeat values. The negative numbers are interpreted counting from the backward. By default, use the flattened input array, and return a flat output array.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8675,15 +9128,15 @@ elements:</p>
<p>Given an array and a shape, this function returns a copy of the array in the new shape.
The shape is a tuple of integers such as (2,3,4).The size of the new shape should be same as the size of the input array.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">reshape</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">) [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">reshape</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)) [...]
</pre></div>
</div>
<p>Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}. The significance of each is explained below:</p>
<ul>
<li><p class="first"><code class="docutils literal"><span class="pre">0</span></code> copy this dimension from the input to the output shape.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
+- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
</pre></div>
</div>
</li>
@@ -8691,33 +9144,33 @@ The shape is a tuple of integers such as (2,3,4).The size of the new shape shoul
keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span clas [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,),</span> <span class="n">output</span> <span class="n">shape</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
+- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
+- input shape = (2,3,4), shape=(-1,), output shape = (24,)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-2</span></code> copy all/remainder of the input dimensions to the output shape.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span> <span cl [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
+- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
+- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-3</span></code> use the product of two consecutive dimensions of the input shape as the output dimension.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span clas [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">),</span> <span cl [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
+- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
+- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
+- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-4</span></code> split one dimension of the input into two dimensions passed subsequent to -4 in shape (can contain -1).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
+- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
</pre></div>
</div>
</li>
@@ -8725,30 +9178,31 @@ At most one dimension of shape can be -1.</p>
<p>If the argument <cite>reverse</cite> is set to 1, then the special values are inferred from right to left.</p>
<blockquote>
<div><p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="n">without</span> <span class="n">reverse</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="k">for</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span>< [...]
-<span class="o">-</span> <span class="k">with</span> <span class="n">reverse</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">output</span> <span class="n">shape</span> <span class="n">will</span> <span class="n">be</span> <span class="p">(</span><span class="mi">50</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>- without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be (40,5)
+- with reverse=1, output shape will be (50,4).
</pre></div>
</div>
</div></blockquote>
<p>Defined in src/operator/tensor/matrix_op.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to reshape.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The target shape</li>
-<li><strong>reverse</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then the special values are inferred from right to left</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Target new shape. One and only one dim can be 0, in which case it will be inferred from the rest of dims</li>
-<li><strong>keep_highest</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Whether keep the highest dim unchanged.If set to true, then the first dim in target_shape is ignored,and always fixed as input</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – The target shape</li>
+<li><strong>reverse</strong> (<em>boolean, optional, default=False</em>) – If true then the special values are inferred from right to left</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=()</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Target new shape. One and only one dim can be 0, in which case it will be inferred from the rest of dims</li>
+<li><strong>keep_highest</strong> (<em>boolean, optional, default=False</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Whether keep the highest dim unchanged.If set to true, then the first dim in target_shape is ignored,and always fixed as input</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8760,7 +9214,7 @@ At most one dimension of shape can be -1.</p>
<dd><p>Reverses the order of elements along given axis while preserving array shape.</p>
<p>Note: reverse and flip are equivalent. We use reverse in the following examples.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">]]</span>
<span class="n">reverse</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><sp [...]
@@ -8772,20 +9226,21 @@ At most one dimension of shape can be -1.</p>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L601</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data array</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The axis which to reverse elements.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>Shape(tuple), required</em>) – The axis which to reverse elements.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8803,24 +9258,25 @@ At most one dimension of shape can be -1.</p>
</ul>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">rint</span><span class="p">([</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">rint</span><span class="p">([</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L282</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8854,28 +9310,29 @@ Tieleman & Hinton, 2012.</p>
<span class="math">\(\eta\)</span> to be 0.001.</p>
<p>Defined in src/operator/optimizer_op.cc:L178</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Gradient</li>
<li><strong>n</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – n</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>gamma1</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.95</em>) – The decay rate of momentum estimates.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e-08</em>) – A small constant for numerical stability.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>clip_weights</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip weights to the range of [-clip_weights, clip_weights] If clip_weights <= 0, weight clipping is turned off. weights = max(min(weights, clip_weights), -clip_weights).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>gamma1</strong> (<em>float, optional, default=0.95</em>) – The decay rate of momentum estimates.</li>
+<li><strong>epsilon</strong> (<em>float, optional, default=1e-08</em>) – A small constant for numerical stability.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>clip_weights</strong> (<em>float, optional, default=-1</em>) – Clip weights to the range of [-clip_weights, clip_weights] If clip_weights <= 0, weight clipping is turned off. weights = max(min(weights, clip_weights), -clip_weights).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8901,8 +9358,8 @@ E[g]_t = \gamma_1 * E[g]_{t-1} + (1 - \gamma_1) * g_t\\
to be 0.9 and the learning rate <span class="math">\(\eta\)</span> to be 0.0001.</p>
<p>Defined in src/operator/optimizer_op.cc:L217</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight</li>
@@ -8910,22 +9367,23 @@ to be 0.9 and the learning rate <span class="math">\(\eta\)</span> to be 0.0001.
<li><strong>n</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – n</li>
<li><strong>g</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – g</li>
<li><strong>delta</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – delta</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>gamma1</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.95</em>) – Decay rate.</li>
-<li><strong>gamma2</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – Decay rate.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e-08</em>) – A small constant for numerical stability.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>clip_weights</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip weights to the range of [-clip_weights, clip_weights] If clip_weights <= 0, weight clipping is turned off. weights = max(min(weights, clip_weights), -clip_weights).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>gamma1</strong> (<em>float, optional, default=0.95</em>) – Decay rate.</li>
+<li><strong>gamma2</strong> (<em>float, optional, default=0.9</em>) – Decay rate.</li>
+<li><strong>epsilon</strong> (<em>float, optional, default=1e-08</em>) – A small constant for numerical stability.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>clip_weights</strong> (<em>float, optional, default=-1</em>) – Clip weights to the range of [-clip_weights, clip_weights] If clip_weights <= 0, weight clipping is turned off. weights = max(min(weights, clip_weights), -clip_weights).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8936,24 +9394,25 @@ to be 0.9 and the learning rate <span class="math">\(\eta\)</span> to be 0.0001.
<code class="descclassname">mxnet.ndarray.</code><code class="descname">round</code><span class="sig-paren">(</span><em>data=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.round" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise rounded value to the nearest integer of the input.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="nb">round</span><span class="p">([</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">round</span><span class="p">([</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span cla [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L266</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -8966,24 +9425,25 @@ to be 0.9 and the learning rate <span class="math">\(\eta\)</span> to be 0.0001.
<div class="math">
\[rsqrt(x) = 1/\sqrt{x}\]</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">rsqrt</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">16</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">rsqrt</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">16</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L383</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9004,33 +9464,34 @@ which is parameterized by the input value at index <em>i</em>. If the shape para
operator is not set, then one sample will be drawn per distribution and the output array
has the same shape as the input array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">lam</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">8.5</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>lam = [ 1.0, 8.5 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_exponential</span><span class="p">(</span><span class="n">lam</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.51837951</span><span class="p">,</span> <span class="mf">0.09994757</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_exponential(lam) = [ 0.51837951, 0.09994757]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_exponential</span><span class="p">(</span><span class="n">lam</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.51837951</span><span class="p">,</span> <span class="mf">0.19866663</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.09994757</span><span class="p">,</span> <span class="mf">0.50447971</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_exponential(lam, shape=(2)) = [[ 0.51837951, 0.19866663],
+ [ 0.09994757, 0.50447971]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L370</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lam</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Lambda (rate) parameters of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9051,35 +9512,36 @@ which is parameterized by the input values at index <em>i</em>. If the shape par
operator is not set, then one sample will be drawn per distribution and the output array
has the same shape as the input arrays.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">alpha</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.5</span> <span class="p">]</span>
-<span class="n">beta</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.7</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>alpha = [ 0.0, 2.5 ]
+beta = [ 1.0, 0.7 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_gamma</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">beta</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span> <span class="p">,</span> <span class="mf">2.25797319</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_gamma(alpha, beta) = [ 0. , 2.25797319]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_gamma</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">beta</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span> <span class="p">,</span> <span class="mf">0.</span> <span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.25797319</span><span class="p">,</span> <span class="mf">1.70734084</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_gamma(alpha, beta, shape=(2)) = [[ 0. , 0. ],
+ [ 2.25797319, 1.70734084]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L368</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>alpha</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Alpha (shape) parameters of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>beta</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Beta (scale) parameters of the distributions.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9101,35 +9563,36 @@ operator is not set, then one sample will be drawn per distribution and the outp
has the same shape as the input arrays.</p>
<p>Samples will always be returned as a floating point data type.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">mu</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.5</span> <span class="p">]</span>
-<span class="n">alpha</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.1</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>mu = [ 2.0, 2.5 ]
+alpha = [ 1.0, 0.1 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_generalized_negative_binomial(mu, alpha) = [ 0., 3.]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_generalized_negative_binomial(mu, alpha, shape=(2)) = [[ 0., 3.],
+ [ 3., 1.]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L379</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>mu</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Means of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>alpha</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Alpha (dispersion) parameters of the distributions.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9150,36 +9613,37 @@ gradient.</p>
<p>Note that the input distribution must be normalized, i.e. <em>data</em> must sum to
1 along its last axis.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">probs</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>probs = [[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_multinomial</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_multinomial(probs) = [3, 0]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_multinomial</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_multinomial(probs, shape=(2)) = [[4, 2],
+ [0, 0]]
-<span class="o">//</span> <span class="n">requests</span> <span class="n">log</span> <span class="n">likelihood</span>
-<span class="n">sample_multinomial</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">get_prob</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class=" [...]
+// requests log likelihood
+sample_multinomial(probs, get_prob=True) = [2, 1], [0.2, 0.3]
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Distribution probabilities. Must sum to one on the last axis.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>get_prob</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to also return the log probability of sampled result. This is usually used for differentiating through stochastic variables, e.g. in reinforcement learning.</li>
-<li><strong>dtype</strong> (<em>{'int32'}</em><em>,</em><em>optional</em><em>, </em><em>default='int32'</em>) – DType of the output in case this can’t be inferred. Only support int32 for now.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>get_prob</strong> (<em>boolean, optional, default=False</em>) – Whether to also return the log probability of sampled result. This is usually used for differentiating through stochastic variables, e.g. in reinforcement learning.</li>
+<li><strong>dtype</strong> (<em>{'int32'},optional, default='int32'</em>) – DType of the output in case this can’t be inferred. Only support int32 for now.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9201,35 +9665,36 @@ operator is not set, then one sample will be drawn per distribution and the outp
has the same shape as the input arrays.</p>
<p>Samples will always be returned as a floating point data type.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">k</span> <span class="o">=</span> <span class="p">[</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">49</span> <span class="p">]</span>
-<span class="n">p</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.4</span> <span class="p">,</span> <span class="mf">0.77</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>k = [ 20, 49 ]
+p = [ 0.4 , 0.77 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">16.</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_negative_binomial(k, p) = [ 15., 16.]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">50.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_negative_binomial(k, p, shape=(2)) = [[ 15., 50.],
+ [ 16., 12.]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L375</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>k</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Limits of unsuccessful experiments.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>p</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Failure probabilities in each experiment.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9250,35 +9715,36 @@ which is parameterized by the input values at index <em>i</em>. If the shape par
operator is not set, then one sample will be drawn per distribution and the output array
has the same shape as the input arrays.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">mu</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.5</span> <span class="p">]</span>
-<span class="n">sigma</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.7</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>mu = [ 0.0, 2.5 ]
+sigma = [ 1.0, 3.7 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.56410581</span><span class="p">,</span> <span class="mf">0.95934606</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_normal(mu, sigma) = [-0.56410581, 0.95934606]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span><span class="o">-</span><span class="mf">0.56410581</span><span class="p">,</span> <span class="mf">0.2928229</span> <span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.95934606</span><span class="p">,</span> <span class="mf">4.48287058</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_normal(mu, sigma, shape=(2)) = [[-0.56410581, 0.2928229 ],
+ [ 0.95934606, 4.48287058]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L365</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>mu</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Means of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>sigma</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Standard deviations of the distributions.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9300,33 +9766,34 @@ operator is not set, then one sample will be drawn per distribution and the outp
has the same shape as the input array.</p>
<p>Samples will always be returned as a floating point data type.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">lam</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">8.5</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>lam = [ 1.0, 8.5 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_poisson</span><span class="p">(</span><span class="n">lam</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">13.</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_poisson(lam) = [ 0., 13.]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_poisson</span><span class="p">(</span><span class="n">lam</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_poisson(lam, shape=(2)) = [[ 0., 4.],
+ [ 13., 8.]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L372</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lam</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Lambda (rate) parameters of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9347,35 +9814,36 @@ which is parameterized by the input values at index <em>i</em>. If the shape par
operator is not set, then one sample will be drawn per distribution and the output array
has the same shape as the input arrays.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">low</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.5</span> <span class="p">]</span>
-<span class="n">high</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.7</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>low = [ 0.0, 2.5 ]
+high = [ 1.0, 3.7 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_uniform</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.40451524</span><span class="p">,</span> <span class="mf">3.18687344</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_uniform(low, high) = [ 0.40451524, 3.18687344]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_uniform</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.40451524</span><span class="p">,</span> <span class="mf">0.18017688</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.18687344</span><span class="p">,</span> <span class="mf">3.68352246</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_uniform(low, high, shape=(2)) = [[ 0.40451524, 0.18017688],
+ [ 3.18687344, 3.68352246]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L363</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>low</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Lower bounds of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>high</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Upper bounds of the distributions.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9392,33 +9860,34 @@ like below:</p>
v_t = \gamma v_{t-1} - \alpha * \nabla J(W_{t-1})\\
W_t = W_{t-1} + v_t\end{split}\]</div>
<p>It updates the weights using:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">v</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">v</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">v</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">v</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
<span class="n">weight</span> <span class="o">+=</span> <span class="n">v</span>
</pre></div>
</div>
<p>Where the parameter <code class="docutils literal"><span class="pre">momentum</span></code> is the decay rate of momentum estimates at each epoch.</p>
<p>Defined in src/operator/optimizer_op.cc:L55</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Gradient</li>
<li><strong>mom</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Momentum</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The decay rate of momentum estimates at each epoch.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0</em>) – The decay rate of momentum estimates at each epoch.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9429,29 +9898,30 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">sgd_update</code><span class="sig-paren">(</span><em>weight=None</em>, <em>grad=None</em>, <em>lr=_Null</em>, <em>wd=_Null</em>, <em>rescale_grad=_Null</em>, <em>clip_gradient=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.sgd_update" title="Permalink to this definition">¶</a></dt>
<dd><p>Update function for Stochastic Gradient Descent (SDG) optimizer.</p>
<p>It updates the weights using:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
</pre></div>
</div>
<p>Defined in src/operator/optimizer_op.cc:L25</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Gradient</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9465,19 +9935,20 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
\[y = 1 / (1 + exp(-x))\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L36</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9488,24 +9959,25 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">sign</code><span class="sig-paren">(</span><em>data=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.sign" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise sign of the input.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">sign</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</ [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sign</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</s [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L251</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9520,19 +9992,20 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
\[sin([0, \pi/4, \pi/2]) = [0, 0.707, 1]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L448</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9546,19 +10019,20 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
\[sinh(x) = 0.5\times(exp(x) - exp(-x))\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L599</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9580,7 +10054,7 @@ and <code class="docutils literal"><span class="pre">end=(e_1,</span> <span clas
<p>The resulting array’s <em>k</em>-th dimension contains elements
from the <em>k</em>-th dimension of the input array with the open range <code class="docutils literal"><span class="pre">[b_k,</span> <span class="pre">e_k)</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">9.</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]]</span>
@@ -9590,21 +10064,22 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L257</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Source input</li>
-<li><strong>begin</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – starting indices for the slice operation, supports negative indices.</li>
-<li><strong>end</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – ending indices for the slice operation, supports negative indices.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>begin</strong> (<em>Shape(tuple), required</em>) – starting indices for the slice operation, supports negative indices.</li>
+<li><strong>end</strong> (<em>Shape(tuple), required</em>) – ending indices for the slice operation, supports negative indices.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9617,7 +10092,7 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
<p>Returns an array slice along a given <cite>axis</cite> starting from the <cite>begin</cite> index
to the <cite>end</cite> index.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">9.</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]]</span>
@@ -9635,22 +10110,23 @@ to the <cite>end</cite> index.</p>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L337</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Source input</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>required</em>) – Axis along which to be sliced, supports negative indexes.</li>
-<li><strong>begin</strong> (<em>int</em><em>, </em><em>required</em>) – The beginning index along the axis to be sliced, supports negative indexes.</li>
-<li><strong>end</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>required</em>) – The ending index along the axis to be sliced, supports negative indexes.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int, required</em>) – Axis along which to be sliced, supports negative indexes.</li>
+<li><strong>begin</strong> (<em>int, required</em>) – The beginning index along the axis to be sliced, supports negative indexes.</li>
+<li><strong>end</strong> (<em>int or None, required</em>) – The ending index along the axis to be sliced, supports negative indexes.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9668,25 +10144,26 @@ to the <cite>end</cite> index.</p>
\end{cases}\end{split}\]</div>
<p>where <span class="math">\(x\)</span> is an element of the tensor <em>lhs</em> and <span class="math">\(\sigma\)</span> is the scalar.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">smooth_l1</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">smooth_l1</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class= [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_scalar_op_extended.cc:L79</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – source input</li>
<li><strong>scalar</strong> (<em>float</em>) – scalar input</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9701,32 +10178,33 @@ to the <cite>end</cite> index.</p>
\[softmax(\mathbf{z})_j = \frac{e^{z_j}}{\sum_{k=1}^K e^{z_k}}\]</div>
<p>for <span class="math">\(j = 1, ..., K\)</span></p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1. 1. 1.]
+ [ 1. 1. 1.]]
-<span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.5</span> <span class="mf">0.5</span> <span class="mf">0.5</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.5</span> <span class="mf">0.5</span> <span class="mf">0.5</span><span class="p">]]</span>
+softmax(x,axis=0) = [[ 0.5 0.5 0.5]
+ [ 0.5 0.5 0.5]]
-<span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">]]</span>
+softmax(x,axis=1) = [[ 0.33333334, 0.33333334, 0.33333334],
+ [ 0.33333334, 0.33333334, 0.33333334]]
</pre></div>
</div>
<p>Defined in src/operator/nn/softmax.cc:L35</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – The axis along which to compute softmax.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int, optional, default='-1'</em>) – The axis along which to compute softmax.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9755,7 +10233,7 @@ to the <cite>end</cite> index.</p>
</li>
</ul>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
<span class="p">[</span><span class="mi">11</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
<span class="n">label</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
@@ -9768,20 +10246,21 @@ to the <cite>end</cite> index.</p>
</div>
<p>Defined in src/operator/loss_binary_op.cc:L40</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input label</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9792,42 +10271,43 @@ to the <cite>end</cite> index.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">sort</code><span class="sig-paren">(</span><em>data=None</em>, <em>axis=_Null</em>, <em>is_ascend=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.sort" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a sorted copy of an input array along the given axis.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1, 4],
+ [ 3, 1]]
-<span class="o">//</span> <span class="n">sorts</span> <span class="n">along</span> <span class="n">the</span> <span class="n">last</span> <span class="n">axis</span>
-<span class="n">sort</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]]</span>
+// sorts along the last axis
+sort(x) = [[ 1., 4.],
+ [ 1., 3.]]
-<span class="o">//</span> <span class="n">flattens</span> <span class="ow">and</span> <span class="n">then</span> <span class="n">sorts</span>
-<span class="n">sort</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]</span>
+// flattens and then sorts
+sort(x) = [ 1., 1., 3., 4.]
-<span class="o">//</span> <span class="n">sorts</span> <span class="n">along</span> <span class="n">the</span> <span class="n">first</span> <span class="n">axis</span>
-<span class="n">sort</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]]</span>
+// sorts along the first axis
+sort(x, axis=0) = [[ 1., 1.],
+ [ 3., 4.]]
-<span class="o">//</span> <span class="ow">in</span> <span class="n">a</span> <span class="n">descend</span> <span class="n">order</span>
-<span class="n">sort</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">is_ascend</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
+// in a descend order
+sort(x, is_ascend=0) = [[ 4., 1.],
+ [ 3., 1.]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/ordering_op.cc:L107</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Axis along which to choose sort the input tensor. If not given, the flattened array is used. Default is -1.</li>
-<li><strong>is_ascend</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to sort in ascending or descending order.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='-1'</em>) – Axis along which to choose sort the input tensor. If not given, the flattened array is used. Default is -1.</li>
+<li><strong>is_ascend</strong> (<em>boolean, optional, default=True</em>) – Whether to sort in ascending or descending order.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9844,36 +10324,36 @@ to the <cite>end</cite> index.</p>
<p><strong>Note</strong> that <cite>num_outputs</cite> should evenly divide the length of the axis
along which to split the array.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]]</span>
-<span class="n">x</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1.]
+ [ 2.]]
+ [[ 3.]
+ [ 4.]]
+ [[ 5.]
+ [ 6.]]]
+x.shape = (3, 2, 1)
-<span class="n">y</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="n">a</span> <span class="nb">list</span> <span class="n">of</span> <span class="mi">2</span> [...]
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]]]</span>
+y = split(x, axis=1, num_outputs=2) // a list of 2 arrays with shape (3, 1, 1)
+y = [[[ 1.]]
+ [[ 3.]]
+ [[ 5.]]]
- <span class="p">[[[</span> <span class="mf">2.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">4.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[[ 2.]]
+ [[ 4.]]
+ [[ 6.]]]
-<span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+y[0].shape = (3, 1, 1)
-<span class="n">z</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">//</span> <span class="n">a</span> <span class="nb">list</span> <span class="n">of</span> <span class="mi">3</span> [...]
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]]</span>
+z = split(x, axis=0, num_outputs=3) // a list of 3 arrays with shape (1, 2, 1)
+z = [[[ 1.]
+ [ 2.]]]
- <span class="p">[[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]]</span>
+ [[[ 3.]
+ [ 4.]]]
- <span class="p">[[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[[ 5.]
+ [ 6.]]]
-<span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+z[0].shape = (1, 2, 1)
</pre></div>
</div>
<p><cite>squeeze_axis=1</cite> removes the axis with length 1 from the shapes of the output arrays.
@@ -9881,36 +10361,37 @@ along which to split the array.</p>
along the <cite>axis</cite> which it is split.
Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils literal"><span class="pre">input.shape[axis]</span> <span class="pre">==</span> <span class="pre">num_outputs</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">squeeze_axis</span><span class="o">=</span>< [...]
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>z = split(x, axis=0, num_outputs=3, squeeze_axis=1) // a list of 3 arrays with shape (2, 1)
+z = [[ 1.]
+ [ 2.]]
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
+ [[ 3.]
+ [ 4.]]
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]</span>
-<span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="p">,</span><span class="mi">1</span> <span class="p">)</span>
+ [[ 5.]
+ [ 6.]]
+z[0].shape = (2 ,1 )
</pre></div>
</div>
<p>Defined in src/operator/slice_channel.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>num_outputs</strong> (<em>int</em><em>, </em><em>required</em>) – Number of splits. Note that this should evenly divide the length of the <cite>axis</cite>.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Axis along which to split.</li>
-<li><strong>squeeze_axis</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true, Removes the axis with length 1 from the shapes of the output arrays. <strong>Note</strong> that setting <cite>squeeze_axis</cite> to <code class="docutils literal"><span class="pre">true</span></code> removes axis with length 1 only along the <cite>axis</cite> which it is split. Also <cite>squeeze_axis</cite> can be set to <code class="docutils literal"><span class [...]
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>num_outputs</strong> (<em>int, required</em>) – Number of splits. Note that this should evenly divide the length of the <cite>axis</cite>.</li>
+<li><strong>axis</strong> (<em>int, optional, default='1'</em>) – Axis along which to split.</li>
+<li><strong>squeeze_axis</strong> (<em>boolean, optional, default=False</em>) – If true, Removes the axis with length 1 from the shapes of the output arrays. <strong>Note</strong> that setting <cite>squeeze_axis</cite> to <code class="docutils literal"><span class="pre">true</span></code> removes axis with length 1 only along the <cite>axis</cite> which it is split. Also <cite>squeeze_axis</cite> can be set to <code class="docutils literal"><span class="pre">true</span></code> only if <c [...]
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9923,24 +10404,25 @@ Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils
<div class="math">
\[\textrm{sqrt}(x) = \sqrt{x}\]</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">sqrt</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sqrt</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L365</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9953,24 +10435,25 @@ Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils
<div class="math">
\[square(x) = x^2\]</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">square</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">square</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L347</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -9984,7 +10467,7 @@ Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils
result. For example, if axis=0 it will be the first dimension and if axis=-1 it
will be the last dimension.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
<span class="n">stack</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
@@ -9994,20 +10477,21 @@ will be the last dimension.</p>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>[</em><em></em><em>]</em><em></em>) – List of arrays to stack</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The axis in the result array along which the input arrays are stacked.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>data</strong> (<em>NDArray[]</em>) – List of arrays to stack</li>
+<li><strong>axis</strong> (<em>int, optional, default='0'</em>) – The axis in the result array along which the input arrays are stacked.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10021,39 +10505,40 @@ will be the last dimension.</p>
in the backward direction. In other words, this operator prevents the contribution
of its inputs to be taken into account for computing gradients.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">v1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-<span class="n">v2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
-<span class="n">b</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
-<span class="n">b_stop_grad</span> <span class="o">=</span> <span class="n">stop_gradient</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">b</span><span class="p">)</span>
-<span class="n">loss</span> <span class="o">=</span> <span class="n">MakeLoss</span><span class="p">(</span><span class="n">b_stop_grad</span> <span class="o">+</span> <span class="n">a</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>v1 = [1, 2]
+v2 = [0, 1]
+a = Variable('a')
+b = Variable('b')
+b_stop_grad = stop_gradient(3 * b)
+loss = MakeLoss(b_stop_grad + a)
-<span class="n">executor</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">simple_bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">a</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">b</span><span class="o">=</span><sp [...]
-<span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="n">v1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">v2</span><span class="p">)</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">outputs</span>
-<span class="p">[</span> <span class="mf">1.</span> <span class="mf">5.</span><span class="p">]</span>
+executor = loss.simple_bind(ctx=cpu(), a=(1,2), b=(1,2))
+executor.forward(is_train=True, a=v1, b=v2)
+executor.outputs
+[ 1. 5.]
-<span class="n">executor</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">grad_arrays</span>
-<span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
+executor.backward()
+executor.grad_arrays
+[ 0. 0.]
+[ 1. 1.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L99</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10068,47 +10553,47 @@ of its inputs to be taken into account for computing gradients.</p>
<p class="last"><cite>sum</cite> and <cite>sum_axis</cite> are equivalent.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">7</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">3</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>data = [[[1,2],[2,3],[1,3]],
+ [[1,4],[4,3],[5,2]],
+ [[7,1],[7,2],[7,3]]]
-<span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="p">[[</span> <span class="mf">4.</span> <span class="mf">8.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">21.</span> <span class="mf">6.</span><span class="p">]]</span>
+sum(data, axis=1)
+[[ 4. 8.]
+ [ 10. 9.]
+ [ 21. 6.]]
-<span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
-<span class="p">[</span> <span class="mf">12.</span> <span class="mf">19.</span> <span class="mf">27.</span><span class="p">]</span>
+sum(data, axis=[1,2])
+[ 12. 19. 27.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10123,47 +10608,47 @@ NOT in axis instead.</p>
<p class="last"><cite>sum</cite> and <cite>sum_axis</cite> are equivalent.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">7</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">3</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>data = [[[1,2],[2,3],[1,3]],
+ [[1,4],[4,3],[5,2]],
+ [[7,1],[7,2],[7,3]]]
-<span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="p">[[</span> <span class="mf">4.</span> <span class="mf">8.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">21.</span> <span class="mf">6.</span><span class="p">]]</span>
+sum(data, axis=1)
+[[ 4. 8.]
+ [ 10. 9.]
+ [ 21. 6.]]
-<span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
-<span class="p">[</span> <span class="mf">12.</span> <span class="mf">19.</span> <span class="mf">27.</span><span class="p">]</span>
+sum(data, axis=[1,2])
+[ 12. 19. 27.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10174,39 +10659,40 @@ NOT in axis instead.</p>
<code class="descclassname">mxnet.ndarray.</code><code class="descname">swapaxes</code><span class="sig-paren">(</span><em>data=None</em>, <em>dim1=_Null</em>, <em>dim2=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.swapaxes" title="Permalink to this definition">¶</a></dt>
<dd><p>Interchanges two axes of an array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
- <span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> x = [[1, 2, 3]])
+ swapaxes(x, 0, 1) = [[ 1],
+ [ 2],
+ [ 3]]
- <span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]]</span> <span class="o">//</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="n">array</span>
+ x = [[[ 0, 1],
+ [ 2, 3]],
+ [[ 4, 5],
+ [ 6, 7]]] // (2,2,2) array
-<span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]]</span>
+swapaxes(x, 0, 2) = [[[ 0, 4],
+ [ 2, 6]],
+ [[ 1, 5],
+ [ 3, 7]]]
</pre></div>
</div>
<p>Defined in src/operator/swapaxis.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input array.</li>
-<li><strong>dim1</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the first axis to be swapped.</li>
-<li><strong>dim2</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the second axis to be swapped.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>dim1</strong> (<em>int (non-negative), optional, default=0</em>) – the first axis to be swapped.</li>
+<li><strong>dim2</strong> (<em>int (non-negative), optional, default=0</em>) – the second axis to be swapped.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10219,7 +10705,7 @@ NOT in axis instead.</p>
<p>This function slices the input array along a particular axis with the provided indices.</p>
<p>Given an input array with shape <code class="docutils literal"><span class="pre">(d0,</span> <span class="pre">d1,</span> <span class="pre">d2)</span></code> and indices with shape <code class="docutils literal"><span class="pre">(i0,</span> <span class="pre">i1)</span></code>, the output
will have shape <code class="docutils literal"><span class="pre">(i0,</span> <span class="pre">i1,</span> <span class="pre">d1,</span> <span class="pre">d2)</span></code>, computed by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">],:,:]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">],:,:]</span>
</pre></div>
</div>
<div class="admonition note">
@@ -10230,36 +10716,37 @@ will have shape <code class="docutils literal"><span class="pre">(i0,</span> <sp
</ul>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1., 2.],
+ [ 3., 4.],
+ [ 5., 6.]]
-<span class="o">//</span> <span class="n">takes</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">take</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]])</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]],</span>
+// takes elements with specified indices along axis 0
+take(x, [[0,1],[1,2]]) = [[[ 1., 2.],
+ [ 3., 4.]],
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[ 3., 4.],
+ [ 5., 6.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/indexing_op.cc:L117</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>a</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
<li><strong>indices</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The indices of the values to be extracted.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The axis of input array to be taken.</li>
-<li><strong>mode</strong> (<em>{'clip'</em><em>, </em><em>'raise'</em><em>, </em><em>'wrap'}</em><em>,</em><em>optional</em><em>, </em><em>default='clip'</em>) – Specify how out-of-bound indices bahave. “clip” means clip to the range. So, if all indices mentioned are too large, they are replaced by the index that addresses the last element along an axis. “wrap” means to wrap around. “raise” means to raise an error.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axis</strong> (<em>int, optional, default='0'</em>) – The axis of input array to be taken.</li>
+<li><strong>mode</strong> (<em>{'clip', 'raise', 'wrap'},optional, default='clip'</em>) – Specify how out-of-bound indices bahave. “clip” means clip to the range. So, if all indices mentioned are too large, they are replaced by the index that addresses the last element along an axis. “wrap” means to wrap around. “raise” means to raise an error.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10274,19 +10761,20 @@ will have shape <code class="docutils literal"><span class="pre">(i0,</span> <sp
\[tan([0, \pi/4, \pi/2]) = [0, 1, -inf]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L507</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10300,19 +10788,20 @@ will have shape <code class="docutils literal"><span class="pre">(i0,</span> <sp
\[tanh(x) = sinh(x) / cosh(x)\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L627</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10326,7 +10815,7 @@ will have shape <code class="docutils literal"><span class="pre">(i0,</span> <sp
there cases:</p>
<ul>
<li><p class="first"><strong>n=d</strong>. Repeat <em>i</em>-th dimension of the input by <code class="docutils literal"><span class="pre">reps[i]</span></code> times:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
<span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span clas [...]
@@ -10338,14 +10827,14 @@ there cases:</p>
</li>
<li><p class="first"><strong>n>d</strong>. <code class="docutils literal"><span class="pre">reps</span></code> is promoted to length <em>n</em> by pre-pending 1’s to it. Thus for
an input shape <code class="docutils literal"><span class="pre">(2,3)</span></code>, <code class="docutils literal"><span class="pre">repos=(2,)</span></code> is treated as <code class="docutils literal"><span class="pre">(1,2)</span></code>:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class= [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class=" [...]
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]]</span>
</pre></div>
</div>
</li>
-<li><p class="first"><strong>n<d< strong="">. The input is promoted to be d-dimensional by prepending new axes. So a
-shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array is promoted to <code class="docutils literal"><span class="pre">(1,2,2)</span></code> for 3-D replication:</d<></strong></p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,< [...]
+<li><p class="first"><strong>n<d</strong>. The input is promoted to be d-dimensional by prepending new axes. So a
+shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array is promoted to <code class="docutils literal"><span class="pre">(1,2,2)</span></code> for 3-D replication:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</ [...]
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]],</span>
@@ -10360,20 +10849,21 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
</ul>
<p>Defined in src/operator/tensor/matrix_op.cc:L560</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data array</li>
-<li><strong>reps</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The number of times for repeating the tensor a. If reps has length d, the result will have dimension of max(d, a.ndim); If a.ndim < d, a is promoted to be d-dimensional by prepending new axes. If a.ndim > d, reps is promoted to a.ndim by pre-pending 1’s to it.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>reps</strong> (<em>Shape(tuple), required</em>) – The number of times for repeating the tensor a. If reps has length d, the result will have dimension of max(d, a.ndim); If a.ndim < d, a is promoted to be d-dimensional by prepending new axes. If a.ndim > d, reps is promoted to a.ndim by pre-pending 1’s to it.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10384,49 +10874,50 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
<code class="descclassname">mxnet.ndarray.</code><code class="descname">topk</code><span class="sig-paren">(</span><em>data=None</em>, <em>axis=_Null</em>, <em>k=_Null</em>, <em>ret_typ=_Null</em>, <em>is_ascend=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.topk" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the top <em>k</em> elements in an input array along the given axis.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 0.3, 0.2, 0.4],
+ [ 0.1, 0.3, 0.2]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">an</span> <span class="n">index</span> <span class="n">of</span> <span class="n">the</span> <span class="n">largest</span> <span class="n">element</span> <span class="n">on</span> <span class="n">last</span> <span class="n">axis</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">1.</span><span class="p">]]</span>
+// returns an index of the largest element on last axis
+topk(x) = [[ 2.],
+ [ 1.]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">the</span> <span class="n">value</span> <span class="n">of</span> <span class="n">top</span><span class="o">-</span><span class="mi">2</span> <span class="n">largest</span> <span class="n">elements</span> <span class="n">on</span> <span class="n">last</span> <span class="n">axis</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">ret_typ</span><span class="o">=</span><span class="s1">'value'</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+// returns the value of top-2 largest elements on last axis
+topk(x, ret_typ='value', k=2) = [[ 0.4, 0.3],
+ [ 0.3, 0.2]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">the</span> <span class="n">value</span> <span class="n">of</span> <span class="n">top</span><span class="o">-</span><span class="mi">2</span> <span class="n">smallest</span> <span class="n">elements</span> <span class="n">on</span> <span class="n">last</span> <span class="n">axis</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">ret_typ</span><span class="o">=</span><span class="s1">'value'</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">is_ascend</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.2</span> [...]
- <span class="p">[</span> <span class="mf">0.1</span> <span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+// returns the value of top-2 smallest elements on last axis
+topk(x, ret_typ='value', k=2, is_ascend=1) = [[ 0.2 , 0.3],
+ [ 0.1 , 0.2]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">the</span> <span class="n">value</span> <span class="n">of</span> <span class="n">top</span><span class="o">-</span><span class="mi">2</span> <span class="n">largest</span> <span class="n">elements</span> <span class="n">on</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ret_typ</span><span class="o">=</span><span class="s1">'value'</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.3</span><span [...]
- <span class="p">[</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+// returns the value of top-2 largest elements on axis 0
+topk(x, axis=0, ret_typ='value', k=2) = [[ 0.3, 0.3, 0.4],
+ [ 0.1, 0.2, 0.2]]
-<span class="o">//</span> <span class="n">flattens</span> <span class="ow">and</span> <span class="n">then</span> <span class="n">returns</span> <span class="nb">list</span> <span class="n">of</span> <span class="n">both</span> <span class="n">values</span> <span class="ow">and</span> <span class="n">indices</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">ret_typ</span><span class="o">=</span><span class="s1">'both'</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="p">[</span> <s [...]
+// flattens and then returns list of both values and indices
+topk(x, ret_typ='both', k=2) = [[[ 0.4, 0.3], [ 0.3, 0.2]] , [[ 2., 0.], [ 1., 2.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/ordering_op.cc:L44</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Axis along which to choose the top k indices. If not given, the flattened array is used. Default is -1.</li>
-<li><strong>k</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Number of top elements to select, should be always smaller than or equal to the element number in the given axis. A global sort is performed if set k < 1.</li>
-<li><strong>ret_typ</strong> (<em>{'both'</em><em>, </em><em>'indices'</em><em>, </em><em>'mask'</em><em>, </em><em>'value'}</em><em>,</em><em>optional</em><em>, </em><em>default='indices'</em>) – The return type.
+<li><strong>axis</strong> (<em>int or None, optional, default='-1'</em>) – Axis along which to choose the top k indices. If not given, the flattened array is used. Default is -1.</li>
+<li><strong>k</strong> (<em>int, optional, default='1'</em>) – Number of top elements to select, should be always smaller than or equal to the element number in the given axis. A global sort is performed if set k < 1.</li>
+<li><strong>ret_typ</strong> (<em>{'both', 'indices', 'mask', 'value'},optional, default='indices'</em>) – The return type.
“value” means to return the top k values, “indices” means to return the indices of the top k values, “mask” means to return a mask array containing 0 and 1. 1 means the top k values. “both” means to return a list of both values and indices of top k elements.</li>
-<li><strong>is_ascend</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to choose k largest or k smallest elements. Top K largest elements will be chosen if set to false.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>is_ascend</strong> (<em>boolean, optional, default=False</em>) – Whether to choose k largest or k smallest elements. Top K largest elements will be chosen if set to false.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10437,7 +10928,7 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
<code class="descclassname">mxnet.ndarray.</code><code class="descname">transpose</code><span class="sig-paren">(</span><em>data=None</em>, <em>axes=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.transpose" title="Permalink to this definition">¶</a></dt>
<dd><p>Permutes the dimensions of an array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
<span class="n">transpose</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
@@ -10464,20 +10955,21 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L177</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Source input</li>
-<li><strong>axes</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Target axis order. By default the axes will be inverted.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>axes</strong> (<em>Shape(tuple), optional, default=()</em>) – Target axis order. By default the axes will be inverted.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10490,24 +10982,25 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
<p>The truncated value of the scalar x is the nearest integer i which is closer to
zero than x is. In short, the fractional part of the signed number x is discarded.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">trunc</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">trunc</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span clas [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L322</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input array.</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10524,29 +11017,30 @@ zero than x is. In short, the fractional part of the signed number x is discarde
<p>Samples are uniformly distributed over the half-open interval <em>[low, high)</em>
(includes <em>low</em>, but excludes <em>high</em>).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cla [...]
<span class="p">[</span> <span class="mf">0.54488319</span><span class="p">,</span> <span class="mf">0.84725171</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L45</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>low</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Lower bound of the distribution.</li>
-<li><strong>high</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Upper bound of the distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>low</strong> (<em>float, optional, default=0</em>) – Lower bound of the distribution.</li>
+<li><strong>high</strong> (<em>float, optional, default=1</em>) – Upper bound of the distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10558,21 +11052,22 @@ zero than x is. In short, the fractional part of the signed number x is discarde
<dd><p>Given three ndarrays, condition, x, and y, return an ndarray with the elements from x or y, depending on the elements from condition are true or false. x and y must have the same shape. If condition has the same shape as x, each element in the output array is from x if the corresponding element in the condition is true, and from y if false. If condition does not have the same shape as x, it must be a 1D array whose size is the same as x’s first dimension size. Each row of the outp [...]
<p>From:src/operator/tensor/control_flow_op.cc:21</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>condition</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – condition array</li>
<li><strong>x</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – </li>
<li><strong>y</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – </li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10584,7 +11079,7 @@ zero than x is. In short, the fractional part of the signed number x is discarde
<dd><p>Return an array of zeros with the same shape and type
as the input array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">zeros_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
@@ -10592,19 +11087,20 @@ as the input array.</p>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</li>
-<li><strong>out</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The output of this function.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
</td>
</tr>
</tbody>
@@ -10618,8 +11114,8 @@ as the input array.</p>
<p>This affects the behavior of modules in MXNet that uses random number generators,
like the dropout operator and <cite>NDArray</cite>‘s random sampling operators.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>seed_state</strong> (<em>int</em>) – The random number seed to set to all devices.</td>
</tr>
@@ -10629,24 +11125,612 @@ like the dropout operator and <cite>NDArray</cite>‘s random sampling operators
<p>Random number generators in MXNet are device specific. Therefore, random numbers
generated from two devices can be different even if they are seeded using the same seed.</p>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span cl [...]
<span class="go">[[ 1.36481571 -0.62203991]</span>
<span class="go"> [-1.4962182 -0.08511394]]</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="go">[[ 1.09544981 -0.20014545]</span>
<span class="go"> [-0.20808885 0.2527658 ]]</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">128</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="go">[[ 0.47400656 -0.75213492]</span>
<span class="go"> [ 0.20251541 0.95352972]]</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">128</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="go">[[ 0.47400656 -0.75213492]</span>
<span class="go"> [ 0.20251541 0.95352972]]</span>
</pre></div>
</div>
</dd></dl>
+<span class="target" id="module-mxnet.contrib.ndarray"></span><p>NDArray namespace used to register contrib functions</p>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.CTCLoss">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">CTCLoss</code><span class="sig-paren">(</span><em>data=None</em>, <em>label=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.ndarray.CTCLoss" title="Permalink to this definition">¶</a></dt>
+<dd><p>Connectionist Temporal Classification Loss.</p>
+<p>The shapes of the inputs and outputs:</p>
+<ul class="simple">
+<li><strong>data</strong>: <em>(sequence_length, batch_size, alphabet_size + 1)</em></li>
+<li><strong>label</strong>: <em>(batch_size, label_sequence_length)</em></li>
+<li><strong>out</strong>: <em>(batch_size)</em>.</li>
+</ul>
+<p><code class="docutils literal"><span class="pre">label</span></code> is a tensor of integers between 1 and <em>alphabet_size</em>. If a
+sequence of labels is shorter than <em>label_sequence_length</em>, use the special
+padding character 0 at the end of the sequence to conform it to the correct
+length. For example, if <em>label_sequence_length</em> = 4, and one has two sequences
+of labels [2, 1] and [3, 2, 2], the resulting <code class="docutils literal"><span class="pre">`label`</span></code> tensor should be
+padded to be:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</s [...]
+</pre></div>
+</div>
+<p>The <code class="docutils literal"><span class="pre">data</span></code> tensor consists of sequences of activation vectors. The layer
+applies a softmax to each vector, which then becomes a vector of probabilities
+over the alphabet. Note that the 0th element of this vector is reserved for the
+special blank character.</p>
+<p><code class="docutils literal"><span class="pre">out</span></code> is a list of CTC loss values, one per example in the batch.</p>
+<p>See <em>Connectionist Temporal Classification: Labelling Unsegmented
+Sequence Data with Recurrent Neural Networks</em>, A. Graves <em>et al</em>. for more
+information.</p>
+<p>Defined in src/operator/contrib/ctc_loss.cc:L81</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the ctc_loss op.</li>
+<li><strong>label</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Ground-truth labels for the loss.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.DeformableConvolution">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">DeformableConvolution</code><span class="sig-paren">(</span><em>data=None</em>, <em>offset=None</em>, <em>weight=None</em>, <em>bias=None</em>, <em>kernel=_Null</em>, <em>stride=_Null</em>, <em>dilate=_Null</em>, <em>pad=_Null</em>, <em>num_filter=_Null</em>, <em>num_group=_Null</em>, <em>num_deformable_group=_Null</em>, <em>workspace=_Null</em>, <em>no_bias=_Null</em>, <em>layout=_Null</em>, <em>out=None</em [...]
+<dd><p>Compute 2-D deformable convolution on 4-D input.</p>
+<p>The deformable convolution operation is described in <a class="reference external" href="https://arxiv.org/abs/1703.06211">https://arxiv.org/abs/1703.06211</a></p>
+<p>For 2-D deformable convolution, the shapes are</p>
+<ul class="simple">
+<li><strong>data</strong>: <em>(batch_size, channel, height, width)</em></li>
+<li><strong>offset</strong>: <em>(batch_size, num_deformable_group * kernel[0] * kernel[1], height, width)</em></li>
+<li><strong>weight</strong>: <em>(num_filter, channel, kernel[0], kernel[1])</em></li>
+<li><strong>bias</strong>: <em>(num_filter,)</em></li>
+<li><strong>out</strong>: <em>(batch_size, num_filter, out_height, out_width)</em>.</li>
+</ul>
+<p>Define:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="n">p</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">d</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span [...]
+</pre></div>
+</div>
+<p>then we have:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><spa [...]
+<span class="n">out_width</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dilate</sp [...]
+</pre></div>
+</div>
+<p>If <code class="docutils literal"><span class="pre">no_bias</span></code> is set to be true, then the <code class="docutils literal"><span class="pre">bias</span></code> term is ignored.</p>
+<p>The default data <code class="docutils literal"><span class="pre">layout</span></code> is <em>NCHW</em>, namely <em>(batch_size, channle, height,
+width)</em>.</p>
+<p>If <code class="docutils literal"><span class="pre">num_group</span></code> is larger than 1, denoted by <em>g</em>, then split the input <code class="docutils literal"><span class="pre">data</span></code>
+evenly into <em>g</em> parts along the channel axis, and also evenly split <code class="docutils literal"><span class="pre">weight</span></code>
+along the first dimension. Next compute the convolution on the <em>i</em>-th part of
+the data with the <em>i</em>-th weight part. The output is obtained by concating all
+the <em>g</em> results.</p>
+<p>If <code class="docutils literal"><span class="pre">num_deformable_group</span></code> is larger than 1, denoted by <em>dg</em>, then split the
+input <code class="docutils literal"><span class="pre">offset</span></code> evenly into <em>dg</em> parts along the channel axis, and also evenly
+split <code class="docutils literal"><span class="pre">out</span></code> evenly into <em>dg</em> parts along the channel axis. Next compute the
+deformable convolution, apply the <em>i</em>-th part of the offset part on the <em>i</em>-th
+out.</p>
+<p>Both <code class="docutils literal"><span class="pre">weight</span></code> and <code class="docutils literal"><span class="pre">bias</span></code> are learnable parameters.</p>
+<p>Defined in src/operator/contrib/deformable_convolution.cc:L81</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the DeformableConvolutionOp.</li>
+<li><strong>offset</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input offset to the DeformableConvolutionOp.</li>
+<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Weight matrix.</li>
+<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Bias parameter.</li>
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution stride: (h, w) or (d, h, w)</li>
+<li><strong>dilate</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution dilate: (h, w) or (d, h, w)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for convolution: (h, w) or (d, h, w)</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), required</em>) – convolution filter(channel) number</li>
+<li><strong>num_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of group partitions.</li>
+<li><strong>num_deformable_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of deformable group partitions.</li>
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=1024</em>) – Maximum temperal workspace allowed for convolution (MB).</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=False</em>) – Whether to disable bias parameter.</li>
+<li><strong>layout</strong> (<em>{None, 'NCDHW', 'NCHW', 'NCW'},optional, default='None'</em>) – Set layout for input, output and weight. Empty for
+default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.DeformablePSROIPooling">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">DeformablePSROIPooling</code><span class="sig-paren">(</span><em>data=None</em>, <em>rois=None</em>, <em>trans=None</em>, <em>spatial_scale=_Null</em>, <em>output_dim=_Null</em>, <em>group_size=_Null</em>, <em>pooled_size=_Null</em>, <em>part_size=_Null</em>, <em>sample_per_part=_Null</em>, <em>trans_std=_Null</em>, <em>no_trans=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class=" [...]
+<dd><p>Performs deformable position-sensitive region-of-interest pooling on inputs.The DeformablePSROIPooling operation is described in <a class="reference external" href="https://arxiv.org/abs/1703.06211.batch_size">https://arxiv.org/abs/1703.06211.batch_size</a> will change to the number of region bounding boxes after DeformablePSROIPooling</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator, a 4D Feature maps</li>
+<li><strong>rois</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data</li>
+<li><strong>trans</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – transition parameter</li>
+<li><strong>spatial_scale</strong> (<em>float, required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</li>
+<li><strong>output_dim</strong> (<em>int, required</em>) – fix output dim</li>
+<li><strong>group_size</strong> (<em>int, required</em>) – fix group size</li>
+<li><strong>pooled_size</strong> (<em>int, required</em>) – fix pooled size</li>
+<li><strong>part_size</strong> (<em>int, optional, default='0'</em>) – fix part size</li>
+<li><strong>sample_per_part</strong> (<em>int, optional, default='1'</em>) – fix samples per part</li>
+<li><strong>trans_std</strong> (<em>float, optional, default=0</em>) – fix transition std</li>
+<li><strong>no_trans</strong> (<em>boolean, optional, default=False</em>) – Whether to disable trans parameter.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.MultiBoxDetection">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">MultiBoxDetection</code><span class="sig-paren">(</span><em>cls_prob=None</em>, <em>loc_pred=None</em>, <em>anchor=None</em>, <em>clip=_Null</em>, <em>threshold=_Null</em>, <em>background_id=_Null</em>, <em>nms_threshold=_Null</em>, <em>force_suppress=_Null</em>, <em>variances=_Null</em>, <em>nms_topk=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class= [...]
+<dd><p>Convert multibox detection predictions.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>cls_prob</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Class probabilities.</li>
+<li><strong>loc_pred</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Location regression predictions.</li>
+<li><strong>anchor</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Multibox prior anchor boxes</li>
+<li><strong>clip</strong> (<em>boolean, optional, default=True</em>) – Clip out-of-boundary boxes.</li>
+<li><strong>threshold</strong> (<em>float, optional, default=0.01</em>) – Threshold to be a positive prediction.</li>
+<li><strong>background_id</strong> (<em>int, optional, default='0'</em>) – Background id.</li>
+<li><strong>nms_threshold</strong> (<em>float, optional, default=0.5</em>) – Non-maximum suppression threshold.</li>
+<li><strong>force_suppress</strong> (<em>boolean, optional, default=False</em>) – Suppress all detections regardless of class_id.</li>
+<li><strong>variances</strong> (<em>, optional, default=(0.1,0.1,0.2,0.2)</em>) – Variances to be decoded from box regression output.</li>
+<li><strong>nms_topk</strong> (<em>int, optional, default='-1'</em>) – Keep maximum top k detections before nms, -1 for no limit.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.MultiBoxPrior">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">MultiBoxPrior</code><span class="sig-paren">(</span><em>data=None</em>, <em>sizes=_Null</em>, <em>ratios=_Null</em>, <em>clip=_Null</em>, <em>steps=_Null</em>, <em>offsets=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.ndarray.MultiBoxPrior" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate prior(anchor) boxes from data, sizes and ratios.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data.</li>
+<li><strong>sizes</strong> (<em>, optional, default=(1,)</em>) – List of sizes of generated MultiBoxPriores.</li>
+<li><strong>ratios</strong> (<em>, optional, default=(1,)</em>) – List of aspect ratios of generated MultiBoxPriores.</li>
+<li><strong>clip</strong> (<em>boolean, optional, default=False</em>) – Whether to clip out-of-boundary boxes.</li>
+<li><strong>steps</strong> (<em>, optional, default=(-1,-1)</em>) – Priorbox step across y and x, -1 for auto calculation.</li>
+<li><strong>offsets</strong> (<em>, optional, default=(0.5,0.5)</em>) – Priorbox center offsets, y and x respectively</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.MultiBoxTarget">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">MultiBoxTarget</code><span class="sig-paren">(</span><em>anchor=None</em>, <em>label=None</em>, <em>cls_pred=None</em>, <em>overlap_threshold=_Null</em>, <em>ignore_label=_Null</em>, <em>negative_mining_ratio=_Null</em>, <em>negative_mining_thresh=_Null</em>, <em>minimum_negative_samples=_Null</em>, <em>variances=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</sp [...]
+<dd><p>Compute Multibox training targets</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>anchor</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Generated anchor boxes.</li>
+<li><strong>label</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Object detection labels.</li>
+<li><strong>cls_pred</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Class predictions.</li>
+<li><strong>overlap_threshold</strong> (<em>float, optional, default=0.5</em>) – Anchor-GT overlap threshold to be regarded as a possitive match.</li>
+<li><strong>ignore_label</strong> (<em>float, optional, default=-1</em>) – Label for ignored anchors.</li>
+<li><strong>negative_mining_ratio</strong> (<em>float, optional, default=-1</em>) – Max negative to positive samples ratio, use -1 to disable mining</li>
+<li><strong>negative_mining_thresh</strong> (<em>float, optional, default=0.5</em>) – Threshold used for negative mining.</li>
+<li><strong>minimum_negative_samples</strong> (<em>int, optional, default='0'</em>) – Minimum number of negative samples.</li>
+<li><strong>variances</strong> (<em>, optional, default=(0.1,0.1,0.2,0.2)</em>) – Variances to be encoded in box regression target.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.MultiProposal">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">MultiProposal</code><span class="sig-paren">(</span><em>cls_score=None</em>, <em>bbox_pred=None</em>, <em>im_info=None</em>, <em>rpn_pre_nms_top_n=_Null</em>, <em>rpn_post_nms_top_n=_Null</em>, <em>threshold=_Null</em>, <em>rpn_min_size=_Null</em>, <em>scales=_Null</em>, <em>ratios=_Null</em>, <em>feature_stride=_Null</em>, <em>output_score=_Null</em>, <em>iou_loss=_Null</em>, <em>out=None</em>, <em>name=None [...]
+<dd><p>Generate region proposals via RPN</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>cls_score</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Score of how likely proposal is object.</li>
+<li><strong>bbox_pred</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – BBox Predicted deltas from anchors for proposals</li>
+<li><strong>im_info</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Image size and scale.</li>
+<li><strong>rpn_pre_nms_top_n</strong> (<em>int, optional, default='6000'</em>) – Number of top scoring boxes to keep after applying NMS to RPN proposals</li>
+<li><strong>rpn_post_nms_top_n</strong> (<em>int, optional, default='300'</em>) – Overlap threshold used for non-maximumsuppresion(suppress boxes with IoU >= this threshold</li>
+<li><strong>threshold</strong> (<em>float, optional, default=0.7</em>) – NMS value, below which to suppress.</li>
+<li><strong>rpn_min_size</strong> (<em>int, optional, default='16'</em>) – Minimum height or width in proposal</li>
+<li><strong>scales</strong> (<em>, optional, default=(4,8,16,32)</em>) – Used to generate anchor windows by enumerating scales</li>
+<li><strong>ratios</strong> (<em>, optional, default=(0.5,1,2)</em>) – Used to generate anchor windows by enumerating ratios</li>
+<li><strong>feature_stride</strong> (<em>int, optional, default='16'</em>) – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.</li>
+<li><strong>output_score</strong> (<em>boolean, optional, default=False</em>) – Add score to outputs</li>
+<li><strong>iou_loss</strong> (<em>boolean, optional, default=False</em>) – Usage of IoU Loss</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.PSROIPooling">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">PSROIPooling</code><span class="sig-paren">(</span><em>data=None</em>, <em>rois=None</em>, <em>spatial_scale=_Null</em>, <em>output_dim=_Null</em>, <em>pooled_size=_Null</em>, <em>group_size=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.ndarray.PSROIPooling" title="Permalink to this definition">¶</a></dt>
+<dd><p>Performs region-of-interest pooling on inputs. Resize bounding box coordinates by spatial_scale and crop input feature maps accordingly. The cropped feature maps are pooled by max pooling to a fixed size output indicated by pooled_size. batch_size will change to the number of region bounding boxes after PSROIPooling</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator, a 4D Feature maps</li>
+<li><strong>rois</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data</li>
+<li><strong>spatial_scale</strong> (<em>float, required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</li>
+<li><strong>output_dim</strong> (<em>int, required</em>) – fix output dim</li>
+<li><strong>pooled_size</strong> (<em>int, required</em>) – fix pooled size</li>
+<li><strong>group_size</strong> (<em>int, optional, default='0'</em>) – fix group size</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.Proposal">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">Proposal</code><span class="sig-paren">(</span><em>cls_score=None</em>, <em>bbox_pred=None</em>, <em>im_info=None</em>, <em>rpn_pre_nms_top_n=_Null</em>, <em>rpn_post_nms_top_n=_Null</em>, <em>threshold=_Null</em>, <em>rpn_min_size=_Null</em>, <em>scales=_Null</em>, <em>ratios=_Null</em>, <em>feature_stride=_Null</em>, <em>output_score=_Null</em>, <em>iou_loss=_Null</em>, <em>out=None</em>, <em>name=None</em> [...]
+<dd><p>Generate region proposals via RPN</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>cls_score</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Score of how likely proposal is object.</li>
+<li><strong>bbox_pred</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – BBox Predicted deltas from anchors for proposals</li>
+<li><strong>im_info</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Image size and scale.</li>
+<li><strong>rpn_pre_nms_top_n</strong> (<em>int, optional, default='6000'</em>) – Number of top scoring boxes to keep after applying NMS to RPN proposals</li>
+<li><strong>rpn_post_nms_top_n</strong> (<em>int, optional, default='300'</em>) – Overlap threshold used for non-maximumsuppresion(suppress boxes with IoU >= this threshold</li>
+<li><strong>threshold</strong> (<em>float, optional, default=0.7</em>) – NMS value, below which to suppress.</li>
+<li><strong>rpn_min_size</strong> (<em>int, optional, default='16'</em>) – Minimum height or width in proposal</li>
+<li><strong>scales</strong> (<em>, optional, default=(4,8,16,32)</em>) – Used to generate anchor windows by enumerating scales</li>
+<li><strong>ratios</strong> (<em>, optional, default=(0.5,1,2)</em>) – Used to generate anchor windows by enumerating ratios</li>
+<li><strong>feature_stride</strong> (<em>int, optional, default='16'</em>) – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.</li>
+<li><strong>output_score</strong> (<em>boolean, optional, default=False</em>) – Add score to outputs</li>
+<li><strong>iou_loss</strong> (<em>boolean, optional, default=False</em>) – Usage of IoU Loss</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.count_sketch">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">count_sketch</code><span class="sig-paren">(</span><em>data=None</em>, <em>h=None</em>, <em>s=None</em>, <em>out_dim=_Null</em>, <em>processing_batch_size=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.ndarray.count_sketch" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply CountSketch to input: map a d-dimension data to k-dimension data”</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last"><cite>count_sketch</cite> is only available on GPU.</p>
+</div>
+<p>Assume input data has shape (N, d), sign hash table s has shape (N, d),
+index hash table h has shape (N, d) and mapping dimension out_dim = k,
+each element in s is either +1 or -1, each element in h is random integer from 0 to k-1.
+Then the operator computs:</p>
+<div class="math">
+\[out[h[i]] += data[i] * s[i]\]</div>
+<dl class="docutils">
+<dt>Example::</dt>
+<dd><p class="first">out_dim = 5
+x = [[1.2, 2.5, 3.4],[3.2, 5.7, 6.6]]
+h = [0, 3, 4]
+s = [1, -1, 1]
+mx.contrib.ndarray.count_sketch(data=x, h=h, s=s, out_dim = 5) = [[1.2, 0, 0, -2.5, 3.4],</p>
+<blockquote class="last">
+<div>[3.2, 0, 0, -5.7, 6.6]]</div></blockquote>
+</dd>
+</dl>
+<p>Defined in src/operator/contrib/count_sketch.cc:L47</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the CountSketchOp.</li>
+<li><strong>h</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The index vector</li>
+<li><strong>s</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The sign vector</li>
+<li><strong>out_dim</strong> (<em>int, required</em>) – The output dimension.</li>
+<li><strong>processing_batch_size</strong> (<em>int, optional, default='32'</em>) – How many sketch vectors to process at one time.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.ctc_loss">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">ctc_loss</code><span class="sig-paren">(</span><em>data=None</em>, <em>label=None</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.ndarray.ctc_loss" title="Permalink to this definition">¶</a></dt>
+<dd><p>Connectionist Temporal Classification Loss.</p>
+<p>The shapes of the inputs and outputs:</p>
+<ul class="simple">
+<li><strong>data</strong>: <em>(sequence_length, batch_size, alphabet_size + 1)</em></li>
+<li><strong>label</strong>: <em>(batch_size, label_sequence_length)</em></li>
+<li><strong>out</strong>: <em>(batch_size)</em>.</li>
+</ul>
+<p><code class="docutils literal"><span class="pre">label</span></code> is a tensor of integers between 1 and <em>alphabet_size</em>. If a
+sequence of labels is shorter than <em>label_sequence_length</em>, use the special
+padding character 0 at the end of the sequence to conform it to the correct
+length. For example, if <em>label_sequence_length</em> = 4, and one has two sequences
+of labels [2, 1] and [3, 2, 2], the resulting <code class="docutils literal"><span class="pre">`label`</span></code> tensor should be
+padded to be:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</s [...]
+</pre></div>
+</div>
+<p>The <code class="docutils literal"><span class="pre">data</span></code> tensor consists of sequences of activation vectors. The layer
+applies a softmax to each vector, which then becomes a vector of probabilities
+over the alphabet. Note that the 0th element of this vector is reserved for the
+special blank character.</p>
+<p><code class="docutils literal"><span class="pre">out</span></code> is a list of CTC loss values, one per example in the batch.</p>
+<p>See <em>Connectionist Temporal Classification: Labelling Unsegmented
+Sequence Data with Recurrent Neural Networks</em>, A. Graves <em>et al</em>. for more
+information.</p>
+<p>Defined in src/operator/contrib/ctc_loss.cc:L81</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the ctc_loss op.</li>
+<li><strong>label</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Ground-truth labels for the loss.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.dequantize">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">dequantize</code><span class="sig-paren">(</span><em>input=None</em>, <em>min_range=None</em>, <em>max_range=None</em>, <em>out_type=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.ndarray.dequantize" title="Permalink to this definition">¶</a></dt>
+<dd><p>Dequantize the input tensor into a float tensor.
+[min_range, max_range] are scalar floats that spcify the range for
+the output data.</p>
+<p>Each value of the tensor will undergo the following:</p>
+<p><cite>out[i] = min_range + (in[i] * (max_range - min_range) / range(INPUT_TYPE))</cite></p>
+<p>here <cite>range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()</cite></p>
+<p>Defined in src/operator/contrib/dequantize.cc:L22</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – A ndarray/symbol of type <cite>uint8</cite></li>
+<li><strong>min_range</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The minimum scalar value possibly produced for the input</li>
+<li><strong>max_range</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The maximum scalar value possibly produced for the input</li>
+<li><strong>out_type</strong> (<em>{'float32'}, required</em>) – Output data type.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.fft">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">fft</code><span class="sig-paren">(</span><em>data=None</em>, <em>compute_size=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.ndarray.fft" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply 1D FFT to input”</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last"><cite>fft</cite> is only available on GPU.</p>
+</div>
+<p>Currently accept 2 input data shapes: (N, d) or (N1, N2, N3, d), data can only be real numbers.
+The output data has shape: (N, 2*d) or (N1, N2, N3, 2*d). The format is: [real0, imag0, real1, imag1, ...].</p>
+<dl class="docutils">
+<dt>Example::</dt>
+<dd>data = np.random.normal(0,1,(3,4))
+out = mx.contrib.ndarray.fft(data = mx.nd.array(data,ctx = mx.gpu(0)))</dd>
+</dl>
+<p>Defined in src/operator/contrib/fft.cc:L40</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the FFTOp.</li>
+<li><strong>compute_size</strong> (<em>int, optional, default='128'</em>) – Maximum size of sub-batch to be forwarded at one time</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.ifft">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">ifft</code><span class="sig-paren">(</span><em>data=None</em>, <em>compute_size=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.ndarray.ifft" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply 1D ifft to input”</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last"><cite>ifft</cite> is only available on GPU.</p>
+</div>
+<p>Currently accept 2 input data shapes: (N, d) or (N1, N2, N3, d). Data is in format: [real0, imag0, real1, imag1, ...].
+Last dimension must be an even number.
+The output data has shape: (N, d/2) or (N1, N2, N3, d/2). It is only the real part of the result.</p>
+<dl class="docutils">
+<dt>Example::</dt>
+<dd>data = np.random.normal(0,1,(3,4))
+out = mx.contrib.ndarray.ifft(data = mx.nd.array(data,ctx = mx.gpu(0)))</dd>
+</dl>
+<p>Defined in src/operator/contrib/ifft.cc:L42</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data to the IFFTOp.</li>
+<li><strong>compute_size</strong> (<em>int, optional, default='128'</em>) – Maximum size of sub-batch to be forwarded at one time</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.ndarray.quantize">
+<code class="descclassname">mxnet.contrib.ndarray.</code><code class="descname">quantize</code><span class="sig-paren">(</span><em>input=None</em>, <em>min_range=None</em>, <em>max_range=None</em>, <em>out_type=_Null</em>, <em>out=None</em>, <em>name=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.ndarray.quantize" title="Permalink to this definition">¶</a></dt>
+<dd><p>Quantize a input tensor from float to <cite>out_type</cite>,
+with user-specified <cite>min_range</cite> and <cite>max_range</cite>.</p>
+<p>[min_range, max_range] are scalar floats that spcify the range for
+the input data. Each value of the tensor will undergo the following:</p>
+<p><cite>out[i] = (in[i] - min_range) * range(OUTPUT_TYPE) / (max_range - min_range)</cite></p>
+<p>here <cite>range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()</cite></p>
+<p>Defined in src/operator/contrib/quantize.cc:L22</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – A ndarray/symbol of type <cite>float32</cite></li>
+<li><strong>min_range</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The minimum scalar value possibly produced for the input</li>
+<li><strong>max_range</strong> (<a class="reference internal" href="#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The maximum scalar value possibly produced for the input</li>
+<li><strong>out_type</strong> (<em>{'uint8'},optional, default='uint8'</em>) – Output data type.</li>
+<li><strong>out</strong> (<em>NDArray, optional</em>) – The output NDArray to hold the result.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The output of this function.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">NDArray or list of NDArrays</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
<script>auto_index("api-reference");</script></div>
</div>
<div class="container">
@@ -10700,6 +11784,7 @@ generated from two devices can be different even if they are seeded using the sa
<li><a class="reference internal" href="#more">More</a></li>
</ul>
</li>
+<li><a class="reference internal" href="#contrib">Contrib</a></li>
<li><a class="reference internal" href="#api-reference">API Reference</a></li>
</ul>
</li>
diff --git a/versions/master/api/python/optimization.html b/versions/master/api/python/optimization.html
index 4529c3f..ad8a7c3 100644
--- a/versions/master/api/python/optimization.html
+++ b/versions/master/api/python/optimization.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Optimization: initialize and update weights — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
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+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
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@@ -18,7 +18,7 @@
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FILE_SUFFIX: '.html',
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- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
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@@ -48,13 +48,11 @@
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-<link href="index.html" rel="up" title="MXNet - Python API"/>
+<link href="index.html" rel="up" title="MXNet - Python API">
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-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
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<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -229,12 +227,13 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
<li class="toctree-l3"><a class="reference internal" href="kvstore.html">KVStore API</a></li>
<li class="toctree-l3"><a class="reference internal" href="io.html">Data Loading API</a></li>
<li class="toctree-l3"><a class="reference internal" href="image.html">Image API</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Optimization: initialize and update weights</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Optimization: initialize and update weights</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
<li class="toctree-l4"><a class="reference internal" href="#the-mxnet-initializer-package">The <code class="docutils literal"><span class="pre">mxnet.initializer</span></code> package</a></li>
<li class="toctree-l4"><a class="reference internal" href="#the-mxnet-optimizer-package">The <code class="docutils literal"><span class="pre">mxnet.optimizer</span></code> package</a></li>
@@ -269,8 +268,8 @@ Previous Navbar Layout End -->
during training</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#module-mxnet.initializer" title="mxnet.initializer"><code class="xref py py-obj docutils literal"><span class="pre">mxnet.initializer</span></code></a></td>
@@ -316,8 +315,8 @@ various parameters, such as set bias to 1, except for the weight. Other classes
then defines how to initialize the weight.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><code class="xref py py-obj docutils literal"><span class="pre">Initializer</span></code></a></td>
@@ -369,8 +368,8 @@ then defines how to initialize the weight.</p>
implements one weight updating function.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-obj docutils literal"><span class="pre">Optimizer</span></code></a></td>
@@ -409,8 +408,8 @@ implements one weight updating function.</p>
implement various schemes to change the learning rate during training.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.lr_scheduler.LRScheduler" title="mxnet.lr_scheduler.LRScheduler"><code class="xref py py-obj docutils literal"><span class="pre">LRScheduler</span></code></a></td>
@@ -450,23 +449,23 @@ for examples.</p>
<script src="../../_static/js/auto_module_index.js" type="text/javascript"></script><span class="target" id="module-mxnet.optimizer"></span><p>Weight updating functions.</p>
<dl class="class">
<dt id="mxnet.optimizer.Optimizer">
-<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">Optimizer</code><span class="sig-paren">(</span><em>rescale_grad=1.0</em>, <em>param_idx2name=None</em>, <em>wd=0.0</em>, <em>clip_gradient=None</em>, <em>learning_rate=0.01</em>, <em>lr_scheduler=None</em>, <em>sym=None</em>, <em>begin_num_update=0</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Optimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">Optimizer</code><span class="sig-paren">(</span><em>rescale_grad=1.0</em>, <em>param_idx2name=None</em>, <em>wd=0.0</em>, <em>clip_gradient=None</em>, <em>learning_rate=0.01</em>, <em>lr_scheduler=None</em>, <em>sym=None</em>, <em>begin_num_update=0</em>, <em>param_dict=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Optimizer" title="Permalink t [...]
<dd><p>The base class inherited by all optimizers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em>) – Multiply the gradient with <cite>rescale_grad</cite> before updating. Often
+<li><strong>rescale_grad</strong> (<em>float, optional</em>) – Multiply the gradient with <cite>rescale_grad</cite> before updating. Often
choose to be <code class="docutils literal"><span class="pre">1.0/batch_size</span></code>.</li>
-<li><strong>param_idx2name</strong> (<em>dict from int to string</em><em>, </em><em>optional</em>) – A dictionary that maps int index to string name.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em>) – Clip the gradient by projecting onto the box <code class="docutils literal"><span class="pre">[-clip_gradient,</span> <span class="pre">clip_gradient]</span></code>.</li>
-<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>optional</em>) – The initial learning rate.</li>
-<li><strong>lr_scheduler</strong> (<a class="reference internal" href="#mxnet.lr_scheduler.LRScheduler" title="mxnet.lr_scheduler.LRScheduler"><em>LRScheduler</em></a><em>, </em><em>optional</em>) – The learning rate scheduler.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em>) – The weight decay (or L2 regularization) coefficient. Modifies objective
+<li><strong>param_idx2name</strong> (<em>dict from int to string, optional</em>) – A dictionary that maps int index to string name.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional</em>) – Clip the gradient by projecting onto the box <code class="docutils literal"><span class="pre">[-clip_gradient,</span> <span class="pre">clip_gradient]</span></code>.</li>
+<li><strong>learning_rate</strong> (<em>float, optional</em>) – The initial learning rate.</li>
+<li><strong>lr_scheduler</strong> (<em>LRScheduler, optional</em>) – The learning rate scheduler.</li>
+<li><strong>wd</strong> (<em>float, optional</em>) – The weight decay (or L2 regularization) coefficient. Modifies objective
by adding a penalty for having large weights.</li>
-<li><strong>sym</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>optional</em>) – The Symbol this optimizer is applying to.</li>
-<li><strong>begin_num_update</strong> (<em>int</em><em>, </em><em>optional</em>) – The initial number of updates.</li>
+<li><strong>sym</strong> (<em>Symbol, optional</em>) – The Symbol this optimizer is applying to.</li>
+<li><strong>begin_num_update</strong> (<em>int, optional</em>) – The initial number of updates.</li>
</ul>
</td>
</tr>
@@ -479,12 +478,12 @@ by adding a penalty for having large weights.</li>
<p>Once an optimizer is registered, we can create an instance of this
optimizer with <cite>create_optimizer</cite> later.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nd">@mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">register</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nd">@mx.optimizer.Optimizer.register</span>
<span class="gp">... </span><span class="k">class</span> <span class="nc">MyOptimizer</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">pass</span>
<span class="gp">>>> </span><span class="n">optim</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'MyOptimizer'</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">optim</span><span class="p">))</span>
-<span class="go"><class '__main__.myoptimizer'=""></class></span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">optim</span><span class="p">))</span>
+<span class="go"><class '__main__.MyOptimizer'></span>
</pre></div>
</div>
</dd></dl>
@@ -497,8 +496,8 @@ optimizer with <cite>create_optimizer</cite> later.</p>
<p class="last">We can use the alias <cite>create</cite> for <code class="docutils literal"><span class="pre">Optimizer.create_optimizer</span></code>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>str</em>) – Name of the optimizer. Should be the name
@@ -516,12 +515,12 @@ of a subclass of Optimizer. Case insensitive.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sgd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'sgd'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sgd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'sgd'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">sgd</span><span class="p">)</span>
-<span class="go"><class 'mxnet.optimizer.sgd'=""></class></span>
+<span class="go"><class 'mxnet.optimizer.SGD'></span>
<span class="gp">>>> </span><span class="n">adam</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'adam'</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=.</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">adam</span><span class="p">)</span>
-<span class="go"><class 'mxnet.optimizer.adam'=""></class></span>
+<span class="go"><class 'mxnet.optimizer.Adam'></span>
</pre></div>
</div>
</dd></dl>
@@ -534,8 +533,8 @@ to gradients in order to update weights. This function creates state
for a given weight which will be used in <cite>update</cite>. This function is
called only once for each weight.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>index</strong> (<em>int</em>) – An unique index to identify the weight.</li>
@@ -543,7 +542,8 @@ called only once for each weight.</p>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>state</strong> – The state associated with the weight.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>state</strong> –
+The state associated with the weight.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">any obj</p>
@@ -557,8 +557,8 @@ called only once for each weight.</p>
<code class="descname">update</code><span class="sig-paren">(</span><em>index</em>, <em>weight</em>, <em>grad</em>, <em>state</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Optimizer.update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updates the given parameter using the corresponding gradient and state.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>index</strong> (<em>int</em>) – The unique index of the parameter into the individual learning
@@ -591,8 +591,8 @@ the global learning rate <cite>self.lr</cite> and its multiplier.</p>
can be set with <cite>lr_mult</cite> argument in the constructor.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>args_lr_mult</strong> (<em>dict of str/int to float</em>) – <p>For each of its key-value entries, the learning rate multipler for the
parameter specified in the key will be set as the given value.</p>
@@ -622,8 +622,8 @@ parameters whose name don’t end with <code class="docutils literal"><span clas
can be set with its <cite>wd_mult</cite> argument in the constructor.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>args_wd_mult</strong> (<em>dict of string/int to float</em>) – <p>For each of its key-value entries, the weight decay multipler for the
parameter specified in the key will be set as the given value.</p>
@@ -647,12 +647,12 @@ compatibility, and we recommend to use the name instead.</p>
<p>Once an optimizer is registered, we can create an instance of this
optimizer with <cite>create_optimizer</cite> later.</p>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nd">@mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">register</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nd">@mx.optimizer.Optimizer.register</span>
<span class="gp">... </span><span class="k">class</span> <span class="nc">MyOptimizer</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">pass</span>
<span class="gp">>>> </span><span class="n">optim</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'MyOptimizer'</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">optim</span><span class="p">))</span>
-<span class="go"><class '__main__.myoptimizer'=""></class></span>
+<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">optim</span><span class="p">))</span>
+<span class="go"><class '__main__.MyOptimizer'></span>
</pre></div>
</div>
</dd></dl>
@@ -661,7 +661,7 @@ optimizer with <cite>create_optimizer</cite> later.</p>
<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>momentum=0.0</em>, <em>multi_precision=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.SGD" title="Permalink to this definition">¶</a></dt>
<dd><p>The SGD optimizer with momentum and weight decay.</p>
<p>The optimizer updates the weight by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span> <span class="o">+</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">rescale_grad</span> <span class="o">*</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">clip_gradient</span><spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span> <span class="o">+</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">rescale_grad</span> <span class="o">*</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">clip_gradient</span><span [...]
<span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">-</span> <span class="n">state</span>
</pre></div>
</div>
@@ -670,12 +670,12 @@ optimizer with <cite>create_optimizer</cite> later.</p>
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em>) – The momentum value.</li>
-<li><strong>multi_precision</strong> (<em>bool</em><em>, </em><em>optional</em>) – <p>Flag to control the internal precision of the optimizer.
+<li><strong>momentum</strong> (<em>float, optional</em>) – The momentum value.</li>
+<li><strong>multi_precision</strong> (<em>bool, optional</em>) – <p>Flag to control the internal precision of the optimizer.
<code class="docutils literal"><span class="pre">False</span></code> results in using the same precision as the weights (default),
<code class="docutils literal"><span class="pre">True</span></code> makes internal 32-bit copy of the weights and applies gradients</p>
<blockquote>
@@ -698,12 +698,12 @@ available at <a class="reference external" href="https://arxiv.org/abs/1609.0832
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em>) – The momentum value.</li>
-<li><strong>lamda</strong> (<em>float</em><em>, </em><em>optional</em>) – Scale DC value.</li>
+<li><strong>momentum</strong> (<em>float, optional</em>) – The momentum value.</li>
+<li><strong>lamda</strong> (<em>float, optional</em>) – Scale DC value.</li>
</ul>
</td>
</tr>
@@ -715,7 +715,7 @@ by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.
<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">NAG</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.NAG" title="Permalink to this definition">¶</a></dt>
<dd><p>Nesterov accelerated SGD.</p>
<p>This optimizer updates each weight by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span> <span class="o">+</span> <span class="n">grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span> <span class="o">+</span> <span class="n">grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span>
<span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">-</span> <span class="p">(</span><span class="n">lr</span> <span class="o">*</span> <span class="p">(</span><span class="n">grad</span> <span class="o">+</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">state</span><span class="p">))</span>
</pre></div>
</div>
@@ -744,13 +744,13 @@ Stochastic Optimization</em>, available at <a class="reference external" href="h
by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<p>For details of the update algorithm, see <code class="xref py py-class docutils literal"><span class="pre">ndarray.adam_update</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>beta1</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the first moment estimates.</li>
-<li><strong>beta2</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the second moment estimates.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – Small value to avoid division by 0.</li>
+<li><strong>beta1</strong> (<em>float, optional</em>) – Exponential decay rate for the first moment estimates.</li>
+<li><strong>beta2</strong> (<em>float, optional</em>) – Exponential decay rate for the second moment estimates.</li>
+<li><strong>epsilon</strong> (<em>float, optional</em>) – Small value to avoid division by 0.</li>
</ul>
</td>
</tr>
@@ -767,10 +767,10 @@ Methods for Online Learning and Stochastic Optimization</em>, and available at
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em>) – Small value to avoid division by 0.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>eps</strong> (<em>float, optional</em>) – Small value to avoid division by 0.</td>
</tr>
</tbody>
</table>
@@ -790,17 +790,17 @@ For details of the update algorithm see <a class="reference internal" href="ndar
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>gamma1</strong> (<em>float</em><em>, </em><em>optional</em>) – A decay factor of moving average over past squared gradient.</li>
-<li><strong>gamma2</strong> (<em>float</em><em>, </em><em>optional</em>) – A “momentum” factor. Only used if <cite>centered`=``True`</cite>.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – Small value to avoid division by 0.</li>
-<li><strong>centered</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flag to control which version of RMSProp to use.
+<li><strong>gamma1</strong> (<em>float, optional</em>) – A decay factor of moving average over past squared gradient.</li>
+<li><strong>gamma2</strong> (<em>float, optional</em>) – A “momentum” factor. Only used if <cite>centered`=``True`</cite>.</li>
+<li><strong>epsilon</strong> (<em>float, optional</em>) – Small value to avoid division by 0.</li>
+<li><strong>centered</strong> (<em>bool, optional</em>) – Flag to control which version of RMSProp to use.
<code class="docutils literal"><span class="pre">True</span></code> will use Graves’s version of <cite>RMSProp</cite>,
<code class="docutils literal"><span class="pre">False</span></code> will use Tieleman & Hinton’s version of <cite>RMSProp</cite>.</li>
-<li><strong>clip_weights</strong> (<em>float</em><em>, </em><em>optional</em>) – Clips weights into range <code class="docutils literal"><span class="pre">[-clip_weights,</span> <span class="pre">clip_weights]</span></code>.</li>
+<li><strong>clip_weights</strong> (<em>float, optional</em>) – Clips weights into range <code class="docutils literal"><span class="pre">[-clip_weights,</span> <span class="pre">clip_weights]</span></code>.</li>
</ul>
</td>
</tr>
@@ -816,8 +816,8 @@ learning rate method</em>, available at <a class="reference external" href="http
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>rho</strong> (<em>float</em>) – Decay rate for both squared gradients and delta.</li>
@@ -835,15 +835,16 @@ by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.
<p>Referenced from <em>Ad Click Prediction: a View from the Trenches</em>, available at
<a class="reference external" href="http://dl.acm.org/citation.cfm?id=2488200">http://dl.acm.org/citation.cfm?id=2488200</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>lamda1</strong> (<em>float</em><em>, </em><em>optional</em>) – L1 regularization coefficient.</li>
-<li><strong>learning_rate</strong> (<em>float</em><em>, </em><em>optional</em>) – The initial learning rate.</li>
-<li><strong>beta</strong> (<em>float</em><em>, </em><em>optional</em>) – Per-coordinate learning rate correlation parameter.</li>
+<li><strong>lamda1</strong> (<em>float, optional</em>) – L1 regularization coefficient.</li>
+<li><strong>learning_rate</strong> (<em>float, optional</em>) – The initial learning rate.</li>
+<li><strong>beta</strong> (<em>float, optional</em>) – Per-coordinate learning rate correlation parameter.</li>
<li><strong>eta</strong> – <div class="math">
-\[\eta_{t,i} = \frac{learningrate}{\beta+\sqrt{\sum_{s=1}^tg_{s,i}^t}}\]</div>
+</div>
+<p>eta_{t,i} = frac{learningrate}{beta+sqrt{sum_{s=1}^tg_{s,i}^t}}</p>
</li>
</ul>
</td>
@@ -860,12 +861,12 @@ available at <a class="reference external" href="http://arxiv.org/abs/1412.6980"
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>beta1</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the first moment estimates.</li>
-<li><strong>beta2</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the second moment estimates.</li>
+<li><strong>beta1</strong> (<em>float, optional</em>) – Exponential decay rate for the first moment estimates.</li>
+<li><strong>beta2</strong> (<em>float, optional</em>) – Exponential decay rate for the second moment estimates.</li>
</ul>
</td>
</tr>
@@ -882,14 +883,14 @@ at <a class="reference external" href="http://cs229.stanford.edu/proj2015/054_re
<p>This optimizer accepts the following parameters in addition to those accepted
by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><code class="xref py py-class docutils literal"><span class="pre">Optimizer</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>beta1</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the first moment estimates.</li>
-<li><strong>beta2</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the second moment estimates.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – Small value to avoid division by 0.</li>
-<li><strong>schedule_decay</strong> (<em>float</em><em>, </em><em>optional</em>) – Exponential decay rate for the momentum schedule</li>
+<li><strong>beta1</strong> (<em>float, optional</em>) – Exponential decay rate for the first moment estimates.</li>
+<li><strong>beta2</strong> (<em>float, optional</em>) – Exponential decay rate for the second moment estimates.</li>
+<li><strong>epsilon</strong> (<em>float, optional</em>) – Small value to avoid division by 0.</li>
+<li><strong>schedule_decay</strong> (<em>float, optional</em>) – Exponential decay rate for the momentum schedule</li>
</ul>
</td>
</tr>
@@ -920,8 +921,8 @@ by <a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.
<p class="last">We can use the alias <cite>create</cite> for <code class="docutils literal"><span class="pre">Optimizer.create_optimizer</span></code>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>str</em>) – Name of the optimizer. Should be the name
@@ -939,12 +940,12 @@ of a subclass of Optimizer. Case insensitive.</li>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sgd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'sgd'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sgd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">(</span><span class="s1">'sgd'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">sgd</span><span class="p">)</span>
-<span class="go"><class 'mxnet.optimizer.sgd'=""></class></span>
+<span class="go"><class 'mxnet.optimizer.SGD'></span>
<span class="gp">>>> </span><span class="n">adam</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'adam'</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=.</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">adam</span><span class="p">)</span>
-<span class="go"><class 'mxnet.optimizer.adam'=""></class></span>
+<span class="go"><class 'mxnet.optimizer.Adam'></span>
</pre></div>
</div>
</dd></dl>
@@ -953,6 +954,11 @@ of a subclass of Optimizer. Case insensitive.</li>
<em class="property">class </em><code class="descclassname">mxnet.optimizer.</code><code class="descname">Updater</code><span class="sig-paren">(</span><em>optimizer</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Updater" title="Permalink to this definition">¶</a></dt>
<dd><p>Updater for kvstore.</p>
<dl class="method">
+<dt id="mxnet.optimizer.Updater.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>index</em>, <em>grad</em>, <em>weight</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Updater.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Updates weight given gradient and index.</p>
+</dd></dl>
+<dl class="method">
<dt id="mxnet.optimizer.Updater.set_states">
<code class="descname">set_states</code><span class="sig-paren">(</span><em>states</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.Updater.set_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets updater states.</p>
@@ -968,12 +974,13 @@ of a subclass of Optimizer. Case insensitive.</li>
<code class="descclassname">mxnet.optimizer.</code><code class="descname">get_updater</code><span class="sig-paren">(</span><em>optimizer</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.optimizer.get_updater" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a closure of the updater needed for kvstore.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>optimizer</strong> (<a class="reference internal" href="#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – The optimizer.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>updater</strong> – The closure of the updater.</td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>updater</strong> –
+The closure of the updater.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">function</td>
</tr>
@@ -988,30 +995,50 @@ of a subclass of Optimizer. Case insensitive.</li>
<p>A scheduler returns a new learning rate based on the number of updates that have
been performed.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>base_lr</strong> (<em>float, optional</em>) – The initial learning rate.</td>
+</tr>
+</tbody>
+</table>
+<dl class="method">
+<dt id="mxnet.lr_scheduler.LRScheduler.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>num_update</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.lr_scheduler.LRScheduler.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return a new learning rate.</p>
+<p>The <code class="docutils literal"><span class="pre">num_update</span></code> is the upper bound of the number of updates applied to
+every weight.</p>
+<p>Assume the optimizer has updated <em>i</em>-th weight by <em>k_i</em> times, namely
+<code class="docutils literal"><span class="pre">optimizer.update(i,</span> <span class="pre">weight_i)</span></code> is called by <em>k_i</em> times. Then:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span>num_update = max([k_i for all i])
+</pre></div>
+</div>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>base_lr</strong> (<em>float</em><em>, </em><em>optional</em>) – The initial learning rate.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>num_update</strong> (<em>int</em>) – the maximal number of updates applied to a weight.</td>
</tr>
</tbody>
</table>
</dd></dl>
+</dd></dl>
<dl class="class">
<dt id="mxnet.lr_scheduler.FactorScheduler">
<em class="property">class </em><code class="descclassname">mxnet.lr_scheduler.</code><code class="descname">FactorScheduler</code><span class="sig-paren">(</span><em>step</em>, <em>factor=1</em>, <em>stop_factor_lr=1e-08</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.lr_scheduler.FactorScheduler" title="Permalink to this definition">¶</a></dt>
<dd><p>Reduce the learning rate by a factor for every <em>n</em> steps.</p>
<p>It returns a new learning rate by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">base_lr</span> <span class="o">*</span> <span class="nb">pow</span><span class="p">(</span><span class="n">factor</span><span class="p">,</span> <span class="n">floor</span><span class="p">(</span><span class="n">num_update</span><span class="o">/</span><span class="n">step</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">base_lr</span> <span class="o">*</span> <span class="nb">pow</span><span class="p">(</span><span class="n">factor</span><span class="p">,</span> <span class="n">floor</span><span class="p">(</span><span class="n">num_update</span><span class="o">/</span><span class="n">step</span><span class="p">))</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>step</strong> (<em>int</em>) – Changes the learning rate for every n updates.</li>
-<li><strong>factor</strong> (<em>float</em><em>, </em><em>optional</em>) – The factor to change the learning rate.</li>
-<li><strong>stop_factor_lr</strong> (<em>float</em><em>, </em><em>optional</em>) – Stop updating the learning rate if it is less than this value.</li>
+<li><strong>factor</strong> (<em>float, optional</em>) – The factor to change the learning rate.</li>
+<li><strong>stop_factor_lr</strong> (<em>float, optional</em>) – Stop updating the learning rate if it is less than this value.</li>
</ul>
</td>
</tr>
@@ -1023,16 +1050,16 @@ been performed.</p>
<em class="property">class </em><code class="descclassname">mxnet.lr_scheduler.</code><code class="descname">MultiFactorScheduler</code><span class="sig-paren">(</span><em>step</em>, <em>factor=1</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.lr_scheduler.MultiFactorScheduler" title="Permalink to this definition">¶</a></dt>
<dd><p>Reduce the learning rate by given a list of steps.</p>
<p>Assume there exists <em>k</em> such that:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">step</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o"><=</span> <span class="n">num_update</span> <span class="ow">and</span> <span class="n">num_update</span> <span class="o"><</span> <span class="n">step</span><span class="p">[</span><span class="n">k</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">step</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o"><=</span> <span class="n">num_update</span> <span class="ow">and</span> <span class="n">num_update</span> <span class="o"><</span> <span class="n">step</span><span class="p">[</span><span class="n">k</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
</pre></div>
</div>
<p>Then calculate the new learning rate by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">base_lr</span> <span class="o">*</span> <span class="nb">pow</span><span class="p">(</span><span class="n">factor</span><span class="p">,</span> <span class="n">k</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">base_lr</span> <span class="o">*</span> <span class="nb">pow</span><span class="p">(</span><span class="n">factor</span><span class="p">,</span> <span class="n">k</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>step</strong> (<em>list of int</em>) – The list of steps to schedule a change</li>
@@ -1049,12 +1076,12 @@ been performed.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">InitDesc</code><a class="headerlink" href="#mxnet.initializer.InitDesc" title="Permalink to this definition">¶</a></dt>
<dd><p>Descriptor for the initialization pattern.</p>
<dl class="docutils">
-<dt>name</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">str</span><dd>Name of variable.</dd>
-<dt>attrs</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">dict of str to str</span><dd>Attributes of this variable taken from <code class="docutils literal"><span class="pre">Symbol.attr_dict</span></code>.</dd>
-<dt>global_init</dt>
-<span class="classifier-delimiter">:</span> <span class="classifier">Initializer</span><dd>Global initializer to fallback to.</dd>
+<dt>name <span class="classifier-delimiter">:</span> <span class="classifier">str</span></dt>
+<dd>Name of variable.</dd>
+<dt>attrs <span class="classifier-delimiter">:</span> <span class="classifier">dict of str to str</span></dt>
+<dd>Attributes of this variable taken from <code class="docutils literal"><span class="pre">Symbol.attr_dict</span></code>.</dd>
+<dt>global_init <span class="classifier-delimiter">:</span> <span class="classifier">Initializer</span></dt>
+<dd>Global initializer to fallback to.</dd>
</dl>
</dd></dl>
<dl class="class">
@@ -1066,8 +1093,8 @@ been performed.</p>
<code class="descname">set_verbosity</code><span class="sig-paren">(</span><em>verbose=False</em>, <em>print_func=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Initializer.set_verbosity" title="Permalink to this definition">¶</a></dt>
<dd><p>Switch on/off verbose mode</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>verbose</strong> (<em>bool</em>) – switch on/off verbose mode</li>
@@ -1085,8 +1112,8 @@ absolute value str((<a href="#id1"><span class="problematic" id="id2">|x|</span>
<code class="descname">dumps</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Initializer.dumps" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves the initializer to string</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">JSON formatted string that describes the initializer.</td>
</tr>
@@ -1095,7 +1122,7 @@ absolute value str((<a href="#id1"><span class="problematic" id="id2">|x|</span>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Create initializer and retrieve its parameters</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Create initializer and retrieve its parameters</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">init</span><span class="o">.</span><span class="n">dumps</span><span class="p">()</span>
@@ -1106,6 +1133,23 @@ absolute value str((<a href="#id1"><span class="problematic" id="id2">|x|</span>
</pre></div>
</div>
</dd></dl>
+<dl class="method">
+<dt id="mxnet.initializer.Initializer.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>desc</em>, <em>arr</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Initializer.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Initialize an array</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>desc</strong> (<a class="reference internal" href="#mxnet.initializer.InitDesc" title="mxnet.initializer.InitDesc"><em>InitDesc</em></a>) – Initialization pattern descriptor.</li>
+<li><strong>arr</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The array to be initialized.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
</dd></dl>
<dl class="function">
<dt id="mxnet.initializer.register">
@@ -1115,21 +1159,21 @@ absolute value str((<a href="#id1"><span class="problematic" id="id2">|x|</span>
required functions like <cite>_init_weight</cite> and <cite>_init_bias</cite>. The created initializer must be
registered using <cite>mx.init.register</cite> before it can be called by name.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>klass</strong> (<em>class</em>) – A subclass of <cite>mx.init.Initializer</cite> that needs to be registered as a custom initializer.</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Create and register a custom initializer that</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Create and register a custom initializer that</span>
<span class="gp">... </span><span class="c1"># initializes weights to 0.1 and biases to 1.</span>
<span class="gp">...</span>
-<span class="gp">>>> </span><span class="nd">@mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">register</span>
+<span class="gp">>>> </span><span class="nd">@mx.init.register</span>
<span class="gp">... </span><span class="nd">@alias</span><span class="p">(</span><span class="s1">'myinit'</span><span class="p">)</span>
<span class="gp">... </span><span class="k">class</span> <span class="nc">CustomInit</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Initializer</span><span class="p">):</span>
-<span class="gp">... </span> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="gp">... </span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="gp">... </span> <span class="nb">super</span><span class="p">(</span><span class="n">CustomInit</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="gp">... </span> <span class="k">def</span> <span class="nf">_init_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">arr</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="mf">0.1</span>
@@ -1151,11 +1195,11 @@ registered using <cite>mx.init.register</cite> before it can be called by name.<
<p><strong>Note</strong> Load will drop <code class="docutils literal"><span class="pre">arg:</span></code> or <code class="docutils literal"><span class="pre">aux:</span></code> from name and
initialize the variables that match with the prefix dropped.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>param</strong> (<em>str</em><em> or </em><em>dict of str->`NDArray`</em>) – Parameter file or dict mapping name to NDArray.</li>
+<li><strong>param</strong> (<em>str or dict of str->`NDArray`</em>) – Parameter file or dict mapping name to NDArray.</li>
<li><strong>default_init</strong> (<a class="reference internal" href="#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Default initializer when name is not found in <cite>param</cite>.</li>
<li><strong>verbose</strong> (<em>bool</em>) – Flag for enabling logging of source when initializing.</li>
</ul>
@@ -1169,8 +1213,8 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">Mixed</code><span class="sig-paren">(</span><em>patterns</em>, <em>initializers</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Mixed" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize parameters using multiple initializers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>patterns</strong> (<em>list of str</em>) – List of regular expressions matching parameter names.</li>
@@ -1181,7 +1225,7 @@ initialize the variables that match with the prefix dropped.</p>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize biases to zero</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize biases to zero</span>
<span class="gp">... </span><span class="c1"># and every other parameter to random values with uniform distribution.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">initializer</span><span class="o">.</span><span class="n">Mixed</span><span class="p">([</span><span class="s1">'bias'</span><span class="p">,</span> <span class="s1">'.*'</span><span class="p">],</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Ze [...]
@@ -1189,8 +1233,8 @@ initialize the variables that match with the prefix dropped.</p>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected1_weight</span>
<span class="go">[[ 0.0097627 0.01856892 0.04303787]]</span>
@@ -1204,14 +1248,14 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">Zero</code><a class="headerlink" href="#mxnet.initializer.Zero" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes weights to zero.</p>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights to zero.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights to zero.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">initializer</span><span class="o">.</span><span class="n">Zero</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">init</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected0_weight</span>
<span class="go">[[ 0. 0. 0.]]</span>
@@ -1223,14 +1267,14 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">One</code><a class="headerlink" href="#mxnet.initializer.One" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes weights to one.</p>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights to one.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights to one.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">initializer</span><span class="o">.</span><span class="n">One</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">init</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected0_weight</span>
<span class="go">[[ 1. 1. 1.]]</span>
@@ -1242,8 +1286,8 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">Constant</code><span class="sig-paren">(</span><em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Constant" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes the weights to a scalar value.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>value</strong> (<em>float</em>) – Fill value.</td>
</tr>
@@ -1255,25 +1299,25 @@ initialize the variables that match with the prefix dropped.</p>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">Uniform</code><span class="sig-paren">(</span><em>scale=0.07</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.Uniform" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes weights with random values uniformly sampled from a given range.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em>) – The bound on the range of the generated random values.
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>scale</strong> (<em>float, optional</em>) – The bound on the range of the generated random values.
Values are generated from the range [-<cite>scale</cite>, <cite>scale</cite>].
Default scale is 0.07.</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights</span>
<span class="gp">>>> </span><span class="c1"># to random values uniformly sampled between -0.1 and 0.1.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Uniform</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">init</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected0_weight</span>
<span class="go">[[ 0.01360891 -0.02144304 0.08511933]]</span>
@@ -1286,24 +1330,24 @@ Default scale is 0.07.</td>
<dd><p>Initializes weights with random values sampled from a normal distribution
with a mean of zero and standard deviation of <cite>sigma</cite>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>sigma</strong> (<em>float</em><em>, </em><em>optional</em>) – Standard deviation of the normal distribution.
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>sigma</strong> (<em>float, optional</em>) – Standard deviation of the normal distribution.
Default standard deviation is 0.01.</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Given 'module', an instance of 'mxnet.module.Module', initialize weights</span>
<span class="gp">>>> </span><span class="c1"># to random values sampled from a normal distribution.</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">init</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">module</span><span class="o">.</span><span class="n">init_params</span><span class="p">(</span><span class="n">init</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dictionary</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dictionary</span><span class="p">:</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="gp">...</span>
<span class="go">fullyconnected0_weight</span>
<span class="go">[[-0.3214761 -0.12660924 0.53789419]]</span>
@@ -1318,8 +1362,8 @@ Default standard deviation is 0.01.</td>
learning in deep linear neural networks</em>, available at
<a class="reference external" href="https://arxiv.org/abs/1312.6120">https://arxiv.org/abs/1312.6120</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>scale</strong> (<em>float optional</em>) – Scaling factor of weight.</li>
@@ -1348,13 +1392,13 @@ Similarly when <cite>factor_type</cite> is <code class="docutils literal"><span
the initializer fills the weights with numbers from normal distribution with
a standard deviation of <span class="math">\(\sqrt{\frac{3.}{0.5 * (n_{in} + n_{out})}}\)</span>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>rnd_type</strong> (<em>str</em><em>, </em><em>optional</em>) – Random generator type, can be <code class="docutils literal"><span class="pre">'gaussian'</span></code> or <code class="docutils literal"><span class="pre">'uniform'</span></code>.</li>
-<li><strong>factor_type</strong> (<em>str</em><em>, </em><em>optional</em>) – Can be <code class="docutils literal"><span class="pre">'avg'</span></code>, <code class="docutils literal"><span class="pre">'in'</span></code>, or <code class="docutils literal"><span class="pre">'out'</span></code>.</li>
-<li><strong>magnitude</strong> (<em>float</em><em>, </em><em>optional</em>) – Scale of random number.</li>
+<li><strong>rnd_type</strong> (<em>str, optional</em>) – Random generator type, can be <code class="docutils literal"><span class="pre">'gaussian'</span></code> or <code class="docutils literal"><span class="pre">'uniform'</span></code>.</li>
+<li><strong>factor_type</strong> (<em>str, optional</em>) – Can be <code class="docutils literal"><span class="pre">'avg'</span></code>, <code class="docutils literal"><span class="pre">'in'</span></code>, or <code class="docutils literal"><span class="pre">'out'</span></code>.</li>
+<li><strong>magnitude</strong> (<em>float, optional</em>) – Scale of random number.</li>
</ul>
</td>
</tr>
@@ -1371,12 +1415,12 @@ Human-Level Performance on ImageNet Classification</em>, available at
<p>This initializer is proposed for initialization related to ReLu activation,
it maked some changes on top of Xavier method.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>factor_type</strong> (<em>str</em><em>, </em><em>optional</em>) – Can be <code class="docutils literal"><span class="pre">'avg'</span></code>, <code class="docutils literal"><span class="pre">'in'</span></code>, or <code class="docutils literal"><span class="pre">'out'</span></code>.</li>
-<li><strong>slope</strong> (<em>float</em><em>, </em><em>optional</em>) – initial slope of any PReLU (or similar) nonlinearities.</li>
+<li><strong>factor_type</strong> (<em>str, optional</em>) – Can be <code class="docutils literal"><span class="pre">'avg'</span></code>, <code class="docutils literal"><span class="pre">'in'</span></code>, or <code class="docutils literal"><span class="pre">'out'</span></code>.</li>
+<li><strong>slope</strong> (<em>float, optional</em>) – initial slope of any PReLU (or similar) nonlinearities.</li>
</ul>
</td>
</tr>
@@ -1394,10 +1438,10 @@ it maked some changes on top of Xavier method.</p>
<dd><p>Initialize all bias of an LSTMCell to 0.0 except for
the forget gate whose bias is set to custom value.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>forget_bias</strong> (<em>float</em><em>, </em><em>default 1.0</em>) – bias for the forget gate. Jozefowicz et al. 2015 recommends
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>forget_bias</strong> (<em>float, default 1.0</em>) – bias for the forget gate. Jozefowicz et al. 2015 recommends
setting this to 1.0.</td>
</tr>
</tbody>
@@ -1408,8 +1452,8 @@ setting this to 1.0.</td>
<em class="property">class </em><code class="descclassname">mxnet.initializer.</code><code class="descname">FusedRNN</code><span class="sig-paren">(</span><em>init</em>, <em>num_hidden</em>, <em>num_layers</em>, <em>mode</em>, <em>bidirectional=False</em>, <em>forget_bias=1.0</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.initializer.FusedRNN" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize parameters for fused rnn layers.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>init</strong> (<a class="reference internal" href="#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – initializer applied to unpacked weights. Fall back to global
diff --git a/versions/master/api/python/rnn.html b/versions/master/api/python/rnn.html
index 9adf972..70f987c 100644
--- a/versions/master/api/python/rnn.html
+++ b/versions/master/api/python/rnn.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>RNN Cell API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="index.html" rel="up" title="MXNet - Python API"/>
+<link href="index.html" rel="up" title="MXNet - Python API">
<link href="kvstore.html" rel="next" title="KVStore API"/>
<link href="gluon.html" rel="prev" title="Gluon Package"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
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<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
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<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -229,8 +227,9 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
<li class="toctree-l3"><a class="reference internal" href="symbol.html">Symbol API</a></li>
<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">RNN Cell API</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">RNN Cell API</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
<li class="toctree-l4"><a class="reference internal" href="#the-rnn-module">The <code class="docutils literal"><span class="pre">rnn</span></code> module</a></li>
<li class="toctree-l4"><a class="reference internal" href="#api-reference">API Reference</a></li>
@@ -278,8 +277,8 @@ Previous Navbar Layout End -->
<span id="cell-interfaces"></span><h3>Cell interfaces<a class="headerlink" href="#cell-interfaces" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.rnn.BaseRNNCell.__call__" title="mxnet.rnn.BaseRNNCell.__call__"><code class="xref py py-obj docutils literal"><span class="pre">BaseRNNCell.__call__</span></code></a></td>
@@ -374,8 +373,8 @@ The list of all the used cells should be provided as the first argument to those
<p><code class="docutils literal"><span class="pre">rnn</span></code> module supports the following RNN cell types.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.rnn.LSTMCell" title="mxnet.rnn.LSTMCell"><code class="xref py py-obj docutils literal"><span class="pre">LSTMCell</span></code></a></td>
@@ -394,8 +393,8 @@ The list of all the used cells should be provided as the first argument to those
<span id="modifier-cells"></span><h3>Modifier cells<a class="headerlink" href="#modifier-cells" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.rnn.BidirectionalCell" title="mxnet.rnn.BidirectionalCell"><code class="xref py py-obj docutils literal"><span class="pre">BidirectionalCell</span></code></a></td>
@@ -463,8 +462,8 @@ RNN models.</p>
<span id="multi-layer-cells"></span><h3>Multi-layer cells<a class="headerlink" href="#multi-layer-cells" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.rnn.SequentialRNNCell" title="mxnet.rnn.SequentialRNNCell"><code class="xref py py-obj docutils literal"><span class="pre">SequentialRNNCell</span></code></a></td>
@@ -502,8 +501,8 @@ top. When unrolling, the output of a lower-level cell is automatically passed to
<span id="fused-rnn-cell"></span><h3>Fused RNN cell<a class="headerlink" href="#fused-rnn-cell" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.rnn.FusedRNNCell" title="mxnet.rnn.FusedRNNCell"><code class="xref py py-obj docutils literal"><span class="pre">FusedRNNCell</span></code></a></td>
@@ -572,8 +571,8 @@ and CPU-compatible <code class="docutils literal"><span class="pre">SequentialRN
<span id="rnn-checkpoint-methods-and-parameters"></span><h3>RNN checkpoint methods and parameters<a class="headerlink" href="#rnn-checkpoint-methods-and-parameters" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.rnn.save_rnn_checkpoint" title="mxnet.rnn.save_rnn_checkpoint"><code class="xref py py-obj docutils literal"><span class="pre">save_rnn_checkpoint</span></code></a></td>
@@ -589,8 +588,8 @@ and CPU-compatible <code class="docutils literal"><span class="pre">SequentialRN
</table>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
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<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><code class="xref py py-obj docutils literal"><span class="pre">RNNParams</span></code></a></td>
@@ -623,8 +622,8 @@ conversion transparently based on the provided cells.</p>
<span id="i-o-utilities"></span><h3>I/O utilities<a class="headerlink" href="#i-o-utilities" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.rnn.BucketSentenceIter" title="mxnet.rnn.BucketSentenceIter"><code class="xref py py-obj docutils literal"><span class="pre">BucketSentenceIter</span></code></a></td>
@@ -644,14 +643,14 @@ conversion transparently based on the provided cells.</p>
<em class="property">class </em><code class="descclassname">mxnet.rnn.</code><code class="descname">BaseRNNCell</code><span class="sig-paren">(</span><em>prefix=''</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.BaseRNNCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Abstract base class for RNN cells</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>prefix</strong> (<em>str</em><em>, </em><em>optional</em>) – Prefix for names of layers
+<li><strong>prefix</strong> (<em>str, optional</em>) – Prefix for names of layers
(this prefix is also used for names of weights if <cite>params</cite> is None
i.e. if <cite>params</cite> are being created and not reused)</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None.</em>) – Container for weight sharing between cells.
+<li><strong>params</strong> (<em>RNNParams, default None.</em>) – Container for weight sharing between cells.
A new RNNParams container is created if <cite>params</cite> is None.</li>
</ul>
</td>
@@ -663,8 +662,8 @@ A new RNNParams container is created if <cite>params</cite> is None.</li>
<code class="descname">__call__</code><span class="sig-paren">(</span><em>inputs</em>, <em>states</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.BaseRNNCell.__call__" title="Permalink to this definition">¶</a></dt>
<dd><p>Unroll the RNN for one time step.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>inputs</strong> (<em>sym.Variable</em>) – input symbol, 2D, batch * num_units</li>
@@ -673,9 +672,11 @@ A new RNNParams container is created if <cite>params</cite> is None.</li>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>output</strong> (<em>Symbol</em>) – Symbol corresponding to the output from the RNN when unrolling
+<li><strong>output</strong> (<em>Symbol</em>) –
+Symbol corresponding to the output from the RNN when unrolling
for a single time step.</li>
-<li><strong>states</strong> (<em>nested list of Symbol</em>) – The new state of this RNN after this unrolling.
+<li><strong>states</strong> (<em>nested list of Symbol</em>) –
+The new state of this RNN after this unrolling.
The type of this symbol is same as the output of begin_state().
This can be used as input state to the next time step
of this RNN.</li>
@@ -700,6 +701,45 @@ of this RNN.</li>
<code class="descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.BaseRNNCell.reset" title="Permalink to this definition">¶</a></dt>
<dd><p>Reset before re-using the cell for another graph.</p>
</dd></dl>
+<dl class="method">
+<dt>
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>inputs</em>, <em>states</em><span class="sig-paren">)</span></dt>
+<dd><p>Unroll the RNN for one time step.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>inputs</strong> (<em>sym.Variable</em>) – input symbol, 2D, batch * num_units</li>
+<li><strong>states</strong> (<em>list of sym.Variable</em>) – RNN state from previous step or the output of begin_state().</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
+<li><strong>output</strong> (<em>Symbol</em>) –
+Symbol corresponding to the output from the RNN when unrolling
+for a single time step.</li>
+<li><strong>states</strong> (<em>nested list of Symbol</em>) –
+The new state of this RNN after this unrolling.
+The type of this symbol is same as the output of begin_state().
+This can be used as input state to the next time step
+of this RNN.</li>
+</ul>
+</p>
+</td>
+</tr>
+</tbody>
+</table>
+<div class="admonition seealso">
+<p class="first admonition-title">See also</p>
+<dl class="last docutils">
+<dt><a class="reference internal" href="#mxnet.rnn.BaseRNNCell.begin_state" title="mxnet.rnn.BaseRNNCell.begin_state"><code class="xref py py-meth docutils literal"><span class="pre">begin_state()</span></code></a></dt>
+<dd>This function can provide the states for the first time step.</dd>
+<dt><a class="reference internal" href="#mxnet.rnn.BaseRNNCell.unroll" title="mxnet.rnn.BaseRNNCell.unroll"><code class="xref py py-meth docutils literal"><span class="pre">unroll()</span></code></a></dt>
+<dd>This function unrolls an RNN for a given number of (>=1) time steps.</dd>
+</dl>
+</div>
+</dd></dl>
<dl class="attribute">
<dt id="mxnet.rnn.BaseRNNCell.params">
<code class="descname">params</code><a class="headerlink" href="#mxnet.rnn.BaseRNNCell.params" title="Permalink to this definition">¶</a></dt>
@@ -717,23 +757,25 @@ of this RNN.</li>
</dd></dl>
<dl class="method">
<dt id="mxnet.rnn.BaseRNNCell.begin_state">
-<code class="descname">begin_state</code><span class="sig-paren">(</span><em>func=<function zeros=""></function></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.BaseRNNCell.begin_state" title="Permalink to this definition">¶</a></dt>
+<code class="descname">begin_state</code><span class="sig-paren">(</span><em>func=<function zeros></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.BaseRNNCell.begin_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Initial state for this cell.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>func</strong> (<em>callable</em><em>, </em><em>default symbol.zeros</em>) – Function for creating initial state. Can be symbol.zeros,
+<li><strong>func</strong> (<em>callable, default symbol.zeros</em>) – Function for creating initial state. Can be symbol.zeros,
symbol.uniform, symbol.Variable etc.
Use symbol.Variable if you want to directly
feed input as states.</li>
-<li><strong>**kwargs</strong> – more keyword arguments passed to func. For example
-mean, std, dtype, etc.</li>
+<li><strong>**kwargs</strong> – <p>more keyword arguments passed to func. For example
+mean, std, dtype, etc.</p>
+</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>states</strong> – Starting states for the first RNN step.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>states</strong> –
+Starting states for the first RNN step.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">nested list of Symbol</p>
@@ -753,13 +795,14 @@ In <cite>mod.get_params()[0]</cite>, the lstm parameters are all represented as
more readable lstm parameters - c, f, i, o gates for i2h (input to hidden) and
h2h (hidden to hidden) weights.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>args</strong> (<em>dict of str -> NDArray</em>) – Dictionary containing packed weights.
usually from <cite>Module.get_params()[0]</cite>.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>args</strong> – Dictionary with unpacked weights associated with
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>args</strong> –
+Dictionary with unpacked weights associated with
this cell.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">dict of str -> NDArray</td>
@@ -780,12 +823,13 @@ this cell.</td>
<dd><p>Pack separate weight matrices into a single packed
weight.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>args</strong> (<em>dict of str -> NDArray</em>) – Dictionary containing unpacked weights.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>args</strong> – Dictionary with packed weights associated with
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>args</strong> –
+Dictionary with packed weights associated with
this cell.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">dict of str -> NDArray</td>
@@ -798,12 +842,12 @@ this cell.</td>
<code class="descname">unroll</code><span class="sig-paren">(</span><em>length</em>, <em>inputs</em>, <em>begin_state=None</em>, <em>layout='NTC'</em>, <em>merge_outputs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.BaseRNNCell.unroll" title="Permalink to this definition">¶</a></dt>
<dd><p>Unroll an RNN cell across time steps.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>length</strong> (<em>int</em>) – Number of steps to unroll.</li>
-<li><strong>inputs</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>list of Symbol</em><em>, or </em><em>None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output
+<li><strong>inputs</strong> (<em>Symbol, list of Symbol, or None</em>) – <p>If <cite>inputs</cite> is a single Symbol (usually the output
of Embedding symbol), it should have shape
(batch_size, length, ...) if layout == ‘NTC’,
or (length, batch_size, ...) if layout == ‘TNC’.</p>
@@ -811,12 +855,12 @@ or (length, batch_size, ...) if layout == ‘TNC’.</p>
previous unroll), they should all have shape
(batch_size, ...).</p>
</li>
-<li><strong>begin_state</strong> (<em>nested list of Symbol</em><em>, </em><em>default None</em>) – Input states created by <cite>begin_state()</cite>
+<li><strong>begin_state</strong> (<em>nested list of Symbol, default None</em>) – Input states created by <cite>begin_state()</cite>
or output state of another cell.
Created from <cite>begin_state()</cite> if None.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs
+<li><strong>layout</strong> (<em>str, optional</em>) – <cite>layout</cite> of input symbol. Only used if inputs
is a single Symbol.</li>
-<li><strong>merge_outputs</strong> (<em>bool</em><em>, </em><em>optional</em>) – If False, return outputs as a list of Symbols.
+<li><strong>merge_outputs</strong> (<em>bool, optional</em>) – If False, return outputs as a list of Symbols.
If True, concatenate output across time steps
and return a single symbol with shape
(batch_size, length, ...) if layout == ‘NTC’,
@@ -826,10 +870,12 @@ If None, output whatever is faster.</li>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) – Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols
+<li><strong>outputs</strong> (<em>list of Symbol or Symbol</em>) –
+Symbol (if <cite>merge_outputs</cite> is True) or list of Symbols
(if <cite>merge_outputs</cite> is False) corresponding to the output from
the RNN from this unrolling.</li>
-<li><strong>states</strong> (<em>nested list of Symbol</em>) – The new state of this RNN after this unrolling.
+<li><strong>states</strong> (<em>nested list of Symbol</em>) –
+The new state of this RNN after this unrolling.
The type of this symbol is same as the output of begin_state().</li>
</ul>
</p>
@@ -844,14 +890,14 @@ The type of this symbol is same as the output of begin_state().</li>
<em class="property">class </em><code class="descclassname">mxnet.rnn.</code><code class="descname">LSTMCell</code><span class="sig-paren">(</span><em>num_hidden</em>, <em>prefix='lstm_'</em>, <em>params=None</em>, <em>forget_bias=1.0</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.LSTMCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Long-Short Term Memory (LSTM) network cell.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>num_hidden</strong> (<em>int</em>) – Number of units in output symbol.</li>
-<li><strong>prefix</strong> (str, default ‘<a href="#id1"><span class="problematic" id="id2">lstm_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li>
-<li><strong>forget_bias</strong> (<em>bias added to forget gate</em><em>, </em><em>default 1.0.</em>) – Jozefowicz et al. 2015 recommends setting this to 1.0</li>
+<li><strong>prefix</strong> (str, default ‘<a href="#id5"><span class="problematic" id="id6">lstm_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li>
+<li><strong>params</strong> (<em>RNNParams, default None</em>) – Container for weight sharing between cells. Created if None.</li>
+<li><strong>forget_bias</strong> (<em>bias added to forget gate, default 1.0.</em>) – Jozefowicz et al. 2015 recommends setting this to 1.0</li>
</ul>
</td>
</tr>
@@ -865,13 +911,13 @@ The type of this symbol is same as the output of begin_state().</li>
Note: this is an implementation of the cuDNN version of GRUs
(slight modification compared to Cho et al. 2014).</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>num_hidden</strong> (<em>int</em>) – Number of units in output symbol.</li>
-<li><strong>prefix</strong> (str, default ‘<a href="#id3"><span class="problematic" id="id4">gru_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li>
+<li><strong>prefix</strong> (str, default ‘<a href="#id7"><span class="problematic" id="id8">gru_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li>
+<li><strong>params</strong> (<em>RNNParams, default None</em>) – Container for weight sharing between cells. Created if None.</li>
</ul>
</td>
</tr>
@@ -883,14 +929,14 @@ Note: this is an implementation of the cuDNN version of GRUs
<em class="property">class </em><code class="descclassname">mxnet.rnn.</code><code class="descname">RNNCell</code><span class="sig-paren">(</span><em>num_hidden</em>, <em>activation='tanh'</em>, <em>prefix='rnn_'</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.RNNCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Simple recurrent neural network cell.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>num_hidden</strong> (<em>int</em>) – Number of units in output symbol.</li>
-<li><strong>activation</strong> (<em>str</em><em> or </em><a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>, </em><em>default 'tanh'</em>) – Type of activation function. Options are ‘relu’ and ‘tanh’.</li>
-<li><strong>prefix</strong> (str, default ‘<a href="#id5"><span class="problematic" id="id6">rnn_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li>
+<li><strong>activation</strong> (<em>str or Symbol, default 'tanh'</em>) – Type of activation function. Options are ‘relu’ and ‘tanh’.</li>
+<li><strong>prefix</strong> (str, default ‘<a href="#id9"><span class="problematic" id="id10">rnn_</span></a>‘) – Prefix for name of layers (and name of weight if params is None).</li>
+<li><strong>params</strong> (<em>RNNParams, default None</em>) – Container for weight sharing between cells. Created if None.</li>
</ul>
</td>
</tr>
@@ -904,21 +950,21 @@ Note: this is an implementation of the cuDNN version of GRUs
Improves speed but is less flexible. Currently only
supported if using cuDNN on GPU.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>num_hidden</strong> (<em>int</em>) – Number of units in output symbol.</li>
-<li><strong>num_layers</strong> (<em>int</em><em>, </em><em>default 1</em>) – Number of layers in the cell.</li>
-<li><strong>mode</strong> (<em>str</em><em>, </em><em>default 'lstm'</em>) – Type of RNN. options are ‘rnn_relu’, ‘rnn_tanh’, ‘lstm’, ‘gru’.</li>
-<li><strong>bidirectional</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to use bidirectional unroll. The output dimension size is doubled if bidrectional.</li>
-<li><strong>dropout</strong> (<em>float</em><em>, </em><em>default 0.</em>) – Fraction of the input that gets dropped out during training time.</li>
-<li><strong>get_next_state</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to return the states that can be used as starting states next time.</li>
-<li><strong>forget_bias</strong> (<em>bias added to forget gate</em><em>, </em><em>default 1.0.</em>) – Jozefowicz et al. 2015 recommends setting this to 1.0</li>
-<li><strong>prefix</strong> (str, default ‘$mode_’ such as ‘<a href="#id7"><span class="problematic" id="id8">lstm_</span></a>‘) – Prefix for names of layers
+<li><strong>num_layers</strong> (<em>int, default 1</em>) – Number of layers in the cell.</li>
+<li><strong>mode</strong> (<em>str, default 'lstm'</em>) – Type of RNN. options are ‘rnn_relu’, ‘rnn_tanh’, ‘lstm’, ‘gru’.</li>
+<li><strong>bidirectional</strong> (<em>bool, default False</em>) – Whether to use bidirectional unroll. The output dimension size is doubled if bidrectional.</li>
+<li><strong>dropout</strong> (<em>float, default 0.</em>) – Fraction of the input that gets dropped out during training time.</li>
+<li><strong>get_next_state</strong> (<em>bool, default False</em>) – Whether to return the states that can be used as starting states next time.</li>
+<li><strong>forget_bias</strong> (<em>bias added to forget gate, default 1.0.</em>) – Jozefowicz et al. 2015 recommends setting this to 1.0</li>
+<li><strong>prefix</strong> (str, default ‘$mode_’ such as ‘<a href="#id11"><span class="problematic" id="id12">lstm_</span></a>‘) – Prefix for names of layers
(this prefix is also used for names of weights if <cite>params</cite> is None
i.e. if <cite>params</cite> are being created and not reused)</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li>
+<li><strong>params</strong> (<em>RNNParams, default None</em>) – Container for weight sharing between cells. Created if None.</li>
</ul>
</td>
</tr>
@@ -929,10 +975,11 @@ i.e. if <cite>params</cite> are being created and not reused)</li>
<code class="descname">unfuse</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.FusedRNNCell.unfuse" title="Permalink to this definition">¶</a></dt>
<dd><p>Unfuse the fused RNN in to a stack of rnn cells.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>cell</strong> – unfused cell that can be used for stepping, and can run on CPU.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>cell</strong> –
+unfused cell that can be used for stepping, and can run on CPU.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#mxnet.rnn.SequentialRNNCell" title="mxnet.rnn.SequentialRNNCell">SequentialRNNCell</a></td>
</tr>
@@ -945,10 +992,10 @@ i.e. if <cite>params</cite> are being created and not reused)</li>
<em class="property">class </em><code class="descclassname">mxnet.rnn.</code><code class="descname">SequentialRNNCell</code><span class="sig-paren">(</span><em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.SequentialRNNCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Sequantially stacking multiple RNN cells.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>params</strong> (<a class="reference internal" href="#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>params</strong> (<em>RNNParams, default None</em>) – Container for weight sharing between cells. Created if None.</td>
</tr>
</tbody>
</table>
@@ -957,8 +1004,8 @@ i.e. if <cite>params</cite> are being created and not reused)</li>
<code class="descname">add</code><span class="sig-paren">(</span><em>cell</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.SequentialRNNCell.add" title="Permalink to this definition">¶</a></dt>
<dd><p>Append a cell into the stack.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>cell</strong> (<a class="reference internal" href="#mxnet.rnn.BaseRNNCell" title="mxnet.rnn.BaseRNNCell"><em>BaseRNNCell</em></a>) – The cell to be appended. During unroll, previous cell’s output (or raw inputs if
no previous cell) is used as the input to this cell.</td>
@@ -972,15 +1019,15 @@ no previous cell) is used as the input to this cell.</td>
<em class="property">class </em><code class="descclassname">mxnet.rnn.</code><code class="descname">BidirectionalCell</code><span class="sig-paren">(</span><em>l_cell</em>, <em>r_cell</em>, <em>params=None</em>, <em>output_prefix='bi_'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.BidirectionalCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Bidirectional RNN cell.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>l_cell</strong> (<a class="reference internal" href="#mxnet.rnn.BaseRNNCell" title="mxnet.rnn.BaseRNNCell"><em>BaseRNNCell</em></a>) – cell for forward unrolling</li>
<li><strong>r_cell</strong> (<a class="reference internal" href="#mxnet.rnn.BaseRNNCell" title="mxnet.rnn.BaseRNNCell"><em>BaseRNNCell</em></a>) – cell for backward unrolling</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None.</em>) – Container for weight sharing between cells.
+<li><strong>params</strong> (<em>RNNParams, default None.</em>) – Container for weight sharing between cells.
A new RNNParams container is created if <cite>params</cite> is None.</li>
-<li><strong>output_prefix</strong> (str, default ‘<a href="#id9"><span class="problematic" id="id10">bi_</span></a>‘) – prefix for name of output</li>
+<li><strong>output_prefix</strong> (str, default ‘<a href="#id13"><span class="problematic" id="id14">bi_</span></a>‘) – prefix for name of output</li>
</ul>
</td>
</tr>
@@ -992,16 +1039,16 @@ A new RNNParams container is created if <cite>params</cite> is None.</li>
<em class="property">class </em><code class="descclassname">mxnet.rnn.</code><code class="descname">DropoutCell</code><span class="sig-paren">(</span><em>dropout</em>, <em>prefix='dropout_'</em>, <em>params=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.DropoutCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply dropout on input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>dropout</strong> (<em>float</em>) – Percentage of elements to drop out, which
is 1 - percentage to retain.</li>
-<li><strong>prefix</strong> (str, default ‘<a href="#id11"><span class="problematic" id="id12">dropout_</span></a>‘) – Prefix for names of layers
+<li><strong>prefix</strong> (str, default ‘<a href="#id15"><span class="problematic" id="id16">dropout_</span></a>‘) – Prefix for names of layers
(this prefix is also used for names of weights if <cite>params</cite> is None
i.e. if <cite>params</cite> are being created and not reused)</li>
-<li><strong>params</strong> (<a class="reference internal" href="#mxnet.rnn.RNNParams" title="mxnet.rnn.RNNParams"><em>RNNParams</em></a><em>, </em><em>default None</em>) – Container for weight sharing between cells. Created if None.</li>
+<li><strong>params</strong> (<em>RNNParams, default None</em>) – Container for weight sharing between cells. Created if None.</li>
</ul>
</td>
</tr>
@@ -1013,13 +1060,13 @@ i.e. if <cite>params</cite> are being created and not reused)</li>
<em class="property">class </em><code class="descclassname">mxnet.rnn.</code><code class="descname">ZoneoutCell</code><span class="sig-paren">(</span><em>base_cell</em>, <em>zoneout_outputs=0.0</em>, <em>zoneout_states=0.0</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.ZoneoutCell" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply Zoneout on base cell.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>base_cell</strong> (<a class="reference internal" href="#mxnet.rnn.BaseRNNCell" title="mxnet.rnn.BaseRNNCell"><em>BaseRNNCell</em></a>) – Cell on whose states to perform zoneout.</li>
-<li><strong>zoneout_outputs</strong> (<em>float</em><em>, </em><em>default 0.</em>) – Fraction of the output that gets dropped out during training time.</li>
-<li><strong>zoneout_states</strong> (<em>float</em><em>, </em><em>default 0.</em>) – Fraction of the states that gets dropped out during training time.</li>
+<li><strong>zoneout_outputs</strong> (<em>float, default 0.</em>) – Fraction of the output that gets dropped out during training time.</li>
+<li><strong>zoneout_states</strong> (<em>float, default 0.</em>) – Fraction of the states that gets dropped out during training time.</li>
</ul>
</td>
</tr>
@@ -1033,8 +1080,8 @@ i.e. if <cite>params</cite> are being created and not reused)</li>
(<a class="reference external" href="https://arxiv.org/abs/1609.08144">https://arxiv.org/abs/1609.08144</a>).</p>
<p>Output of the cell is output of the base cell plus input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>base_cell</strong> (<a class="reference internal" href="#mxnet.rnn.BaseRNNCell" title="mxnet.rnn.BaseRNNCell"><em>BaseRNNCell</em></a>) – Cell on whose outputs to add residual connection.</td>
</tr>
@@ -1047,8 +1094,8 @@ i.e. if <cite>params</cite> are being created and not reused)</li>
<dd><p>Container for holding variables.
Used by RNN cells for parameter sharing between cells.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>prefix</strong> (<em>str</em>) – Names of all variables created by this container will
be prepended with prefix.</td>
@@ -1060,12 +1107,13 @@ be prepended with prefix.</td>
<code class="descname">get</code><span class="sig-paren">(</span><em>name</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.rnn.RNNParams.get" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the variable given a name if one exists or create a new one if missing.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>name</strong> (<em>str</em>) – name of the variable</li>
-<li><strong>**kwargs</strong> – more arguments that’s passed to symbol.Variable</li>
+<li><strong>**kwargs</strong> – <p>more arguments that’s passed to symbol.Variable</p>
+</li>
</ul>
</td>
</tr>
@@ -1080,18 +1128,18 @@ be prepended with prefix.</td>
The label at each sequence step is the following token
in the sequence.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>sentences</strong> (<em>list of list of int</em>) – Encoded sentences.</li>
<li><strong>batch_size</strong> (<em>int</em>) – Batch size of the data.</li>
-<li><strong>invalid_label</strong> (<em>int</em><em>, </em><em>optional</em>) – Key for invalid label, e.g. <end-of-sentence>. The default is -1.</end-of-sentence></li>
-<li><strong>dtype</strong> (<em>str</em><em>, </em><em>optional</em>) – Data type of the encoding. The default data type is ‘float32’.</li>
-<li><strong>buckets</strong> (<em>list of int</em><em>, </em><em>optional</em>) – Size of the data buckets. Automatically generated if None.</li>
-<li><strong>data_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of the data. The default name is ‘data’.</li>
-<li><strong>label_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of the label. The default name is ‘softmax_label’.</li>
-<li><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – Format of data and label. ‘NT’ means (batch_size, length)
+<li><strong>invalid_label</strong> (<em>int, optional</em>) – Key for invalid label, e.g. <end-of-sentence>. The default is -1.</li>
+<li><strong>dtype</strong> (<em>str, optional</em>) – Data type of the encoding. The default data type is ‘float32’.</li>
+<li><strong>buckets</strong> (<em>list of int, optional</em>) – Size of the data buckets. Automatically generated if None.</li>
+<li><strong>data_name</strong> (<em>str, optional</em>) – Name of the data. The default name is ‘data’.</li>
+<li><strong>label_name</strong> (<em>str, optional</em>) – Name of the label. The default name is ‘softmax_label’.</li>
+<li><strong>layout</strong> (<em>str, optional</em>) – Format of data and label. ‘NT’ means (batch_size, length)
and ‘TN’ means (length, batch_size).</li>
</ul>
</td>
@@ -1116,23 +1164,25 @@ and ‘TN’ means (length, batch_size).</li>
from string tokens to integer indices. Unknown keys
will be added to vocabulary.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>sentences</strong> (<em>list of list of str</em>) – A list of sentences to encode. Each sentence
should be a list of string tokens.</li>
-<li><strong>vocab</strong> (<em>None</em><em> or </em><em>dict of str -> int</em>) – Optional input Vocabulary</li>
-<li><strong>invalid_label</strong> (<em>int</em><em>, </em><em>default -1</em>) – Index for invalid token, like <end-of-sentence></end-of-sentence></li>
-<li><strong>invalid_key</strong> (<em>str</em><em>, </em><em>default 'n'</em>) – Key for invalid token. Use ‘n’ for end
+<li><strong>vocab</strong> (<em>None or dict of str -> int</em>) – Optional input Vocabulary</li>
+<li><strong>invalid_label</strong> (<em>int, default -1</em>) – Index for invalid token, like <end-of-sentence></li>
+<li><strong>invalid_key</strong> (<em>str, default 'n'</em>) – Key for invalid token. Use ‘n’ for end
of sentence by default.</li>
<li><strong>start_label</strong> (<em>int</em>) – lowest index.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>result</strong> (<em>list of list of int</em>) – encoded sentences</li>
-<li><strong>vocab</strong> (<em>dict of str -> int</em>) – result vocabulary</li>
+<li><strong>result</strong> (<em>list of list of int</em>) –
+encoded sentences</li>
+<li><strong>vocab</strong> (<em>dict of str -> int</em>) –
+result vocabulary</li>
</ul>
</p>
</td>
@@ -1146,11 +1196,11 @@ of sentence by default.</li>
<dd><p>Save checkpoint for model using RNN cells.
Unpacks weight before saving.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>cells</strong> (<a class="reference internal" href="gluon.html#mxnet.gluon.rnn.RNNCell" title="mxnet.gluon.rnn.RNNCell"><em>RNNCell</em></a><em> or </em><em>list of RNNCells</em>) – The RNN cells used by this symbol.</li>
+<li><strong>cells</strong> (<em>RNNCell or list of RNNCells</em>) – The RNN cells used by this symbol.</li>
<li><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</li>
<li><strong>epoch</strong> (<em>int</em>) – The epoch number of the model.</li>
<li><strong>symbol</strong> (<a class="reference internal" href="symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input symbol</li>
@@ -1173,20 +1223,23 @@ Unpacks weight before saving.</p>
<dd><p>Load model checkpoint from file.
Pack weights after loading.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>cells</strong> (<a class="reference internal" href="gluon.html#mxnet.gluon.rnn.RNNCell" title="mxnet.gluon.rnn.RNNCell"><em>RNNCell</em></a><em> or </em><em>list of RNNCells</em>) – The RNN cells used by this symbol.</li>
+<li><strong>cells</strong> (<em>RNNCell or list of RNNCells</em>) – The RNN cells used by this symbol.</li>
<li><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</li>
<li><strong>epoch</strong> (<em>int</em>) – Epoch number of model we would like to load.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>symbol</strong> (<em>Symbol</em>) – The symbol configuration of computation network.</li>
-<li><strong>arg_params</strong> (<em>dict of str to NDArray</em>) – Model parameter, dict of name to NDArray of net’s weights.</li>
-<li><strong>aux_params</strong> (<em>dict of str to NDArray</em>) – Model parameter, dict of name to NDArray of net’s auxiliary states.</li>
+<li><strong>symbol</strong> (<em>Symbol</em>) –
+The symbol configuration of computation network.</li>
+<li><strong>arg_params</strong> (<em>dict of str to NDArray</em>) –
+Model parameter, dict of name to NDArray of net’s weights.</li>
+<li><strong>aux_params</strong> (<em>dict of str to NDArray</em>) –
+Model parameter, dict of name to NDArray of net’s auxiliary states.</li>
</ul>
</p>
</td>
@@ -1205,17 +1258,18 @@ Pack weights after loading.</p>
<dd><p>Make a callback to checkpoint Module to prefix every epoch.
unpacks weights used by cells before saving.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>cells</strong> (<a class="reference internal" href="gluon.html#mxnet.gluon.rnn.RNNCell" title="mxnet.gluon.rnn.RNNCell"><em>RNNCell</em></a><em> or </em><em>list of RNNCells</em>) – The RNN cells used by this symbol.</li>
+<li><strong>cells</strong> (<em>RNNCell or list of RNNCells</em>) – The RNN cells used by this symbol.</li>
<li><strong>prefix</strong> (<em>str</em>) – The file prefix to checkpoint to</li>
<li><strong>period</strong> (<em>int</em>) – How many epochs to wait before checkpointing. Default is 1.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>callback</strong> – The callback function that can be passed as iter_end_callback to fit.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>callback</strong> –
+The callback function that can be passed as iter_end_callback to fit.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">function</p>
diff --git a/versions/master/api/python/symbol.html b/versions/master/api/python/symbol.html
index d59800c..28bede0 100644
--- a/versions/master/api/python/symbol.html
+++ b/versions/master/api/python/symbol.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Symbol API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="index.html" rel="up" title="MXNet - Python API"/>
+<link href="index.html" rel="up" title="MXNet - Python API">
<link href="module.html" rel="next" title="Module API"/>
<link href="ndarray.html" rel="prev" title="NDArray API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
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@@ -227,17 +225,19 @@ Previous Navbar Layout End -->
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Python Documents</a><ul class="current">
<li class="toctree-l2 current"><a class="reference internal" href="index.html#table-of-contents">Table of contents</a><ul class="current">
<li class="toctree-l3"><a class="reference internal" href="ndarray.html">NDArray API</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Symbol API</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Symbol API</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
<li class="toctree-l4"><a class="reference internal" href="#the-symbol-class">The <code class="docutils literal"><span class="pre">Symbol</span></code> class</a></li>
<li class="toctree-l4"><a class="reference internal" href="#symbol-creation-routines">Symbol creation routines</a></li>
<li class="toctree-l4"><a class="reference internal" href="#symbol-manipulation-routines">Symbol manipulation routines</a></li>
<li class="toctree-l4"><a class="reference internal" href="#mathematical-functions">Mathematical functions</a></li>
<li class="toctree-l4"><a class="reference internal" href="#neural-network">Neural network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#contrib">Contrib</a></li>
<li class="toctree-l4"><a class="reference internal" href="#api-reference">API Reference</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="module.html">Module API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="autograd.html">Autograd Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="gluon.html">Gluon Package</a></li>
<li class="toctree-l3"><a class="reference internal" href="rnn.html">RNN Cell API</a></li>
<li class="toctree-l3"><a class="reference internal" href="kvstore.html">KVStore API</a></li>
@@ -269,8 +269,8 @@ Previous Navbar Layout End -->
<p>This document lists the routines of the symbolic expression package:</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#module-mxnet.symbol" title="mxnet.symbol"><code class="xref py py-obj docutils literal"><span class="pre">mxnet.symbol</span></code></a></td>
@@ -291,17 +291,17 @@ or an output of another symbol.</p>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-<span class="go"><class 'mxnet.symbol.symbol'=""></class></span>
+<span class="go"><class 'mxnet.symbol.Symbol'></span>
<span class="gp">>>> </span><span class="n">e</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class [...]
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">e</span><span class="o">.</span><span class="n">forward</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">y</span>
-<span class="go">[<ndarray 2="" @cpu(0)="">]</ndarray></span>
+<span class="go">[<NDArray 2 @cpu(0)>]</span>
<span class="gp">>>> </span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([ 4., 7.], dtype=float32)</span>
</pre></div>
</div>
<p>A detailed tutorial is available at <a class="reference external" href="http://mxnet.io/tutorials/basic/symbol.html">Symbol - Neural network graphs and auto-differentiation</a>.
-<br><br/></br></p>
+<br/><br/></p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>most operators provided in <code class="docutils literal"><span class="pre">symbol</span></code> are similar to those in <code class="docutils literal"><span class="pre">ndarray</span></code>
@@ -326,11 +326,11 @@ explicitly.</li>
<p>Composite multiple symbols into a new one by an operator.</p>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__call__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__call__" title="mxnet.symbol.Symbol.__call__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__call__</span></code></a></td>
<td>Composes symbol using inputs.</td>
</tr>
</tbody>
@@ -339,38 +339,38 @@ explicitly.</li>
<span id="arithmetic-operations"></span><h4>Arithmetic operations<a class="headerlink" href="#arithmetic-operations" title="Permalink to this headline">¶</a></h4>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__add__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__add__" title="mxnet.symbol.Symbol.__add__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__add__</span></code></a></td>
<td>x.__add__(y) <=> x+y</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__sub__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__sub__" title="mxnet.symbol.Symbol.__sub__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__sub__</span></code></a></td>
<td>x.__sub__(y) <=> x-y</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__rsub__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__rsub__" title="mxnet.symbol.Symbol.__rsub__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__rsub__</span></code></a></td>
<td>x.__rsub__(y) <=> y-x</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__neg__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__neg__" title="mxnet.symbol.Symbol.__neg__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__neg__</span></code></a></td>
<td>x.__neg__() <=> -x</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__mul__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__mul__" title="mxnet.symbol.Symbol.__mul__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__mul__</span></code></a></td>
<td>x.__mul__(y) <=> x*y</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__div__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__div__" title="mxnet.symbol.Symbol.__div__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__div__</span></code></a></td>
<td>x.__div__(y) <=> x/y</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__rdiv__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__rdiv__" title="mxnet.symbol.Symbol.__rdiv__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__rdiv__</span></code></a></td>
<td>x.__rdiv__(y) <=> y/x</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__mod__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__mod__" title="mxnet.symbol.Symbol.__mod__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__mod__</span></code></a></td>
<td>x.__mod__(y) <=> x%y</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__rmod__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__rmod__" title="mxnet.symbol.Symbol.__rmod__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__rmod__</span></code></a></td>
<td>x.__rmod__(y) <=> y%x</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__pow__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__pow__" title="mxnet.symbol.Symbol.__pow__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__pow__</span></code></a></td>
<td>x.__pow__(y) <=> x**y</td>
</tr>
</tbody>
@@ -380,26 +380,26 @@ explicitly.</li>
<span id="comparison-operators"></span><h4>Comparison operators<a class="headerlink" href="#comparison-operators" title="Permalink to this headline">¶</a></h4>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__lt__</span></code></td>
-<td>x.__lt__(y) <=> x<y< td="">
-</y<></td></tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__le__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__lt__" title="mxnet.symbol.Symbol.__lt__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__lt__</span></code></a></td>
+<td>x.__lt__(y) <=> x<y</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__le__" title="mxnet.symbol.Symbol.__le__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__le__</span></code></a></td>
<td>x.__le__(y) <=> x<=y</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__gt__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__gt__" title="mxnet.symbol.Symbol.__gt__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__gt__</span></code></a></td>
<td>x.__gt__(y) <=> x>y</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__ge__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__ge__" title="mxnet.symbol.Symbol.__ge__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__ge__</span></code></a></td>
<td>x.__ge__(y) <=> x>=y</td>
</tr>
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__eq__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__eq__" title="mxnet.symbol.Symbol.__eq__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__eq__</span></code></a></td>
<td>x.__eq__(y) <=> x==y</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__ne__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__ne__" title="mxnet.symbol.Symbol.__ne__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__ne__</span></code></a></td>
<td>x.__ne__(y) <=> x!=y</td>
</tr>
</tbody>
@@ -410,8 +410,8 @@ explicitly.</li>
<span id="query-information"></span><h3>Query information<a class="headerlink" href="#query-information" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.name" title="mxnet.symbol.Symbol.name"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.name</span></code></a></td>
@@ -442,14 +442,14 @@ explicitly.</li>
<span id="get-internal-and-output-symbol"></span><h3>Get internal and output symbol<a class="headerlink" href="#get-internal-and-output-symbol" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
-<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__getitem__</span></code></td>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__getitem__" title="mxnet.symbol.Symbol.__getitem__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__getitem__</span></code></a></td>
<td>x.__getitem__(i) <=> x[i]</td>
</tr>
-<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__iter__</span></code></td>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.Symbol.__iter__" title="mxnet.symbol.Symbol.__iter__"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.__iter__</span></code></a></td>
<td>Returns a generator object of symbol.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.get_internals" title="mxnet.symbol.Symbol.get_internals"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.get_internals</span></code></a></td>
@@ -465,8 +465,8 @@ explicitly.</li>
<span id="inference-type-and-shape"></span><h3>Inference type and shape<a class="headerlink" href="#inference-type-and-shape" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.infer_type" title="mxnet.symbol.Symbol.infer_type"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.infer_type</span></code></a></td>
@@ -485,8 +485,8 @@ explicitly.</li>
<span id="bind"></span><h3>Bind<a class="headerlink" href="#bind" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.bind" title="mxnet.symbol.Symbol.bind"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.bind</span></code></a></td>
@@ -502,8 +502,8 @@ explicitly.</li>
<span id="save"></span><h3>Save<a class="headerlink" href="#save" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Symbol.save" title="mxnet.symbol.Symbol.save"><code class="xref py py-obj docutils literal"><span class="pre">Symbol.save</span></code></a></td>
@@ -523,8 +523,8 @@ explicitly.</li>
<span id="symbol-creation-routines"></span><h2>Symbol creation routines<a class="headerlink" href="#symbol-creation-routines" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.var" title="mxnet.symbol.var"><code class="xref py py-obj docutils literal"><span class="pre">var</span></code></a></td>
@@ -548,8 +548,8 @@ explicitly.</li>
<span id="changing-shape-and-type"></span><h3>Changing shape and type<a class="headerlink" href="#changing-shape-and-type" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.cast" title="mxnet.symbol.cast"><code class="xref py py-obj docutils literal"><span class="pre">cast</span></code></a></td>
@@ -571,8 +571,8 @@ explicitly.</li>
<span id="expanding-elements"></span><h3>Expanding elements<a class="headerlink" href="#expanding-elements" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.broadcast_to" title="mxnet.symbol.broadcast_to"><code class="xref py py-obj docutils literal"><span class="pre">broadcast_to</span></code></a></td>
@@ -597,8 +597,8 @@ explicitly.</li>
<span id="rearranging-elements"></span><h3>Rearranging elements<a class="headerlink" href="#rearranging-elements" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.transpose" title="mxnet.symbol.transpose"><code class="xref py py-obj docutils literal"><span class="pre">transpose</span></code></a></td>
@@ -617,8 +617,8 @@ explicitly.</li>
<span id="joining-and-splitting-symbols"></span><h3>Joining and splitting symbols<a class="headerlink" href="#joining-and-splitting-symbols" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.concat" title="mxnet.symbol.concat"><code class="xref py py-obj docutils literal"><span class="pre">concat</span></code></a></td>
@@ -634,8 +634,8 @@ explicitly.</li>
<span id="indexing-routines"></span><h3>Indexing routines<a class="headerlink" href="#indexing-routines" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.slice" title="mxnet.symbol.slice"><code class="xref py py-obj docutils literal"><span class="pre">slice</span></code></a></td>
@@ -663,8 +663,8 @@ explicitly.</li>
<span id="id1"></span><h3>Arithmetic operations<a class="headerlink" href="#arithmetic-operations" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.broadcast_add" title="mxnet.symbol.broadcast_add"><code class="xref py py-obj docutils literal"><span class="pre">broadcast_add</span></code></a></td>
@@ -704,8 +704,8 @@ explicitly.</li>
<span id="trigonometric-functions"></span><h3>Trigonometric functions<a class="headerlink" href="#trigonometric-functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.sin" title="mxnet.symbol.sin"><code class="xref py py-obj docutils literal"><span class="pre">sin</span></code></a></td>
@@ -745,8 +745,8 @@ explicitly.</li>
<span id="hyperbolic-functions"></span><h3>Hyperbolic functions<a class="headerlink" href="#hyperbolic-functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.sinh" title="mxnet.symbol.sinh"><code class="xref py py-obj docutils literal"><span class="pre">sinh</span></code></a></td>
@@ -774,8 +774,8 @@ explicitly.</li>
<span id="reduce-functions"></span><h3>Reduce functions<a class="headerlink" href="#reduce-functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.sum" title="mxnet.symbol.sum"><code class="xref py py-obj docutils literal"><span class="pre">sum</span></code></a></td>
@@ -809,8 +809,8 @@ explicitly.</li>
<span id="rounding"></span><h3>Rounding<a class="headerlink" href="#rounding" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.round" title="mxnet.symbol.round"><code class="xref py py-obj docutils literal"><span class="pre">round</span></code></a></td>
@@ -838,8 +838,8 @@ explicitly.</li>
<span id="exponents-and-logarithms"></span><h3>Exponents and logarithms<a class="headerlink" href="#exponents-and-logarithms" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.exp" title="mxnet.symbol.exp"><code class="xref py py-obj docutils literal"><span class="pre">exp</span></code></a></td>
@@ -867,8 +867,8 @@ explicitly.</li>
<span id="powers"></span><h3>Powers<a class="headerlink" href="#powers" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.broadcast_power" title="mxnet.symbol.broadcast_power"><code class="xref py py-obj docutils literal"><span class="pre">broadcast_power</span></code></a></td>
@@ -890,8 +890,8 @@ explicitly.</li>
<span id="logic-functions"></span><h3>Logic functions<a class="headerlink" href="#logic-functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.broadcast_equal" title="mxnet.symbol.broadcast_equal"><code class="xref py py-obj docutils literal"><span class="pre">broadcast_equal</span></code></a></td>
@@ -919,8 +919,8 @@ explicitly.</li>
<span id="random-sampling"></span><h3>Random sampling<a class="headerlink" href="#random-sampling" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.random_uniform" title="mxnet.symbol.random_uniform"><code class="xref py py-obj docutils literal"><span class="pre">random_uniform</span></code></a></td>
@@ -975,8 +975,8 @@ explicitly.</li>
<span id="sorting-and-searching"></span><h3>Sorting and searching<a class="headerlink" href="#sorting-and-searching" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.sort" title="mxnet.symbol.sort"><code class="xref py py-obj docutils literal"><span class="pre">sort</span></code></a></td>
@@ -1001,8 +1001,8 @@ explicitly.</li>
<span id="linear-algebra"></span><h3>Linear Algebra<a class="headerlink" href="#linear-algebra" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.linalg_gemm" title="mxnet.symbol.linalg_gemm"><code class="xref py py-obj docutils literal"><span class="pre">linalg_gemm</span></code></a></td>
@@ -1033,8 +1033,8 @@ explicitly.</li>
<span id="miscellaneous"></span><h3>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.maximum" title="mxnet.symbol.maximum"><code class="xref py py-obj docutils literal"><span class="pre">maximum</span></code></a></td>
@@ -1074,8 +1074,8 @@ explicitly.</li>
<span id="basic"></span><h3>Basic<a class="headerlink" href="#basic" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.FullyConnected" title="mxnet.symbol.FullyConnected"><code class="xref py py-obj docutils literal"><span class="pre">FullyConnected</span></code></a></td>
@@ -1109,8 +1109,8 @@ explicitly.</li>
<span id="more"></span><h3>More<a class="headerlink" href="#more" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
-<col width="10%"></col>
-<col width="90%"></col>
+<col width="10%"/>
+<col width="90%"/>
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.Correlation" title="mxnet.symbol.Correlation"><code class="xref py py-obj docutils literal"><span class="pre">Correlation</span></code></a></td>
@@ -1147,7 +1147,7 @@ explicitly.</li>
<td>Applies dropout operation to input array.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#mxnet.symbol.BilinearSampler" title="mxnet.symbol.BilinearSampler"><code class="xref py py-obj docutils literal"><span class="pre">BilinearSampler</span></code></a></td>
-<td>Applies bilinear sampling to input feature map, which is the key of “[NIPS2015] Spatial Transformer Networks” output[batch, channel, y_dst, x_dst] = G(data[batch, channel, y_src, x_src) x_dst, y_dst enumerate all spatial locations in output x_src = grid[batch, 0, y_dst, x_dst] y_src = grid[batch, 1, y_dst, x_dst] G() denotes the bilinear interpolation kernel The out-boundary points will be padded as zeros.</td>
+<td>Applies bilinear sampling to input feature map.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#mxnet.symbol.GridGenerator" title="mxnet.symbol.GridGenerator"><code class="xref py py-obj docutils literal"><span class="pre">GridGenerator</span></code></a></td>
<td>Generates 2D sampling grid for bilinear sampling.</td>
@@ -1192,6 +1192,67 @@ explicitly.</li>
</table>
</div>
</div>
+<div class="section" id="contrib">
+<span id="contrib"></span><h2>Contrib<a class="headerlink" href="#contrib" title="Permalink to this headline">¶</a></h2>
+<div class="admonition warning">
+<p class="first admonition-title">Warning</p>
+<p class="last">This package contains experimental APIs and may change in the near future.</p>
+</div>
+<p>The <code class="docutils literal"><span class="pre">contrib.symbol</span></code> module contains many useful experimental APIs for new features. This is a place for the community to try out the new features, so that feature contributors can receive feedback.</p>
+<table border="1" class="longtable docutils">
+<colgroup>
+<col width="10%"/>
+<col width="90%"/>
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.symbol.CTCLoss" title="mxnet.contrib.symbol.CTCLoss"><code class="xref py py-obj docutils literal"><span class="pre">CTCLoss</span></code></a></td>
+<td>Connectionist Temporal Classification Loss.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.symbol.DeformableConvolution" title="mxnet.contrib.symbol.DeformableConvolution"><code class="xref py py-obj docutils literal"><span class="pre">DeformableConvolution</span></code></a></td>
+<td>Compute 2-D deformable convolution on 4-D input.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.symbol.DeformablePSROIPooling" title="mxnet.contrib.symbol.DeformablePSROIPooling"><code class="xref py py-obj docutils literal"><span class="pre">DeformablePSROIPooling</span></code></a></td>
+<td>Performs deformable position-sensitive region-of-interest pooling on inputs.The DeformablePSROIPooling operation is described in <a class="reference external" href="https://arxiv.org/abs/1703.06211.batch_size">https://arxiv.org/abs/1703.06211.batch_size</a> will change to the number of region bounding boxes after DeformablePSROIPooling</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.symbol.MultiBoxDetection" title="mxnet.contrib.symbol.MultiBoxDetection"><code class="xref py py-obj docutils literal"><span class="pre">MultiBoxDetection</span></code></a></td>
+<td>Convert multibox detection predictions.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.symbol.MultiBoxPrior" title="mxnet.contrib.symbol.MultiBoxPrior"><code class="xref py py-obj docutils literal"><span class="pre">MultiBoxPrior</span></code></a></td>
+<td>Generate prior(anchor) boxes from data, sizes and ratios.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.symbol.MultiBoxTarget" title="mxnet.contrib.symbol.MultiBoxTarget"><code class="xref py py-obj docutils literal"><span class="pre">MultiBoxTarget</span></code></a></td>
+<td>Compute Multibox training targets</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.symbol.MultiProposal" title="mxnet.contrib.symbol.MultiProposal"><code class="xref py py-obj docutils literal"><span class="pre">MultiProposal</span></code></a></td>
+<td>Generate region proposals via RPN</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.symbol.PSROIPooling" title="mxnet.contrib.symbol.PSROIPooling"><code class="xref py py-obj docutils literal"><span class="pre">PSROIPooling</span></code></a></td>
+<td>Performs region-of-interest pooling on inputs.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.symbol.Proposal" title="mxnet.contrib.symbol.Proposal"><code class="xref py py-obj docutils literal"><span class="pre">Proposal</span></code></a></td>
+<td>Generate region proposals via RPN</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.symbol.count_sketch" title="mxnet.contrib.symbol.count_sketch"><code class="xref py py-obj docutils literal"><span class="pre">count_sketch</span></code></a></td>
+<td>Apply CountSketch to input: map a d-dimension data to k-dimension data”</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.symbol.ctc_loss" title="mxnet.contrib.symbol.ctc_loss"><code class="xref py py-obj docutils literal"><span class="pre">ctc_loss</span></code></a></td>
+<td>Connectionist Temporal Classification Loss.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.symbol.dequantize" title="mxnet.contrib.symbol.dequantize"><code class="xref py py-obj docutils literal"><span class="pre">dequantize</span></code></a></td>
+<td>Dequantize the input tensor into a float tensor.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.symbol.fft" title="mxnet.contrib.symbol.fft"><code class="xref py py-obj docutils literal"><span class="pre">fft</span></code></a></td>
+<td>Apply 1D FFT to input”</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="#mxnet.contrib.symbol.ifft" title="mxnet.contrib.symbol.ifft"><code class="xref py py-obj docutils literal"><span class="pre">ifft</span></code></a></td>
+<td>Apply 1D ifft to input”</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="#mxnet.contrib.symbol.quantize" title="mxnet.contrib.symbol.quantize"><code class="xref py py-obj docutils literal"><span class="pre">quantize</span></code></a></td>
+<td>Quantize a input tensor from float to <cite>out_type</cite>, with user-specified <cite>min_range</cite> and <cite>max_range</cite>.</td>
+</tr>
+</tbody>
+</table>
+</div>
<div class="section" id="api-reference">
<span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline">¶</a></h2>
<script src="../../_static/js/auto_module_index.js" type="text/javascript"></script><span class="target" id="module-mxnet.symbol"></span><p>Symbolic configuration API of MXNet.</p>
@@ -1199,15 +1260,259 @@ explicitly.</li>
<dt id="mxnet.symbol.Symbol">
<em class="property">class </em><code class="descclassname">mxnet.symbol.</code><code class="descname">Symbol</code><span class="sig-paren">(</span><em>handle</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol" title="Permalink to this definition">¶</a></dt>
<dd><p>Symbol is symbolic graph of the mxnet.</p>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__repr__">
+<code class="descname">__repr__</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__repr__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Gets a string representation of the symbol.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__iter__">
+<code class="descname">__iter__</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__iter__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a generator object of symbol.</p>
+<p>One can loop through the returned object list to get outputs.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span><span class="o">+</span><span class="n">b</span>
+<span class="gp">>>> </span><span class="n">d</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'d'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">e</span> <span class="o">=</span> <span class="n">d</span><span class="o">+</span><span class="n">c</span>
+<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">e</span><span class="o">.</span><span class="n">get_children</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="n">out</span>
+<span class="go"><Symbol Grouped></span>
+<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">out</span><span class="p">:</span>
+<span class="gp">... </span> <span class="n">i</span>
+<span class="gp">...</span>
+<span class="go"><Symbol d></span>
+<span class="go"><Symbol _plus0></span>
+</pre></div>
+</div>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__add__">
+<code class="descname">__add__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__add__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__add__(y) <=> x+y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_add</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__sub__">
+<code class="descname">__sub__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__sub__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__sub__(y) <=> x-y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_sub</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__rsub__">
+<code class="descname">__rsub__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__rsub__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__rsub__(y) <=> y-x</p>
+<p>Only <cite>NDArray</cite> is supported for now.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="mi">3</span>
+<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="fm">__rsub__</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[-2., -2., -2.],</span>
+<span class="go"> [-2., -2., -2.]], dtype=float32)</span>
+</pre></div>
+</div>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__mul__">
+<code class="descname">__mul__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__mul__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__mul__(y) <=> x*y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_mul</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__div__">
+<code class="descname">__div__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__div__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__div__(y) <=> x/y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_div</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__rdiv__">
+<code class="descname">__rdiv__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__rdiv__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__rdiv__(y) <=> y/x</p>
+<p>Only <cite>NDArray</cite> is supported for now.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="mi">3</span>
+<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="fm">__rdiv__</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 0.33333334, 0.33333334, 0.33333334],</span>
+<span class="go"> [ 0.33333334, 0.33333334, 0.33333334]], dtype=float32)</span>
+</pre></div>
+</div>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__mod__">
+<code class="descname">__mod__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__mod__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__mod__(y) <=> x%y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_mod</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__rmod__">
+<code class="descname">__rmod__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__rmod__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__rmod__(y) <=> y%x</p>
+<p>Only <cite>NDArray</cite> is supported for now.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">*</span><span class="mi">3</span>
+<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="fm">__rmod__</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[ 1., 1., 1.,</span>
+<span class="go"> [ 1., 1., 1., dtype=float32)</span>
+</pre></div>
+</div>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__pow__">
+<code class="descname">__pow__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__pow__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__pow__(y) <=> x**y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_pow</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__neg__">
+<code class="descname">__neg__</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__neg__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__neg__() <=> -x</p>
+<p>Numerical negative, element-wise.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">a</span>
+<span class="go"><Symbol a></span>
+<span class="gp">>>> </span><span class="o">-</span><span class="n">a</span>
+<span class="go"><Symbol _mulscalar0></span>
+<span class="gp">>>> </span><span class="n">a_neg</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="fm">__neg__</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a_neg</span><span class="o">*</span><span class="n">b</span>
+<span class="gp">>>> </span><span class="n">ex</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">a</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n"> [...]
+<span class="gp">>>> </span><span class="n">ex</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="go">array([[-1., -1., -1.],</span>
+<span class="go"> [-1., -1., -1.]], dtype=float32)</span>
+</pre></div>
+</div>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__deepcopy__">
+<code class="descname">__deepcopy__</code><span class="sig-paren">(</span><em>_</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__deepcopy__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a deep copy of the input object.</p>
+<p>This function returns a deep copy of the input object including the current state
+of all its parameters such as weights, biases, etc.</p>
+<p>Any changes made to the deep copy do not reflect in the original object.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">copy</span>
+<span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">data_1</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">data_1</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">data</span>
+<span class="gp">>>> </span><span class="n">data_1</span><span class="o">.</span><span class="n">tojson</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="n">data_1</span> <span class="ow">is</span> <span class="n">data</span> <span class="c1"># Data got modified</span>
+<span class="go">False</span>
+</pre></div>
+</div>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__eq__">
+<code class="descname">__eq__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__eq__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__eq__(y) <=> x==y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_equal</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__ne__">
+<code class="descname">__ne__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__ne__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__ne__(y) <=> x!=y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_not_equal</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__gt__">
+<code class="descname">__gt__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__gt__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__gt__(y) <=> x>y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_greater</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__ge__">
+<code class="descname">__ge__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__ge__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__ge__(y) <=> x>=y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_greater_equal</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__lt__">
+<code class="descname">__lt__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__lt__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__lt__(y) <=> x<y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_lesser</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__le__">
+<code class="descname">__le__</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__le__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__le__(y) <=> x<=y</p>
+<p>Scalar input is supported.
+Broadcasting is not supported. Use <cite>broadcast_lesser_equal</cite> instead.</p>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__call__">
+<code class="descname">__call__</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Composes symbol using inputs.</p>
+<p>x.__call__(y, z) <=> x(y,z)</p>
+<p>This function internally calls <cite>_compose</cite> to compose the symbol and
+returns the composed symbol.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">net1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc1'</span><span class="p">,</span> <span class="n">num_hidden</span><span c [...]
+<span class="gp">>>> </span><span class="n">net2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'fc3'</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">composed</span> <span class="o">=</span> <span class="n">net2</span><span class="p">(</span><span class="n">fc3_data</span><span class="o">=</span><span class="n">net1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'composed'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">composed</span>
+<span class="go"><Symbol composed></span>
+<span class="gp">>>> </span><span class="n">called</span> <span class="o">=</span> <span class="n">net2</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">fc3_data</span><span class="o">=</span><span class="n">net1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'composed'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">called</span>
+<span class="go"><Symbol composed></span>
+</pre></div>
+</div>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>args</strong> – Positional arguments.</li>
+<li><strong>kwargs</strong> – Keyword arguments.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">The resulting symbol.</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="method">
+<dt id="mxnet.symbol.Symbol.__getitem__">
+<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.__getitem__" title="Permalink to this definition">¶</a></dt>
+<dd><p>x.__getitem__(i) <=> x[i]</p>
+<p>Returns a sliced view of the input symbol.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="go"><Symbol a></span>
+<span class="gp">>>> </span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go"><Symbol a></span>
+</pre></div>
+</div>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>index</strong> (<em>int or str</em>) – Indexing key</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
<dl class="attribute">
<dt id="mxnet.symbol.Symbol.name">
<code class="descname">name</code><a class="headerlink" href="#mxnet.symbol.Symbol.name" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets name string from the symbol, this function only works for non-grouped symbol.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>value</strong> – The name of this symbol, returns <code class="docutils literal"><span class="pre">None</span></code> for grouped symbol.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>value</strong> –
+The name of this symbol, returns <code class="docutils literal"><span class="pre">None</span></code> for grouped symbol.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">str</td>
</tr>
@@ -1219,18 +1524,19 @@ explicitly.</li>
<code class="descname">attr</code><span class="sig-paren">(</span><em>key</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.attr" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the attribute string for corresponding input key from the symbol.</p>
<p>This function only works for non-grouped symbols.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'mood'</span><span class="p">:</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'mood'</span><span class="p">:</span> < [...]
<span class="gp">>>> </span><span class="n">data</span><span class="o">.</span><span class="n">attr</span><span class="p">(</span><span class="s1">'mood'</span><span class="p">)</span>
<span class="go">'angry'</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>key</strong> (<em>str</em>) – The key corresponding to the desired attribute.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>value</strong> – The desired attribute value, returns <code class="docutils literal"><span class="pre">None</span></code> if the attribute does not exist.</td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>value</strong> –
+The desired attribute value, returns <code class="docutils literal"><span class="pre">None</span></code> if the attribute does not exist.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">str</td>
</tr>
@@ -1241,16 +1547,17 @@ explicitly.</li>
<dt id="mxnet.symbol.Symbol.list_attr">
<code class="descname">list_attr</code><span class="sig-paren">(</span><em>recursive=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.list_attr" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets all attributes from the symbol.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'mood'</span><span class="p">:</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'mood'</span><span class="p">:</span> < [...]
<span class="gp">>>> </span><span class="n">data</span><span class="o">.</span><span class="n">list_attr</span><span class="p">()</span>
<span class="go">{'mood': 'angry'}</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>ret</strong> – A dictionary mapping attribute keys to values.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>ret</strong> –
+A dictionary mapping attribute keys to values.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">Dict of str to str</td>
</tr>
@@ -1261,7 +1568,7 @@ explicitly.</li>
<dt id="mxnet.symbol.Symbol.attr_dict">
<code class="descname">attr_dict</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.attr_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Recursively gets all attributes from the symbol and its children.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'a1'</span><span class="p">:</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'a1'</span><span class="p">:</span><span clas [...]
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'b1'</span><span class="p">:</span><span class="s1">'b2'</span><span class="p">})</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span><span class="o">+</span><span class="n">b</span>
<span class="gp">>>> </span><span class="n">c</span><span class="o">.</span><span class="n">attr_dict</span><span class="p">()</span>
@@ -1269,10 +1576,11 @@ explicitly.</li>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>ret</strong> – There is a key in the returned dict for every child with non-empty attribute set.
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>ret</strong> –
+There is a key in the returned dict for every child with non-empty attribute set.
For each symbol, the name of the symbol is its key in the dict
and the correspond value is that symbol’s attribute list (itself a dictionary).</td>
</tr>
@@ -1287,21 +1595,22 @@ and the correspond value is that symbol’s attribute list (itself a dictionary)
<dd><p>Gets a new grouped symbol <cite>sgroup</cite>. The output of <cite>sgroup</cite> is a list of
outputs of all of the internal nodes.</p>
<p>Consider the following code:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="gp">>>> </span><span class="n">d</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">get_internals</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">d</span>
-<span class="go"><symbol grouped=""></symbol></span>
+<span class="go"><Symbol Grouped></span>
<span class="gp">>>> </span><span class="n">d</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()</span>
<span class="go">['a', 'b', '_plus4_output']</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>sgroup</strong> – A symbol group containing all internal and leaf nodes of the computation graph
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>sgroup</strong> –
+A symbol group containing all internal and leaf nodes of the computation graph
used to compute the symbol.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></td>
@@ -1314,13 +1623,13 @@ used to compute the symbol.</td>
<code class="descname">get_children</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.get_children" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets a new grouped symbol whose output contains
inputs to output nodes of the original symbol.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'y'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'z'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">y</span><span class="o">+</span><span class="n">z</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">x</span><span class="o">+</span><span class="n">a</span>
<span class="gp">>>> </span><span class="n">b</span><span class="o">.</span><span class="n">get_children</span><span class="p">()</span>
-<span class="go"><symbol grouped=""></symbol></span>
+<span class="go"><Symbol Grouped></span>
<span class="gp">>>> </span><span class="n">b</span><span class="o">.</span><span class="n">get_children</span><span class="p">()</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()</span>
<span class="go">['x', '_plus10_output']</span>
<span class="gp">>>> </span><span class="n">b</span><span class="o">.</span><span class="n">get_children</span><span class="p">()</span><span class="o">.</span><span class="n">get_children</span><span class="p">()</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()</span>
@@ -1328,13 +1637,14 @@ inputs to output nodes of the original symbol.</p>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>sgroup</strong> – The children of the head node. If the symbol has no
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>sgroup</strong> –
+The children of the head node. If the symbol has no
inputs then <code class="docutils literal"><span class="pre">None</span></code> will be returned.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a> or None</td>
+<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">Symbol or None</td>
</tr>
</tbody>
</table>
@@ -1343,7 +1653,7 @@ inputs then <code class="docutils literal"><span class="pre">None</span></code>
<dt id="mxnet.symbol.Symbol.list_arguments">
<code class="descname">list_arguments</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.list_arguments" title="Permalink to this definition">¶</a></dt>
<dd><p>Lists all the arguments in the symbol.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="gp">>>> </span><span class="n">c</span><span class="o">.</span><span class="n">list_arguments</span>
@@ -1351,10 +1661,11 @@ inputs then <code class="docutils literal"><span class="pre">None</span></code>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>args</strong> – List containing the names of all the arguments required to compute the symbol.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>args</strong> –
+List containing the names of all the arguments required to compute the symbol.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">list of string</td>
</tr>
@@ -1365,7 +1676,7 @@ inputs then <code class="docutils literal"><span class="pre">None</span></code>
<dt id="mxnet.symbol.Symbol.list_outputs">
<code class="descname">list_outputs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.list_outputs" title="Permalink to this definition">¶</a></dt>
<dd><p>Lists all the outputs in the symbol.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="gp">>>> </span><span class="n">c</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()</span>
@@ -1373,8 +1684,8 @@ inputs then <code class="docutils literal"><span class="pre">None</span></code>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">List of all the outputs.
For most symbols, this list contains only the name of this symbol.
@@ -1390,7 +1701,7 @@ in the group.</td>
<dt id="mxnet.symbol.Symbol.list_auxiliary_states">
<code class="descname">list_auxiliary_states</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.list_auxiliary_states" title="Permalink to this definition">¶</a></dt>
<dd><p>Lists all the auxiliary states in the symbol.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="gp">>>> </span><span class="n">c</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()</span>
@@ -1398,7 +1709,7 @@ in the group.</td>
</pre></div>
</div>
<p>Example of auxiliary states in <cite>BatchNorm</cite>.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">weight</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'fc1_weight'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">fc1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">name</span><span cla [...]
<span class="gp">>>> </span><span class="n">fc2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'batchnorm0'</span><span class="p">)</span>
@@ -1407,10 +1718,11 @@ in the group.</td>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>aux_states</strong> – List of the auxiliary states in input symbol.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>aux_states</strong> –
+List of the auxiliary states in input symbol.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">list of str</td>
</tr>
@@ -1427,17 +1739,18 @@ Most operators do not have auxiliary states.</p>
<code class="descname">list_inputs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.list_inputs" title="Permalink to this definition">¶</a></dt>
<dd><p>Lists all arguments and auxiliary states of this Symbol.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>inputs</strong> – List of all inputs.</td>
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>inputs</strong> –
+List of all inputs.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">list of str</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">bn</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'bn'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">bn</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'bn'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">bn</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()</span>
<span class="go">['bn_data', 'bn_gamma', 'bn_beta']</span>
<span class="gp">>>> </span><span class="n">bn</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()</span>
@@ -1456,35 +1769,40 @@ for some arguments.</p>
or keyword argument way as input. It returns a tuple of <cite>None</cite> values
if there is not enough information to deduce the missing types.</p>
<p>Inconsistencies in the known types will cause an error to be raised.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="gp">>>> </span><span class="n">arg_types</span><span class="p">,</span> <span class="n">out_types</span><span class="p">,</span> <span class="n">aux_types</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">infer_type</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">arg_types</span>
-<span class="go">[<type 'numpy.float32'="">, <type 'numpy.float32'="">]</type></type></span>
+<span class="go">[<type 'numpy.float32'>, <type 'numpy.float32'>]</span>
<span class="gp">>>> </span><span class="n">out_types</span>
-<span class="go">[<type 'numpy.float32'="">]</type></span>
+<span class="go">[<type 'numpy.float32'>]</span>
<span class="gp">>>> </span><span class="n">aux_types</span>
<span class="go">[]</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>*args</strong> – Type of known arguments in a positional way.
-Unknown type can be marked as None.</li>
-<li><strong>**kwargs</strong> – Keyword arguments of known types.</li>
+<li><strong>*args</strong> – <p>Type of known arguments in a positional way.
+Unknown type can be marked as None.</p>
+</li>
+<li><strong>**kwargs</strong> – <p>Keyword arguments of known types.</p>
+</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>arg_types</strong> (<em>list of numpy.dtype or None</em>) – List of argument types.
+<li><strong>arg_types</strong> (<em>list of numpy.dtype or None</em>) –
+List of argument types.
The order is same as the order of list_arguments().</li>
-<li><strong>out_types</strong> (<em>list of numpy.dtype or None</em>) – List of output types.
+<li><strong>out_types</strong> (<em>list of numpy.dtype or None</em>) –
+List of output types.
The order is same as the order of list_outputs().</li>
-<li><strong>aux_types</strong> (<em>list of numpy.dtype or None</em>) – List of auxiliary state types.
+<li><strong>aux_types</strong> (<em>list of numpy.dtype or None</em>) –
+List of auxiliary state types.
The order is same as the order of list_auxiliary_states().</li>
</ul>
</p>
@@ -1501,7 +1819,7 @@ some arguments.</p>
<p>This function takes the known shapes of some arguments in either positional way
or keyword argument way as input. It returns a tuple of <cite>None</cite> values
if there is not enough information to deduce the missing shapes.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="gp">>>> </span><span class="n">arg_shapes</span><span class="p">,</span> <span class="n">out_shapes</span><span class="p">,</span> <span class="n">aux_shapes</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">infer_shape</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
@@ -1517,7 +1835,7 @@ if there is not enough information to deduce the missing shapes.</p>
</div>
<p>Inconsistencies in the known shapes will cause an error to be raised.
See the following example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc1'</span><span class="p">,</span> <span class="n">num_hidden</span><span class [...]
<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc2'</span><span class="p">,</span> <span class="n">num_hidden</span><span class= [...]
@@ -1528,22 +1846,27 @@ See the following example:</p>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>*args</strong> – Shape of arguments in a positional way.
-Unknown shape can be marked as None.</li>
-<li><strong>**kwargs</strong> – Keyword arguments of the known shapes.</li>
+<li><strong>*args</strong> – <p>Shape of arguments in a positional way.
+Unknown shape can be marked as None.</p>
+</li>
+<li><strong>**kwargs</strong> – <p>Keyword arguments of the known shapes.</p>
+</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>arg_shapes</strong> (<em>list of tuple or None</em>) – List of argument shapes.
+<li><strong>arg_shapes</strong> (<em>list of tuple or None</em>) –
+List of argument shapes.
The order is same as the order of list_arguments().</li>
-<li><strong>out_shapes</strong> (<em>list of tuple or None</em>) – List of output shapes.
+<li><strong>out_shapes</strong> (<em>list of tuple or None</em>) –
+List of output shapes.
The order is same as the order of list_outputs().</li>
-<li><strong>aux_shapes</strong> (<em>list of tuple or None</em>) – List of auxiliary state shapes.
+<li><strong>aux_shapes</strong> (<em>list of tuple or None</em>) –
+List of auxiliary state shapes.
The order is same as the order of list_auxiliary_states().</li>
</ul>
</p>
@@ -1560,7 +1883,7 @@ The order is same as the order of list_auxiliary_states().</li>
except that this function can return partial results.</p>
<p>In the following example, information about fc2 is not available. So, <cite>infer_shape</cite>
will return a tuple of <cite>None</cite> values but <cite>infer_shape_partial</cite> will return partial values.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">prev</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'prev'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">fc1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc1'</span><span class="p">,</span> <span class="n">num_hidden</span><span clas [...]
<span class="gp">>>> </span><span class="n">fc2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">prev</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc2'</span><span class="p">,</span> <span class="n">num_hidden</span><span clas [...]
@@ -1577,22 +1900,27 @@ will return a tuple of <cite>None</cite> values but <cite>infer_shape_partial</c
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>*args</strong> – Shape of arguments in a positional way.
-Unknown shape can be marked as None</li>
-<li><strong>**kwargs</strong> – Keyword arguments of known shapes.</li>
+<li><strong>*args</strong> – <p>Shape of arguments in a positional way.
+Unknown shape can be marked as None</p>
+</li>
+<li><strong>**kwargs</strong> – <p>Keyword arguments of known shapes.</p>
+</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>arg_shapes</strong> (<em>list of tuple or None</em>) – List of argument shapes.
+<li><strong>arg_shapes</strong> (<em>list of tuple or None</em>) –
+List of argument shapes.
The order is same as the order of list_arguments().</li>
-<li><strong>out_shapes</strong> (<em>list of tuple or None</em>) – List of output shapes.
+<li><strong>out_shapes</strong> (<em>list of tuple or None</em>) –
+List of output shapes.
The order is same as the order of list_outputs().</li>
-<li><strong>aux_shapes</strong> (<em>list of tuple or None</em>) – List of auxiliary state shapes.
+<li><strong>aux_shapes</strong> (<em>list of tuple or None</em>) –
+List of auxiliary state shapes.
The order is same as the order of list_auxiliary_states().</li>
</ul>
</p>
@@ -1608,8 +1936,8 @@ The order is same as the order of list_auxiliary_states().</li>
<p>It contains Symbol output, variables and operators in the computation graph
with their inputs, variables and attributes.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Debug string of the symbol.</td>
</tr>
@@ -1618,12 +1946,12 @@ with their inputs, variables and attributes.</p>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="gp">>>> </span><span class="n">d</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">c</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">d</span><span class="o">.</span><span class="n">debug_str</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">d</span><span class="o">.</span><span class="n">debug_str</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">d</span><span class="o">.</span><span class="n">debug_str</span><span class="p">()</span>
<span class="go">Symbol Outputs:</span>
<span class="go"> output[0]=fullyconnected0(0)</span>
<span class="go">Variable:a</span>
@@ -1665,8 +1993,8 @@ This means the model saved by one language binding can be loaded by a different
language binding of <cite>MXNet</cite>.
You also get the benefit of being able to directly load/save from cloud storage(S3, HDFS).</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fname</strong> (<em>str</em>) – <p>The name of the file.</p>
<ul class="simple">
@@ -1706,11 +2034,11 @@ Allows specifying data types.</p>
<p>This function simplifies the binding procedure. You need to specify only input data shapes.
Before binding the executor, the function allocates arguments and auxiliary states
that were not explicitly specified. Allows specifying data types.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">exe</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">simple_bind</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">grad_req</span>< [...]
<span class="gp">>>> </span><span class="n">exe</span><span class="o">.</span><span class="n">forward</span><span class="p">()</span>
-<span class="go">[<ndarray 5x4="" @cpu(0)="">]</ndarray></span>
+<span class="go">[<NDArray 5x4 @cpu(0)>]</span>
<span class="gp">>>> </span><span class="n">exe</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 0., 0., 0., 0.],</span>
<span class="go"> [ 0., 0., 0., 0.],</span>
@@ -1718,14 +2046,14 @@ that were not explicitly specified. Allows specifying data types.</p>
<span class="go"> [ 0., 0., 0., 0.],</span>
<span class="go"> [ 0., 0., 0., 0.]], dtype=float32)</span>
<span class="gp">>>> </span><span class="n">exe</span><span class="o">.</span><span class="n">arg_arrays</span>
-<span class="go">[<ndarray 5x4="" @cpu(0)="">, <ndarray 4x4="" @cpu(0)="">, <ndarray 4="" @cpu(0)="">]</ndarray></ndarray></ndarray></span>
+<span class="go">[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]</span>
<span class="gp">>>> </span><span class="n">exe</span><span class="o">.</span><span class="n">grad_arrays</span>
-<span class="go">[<ndarray 5x4="" @cpu(0)="">, <ndarray 4x4="" @cpu(0)="">, <ndarray 4="" @cpu(0)="">]</ndarray></ndarray></ndarray></span>
+<span class="go">[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>ctx</strong> (<em>Context</em>) – The device context the generated executor to run on.</li>
@@ -1750,7 +2078,8 @@ of the current executor is not found in <cite>shared_arg_names</cite>.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>executor</strong> – The generated executor</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>executor</strong> –
+The generated executor</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">mxnet.Executor</p>
@@ -1766,25 +2095,25 @@ of the current executor is not found in <cite>shared_arg_names</cite>.</li>
<p>We first declare the computation and then bind to the data to run.
This function returns an executor which provides method <cite>forward()</cite> method for evaluation
and a <cite>outputs()</cite> method to get all the results.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
-<span class="go"><symbol _plus1=""></symbol></span>
+<span class="go"><Symbol _plus1></span>
<span class="gp">>>> </span><span class="n">ex</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="s1">'a'</span> <span class="p">:</span> <span class="n">mx</span><span cla [...]
<span class="gp">>>> </span><span class="n">ex</span><span class="o">.</span><span class="n">forward</span><span class="p">()</span>
-<span class="go">[<ndarray 2x3="" @cpu(0)="">]</ndarray></span>
+<span class="go">[<NDArray 2x3 @cpu(0)>]</span>
<span class="gp">>>> </span><span class="n">ex</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 2. 2. 2.]</span>
<span class="go">[ 2. 2. 2.]]</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>ctx</strong> (<em>Context</em>) – The device context the generated executor to run on.</li>
-<li><strong>args</strong> (<em>list of NDArray</em><em> or </em><em>dict of str to NDArray</em>) – <p>Input arguments to the symbol.</p>
+<li><strong>args</strong> (<em>list of NDArray or dict of str to NDArray</em>) – <p>Input arguments to the symbol.</p>
<ul>
<li>If the input type is a list of <cite>NDArray</cite>, the order should be same as the order
of <cite>list_arguments()</cite>.</li>
@@ -1805,7 +2134,7 @@ for required argument gradient.
Only the specified argument gradient will be calculated.</li>
</ul>
</li>
-<li><strong>grad_req</strong> (<em>{'write'</em><em>, </em><em>'add'</em><em>, </em><em>'null'}</em><em>, or </em><em>list of str</em><em> or </em><em>dict of str to str</em><em>, </em><em>optional</em>) – <p>To specify how we should update the gradient to the <cite>args_grad</cite>.</p>
+<li><strong>grad_req</strong> (<em>{'write', 'add', 'null'}, or list of str or dict of str to str, optional</em>) – <p>To specify how we should update the gradient to the <cite>args_grad</cite>.</p>
<ul>
<li>‘write’ means everytime gradient is write to specified <cite>args_grad</cite> <cite>NDArray</cite>.</li>
<li>‘add’ means everytime gradient is add to the specified NDArray.</li>
@@ -1829,7 +2158,8 @@ used in parallel with it.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>executor</strong> – The generated executor</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>executor</strong> –
+The generated executor</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Executor</p>
@@ -1857,12 +2187,13 @@ gradient they interested in.</p>
<p class="last">This function is currently not implemented.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>wrt</strong> (<em>Array of String</em>) – keyword arguments of the symbol that the gradients are taken.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>grad</strong> – A gradient Symbol with returns to be the corresponding gradients.</td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>grad</strong> –
+A gradient Symbol with returns to be the corresponding gradients.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></td>
</tr>
@@ -1879,20 +2210,20 @@ For the common use case, where you might repeatedly evaluate with same arguments
eval is slow.
In that case, you should call <cite>bind</cite> once and then repeatedly call forward.
This function allows simpler syntax for less cumbersome introspection.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="gp">>>> </span><span class="n">ex</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">ctx</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class= [...]
<span class="gp">>>> </span><span class="n">ex</span>
-<span class="go">[<ndarray 2x3="" @cpu(0)="">]</ndarray></span>
+<span class="go">[<NDArray 2x3 @cpu(0)>]</span>
<span class="gp">>>> </span><span class="n">ex</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([[ 2., 2., 2.],</span>
<span class="go"> [ 2., 2., 2.]], dtype=float32)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>ctx</strong> (<em>Context</em>) – The device context the generated executor to run on.</li>
@@ -1916,8 +2247,8 @@ This function allows simpler syntax for less cumbersome introspection.</p>
<code class="descname">reshape</code><span class="sig-paren">(</span><em>shape</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Symbol.reshape" title="Permalink to this definition">¶</a></dt>
<dd><p>Shorthand for mxnet.sym.reshape.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>shape</strong> (<em>tuple of int</em>) – The new shape should not change the array size, namely
<code class="docutils literal"><span class="pre">np.prod(new_shape)</span></code> should be equal to <code class="docutils literal"><span class="pre">np.prod(self.shape)</span></code>.
@@ -1936,14 +2267,14 @@ from the length of the array and remaining dimensions.</td>
<dt id="mxnet.symbol.var">
<code class="descclassname">mxnet.symbol.</code><code class="descname">var</code><span class="sig-paren">(</span><em>name</em>, <em>attr=None</em>, <em>shape=None</em>, <em>lr_mult=None</em>, <em>wd_mult=None</em>, <em>dtype=None</em>, <em>init=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.var" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a symbolic variable with specified name.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <sp [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <spa [...]
<span class="gp">>>> </span><span class="n">data</span>
-<span class="go"><symbol data=""></symbol></span>
+<span class="go"><Symbol data></span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>str</em>) – Variable name.</li>
@@ -1953,13 +2284,14 @@ If one has specified a different shape for this variable using
a keyword argument when calling shape inference, this shape information will be ignored.</li>
<li><strong>lr_mult</strong> (<em>float</em>) – The learning rate multiplier for input variable.</li>
<li><strong>wd_mult</strong> (<em>float</em>) – Weight decay multiplier for input variable.</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The dtype for input variable. If not specified, this value will be inferred.</li>
-<li><strong>init</strong> (<a class="reference internal" href="optimization.html#module-mxnet.initializer" title="mxnet.initializer"><em>initializer</em></a><em> (</em><em>mxnet.init.*</em><em>)</em><em></em>) – Initializer for this variable to (optionally) override the default initializer.</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype</em>) – The dtype for input variable. If not specified, this value will be inferred.</li>
+<li><strong>init</strong> (<em>initializer (mxnet.init.*)</em>) – Initializer for this variable to (optionally) override the default initializer.</li>
<li><strong>kwargs</strong> (<em>Additional attribute variables</em>) – Additional attributes must start and end with double underscores.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>variable</strong> – A symbol corresponding to an input to the computation graph.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>variable</strong> –
+A symbol corresponding to an input to the computation graph.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
@@ -1972,14 +2304,14 @@ a keyword argument when calling shape inference, this shape information will be
<dt id="mxnet.symbol.Variable">
<code class="descclassname">mxnet.symbol.</code><code class="descname">Variable</code><span class="sig-paren">(</span><em>name</em>, <em>attr=None</em>, <em>shape=None</em>, <em>lr_mult=None</em>, <em>wd_mult=None</em>, <em>dtype=None</em>, <em>init=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Variable" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a symbolic variable with specified name.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <sp [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">attr</span><span class="o">=</span><span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <spa [...]
<span class="gp">>>> </span><span class="n">data</span>
-<span class="go"><symbol data=""></symbol></span>
+<span class="go"><Symbol data></span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>str</em>) – Variable name.</li>
@@ -1989,13 +2321,14 @@ If one has specified a different shape for this variable using
a keyword argument when calling shape inference, this shape information will be ignored.</li>
<li><strong>lr_mult</strong> (<em>float</em>) – The learning rate multiplier for input variable.</li>
<li><strong>wd_mult</strong> (<em>float</em>) – Weight decay multiplier for input variable.</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The dtype for input variable. If not specified, this value will be inferred.</li>
-<li><strong>init</strong> (<a class="reference internal" href="optimization.html#module-mxnet.initializer" title="mxnet.initializer"><em>initializer</em></a><em> (</em><em>mxnet.init.*</em><em>)</em><em></em>) – Initializer for this variable to (optionally) override the default initializer.</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype</em>) – The dtype for input variable. If not specified, this value will be inferred.</li>
+<li><strong>init</strong> (<em>initializer (mxnet.init.*)</em>) – Initializer for this variable to (optionally) override the default initializer.</li>
<li><strong>kwargs</strong> (<em>Additional attribute variables</em>) – Additional attributes must start and end with double underscores.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>variable</strong> – A symbol corresponding to an input to the computation graph.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>variable</strong> –
+A symbol corresponding to an input to the computation graph.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
@@ -2008,19 +2341,20 @@ a keyword argument when calling shape inference, this shape information will be
<dt id="mxnet.symbol.Group">
<code class="descclassname">mxnet.symbol.</code><code class="descname">Group</code><span class="sig-paren">(</span><em>symbols</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Group" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a symbol that contains a collection of other symbols, grouped together.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Group</span><span class="p">([</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">])</span>
-<span class="go"><symbol grouped=""></symbol></span>
+<span class="go"><Symbol Grouped></span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>symbols</strong> (<em>list</em>) – List of symbols to be grouped.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>sym</strong> – A group symbol.</td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>sym</strong> –
+A group symbol.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></td>
</tr>
@@ -2036,8 +2370,8 @@ The advantage of load/save is the file is language agnostic.
This means the file saved using save can be loaded by other language binding of mxnet.
You also get the benefit being able to directly load/save from cloud storage(S3, HDFS).</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fname</strong> (<em>str</em>) – <p>The name of the file, examples:</p>
<ul class="simple">
@@ -2047,7 +2381,8 @@ You also get the benefit being able to directly load/save from cloud storage(S3,
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>sym</strong> – The loaded symbol.</td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>sym</strong> –
+The loaded symbol.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></td>
</tr>
@@ -2066,12 +2401,13 @@ You also get the benefit being able to directly load/save from cloud storage(S3,
<code class="descclassname">mxnet.symbol.</code><code class="descname">load_json</code><span class="sig-paren">(</span><em>json_str</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.load_json" title="Permalink to this definition">¶</a></dt>
<dd><p>Loads symbol from json string.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>json_str</strong> (<em>str</em>) – A JSON string.</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>sym</strong> – The loaded symbol.</td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>sym</strong> –
+The loaded symbol.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></td>
</tr>
@@ -2092,25 +2428,25 @@ You also get the benefit being able to directly load/save from cloud storage(S3,
<p>Both inputs can be Symbol or scalar number.
Broadcasting is not supported. Use <cite>broadcast_pow</cite> instead.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>base</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>scalar</em>) – The base symbol</li>
-<li><strong>exp</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>scalar</em>) – The exponent symbol</li>
+<li><strong>base</strong> (<em>Symbol or scalar</em>) – The base symbol</li>
+<li><strong>exp</strong> (<em>Symbol or scalar</em>) – The exponent symbol</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The bases in x raised to the exponents in y.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a> or scalar</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Symbol or scalar</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="go">8</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'y'</span><span class="p">)</span>
@@ -2132,25 +2468,25 @@ Broadcasting is not supported. Use <cite>broadcast_pow</cite> instead.</p>
<dd><p>Returns element-wise maximum of the input elements.</p>
<p>Both inputs can be Symbol or scalar number. Broadcasting is not supported.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>left</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>scalar</em>) – First symbol to be compared.</li>
-<li><strong>right</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>scalar</em>) – Second symbol to be compared.</li>
+<li><strong>left</strong> (<em>Symbol or scalar</em>) – First symbol to be compared.</li>
+<li><strong>right</strong> (<em>Symbol or scalar</em>) – Second symbol to be compared.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The element-wise maximum of the input symbols.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a> or scalar</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Symbol or scalar</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">)</span>
<span class="go">3.5</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'y'</span><span class="p">)</span>
@@ -2169,25 +2505,25 @@ Broadcasting is not supported. Use <cite>broadcast_pow</cite> instead.</p>
<dd><p>Returns element-wise minimum of the input elements.</p>
<p>Both inputs can be Symbol or scalar number. Broadcasting is not supported.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>left</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>scalar</em>) – First symbol to be compared.</li>
-<li><strong>right</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>scalar</em>) – Second symbol to be compared.</li>
+<li><strong>left</strong> (<em>Symbol or scalar</em>) – First symbol to be compared.</li>
+<li><strong>right</strong> (<em>Symbol or scalar</em>) – Second symbol to be compared.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The element-wise minimum of the input symbols.</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a> or scalar</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Symbol or scalar</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">)</span>
<span class="go">2</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'y'</span><span class="p">)</span>
@@ -2207,25 +2543,25 @@ Broadcasting is not supported. Use <cite>broadcast_pow</cite> instead.</p>
<p>Equivalent to <span class="math">\(\sqrt(left^2 + right^2)\)</span>, element-wise.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>left</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>scalar</em>) – First leg of the triangle(s).</li>
-<li><strong>right</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>scalar</em>) – Second leg of the triangle(s).</li>
+<li><strong>left</strong> (<em>Symbol or scalar</em>) – First leg of the triangle(s).</li>
+<li><strong>right</strong> (<em>Symbol or scalar</em>) – Second leg of the triangle(s).</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The hypotenuse of the triangle(s)</p>
</td>
</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a> or scalar</p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Symbol or scalar</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="go">5.0</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'y'</span><span class="p">)</span>
@@ -2243,16 +2579,17 @@ Both inputs can be Symbol or scalar number. Broadcasting is not supported.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">zeros</code><span class="sig-paren">(</span><em>shape</em>, <em>dtype=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.zeros" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new symbol of given shape and type, filled with zeros.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>shape</strong> (<em>int</em><em> or </em><em>sequence of ints</em>) – Shape of the new array.</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em><em>, </em><em>optional</em>) – The value type of the inner value, default to <code class="docutils literal"><span class="pre">np.float32</span></code>.</li>
+<li><strong>shape</strong> (<em>int or sequence of ints</em>) – Shape of the new array.</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype, optional</em>) – The value type of the inner value, default to <code class="docutils literal"><span class="pre">np.float32</span></code>.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The created Symbol.</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The created Symbol.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
@@ -2266,16 +2603,42 @@ Both inputs can be Symbol or scalar number. Broadcasting is not supported.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">ones</code><span class="sig-paren">(</span><em>shape</em>, <em>dtype=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.ones" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new symbol of given shape and type, filled with ones.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>shape</strong> (<em>int</em><em> or </em><em>sequence of ints</em>) – Shape of the new array.</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em><em>, </em><em>optional</em>) – The value type of the inner value, default to <code class="docutils literal"><span class="pre">np.float32</span></code>.</li>
+<li><strong>shape</strong> (<em>int or sequence of ints</em>) – Shape of the new array.</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype, optional</em>) – The value type of the inner value, default to <code class="docutils literal"><span class="pre">np.float32</span></code>.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The created Symbol</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The created Symbol</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.symbol.full">
+<code class="descclassname">mxnet.symbol.</code><code class="descname">full</code><span class="sig-paren">(</span><em>shape</em>, <em>val</em>, <em>dtype=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.full" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a new array of given shape and type, filled with the given value <cite>val</cite>.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>shape</strong> (<em>int or sequence of ints</em>) – Shape of the new array.</li>
+<li><strong>val</strong> (<em>scalar</em>) – Fill value.</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype, optional</em>) – The value type of the inner value, default to <code class="docutils literal"><span class="pre">np.float32</span></code>.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The created Symbol</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
@@ -2289,20 +2652,21 @@ Both inputs can be Symbol or scalar number. Broadcasting is not supported.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">arange</code><span class="sig-paren">(</span><em>start</em>, <em>stop=None</em>, <em>step=1.0</em>, <em>repeat=1</em>, <em>name=None</em>, <em>dtype=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.arange" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns evenly spaced values within a given interval.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>start</strong> (<em>number</em>) – Start of interval. The interval includes this value. The default start value is 0.</li>
-<li><strong>stop</strong> (<em>number</em><em>, </em><em>optional</em>) – End of interval. The interval does not include this value.</li>
-<li><strong>step</strong> (<em>number</em><em>, </em><em>optional</em>) – Spacing between values.</li>
-<li><strong>repeat</strong> (<em>int</em><em>, </em><em>optional</em>) – “The repeating time of all elements.
+<li><strong>stop</strong> (<em>number, optional</em>) – End of interval. The interval does not include this value.</li>
+<li><strong>step</strong> (<em>number, optional</em>) – Spacing between values.</li>
+<li><strong>repeat</strong> (<em>int, optional</em>) – “The repeating time of all elements.
E.g repeat=3, the element a will be repeated three times –> a, a, a.</li>
-<li><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em><em>, </em><em>optional</em>) – The value type of the inner value, default to <code class="docutils literal"><span class="pre">np.float32</span></code>.</li>
+<li><strong>dtype</strong> (<em>str or numpy.dtype, optional</em>) – The value type of the inner value, default to <code class="docutils literal"><span class="pre">np.float32</span></code>.</li>
</ul>
</td>
</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> – The created Symbol</p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>out</strong> –
+The created Symbol</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
@@ -2324,13 +2688,13 @@ E.g repeat=3, the element a will be repeated three times –> a, a, a.</li>
</ul>
<p>Defined in src/operator/activation.cc:L73</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array to activation function.</li>
-<li><strong>act_type</strong> (<em>{'relu'</em><em>, </em><em>'sigmoid'</em><em>, </em><em>'softrelu'</em><em>, </em><em>'tanh'}</em><em>, </em><em>required</em>) – Activation function to be applied.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>act_type</strong> (<em>{'relu', 'sigmoid', 'softrelu', 'tanh'}, required</em>) – Activation function to be applied.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2344,16 +2708,16 @@ E.g repeat=3, the element a will be repeated three times –> a, a, a.</li>
</table>
<p class="rubric">Examples</p>
<p>A one-hidden-layer MLP with ReLU activation:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mlp</span> <span class="o">=</span> <span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'proj'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mlp</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">mlp</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'activation'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mlp</span> <span class="o">=</span> <span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">mlp</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'mlp'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mlp</span>
-<span class="go"><symbol mlp=""></symbol></span>
+<span class="go"><Symbol mlp></span>
</pre></div>
</div>
<p>ReLU activation</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">test_suites</span> <span class="o">=</span> <span class="p">[</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">test_suites</span> <span class="o">=</span> <span class="p">[</span>
<span class="gp">... </span><span class="p">(</span><span class="s1">'relu'</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
<span class="gp">... </span><span class="p">(</span><span class="s1">'sigmoid'</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))),</span>
<span class="gp">... </span><span class="p">(</span><span class="s1">'tanh'</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span>
@@ -2364,7 +2728,7 @@ E.g repeat=3, the element a will be repeated three times –> a, a, a.</li>
<span class="gp">... </span><span class="n">op</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="n">act_type</span><span class="o">=</span><span class="n">act_type</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'act'</span><span class="p">)</span>
<span class="gp">... </span><span class="n">y</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">simple_forward</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">act_data</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
<span class="gp">... </span><span class="n">y_np</span> <span class="o">=</span> <span class="n">numpy_impl</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="gp">... </span><span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">: </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">act_type</span><span class="p">,</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">almost_equal</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class= [...]
+<span class="gp">... </span><span class="k">print</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">: </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">act_type</span><span class="p">,</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">almost_equal</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class=" [...]
<span class="go">relu: True</span>
<span class="go">sigmoid: True</span>
<span class="go">tanh: True</span>
@@ -2394,7 +2758,7 @@ have shape <em>(k,)</em>. If <code class="docutils literal"><span class="pre">ou
states, <code class="docutils literal"><span class="pre">moving_mean</span></code> and <code class="docutils literal"><span class="pre">moving_var</span></code>, which are <em>k</em>-length
vectors. They are global statistics for the whole dataset, which are updated
by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">moving_mean</span> <span class="o">=</span> <span class="n">moving_mean</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_mean</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">moving_mean</span> <span class="o">=</span> <span class="n">moving_mean</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_mean</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
<span class="n">moving_var</span> <span class="o">=</span> <span class="n">moving_var</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_var</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
</pre></div>
</div>
@@ -2408,8 +2772,8 @@ axis to be the last item in the input shape.</p>
then set <code class="docutils literal"><span class="pre">gamma</span></code> to 1 and its gradient to 0.</p>
<p>Defined in src/operator/batch_norm.cc:L381</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to batch normalization</li>
@@ -2417,14 +2781,14 @@ then set <code class="docutils literal"><span class="pre">gamma</span></code> to
<li><strong>beta</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – beta array</li>
<li><strong>moving_mean</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – running mean of input</li>
<li><strong>moving_var</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – running variance of input</li>
-<li><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.001</em>) – Epsilon to prevent div 0. Must be bigger than CUDNN_BN_MIN_EPSILON defined in cudnn.h when using cudnn (usually 1e-5)</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – Momentum for moving average</li>
-<li><strong>fix_gamma</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Fix gamma while training</li>
-<li><strong>use_global_stats</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</li>
-<li><strong>output_mean_var</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Output All,normal mean and var</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Specify which shape axis the channel is specified</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Do not select CUDNN operator, if available</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>eps</strong> (<em>double, optional, default=0.001</em>) – Epsilon to prevent div 0. Must be no less than CUDNN_BN_MIN_EPSILON defined in cudnn.h when using cudnn (usually 1e-5)</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0.9</em>) – Momentum for moving average</li>
+<li><strong>fix_gamma</strong> (<em>boolean, optional, default=True</em>) – Fix gamma while training</li>
+<li><strong>use_global_stats</strong> (<em>boolean, optional, default=False</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</li>
+<li><strong>output_mean_var</strong> (<em>boolean, optional, default=False</em>) – Output All,normal mean and var</li>
+<li><strong>axis</strong> (<em>int, optional, default='1'</em>) – Specify which shape axis the channel is specified</li>
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Do not select CUDNN operator, if available</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2459,7 +2823,7 @@ have shape <em>(k,)</em>. If <code class="docutils literal"><span class="pre">ou
states, <code class="docutils literal"><span class="pre">moving_mean</span></code> and <code class="docutils literal"><span class="pre">moving_var</span></code>, which are <em>k</em>-length
vectors. They are global statistics for the whole dataset, which are updated
by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">moving_mean</span> <span class="o">=</span> <span class="n">moving_mean</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_mean</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">moving_mean</span> <span class="o">=</span> <span class="n">moving_mean</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_mean</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
<span class="n">moving_var</span> <span class="o">=</span> <span class="n">moving_var</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_var</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span>
</pre></div>
</div>
@@ -2470,19 +2834,19 @@ the output. It is often used during inference.</p>
then set <code class="docutils literal"><span class="pre">gamma</span></code> to 1 and its gradient to 0.</p>
<p>Defined in src/operator/batch_norm_v1.cc:L71</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to batch normalization</li>
<li><strong>gamma</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – gamma array</li>
<li><strong>beta</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – beta array</li>
-<li><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.001</em>) – Epsilon to prevent div 0</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – Momentum for moving average</li>
-<li><strong>fix_gamma</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Fix gamma while training</li>
-<li><strong>use_global_stats</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</li>
-<li><strong>output_mean_var</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Output All,normal mean and var</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>eps</strong> (<em>float, optional, default=0.001</em>) – Epsilon to prevent div 0</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0.9</em>) – Momentum for moving average</li>
+<li><strong>fix_gamma</strong> (<em>boolean, optional, default=True</em>) – Fix gamma while training</li>
+<li><strong>use_global_stats</strong> (<em>boolean, optional, default=False</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</li>
+<li><strong>output_mean_var</strong> (<em>boolean, optional, default=False</em>) – Output All,normal mean and var</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2498,25 +2862,79 @@ then set <code class="docutils literal"><span class="pre">gamma</span></code> to
<dl class="function">
<dt id="mxnet.symbol.BilinearSampler">
<code class="descclassname">mxnet.symbol.</code><code class="descname">BilinearSampler</code><span class="sig-paren">(</span><em>data=None</em>, <em>grid=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.BilinearSampler" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="docutils">
-<dt>Applies bilinear sampling to input feature map, which is the key of “[NIPS2015] Spatial Transformer Networks”</dt>
-<dd>output[batch, channel, y_dst, x_dst] = G(data[batch, channel, y_src, x_src)
-x_dst, y_dst enumerate all spatial locations in output
-x_src = grid[batch, 0, y_dst, x_dst]
-y_src = grid[batch, 1, y_dst, x_dst]
-G() denotes the bilinear interpolation kernel</dd>
-</dl>
-<p>The out-boundary points will be padded as zeros. (The boundary is defined to be [-1, 1])
-The shape of output will be (data.shape[0], data.shape[1], grid.shape[2], grid.shape[3])
-The operator assumes that grid has been nomalized. If you want to design a CustomOp to manipulate grid, please refer to GridGeneratorOp.</p>
+<dd><p>Applies bilinear sampling to input feature map.</p>
+<p>Bilinear Sampling is the key of [NIPS2015] “Spatial Transformer Networks”. The usage of the operator is very similar to remap function in OpenCV,
+except that the operator has the backward pass.</p>
+<p>Given <span class="math">\(data\)</span> and <span class="math">\(grid\)</span>, then the output is computed by</p>
+<div class="math">
+\[\begin{split}x_{src} = grid[batch, 0, y_{dst}, x_{dst}] \\
+y_{src} = grid[batch, 1, y_{dst}, x_{dst}] \\
+output[batch, channel, y_{dst}, x_{dst}] = G(data[batch, channel, y_{src}, x_{src})\end{split}\]</div>
+<p><span class="math">\(x_{dst}\)</span>, <span class="math">\(y_{dst}\)</span> enumerate all spatial locations in <span class="math">\(output\)</span>, and <span class="math">\(G()\)</span> denotes the bilinear interpolation kernel.
+The out-boundary points will be padded with zeros.The shape of the output will be (data.shape[0], data.shape[1], grid.shape[2], grid.shape[3]).</p>
+<p>The operator assumes that <span class="math">\(data\)</span> has ‘NCHW’ layout and <span class="math">\(grid\)</span> has been normalized to [-1, 1].</p>
+<p>BilinearSampler often cooperates with GridGenerator which generates sampling grids for BilinearSampler.
+GridGenerator supports two kinds of transformation: <code class="docutils literal"><span class="pre">affine</span></code> and <code class="docutils literal"><span class="pre">warp</span></code>.
+If users want to design a CustomOp to manipulate <span class="math">\(grid\)</span>, please firstly refer to the code of GridGenerator.</p>
+<p>Example 1:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span>## Zoom out data two times
+data = array([[[[1, 4, 3, 6],
+ [1, 8, 8, 9],
+ [0, 4, 1, 5],
+ [1, 0, 1, 3]]]])
+
+affine_matrix = array([[2, 0, 0],
+ [0, 2, 0]])
+
+affine_matrix = reshape(affine_matrix, shape=(1, 6))
+
+grid = GridGenerator(data=affine_matrix, transform_type='affine', target_shape=(4, 4))
+
+out = BilinearSampler(data, grid)
+
+out
+[[[[ 0, 0, 0, 0],
+ [ 0, 3.5, 6.5, 0],
+ [ 0, 1.25, 2.5, 0],
+ [ 0, 0, 0, 0]]]
+</pre></div>
+</div>
+<p>Example 2:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span>## shift data horizontally by -1 pixel
+
+data = array([[[[1, 4, 3, 6],
+ [1, 8, 8, 9],
+ [0, 4, 1, 5],
+ [1, 0, 1, 3]]]])
+
+warp_maxtrix = array([[[[1, 1, 1, 1],
+ [1, 1, 1, 1],
+ [1, 1, 1, 1],
+ [1, 1, 1, 1]],
+ [[0, 0, 0, 0],
+ [0, 0, 0, 0],
+ [0, 0, 0, 0],
+ [0, 0, 0, 0]]]])
+
+grid = GridGenerator(data=warp_matrix, transform_type='warp')
+out = BilinearSampler(data, grid)
+
+out
+[[[[ 4, 3, 6, 0],
+ [ 8, 8, 9, 0],
+ [ 4, 1, 5, 0],
+ [ 0, 1, 3, 0]]]
+</pre></div>
+</div>
+<p>Defined in src/operator/bilinear_sampler.cc:L226</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the BilinearsamplerOp.</li>
<li><strong>grid</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input grid to the BilinearsamplerOp.grid has two channels: x_src, y_src</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2537,32 +2955,32 @@ The operator assumes that grid has been nomalized. If you want to design a Custo
in the backward direction. In other words, this operator prevents the contribution
of its inputs to be taken into account for computing gradients.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">v1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-<span class="n">v2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
-<span class="n">b</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
-<span class="n">b_stop_grad</span> <span class="o">=</span> <span class="n">stop_gradient</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">b</span><span class="p">)</span>
-<span class="n">loss</span> <span class="o">=</span> <span class="n">MakeLoss</span><span class="p">(</span><span class="n">b_stop_grad</span> <span class="o">+</span> <span class="n">a</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>v1 = [1, 2]
+v2 = [0, 1]
+a = Variable('a')
+b = Variable('b')
+b_stop_grad = stop_gradient(3 * b)
+loss = MakeLoss(b_stop_grad + a)
-<span class="n">executor</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">simple_bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">a</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">b</span><span class="o">=</span><sp [...]
-<span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="n">v1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">v2</span><span class="p">)</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">outputs</span>
-<span class="p">[</span> <span class="mf">1.</span> <span class="mf">5.</span><span class="p">]</span>
+executor = loss.simple_bind(ctx=cpu(), a=(1,2), b=(1,2))
+executor.forward(is_train=True, a=v1, b=v2)
+executor.outputs
+[ 1. 5.]
-<span class="n">executor</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">grad_arrays</span>
-<span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
+executor.backward()
+executor.grad_arrays
+[ 0. 0.]
+[ 1. 1.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L99</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2584,20 +3002,20 @@ of its inputs to be taken into account for computing gradients.</p>
<p class="last"><code class="docutils literal"><span class="pre">Cast</span></code> is deprecated. Use <code class="docutils literal"><span class="pre">cast</span></code> instead.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">cast</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cast</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">cast</span><span class="p">([</span><span class="mf">1e20</span><span class="p">,</span> <span class="mf">11.1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float16'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="n">inf</span><span class="p">,</span> <span class="mf">11.09375</span><span class="p">]</span>
<span class="n">cast</span><span class="p">([</span><span class="mi">300</span><span class="p">,</span> <span class="mf">11.1</span><span class="p">,</span> <span class="mf">10.9</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'uint8'</span><span class="p">)</span> <span class="o">=</span> [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L175</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input.</li>
-<li><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>, </em><em>required</em>) – Output data type.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>dtype</strong> (<em>{'float16', 'float32', 'float64', 'int32', 'uint8'}, required</em>) – Output data type.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2623,37 +3041,37 @@ which they will be concatenated.
The dimension of the output array along the concatenated axis will be equal
to the sum of the corresponding dimensions of the input arrays.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">]]</span>
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">]]</span>
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">6</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">],[</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[1,1],[2,2]]
+y = [[3,3],[4,4],[5,5]]
+z = [[6,6], [7,7],[8,8]]
-<span class="n">concat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+concat(x,y,z,dim=0) = [[ 1., 1.],
+ [ 2., 2.],
+ [ 3., 3.],
+ [ 4., 4.],
+ [ 5., 5.],
+ [ 6., 6.],
+ [ 7., 7.],
+ [ 8., 8.]]
-<span class="n">Note</span> <span class="n">that</span> <span class="n">you</span> <span class="n">cannot</span> <span class="n">concat</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span> <span class="n">along</span> <span class="n">dimension</span> <span class="mi">1</span> <span class="n">since</span> <span class="n">dimension</span>
-<span class="mi">0</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">the</span> <span class="n">same</span> <span class="k">for</span> <span class="nb">all</span> <span class="n">the</span> <span class="nb">input</span> <span class="n">arrays</span><span class="o">.</span>
+Note that you cannot concat x,y,z along dimension 1 since dimension
+0 is not the same for all the input arrays.
-<span class="n">concat</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span clas [...]
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+concat(y,z,dim=1) = [[ 3., 3., 6., 6.],
+ [ 4., 4., 7., 7.],
+ [ 5., 5., 8., 8.]]
</pre></div>
</div>
<p>Defined in src/operator/concat.cc:L80
This function support variable length of positional input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em></em><em>]</em><em></em>) – List of arrays to concatenate</li>
-<li><strong>dim</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – the dimension to be concated.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>data</strong> (<em>Symbol[]</em>) – List of arrays to concatenate</li>
+<li><strong>dim</strong> (<em>int, optional, default='1'</em>) – the dimension to be concated.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2667,11 +3085,11 @@ This function support variable length of positional input.</p>
</table>
<p class="rubric">Examples</p>
<p>Concat two (or more) inputs along a specific dimension:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">Concat</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'my-concat'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">c</span>
-<span class="go"><symbol my-concat=""></symbol></span>
+<span class="go"><Symbol my-concat></span>
<span class="gp">>>> </span><span class="n">SymbolDoc</span><span class="o">.</span><span class="n">get_output_shape</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">b</s [...]
<span class="go">{'my-concat_output': (128L, 25L, 3L, 3L)}</span>
</pre></div>
@@ -2697,11 +3115,11 @@ weight[i,j,:,:]\]</div>
<li><strong>out</strong>: <em>(batch_size, num_filter, out_height, out_width)</em>.</li>
</ul>
<p>Define:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="n">p</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">d</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="n">p</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">d</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span [...]
</pre></div>
</div>
<p>then we have:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><sp [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><spa [...]
<span class="n">out_width</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dilate</sp [...]
</pre></div>
</div>
@@ -2747,26 +3165,26 @@ the performance.</li>
</ul>
<p>Defined in src/operator/convolution.cc:L151</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the ConvolutionOp.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight matrix.</li>
<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bias parameter.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – convolution stride: (h, w) or (d, h, w)</li>
-<li><strong>dilate</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – convolution dilate: (h, w) or (d, h, w)</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – pad for convolution: (h, w) or (d, h, w)</li>
-<li><strong>num_filter</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – convolution filter(channel) number</li>
-<li><strong>num_group</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Number of group partitions.</li>
-<li><strong>workspace</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1024</em>) – Maximum temporary workspace allowed for convolution (MB).</li>
-<li><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to disable bias parameter.</li>
-<li><strong>cudnn_tune</strong> (<em>{None</em><em>, </em><em>'fastest'</em><em>, </em><em>'limited_workspace'</em><em>, </em><em>'off'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Whether to pick convolution algo by running performance test.</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Turn off cudnn for this layer.</li>
-<li><strong>layout</strong> (<em>{None</em><em>, </em><em>'NCDHW'</em><em>, </em><em>'NCHW'</em><em>, </em><em>'NCW'</em><em>, </em><em>'NDHWC'</em><em>, </em><em>'NHWC'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Set layout for input, output and weight. Empty for
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution stride: (h, w) or (d, h, w)</li>
+<li><strong>dilate</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution dilate: (h, w) or (d, h, w)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for convolution: (h, w) or (d, h, w)</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), required</em>) – convolution filter(channel) number</li>
+<li><strong>num_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of group partitions.</li>
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=1024</em>) – Maximum temporary workspace allowed for convolution (MB).</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=False</em>) – Whether to disable bias parameter.</li>
+<li><strong>cudnn_tune</strong> (<em>{None, 'fastest', 'limited_workspace', 'off'},optional, default='None'</em>) – Whether to pick convolution algo by running performance test.</li>
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Turn off cudnn for this layer.</li>
+<li><strong>layout</strong> (<em>{None, 'NCDHW', 'NCHW', 'NCW', 'NDHWC', 'NHWC'},optional, default='None'</em>) – Set layout for input, output and weight. Empty for
default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2784,23 +3202,23 @@ default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
<code class="descclassname">mxnet.symbol.</code><code class="descname">Convolution_v1</code><span class="sig-paren">(</span><em>data=None</em>, <em>weight=None</em>, <em>bias=None</em>, <em>kernel=_Null</em>, <em>stride=_Null</em>, <em>dilate=_Null</em>, <em>pad=_Null</em>, <em>num_filter=_Null</em>, <em>num_group=_Null</em>, <em>workspace=_Null</em>, <em>no_bias=_Null</em>, <em>cudnn_tune=_Null</em>, <em>cudnn_off=_Null</em>, <em>layout=_Null</em>, <em>name=None</em>, <em>attr=None</em> [...]
<dd><p>This operator is DEPRECATED. Apply convolution to input then add a bias.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the ConvolutionV1Op.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight matrix.</li>
<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bias parameter.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – convolution stride: (h, w) or (d, h, w)</li>
-<li><strong>dilate</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – convolution dilate: (h, w) or (d, h, w)</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – pad for convolution: (h, w) or (d, h, w)</li>
-<li><strong>num_filter</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – convolution filter(channel) number</li>
-<li><strong>num_group</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Number of group partitions. Equivalent to slicing input into num_group
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution stride: (h, w) or (d, h, w)</li>
+<li><strong>dilate</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution dilate: (h, w) or (d, h, w)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for convolution: (h, w) or (d, h, w)</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), required</em>) – convolution filter(channel) number</li>
+<li><strong>num_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of group partitions. Equivalent to slicing input into num_group
partitions, apply convolution on each, then concatenate the results</li>
-<li><strong>workspace</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1024</em>) – Maximum tmp workspace allowed for convolution (MB).</li>
-<li><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to disable bias parameter.</li>
-<li><strong>cudnn_tune</strong> (<em>{None</em><em>, </em><em>'fastest'</em><em>, </em><em>'limited_workspace'</em><em>, </em><em>'off'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Whether to pick convolution algo by running performance test.
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=1024</em>) – Maximum tmp workspace allowed for convolution (MB).</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=False</em>) – Whether to disable bias parameter.</li>
+<li><strong>cudnn_tune</strong> (<em>{None, 'fastest', 'limited_workspace', 'off'},optional, default='None'</em>) – Whether to pick convolution algo by running performance test.
Leads to higher startup time but may give faster speed. Options are:
‘off’: no tuning
‘limited_workspace’: run test and pick the fastest algorithm that doesn’t exceed workspace limit.
@@ -2808,10 +3226,10 @@ Leads to higher startup time but may give faster speed. Options are:
If set to None (default), behavior is determined by environment
variable MXNET_CUDNN_AUTOTUNE_DEFAULT: 0 for off,
1 for limited workspace (default), 2 for fastest.</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Turn off cudnn for this layer.</li>
-<li><strong>layout</strong> (<em>{None</em><em>, </em><em>'NCDHW'</em><em>, </em><em>'NCHW'</em><em>, </em><em>'NDHWC'</em><em>, </em><em>'NHWC'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Set layout for input, output and weight. Empty for
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Turn off cudnn for this layer.</li>
+<li><strong>layout</strong> (<em>{None, 'NCDHW', 'NCHW', 'NDHWC', 'NHWC'},optional, default='None'</em>) – Set layout for input, output and weight. Empty for
default layout: NCHW for 2d and NCDHW for 3d.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2834,7 +3252,7 @@ the correlation layer lets the network compare each patch from <span class="math
<p>For now we consider only a single comparison of two patches. The ‘correlation’ of two patches centered at <span class="math">\(x_{1}\)</span> in the first map and
<span class="math">\(x_{2}\)</span> in the second map is then defined as:</p>
<div class="math">
-\[c(x_{1}, x_{2}) = \sum_{o \in [-k,k] \times [-k,k]} <f_{1}(x_{1} +="" f_{2}(x_{2}="" o)="" o),="">\]</f_{1}(x_{1}></div>
+\[\begin{split}c(x_{1}, x_{2}) = \sum_{o \in [-k,k] \times [-k,k]} <f_{1}(x_{1} + o), f_{2}(x_{2} + o)>\end{split}\]</div>
<p>for a square patch of size <span class="math">\(K:=2k+1\)</span>.</p>
<p>Note that the equation above is identical to one step of a convolution in neural networks, but instead of convolving data with a filter, it convolves data with other
data. For this reason, it has no training weights.</p>
@@ -2848,19 +3266,19 @@ centered around <span class="math">\(x_{1}\)</span>.</p>
<p>where <span class="math">\(i\)</span> and <span class="math">\(j\)</span> enumerate spatial locations in <span class="math">\(f_{1}\)</span>, and <span class="math">\(q\)</span> denotes the <span class="math">\(q^{th}\)</span> neighborhood of <span class="math">\(x_{i,j}\)</span>.</p>
<p>Defined in src/operator/correlation.cc:L173</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data1</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data1 to the correlation.</li>
<li><strong>data2</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data2 to the correlation.</li>
-<li><strong>kernel_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – kernel size for Correlation must be an odd number</li>
-<li><strong>max_displacement</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Max displacement of Correlation</li>
-<li><strong>stride1</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – stride1 quantize data1 globally</li>
-<li><strong>stride2</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – stride2 quantize data2 within the neighborhood centered around data1</li>
-<li><strong>pad_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – pad for Correlation</li>
-<li><strong>is_multiply</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – operation type is either multiplication or subduction</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>kernel_size</strong> (<em>int (non-negative), optional, default=1</em>) – kernel size for Correlation must be an odd number</li>
+<li><strong>max_displacement</strong> (<em>int (non-negative), optional, default=1</em>) – Max displacement of Correlation</li>
+<li><strong>stride1</strong> (<em>int (non-negative), optional, default=1</em>) – stride1 quantize data1 globally</li>
+<li><strong>stride2</strong> (<em>int (non-negative), optional, default=1</em>) – stride2 quantize data2 within the neighborhood centered around data1</li>
+<li><strong>pad_size</strong> (<em>int (non-negative), optional, default=0</em>) – pad for Correlation</li>
+<li><strong>is_multiply</strong> (<em>boolean, optional, default=True</em>) – operation type is either multiplication or subduction</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2886,15 +3304,15 @@ specify the crop height and width, otherwise the second input symbol’s size wi
<p>Defined in src/operator/crop.cc:L31
This function support variable length of positional input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em></em><em>]</em><em></em>) – Tensor or List of Tensors, the second input will be used as crop_like shape reference</li>
-<li><strong>offset</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em>0</em><em>)</em><em></em>) – crop offset coordinate: (y, x)</li>
-<li><strong>h_w</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em>0</em><em>)</em><em></em>) – crop height and width: (h, w)</li>
-<li><strong>center_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to true, then it will use be the center_crop,or it will crop using the shape of crop_like</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>data</strong> (<em>Symbol or Symbol[]</em>) – Tensor or List of Tensors, the second input will be used as crop_like shape reference</li>
+<li><strong>offset</strong> (<em>Shape(tuple), optional, default=(0,0)</em>) – crop offset coordinate: (y, x)</li>
+<li><strong>h_w</strong> (<em>Shape(tuple), optional, default=(0,0)</em>) – crop height and width: (h, w)</li>
+<li><strong>center_crop</strong> (<em>boolean, optional, default=False</em>) – If set to true, then it will use be the center_crop,or it will crop using the shape of crop_like</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2916,13 +3334,13 @@ The custom operator must be registered before it can be used.
Please check the tutorial here: <a class="reference external" href="http://mxnet.io/how_to/new_op.html">http://mxnet.io/how_to/new_op.html</a>.</p>
<p>Defined in src/operator/custom/custom.cc:L333</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em></em><em>]</em><em></em>) – Input data for the custom operator.</li>
+<li><strong>data</strong> (<em>Symbol[]</em>) – Input data for the custom operator.</li>
<li><strong>op_type</strong> (<em>string</em>) – Name of the custom operator. This is the name that is passed to <cite>mx.operator.register</cite> to register the operator.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2940,27 +3358,27 @@ Please check the tutorial here: <a class="reference external" href="http://mxnet
<code class="descclassname">mxnet.symbol.</code><code class="descname">Deconvolution</code><span class="sig-paren">(</span><em>data=None</em>, <em>weight=None</em>, <em>bias=None</em>, <em>kernel=_Null</em>, <em>stride=_Null</em>, <em>dilate=_Null</em>, <em>pad=_Null</em>, <em>adj=_Null</em>, <em>target_shape=_Null</em>, <em>num_filter=_Null</em>, <em>num_group=_Null</em>, <em>workspace=_Null</em>, <em>no_bias=_Null</em>, <em>cudnn_tune=_Null</em>, <em>cudnn_off=_Null</em>, <em>layout=_N [...]
<dd><p>Computes 2D transposed convolution (aka fractionally strided convolution) of the input tensor. This operation can be seen as the gradient of Convolution operation with respect to its input. Convolution usually reduces the size of the input. Transposed convolution works the other way, going from a smaller input to a larger output while preserving the connectivity pattern.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input tensor to the deconvolution operation.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weights representing the kernel.</li>
<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bias added to the result after the deconvolution operation.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – Deconvolution kernel size: (h, w) or (d, h, w). This is same as the kernel size used for the corresponding convolution</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The stride used for the corresponding convolution: (h, w) or (d, h, w).</li>
-<li><strong>dilate</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Dilation factor for each dimension of the input: (h, w) or (d, h, w).</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The amount of implicit zero padding added during convolution for each dimension of the input: (h, w) or (d, h, w). <code class="docutils literal"><span class="pre">(kernel-1)/2</span></code> is usually a good choice. If <cite>target_shape</cite> is set, <cite>pad</cite> will be ignored and a padding that will [...]
-<li><strong>adj</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Adjustment for output shape: (h, w) or (d, h, w). If <cite>target_shape</cite> is set, <cite>adj</cite> will be ignored and computed accordingly.</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output tensor: (h, w) or (d, h, w).</li>
-<li><strong>num_filter</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Number of output filters.</li>
-<li><strong>num_group</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Number of groups partition.</li>
-<li><strong>workspace</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=512</em>) – Maximum temporal workspace allowed for deconvolution (MB).</li>
-<li><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to disable bias parameter.</li>
-<li><strong>cudnn_tune</strong> (<em>{None</em><em>, </em><em>'fastest'</em><em>, </em><em>'limited_workspace'</em><em>, </em><em>'off'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Whether to pick convolution algorithm by running performance test.</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Turn off cudnn for this layer.</li>
-<li><strong>layout</strong> (<em>{None</em><em>, </em><em>'NCDHW'</em><em>, </em><em>'NCHW'</em><em>, </em><em>'NCW'</em><em>, </em><em>'NDHWC'</em><em>, </em><em>'NHWC'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Set layout for input, output and weight. Empty for default layout, NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – Deconvolution kernel size: (h, w) or (d, h, w). This is same as the kernel size used for the corresponding convolution</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – The stride used for the corresponding convolution: (h, w) or (d, h, w).</li>
+<li><strong>dilate</strong> (<em>Shape(tuple), optional, default=()</em>) – Dilation factor for each dimension of the input: (h, w) or (d, h, w).</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – The amount of implicit zero padding added during convolution for each dimension of the input: (h, w) or (d, h, w). <code class="docutils literal"><span class="pre">(kernel-1)/2</span></code> is usually a good choice. If <cite>target_shape</cite> is set, <cite>pad</cite> will be ignored and a padding that will generate the target shape will be used.</li>
+<li><strong>adj</strong> (<em>Shape(tuple), optional, default=()</em>) – Adjustment for output shape: (h, w) or (d, h, w). If <cite>target_shape</cite> is set, <cite>adj</cite> will be ignored and computed accordingly.</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output tensor: (h, w) or (d, h, w).</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), required</em>) – Number of output filters.</li>
+<li><strong>num_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of groups partition.</li>
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=512</em>) – Maximum temporal workspace allowed for deconvolution (MB).</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=True</em>) – Whether to disable bias parameter.</li>
+<li><strong>cudnn_tune</strong> (<em>{None, 'fastest', 'limited_workspace', 'off'},optional, default='None'</em>) – Whether to pick convolution algorithm by running performance test.</li>
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Turn off cudnn for this layer.</li>
+<li><strong>layout</strong> (<em>{None, 'NCDHW', 'NCHW', 'NCW', 'NDHWC', 'NHWC'},optional, default='None'</em>) – Set layout for input, output and weight. Empty for default layout, NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -2975,44 +3393,46 @@ Please check the tutorial here: <a class="reference external" href="http://mxnet
</dd></dl>
<dl class="function">
<dt id="mxnet.symbol.Dropout">
-<code class="descclassname">mxnet.symbol.</code><code class="descname">Dropout</code><span class="sig-paren">(</span><em>data=None</em>, <em>p=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Dropout" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">mxnet.symbol.</code><code class="descname">Dropout</code><span class="sig-paren">(</span><em>data=None</em>, <em>p=_Null</em>, <em>mode=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.Dropout" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies dropout operation to input array.</p>
<ul class="simple">
<li>During training, each element of the input is set to zero with probability p.
The whole array is rescaled by <span class="math">\(1/(1-p)\)</span> to keep the expected
sum of the input unchanged.</li>
-<li>During testing, this operator does not change the input.</li>
+<li>During testing, this operator does not change the input if mode is ‘training’.
+If mode is ‘always’, the same computaion as during training will be applied.</li>
</ul>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">998</span><span class="p">)</span>
-<span class="n">input_array</span> <span class="o">=</span> <span class="n">array</span><span class="p">([[</span><span class="mf">3.</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]])</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
-<span class="n">dropout</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">)</span>
-<span class="n">executor</span> <span class="o">=</span> <span class="n">dropout</span><span class="o">.</span><span class="n">simple_bind</span><span class="p">(</span><span class="n">a</span> <span class="o">=</span> <span class="n">input_array</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>random.seed(998)
+input_array = array([[3., 0.5, -0.5, 2., 7.],
+ [2., -0.4, 7., 3., 0.2]])
+a = symbol.Variable('a')
+dropout = symbol.Dropout(a, p = 0.2)
+executor = dropout.simple_bind(a = input_array.shape)
-<span class="c1">## If training</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">input_array</span><span class="p">)</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">outputs</span>
-<span class="p">[[</span> <span class="mf">3.75</span> <span class="mf">0.625</span> <span class="o">-</span><span class="mf">0.</span> <span class="mf">2.5</span> <span class="mf">8.75</span> <span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.5</span> <span class="o">-</span><span class="mf">0.5</span> <span class="mf">8.75</span> <span class="mf">3.75</span> <span class="mf">0.</span> <span class="p">]]</span>
+## If training
+executor.forward(is_train = True, a = input_array)
+executor.outputs
+[[ 3.75 0.625 -0. 2.5 8.75 ]
+ [ 2.5 -0.5 8.75 3.75 0. ]]
-<span class="c1">## If testing</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">input_array</span><span class="p">)</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">outputs</span>
-<span class="p">[[</span> <span class="mf">3.</span> <span class="mf">0.5</span> <span class="o">-</span><span class="mf">0.5</span> <span class="mf">2.</span> <span class="mf">7.</span> <span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span> <span class="o">-</span><span class="mf">0.4</span> <span class="mf">7.</span> <span class="mf">3.</span> <span class="mf">0.2</span> <span class="p">]]</span>
+## If testing
+executor.forward(is_train = False, a = input_array)
+executor.outputs
+[[ 3. 0.5 -0.5 2. 7. ]
+ [ 2. -0.4 7. 3. 0.2 ]]
</pre></div>
</div>
-<p>Defined in src/operator/dropout.cc:L58</p>
+<p>Defined in src/operator/dropout.cc:L59</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array to which dropout will be applied.</li>
-<li><strong>p</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.5</em>) – Fraction of the input that gets dropped out during training time.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>p</strong> (<em>float, optional, default=0.5</em>) – Fraction of the input that gets dropped out during training time.</li>
+<li><strong>mode</strong> (<em>{'always', 'training'},optional, default='training'</em>) – Whether to only turn on dropout during training or to also turn on for inference.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3026,18 +3446,18 @@ sum of the input unchanged.</li>
</table>
<p class="rubric">Examples</p>
<p>Apply dropout to corrupt input as zero with probability 0.2:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">data_dp</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="c1"># take larger shapes to be more statistical stable</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="c1"># take larger shapes to be more statistical stable</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">op</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'dp'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># dropout is identity during testing</span>
-<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">simple_forward</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">dp_data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">simple_forward</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">dp_data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">test_utils</span><span class="o">.</span><span class="n">almost_equal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">True</span>
-<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">simple_forward</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">dp_data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">simple_forward</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">dp_data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># expectation is (approximately) unchanged</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">-</span> <span class="n">y</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="o"><</span> <span class="mf">0.1</span>
<span class="go">True</span>
@@ -3056,12 +3476,12 @@ sum of the input unchanged.</li>
<p>Defined in src/operator/tensor/elemwise_sum.cc:L47
This function support variable length of positional input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>args</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em></em><em>]</em><em></em>) – Positional input arguments</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>args</strong> (<em>Symbol[]</em>) – Positional input arguments</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3090,39 +3510,39 @@ All the input values should be integers in the range [0, input_dim).</p>
<p>By default, if any index mentioned is too large, it is replaced by the index that addresses
the last vector in an embedding matrix.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">input_dim</span> <span class="o">=</span> <span class="mi">4</span>
-<span class="n">output_dim</span> <span class="o">=</span> <span class="mi">5</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>input_dim = 4
+output_dim = 5
-<span class="o">//</span> <span class="n">Each</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">weight</span> <span class="n">matrix</span> <span class="n">y</span> <span class="n">represents</span> <span class="n">a</span> <span class="n">word</span><span class="o">.</span> <span class="n">So</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="n">w0</span><span class="p">,</span><span class="n" [...]
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">,</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">,</span> <span class="mf">19.</span><span class="p">]]</span>
+// Each row in weight matrix y represents a word. So, y = (w0,w1,w2,w3)
+y = [[ 0., 1., 2., 3., 4.],
+ [ 5., 6., 7., 8., 9.],
+ [ 10., 11., 12., 13., 14.],
+ [ 15., 16., 17., 18., 19.]]
-<span class="o">//</span> <span class="n">Input</span> <span class="n">array</span> <span class="n">x</span> <span class="n">represents</span> <span class="n">n</span><span class="o">-</span><span class="n">grams</span><span class="p">(</span><span class="mi">2</span><span class="o">-</span><span class="n">gram</span><span class="p">)</span><span class="o">.</span> <span class="n">So</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[(</span [...]
-<span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]</span>
+// Input array x represents n-grams(2-gram). So, x = [(w1,w3), (w0,w2)]
+x = [[ 1., 3.],
+ [ 0., 2.]]
-<span class="o">//</span> <span class="n">Mapped</span> <span class="nb">input</span> <span class="n">x</span> <span class="n">to</span> <span class="n">its</span> <span class="n">vector</span> <span class="n">representation</span> <span class="n">y</span><span class="o">.</span>
-<span class="n">Embedding</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span> [...]
- <span class="p">[</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">,</span> <span class="mf">19.</span><span class="p">]],</span>
+// Mapped input x to its vector representation y.
+Embedding(x, y, 4, 5) = [[[ 5., 6., 7., 8., 9.],
+ [ 15., 16., 17., 18., 19.]],
- <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">,</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">]]]</span>
+ [[ 0., 1., 2., 3., 4.],
+ [ 10., 11., 12., 13., 14.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/indexing_op.cc:L55</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array to the embedding operator.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The embedding weight matrix.</li>
-<li><strong>input_dim</strong> (<em>int</em><em>, </em><em>required</em>) – Vocabulary size of the input indices.</li>
-<li><strong>output_dim</strong> (<em>int</em><em>, </em><em>required</em>) – Dimension of the embedding vectors.</li>
-<li><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='float32'</em>) – Data type of weight.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>input_dim</strong> (<em>int, required</em>) – Vocabulary size of the input indices.</li>
+<li><strong>output_dim</strong> (<em>int, required</em>) – Dimension of the embedding vectors.</li>
+<li><strong>dtype</strong> (<em>{'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='float32'</em>) – Data type of weight.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3137,7 +3557,7 @@ the last vector in an embedding matrix.</p>
<p class="rubric">Examples</p>
<p>Assume we want to map the 26 English alphabet letters to 16-dimensional
vectorial representations.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">vocabulary_size</span> <span class="o">=</span> <span class="mi">26</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">vocabulary_size</span> <span class="o">=</span> <span class="mi">26</span>
<span class="gp">>>> </span><span class="n">embed_dim</span> <span class="o">=</span> <span class="mi">16</span>
<span class="gp">>>> </span><span class="n">seq_len</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'letters'</span><span class="p">)</span>
@@ -3147,7 +3567,7 @@ vectorial representations.</p>
<span class="go">{'embed_output': (10L, 64L, 16L)}</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span> <span class="o">=</span> <span class="p">(</span><span class="mi">26</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span> <span class="o">=</span> <span class="p">(</span><span class="mi">26</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">12</span>
<span class="gp">>>> </span><span class="n">word_vecs</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">random_arrays</span><span class="p">((</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">op</span> <span class="o">=</span> <span class="n">Embedding</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'embed'</span><span class="p">,</span> <span class="n">input_dim</span><span class="o">=</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">output_dim</span><span class="o">=</span><span class="n">embed_dim</span><span class="p">)</span>
@@ -3170,7 +3590,7 @@ vectorial representations.</p>
<p>For an input array with shape <code class="docutils literal"><span class="pre">(d1,</span> <span class="pre">d2,</span> <span class="pre">...,</span> <span class="pre">dk)</span></code>, <cite>flatten</cite> operation reshapes
the input array into an output array of shape <code class="docutils literal"><span class="pre">(d1,</span> <span class="pre">d2*...*dk)</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span>
<span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span>
<span class="p">[</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">]</span>
@@ -3186,12 +3606,12 @@ the input array into an output array of shape <code class="docutils literal"><sp
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L132</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3206,19 +3626,19 @@ the input array into an output array of shape <code class="docutils literal"><sp
<p class="rubric">Examples</p>
<p>Flatten is usually applied before <cite>FullyConnected</cite>, to reshape the 4D tensor
produced by convolutional layers to 2D matrix:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span> <span class="c1"># say this is 4D from some conv/pool</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span> <span class="c1"># say this is 4D from some conv/pool</span>
<span class="gp">>>> </span><span class="n">flatten</span> <span class="o">=</span> <span class="n">Flatten</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'flat'</span><span class="p">)</span> <span class="c1"># now this is 2D</span>
<span class="gp">>>> </span><span class="n">SymbolDoc</span><span class="o">.</span><span class="n">get_output_shape</span><span class="p">(</span><span class="n">flatten</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="go">{'flat_output': (2L, 60L)}</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">test_dims</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">test_dims</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span clas [...]
<span class="gp">>>> </span><span class="n">op</span> <span class="o">=</span> <span class="n">Flatten</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'flat'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">dims</span> <span class="ow">in</span> <span class="n">test_dims</span><span class="p">:</span>
<span class="gp">... </span><span class="n">x</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">random_arrays</span><span class="p">(</span><span class="n">dims</span><span class="p">)</span>
<span class="gp">... </span><span class="n">y</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">simple_forward</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">flat_data</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
<span class="gp">... </span><span class="n">y_np</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">dims</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">dims</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span><spa [...]
-<span class="gp">... </span><span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">: </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">dims</span><span class="p">,</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">almost_equal</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n"> [...]
+<span class="gp">... </span><span class="k">print</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">: </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">dims</span><span class="p">,</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">almost_equal</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y [...]
<span class="go">(2, 3, 4, 5): True</span>
<span class="go">(2, 3): True</span>
<span class="go">(2,): True</span>
@@ -3240,16 +3660,16 @@ produced by convolutional layers to 2D matrix:</p>
<p>If <code class="docutils literal"><span class="pre">no_bias</span></code> is set to be true, then the <code class="docutils literal"><span class="pre">bias</span></code> term is ignored.</p>
<p>Defined in src/operator/fully_connected.cc:L72</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</li>
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight matrix.</li>
<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bias parameter.</li>
-<li><strong>num_hidden</strong> (<em>int</em><em>, </em><em>required</em>) – Number of hidden nodes of the output.</li>
-<li><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to disable bias parameter.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>num_hidden</strong> (<em>int, required</em>) – Number of hidden nodes of the output.</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=False</em>) – Whether to disable bias parameter.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3263,28 +3683,28 @@ produced by convolutional layers to 2D matrix:</p>
</table>
<p class="rubric">Examples</p>
<p>Construct a fully connected operator with target dimension 512.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span> <span class="c1"># or some constructed NN</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span> <span class="c1"># or some constructed NN</span>
<span class="gp">>>> </span><span class="n">op</span> <span class="o">=</span> <span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span>
<span class="gp">... </span><span class="n">num_hidden</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
<span class="gp">... </span><span class="n">name</span><span class="o">=</span><span class="s1">'FC1'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">op</span>
-<span class="go"><symbol fc1=""></symbol></span>
+<span class="go"><Symbol FC1></span>
<span class="gp">>>> </span><span class="n">SymbolDoc</span><span class="o">.</span><span class="n">get_output_shape</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="go">{'FC1_output': (128L, 512L)}</span>
</pre></div>
</div>
<p>A simple 3-layer MLP with ReLU activation:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">net</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">net</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">dim</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="mi">128</span><span class="p">,</span> <span class="mi">64</span><span class="p">]):</span>
<span class="gp">... </span><span class="n">net</span> <span class="o">=</span> <span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'FC</span><span class="si">%d</span><span class="s1">'</span> <span class [...]
<span class="gp">... </span><span class="n">net</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'ReLU</span><span class="si">%d</span><span class="s1">'</span> <span class [...]
<span class="gp">>>> </span><span class="c1"># 10-class predictor (e.g. MNIST)</span>
<span class="gp">>>> </span><span class="n">net</span> <span class="o">=</span> <span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'pred'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">net</span>
-<span class="go"><symbol pred=""></symbol></span>
+<span class="go"><Symbol pred></span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">dim_in</span><span class="p">,</span> <span class="n">dim_out</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">dim_in</span><span class="p">,</span> <span class="n">dim_out</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">random_arrays</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="n">dim_in</span><span class="p">),</span> <span class="p">(</span><span class="n">dim_out</span><span class="p">,</span> <span class=" [...]
<span class="gp">>>> </span><span class="n">op</span> <span class="o">=</span> <span class="n">FullyConnected</span><span class="p">(</span><span class="n">num_hidden</span><span class="o">=</span><span class="n">dim_out</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'FC'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">test_utils</span><span class="o">.</span><span class="n">simple_forward</span><span class="p">(</span><span class="n">op</span><span class="p">,</span> <span class="n">FC_data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">FC_weight</span><span class="o">=</span><span class="n">w</span><span class="p">,</span> <span class="n">FC_bias</span><span [...]
@@ -3300,14 +3720,14 @@ produced by convolutional layers to 2D matrix:</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">GridGenerator</code><span class="sig-paren">(</span><em>data=None</em>, <em>transform_type=_Null</em>, <em>target_shape=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.GridGenerator" title="Permalink to this definition">¶</a></dt>
<dd><p>Generates 2D sampling grid for bilinear sampling.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the function.</li>
-<li><strong>transform_type</strong> (<em>{'affine'</em><em>, </em><em>'warp'}</em><em>, </em><em>required</em>) – The type of transformation. For <cite>affine</cite>, input data should be an affine matrix of size (batch, 6). For <cite>warp</cite>, input data should be an optical flow of size (batch, 2, h, w).</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em>0</em><em>)</em><em></em>) – Specifies the output shape (H, W). This is required if transformation type is <cite>affine</cite>. If transformation type is <cite>warp</cite>, this parameter is ignored.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>transform_type</strong> (<em>{'affine', 'warp'}, required</em>) – The type of transformation. For <cite>affine</cite>, input data should be an affine matrix of size (batch, 6). For <cite>warp</cite>, input data should be an optical flow of size (batch, 2, h, w).</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=(0,0)</em>) – Specifies the output shape (H, W). This is required if transformation type is <cite>affine</cite>. If transformation type is <cite>warp</cite>, this parameter is ignored.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3325,15 +3745,15 @@ produced by convolutional layers to 2D matrix:</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">IdentityAttachKLSparseReg</code><span class="sig-paren">(</span><em>data=None</em>, <em>sparseness_target=_Null</em>, <em>penalty=_Null</em>, <em>momentum=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.IdentityAttachKLSparseReg" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply a sparse regularization to the output a sigmoid activation function.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</li>
-<li><strong>sparseness_target</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.1</em>) – The sparseness target</li>
-<li><strong>penalty</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.001</em>) – The tradeoff parameter for the sparseness penalty</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – The momentum for running average</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>sparseness_target</strong> (<em>float, optional, default=0.1</em>) – The sparseness target</li>
+<li><strong>penalty</strong> (<em>float, optional, default=0.001</em>) – The tradeoff parameter for the sparseness penalty</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0.9</em>) – The momentum for running average</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3362,40 +3782,40 @@ operation is also known as <cite>contrast normalization</cite>.</p>
<p>If the input data is of shape [batch, channel, spacial_dim1, spacial_dim2, ...],
<cite>gamma</cite> and <cite>beta</cite> parameters must be vectors of shape [channel].</p>
<p>This implementation is based on paper:</p>
-<table class="docutils footnote" frame="void" id="id2" rules="none">
-<colgroup><col class="label"></col><col></col></colgroup>
+<table class="docutils footnote" frame="void" id="id14" rules="none">
+<colgroup><col class="label"/><col/></colgroup>
<tbody valign="top">
<tr><td class="label">[1]</td><td>Instance Normalization: The Missing Ingredient for Fast Stylization,
D. Ulyanov, A. Vedaldi, V. Lempitsky, 2016 (arXiv:1607.08022v2).</td></tr>
</tbody>
</table>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Input</span> <span class="n">of</span> <span class="n">shape</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.1</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mf">3.3</span><span class="p">,</span> <span class="mf">4.4</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Input of shape (2,1,2)
+x = [[[ 1.1, 2.2]],
+ [[ 3.3, 4.4]]]
-<span class="o">//</span> <span class="n">gamma</span> <span class="n">parameter</span> <span class="n">of</span> <span class="n">length</span> <span class="mi">1</span>
-<span class="n">gamma</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.5</span><span class="p">]</span>
+// gamma parameter of length 1
+gamma = [1.5]
-<span class="o">//</span> <span class="n">beta</span> <span class="n">parameter</span> <span class="n">of</span> <span class="n">length</span> <span class="mi">1</span>
-<span class="n">beta</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">]</span>
+// beta parameter of length 1
+beta = [0.5]
-<span class="o">//</span> <span class="n">Instance</span> <span class="n">normalization</span> <span class="ow">is</span> <span class="n">calculated</span> <span class="k">with</span> <span class="n">the</span> <span class="n">above</span> <span class="n">formula</span>
-<span class="n">InstanceNorm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">gamma</span><span class="p">,</span><span class="n">beta</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="o">-</span><span class="mf">0.997527</span> <span class="p">,</span> <span class="mf">1.99752665</span><span class="p">]],</span>
- <span class="p">[[</span><span class="o">-</span><span class="mf">0.99752653</span><span class="p">,</span> <span class="mf">1.99752724</span><span class="p">]]]</span>
+// Instance normalization is calculated with the above formula
+InstanceNorm(x,gamma,beta) = [[[-0.997527 , 1.99752665]],
+ [[-0.99752653, 1.99752724]]]
</pre></div>
</div>
<p>Defined in src/operator/instance_norm.cc:L76</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – An n-dimensional input array (n > 2) of the form [batch, channel, spatial_dim1, spatial_dim2, ...].</li>
<li><strong>gamma</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A vector of length ‘channel’, which multiplies the normalized input.</li>
<li><strong>beta</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A vector of length ‘channel’, which is added to the product of the normalized input and the weight.</li>
-<li><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.001</em>) – An <cite>epsilon</cite> parameter to prevent division by 0.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>eps</strong> (<em>float, optional, default=0.001</em>) – An <cite>epsilon</cite> parameter to prevent division by 0.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3413,64 +3833,64 @@ D. Ulyanov, A. Vedaldi, V. Lempitsky, 2016 (arXiv:1607.08022v2).</td></tr>
<code class="descclassname">mxnet.symbol.</code><code class="descname">L2Normalization</code><span class="sig-paren">(</span><em>data=None</em>, <em>eps=_Null</em>, <em>mode=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.L2Normalization" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize the input array using the L2 norm.</p>
<p>For 1-D NDArray, it computes:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out</span> <span class="o">=</span> <span class="n">data</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out</span> <span class="o">=</span> <span class="n">data</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span>
</pre></div>
</div>
<p>For N-D NDArray, if the input array has shape (N, N, ..., N),</p>
<p>with <code class="docutils literal"><span class="pre">mode</span></code> = <code class="docutils literal"><span class="pre">instance</span></code>, it normalizes each instance in the multidimensional
array by its L2 norm.:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="mf">0.</span><span class="o">..</span><span class="n">N</span>
- <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="o">...</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="o">...</span><span class="p">,:]</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>for i in 0...N
+ out[i,:,:,...,:] = data[i,:,:,...,:] / sqrt(sum(data[i,:,:,...,:] ** 2) + eps)
</pre></div>
</div>
<p>with <code class="docutils literal"><span class="pre">mode</span></code> = <code class="docutils literal"><span class="pre">channel</span></code>, it normalizes each channel in the array by its L2 norm.:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="mf">0.</span><span class="o">..</span><span class="n">N</span>
- <span class="n">out</span><span class="p">[:,</span><span class="n">i</span><span class="p">,:,</span><span class="o">...</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:,</span><span class="n">i</span><span class="p">,:,</span><span class="o">...</span><span class="p">,:]</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>for i in 0...N
+ out[:,i,:,...,:] = data[:,i,:,...,:] / sqrt(sum(data[:,i,:,...,:] ** 2) + eps)
</pre></div>
</div>
<p>with <code class="docutils literal"><span class="pre">mode</span></code> = <code class="docutils literal"><span class="pre">spatial</span></code>, it normalizes the cross channel norm for each position
in the array by its L2 norm.:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="mf">2.</span><span class="o">..</span><span class="n">N</span>
- <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="mf">0.</span><span class="o">..</span><span class="n">N</span>
- <span class="n">out</span><span class="p">[</span><span class="o">.....</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">take</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span [...]
- <span class="o">-</span><span class="n">dim</span><span class="o">-</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>for dim in 2...N
+ for i in 0...N
+ out[.....,i,...] = take(out, indices=i, axis=dim) / sqrt(sum(take(out, indices=i, axis=dim) ** 2) + eps)
+ -dim-
</pre></div>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]]]</span>
-
-<span class="n">L2Normalization</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'instance'</span><span class="p">)</span>
-<span class="o">=</span><span class="p">[[[</span> <span class="mf">0.18257418</span> <span class="mf">0.36514837</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.54772252</span> <span class="mf">0.73029673</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">0.24077171</span> <span class="mf">0.24077171</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.60192931</span> <span class="mf">0.72231513</span><span class="p">]]]</span>
-
-<span class="n">L2Normalization</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'channel'</span><span class="p">)</span>
-<span class="o">=</span><span class="p">[[[</span> <span class="mf">0.31622776</span> <span class="mf">0.44721359</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.94868326</span> <span class="mf">0.89442718</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">0.37139067</span> <span class="mf">0.31622776</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.92847669</span> <span class="mf">0.94868326</span><span class="p">]]]</span>
-
-<span class="n">L2Normalization</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'spatial'</span><span class="p">)</span>
-<span class="o">=</span><span class="p">[[[</span> <span class="mf">0.44721359</span> <span class="mf">0.89442718</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.60000002</span> <span class="mf">0.80000001</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">0.70710677</span> <span class="mf">0.70710677</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.6401844</span> <span class="mf">0.76822126</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[1,2],
+ [3,4]],
+ [[2,2],
+ [5,6]]]
+
+L2Normalization(x, mode='instance')
+=[[[ 0.18257418 0.36514837]
+ [ 0.54772252 0.73029673]]
+ [[ 0.24077171 0.24077171]
+ [ 0.60192931 0.72231513]]]
+
+L2Normalization(x, mode='channel')
+=[[[ 0.31622776 0.44721359]
+ [ 0.94868326 0.89442718]]
+ [[ 0.37139067 0.31622776]
+ [ 0.92847669 0.94868326]]]
+
+L2Normalization(x, mode='spatial')
+=[[[ 0.44721359 0.89442718]
+ [ 0.60000002 0.80000001]]
+ [[ 0.70710677 0.70710677]
+ [ 0.6401844 0.76822126]]]
</pre></div>
</div>
<p>Defined in src/operator/l2_normalization.cc:L74</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array to normalize.</li>
-<li><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e-10</em>) – A small constant for numerical stability.</li>
-<li><strong>mode</strong> (<em>{'channel'</em><em>, </em><em>'instance'</em><em>, </em><em>'spatial'}</em><em>,</em><em>optional</em><em>, </em><em>default='instance'</em>) – Specify the dimension along which to compute L2 norm.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>eps</strong> (<em>float, optional, default=1e-10</em>) – A small constant for numerical stability.</li>
+<li><strong>mode</strong> (<em>{'channel', 'instance', 'spatial'},optional, default='instance'</em>) – Specify the dimension along which to compute L2 norm.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3498,16 +3918,16 @@ activity <span class="math">\(b_{x,y}^{i}\)</span> is given by the expression:</
number of kernels in the layer.</p>
<p>Defined in src/operator/lrn.cc:L54</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</li>
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.0001</em>) – The variance scaling parameter <span class="math">\(lpha\)</span> in the LRN expression.</li>
-<li><strong>beta</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.75</em>) – The power parameter <span class="math">\(eta\)</span> in the LRN expression.</li>
-<li><strong>knorm</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – The parameter <span class="math">\(k\)</span> in the LRN expression.</li>
-<li><strong>nsize</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – normalization window width in elements.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>alpha</strong> (<em>float, optional, default=0.0001</em>) – The variance scaling parameter <span class="math">\(lpha\)</span> in the LRN expression.</li>
+<li><strong>beta</strong> (<em>float, optional, default=0.75</em>) – The power parameter <span class="math">\(eta\)</span> in the LRN expression.</li>
+<li><strong>knorm</strong> (<em>float, optional, default=2</em>) – The parameter <span class="math">\(k\)</span> in the LRN expression.</li>
+<li><strong>nsize</strong> (<em>int (non-negative), required</em>) – normalization window width in elements.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3537,16 +3957,16 @@ when the input is negative and has a slope of one when input is positive.</p>
</ul>
<p>Defined in src/operator/leaky_relu.cc:L39</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to activation function.</li>
-<li><strong>act_type</strong> (<em>{'elu'</em><em>, </em><em>'leaky'</em><em>, </em><em>'prelu'</em><em>, </em><em>'rrelu'}</em><em>,</em><em>optional</em><em>, </em><em>default='leaky'</em>) – Activation function to be applied.</li>
-<li><strong>slope</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.25</em>) – Init slope for the activation. (For leaky and elu only)</li>
-<li><strong>lower_bound</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.125</em>) – Lower bound of random slope. (For rrelu only)</li>
-<li><strong>upper_bound</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.334</em>) – Upper bound of random slope. (For rrelu only)</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>act_type</strong> (<em>{'elu', 'leaky', 'prelu', 'rrelu'},optional, default='leaky'</em>) – Activation function to be applied.</li>
+<li><strong>slope</strong> (<em>float, optional, default=0.25</em>) – Init slope for the activation. (For leaky and elu only)</li>
+<li><strong>lower_bound</strong> (<em>float, optional, default=0.125</em>) – Lower bound of random slope. (For rrelu only)</li>
+<li><strong>upper_bound</strong> (<em>float, optional, default=0.334</em>) – Upper bound of random slope. (For rrelu only)</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3575,14 +3995,14 @@ then the squared loss estimated over <span class="math">\(n\)</span> samples is
The parameter <cite>grad_scale</cite> can be used to change this scale to <cite>grad_scale/n</cite>.</p>
<p>Defined in src/operator/regression_output.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the function.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input label to the function.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scale the gradient by a float factor</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scale the gradient by a float factor</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3612,14 +4032,14 @@ It is suitable for binary classification or probability prediction tasks.</p>
The parameter <cite>grad_scale</cite> can be used to change this scale to <cite>grad_scale/n</cite>.</p>
<p>Defined in src/operator/regression_output.cc:L93</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the function.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input label to the function.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scale the gradient by a float factor</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scale the gradient by a float factor</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3648,14 +4068,14 @@ then the mean absolute error (MAE) estimated over <span class="math">\(n\)</span
The parameter <cite>grad_scale</cite> can be used to change this scale to <cite>grad_scale/n</cite>.</p>
<p>Defined in src/operator/regression_output.cc:L72</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the function.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input label to the function.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scale the gradient by a float factor</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scale the gradient by a float factor</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3677,7 +4097,7 @@ the symbol should be an operator with no backward dependency.
The output of this function is the gradient of loss with respect to the input data.</p>
<p>For example, if you are a making a cross entropy loss function. Assume <code class="docutils literal"><span class="pre">out</span></code> is the
predicted output and <code class="docutils literal"><span class="pre">label</span></code> is the true label, then the cross entropy can be defined as:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">cross_entropy</span> <span class="o">=</span> <span class="n">label</span> <span class="o">*</span> <span class="n">log</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">label</span><span class="p">)</span> <span class="o">*</span> <span class="n">log</span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cross_entropy</span> <span class="o">=</span> <span class="n">label</span> <span class="o">*</span> <span class="n">log</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">label</span><span class="p">)</span> <span class="o">*</span> <span class="n">log</span> [...]
<span class="n">loss</span> <span class="o">=</span> <span class="n">MakeLoss</span><span class="p">(</span><span class="n">cross_entropy</span><span class="p">)</span>
</pre></div>
</div>
@@ -3692,15 +4112,15 @@ so that the gradient of the loss will be rescaled in the backpropagation.</p>
</div>
<p>Defined in src/operator/make_loss.cc:L52</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Gradient scale as a supplement to unary and binary operators</li>
-<li><strong>valid_thresh</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – clip each element in the array to 0 when it is less than <code class="docutils literal"><span class="pre">valid_thresh</span></code>. This is used when <code class="docutils literal"><span class="pre">normalization</span></code> is set to <code class="docutils literal"><span class="pre">'valid'</span></code>.</li>
-<li><strong>normalization</strong> (<em>{'batch'</em><em>, </em><em>'null'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='null'</em>) – If this is set to null, the output gradient will not be normalized. If this is set to batch, the output gradient will be divided by the batch size. If this is set to valid, the output gradient will be divided by the number of valid input elements.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Gradient scale as a supplement to unary and binary operators</li>
+<li><strong>valid_thresh</strong> (<em>float, optional, default=0</em>) – clip each element in the array to 0 when it is less than <code class="docutils literal"><span class="pre">valid_thresh</span></code>. This is used when <code class="docutils literal"><span class="pre">normalization</span></code> is set to <code class="docutils literal"><span class="pre">'valid'</span></code>.</li>
+<li><strong>normalization</strong> (<em>{'batch', 'null', 'valid'},optional, default='null'</em>) – If this is set to null, the output gradient will not be normalized. If this is set to batch, the output gradient will be divided by the batch size. If this is set to valid, the output gradient will be divided by the number of valid input elements.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3736,77 +4156,77 @@ to add before and after the elements of the array along dimension <code class="d
The widths of the higher two dimensions <code class="docutils literal"><span class="pre">before_1</span></code>, <code class="docutils literal"><span class="pre">after_1</span></code>, <code class="docutils literal"><span class="pre">before_2</span></code>,
<code class="docutils literal"><span class="pre">after_2</span></code> must be 0.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[[</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[[ 1. 2. 3.]
+ [ 4. 5. 6.]]
- <span class="p">[[</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span><span class="p">]]]</span>
+ [[ 7. 8. 9.]
+ [ 10. 11. 12.]]]
- <span class="p">[[[</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span><span class="p">]]</span>
+ [[[ 11. 12. 13.]
+ [ 14. 15. 16.]]
- <span class="p">[[</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span><span class="p">]]]]</span>
+ [[ 17. 18. 19.]
+ [ 20. 21. 22.]]]]
-<span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">mode</span><span class="o">=</span><span class="s2">"edge"</span><span class="p">,</span> <span class="n">pad_width</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class= [...]
+pad(x,mode="edge", pad_width=(0,0,0,0,1,1,1,1)) =
- <span class="p">[[[[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">6.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">6.</span><span class="p">]]</span>
+ [[[[ 1. 1. 2. 3. 3.]
+ [ 1. 1. 2. 3. 3.]
+ [ 4. 4. 5. 6. 6.]
+ [ 4. 4. 5. 6. 6.]]
- <span class="p">[[</span> <span class="mf">7.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">7.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">12.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">12.</span><span class="p">]]]</span>
+ [[ 7. 7. 8. 9. 9.]
+ [ 7. 7. 8. 9. 9.]
+ [ 10. 10. 11. 12. 12.]
+ [ 10. 10. 11. 12. 12.]]]
- <span class="p">[[[</span> <span class="mf">11.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">11.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">16.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">16.</span><span class="p">]]</span>
+ [[[ 11. 11. 12. 13. 13.]
+ [ 11. 11. 12. 13. 13.]
+ [ 14. 14. 15. 16. 16.]
+ [ 14. 14. 15. 16. 16.]]
- <span class="p">[[</span> <span class="mf">17.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">17.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">22.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">22.</span><span class="p">]]]]</span>
+ [[ 17. 17. 18. 19. 19.]
+ [ 17. 17. 18. 19. 19.]
+ [ 20. 20. 21. 22. 22.]
+ [ 20. 20. 21. 22. 22.]]]]
-<span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"constant"</span><span class="p">,</span> <span class="n">constant_value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">pad_width</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,< [...]
+pad(x, mode="constant", constant_value=0, pad_width=(0,0,0,0,1,1,1,1)) =
- <span class="p">[[[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[[[ 0. 0. 0. 0. 0.]
+ [ 0. 1. 2. 3. 0.]
+ [ 0. 4. 5. 6. 0.]
+ [ 0. 0. 0. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]</span>
+ [[ 0. 0. 0. 0. 0.]
+ [ 0. 7. 8. 9. 0.]
+ [ 0. 10. 11. 12. 0.]
+ [ 0. 0. 0. 0. 0.]]]
- <span class="p">[[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[[ 0. 0. 0. 0. 0.]
+ [ 0. 11. 12. 13. 0.]
+ [ 0. 14. 15. 16. 0.]
+ [ 0. 0. 0. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]]</span>
+ [[ 0. 0. 0. 0. 0.]
+ [ 0. 17. 18. 19. 0.]
+ [ 0. 20. 21. 22. 0.]
+ [ 0. 0. 0. 0. 0.]]]]
</pre></div>
</div>
<p>Defined in src/operator/pad.cc:L747</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – An n-dimensional input array.</li>
-<li><strong>mode</strong> (<em>{'constant'</em><em>, </em><em>'edge'</em><em>, </em><em>'reflect'}</em><em>, </em><em>required</em>) – Padding type to use. “constant” pads with <cite>constant_value</cite> “edge” pads using the edge values of the input array “reflect” pads by reflecting values with respect to the edges.</li>
-<li><strong>pad_width</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – Widths of the padding regions applied to the edges of each axis. It is a tuple of integer padding widths for each axis of the format <code class="docutils literal"><span class="pre">(before_1,</span> <span class="pre">after_1,</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">before_N,</span> <span class="pre">after_N)</span></code>. It s [...]
-<li><strong>constant_value</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The value used for padding when <cite>mode</cite> is “constant”.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>mode</strong> (<em>{'constant', 'edge', 'reflect'}, required</em>) – Padding type to use. “constant” pads with <cite>constant_value</cite> “edge” pads using the edge values of the input array “reflect” pads by reflecting values with respect to the edges.</li>
+<li><strong>pad_width</strong> (<em>Shape(tuple), required</em>) – Widths of the padding regions applied to the edges of each axis. It is a tuple of integer padding widths for each axis of the format <code class="docutils literal"><span class="pre">(before_1,</span> <span class="pre">after_1,</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">before_N,</span> <span class="pre">after_N)</span></code>. It should be of length <code class="docutils literal"><spa [...]
+<li><strong>constant_value</strong> (<em>double, optional, default=0</em>) – The value used for padding when <cite>mode</cite> is “constant”.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3833,7 +4253,7 @@ The widths of the higher two dimensions <code class="docutils literal"><span cla
<li><p class="first"><strong>data</strong>: <em>(batch_size, channel, height, width)</em></p>
</li>
<li><p class="first"><strong>out</strong>: <em>(batch_size, num_filter, out_height, out_width)</em>, with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><s [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
</pre></div>
</div>
@@ -3842,12 +4262,12 @@ The widths of the higher two dimensions <code class="docutils literal"><span cla
<p>The definition of <em>f</em> depends on <code class="docutils literal"><span class="pre">pooling_convention</span></code>, which has two options:</p>
<ul>
<li><p class="first"><strong>valid</strong> (default):</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><s [...]
</pre></div>
</div>
</li>
<li><p class="first"><strong>full</strong>, which is compatible with Caffe:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><s [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><sp [...]
</pre></div>
</div>
</li>
@@ -3865,19 +4285,19 @@ The widths of the higher two dimensions <code class="docutils literal"><span cla
height, width)</em>.</p>
<p>Defined in src/operator/pooling.cc:L116</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator.</li>
-<li><strong>global_pool</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Ignore kernel size, do global pooling based on current input feature map.</li>
-<li><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Turn off cudnn pooling and use MXNet pooling operator.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – pooling kernel size: (y, x) or (d, y, x)</li>
-<li><strong>pool_type</strong> (<em>{'avg'</em><em>, </em><em>'max'</em><em>, </em><em>'sum'}</em><em>, </em><em>required</em>) – Pooling type to be applied.</li>
-<li><strong>pooling_convention</strong> (<em>{'full'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='valid'</em>) – Pooling convention to be applied.</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – stride: for pooling (y, x) or (d, y, x)</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – pad for pooling: (y, x) or (d, y, x)</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>global_pool</strong> (<em>boolean, optional, default=False</em>) – Ignore kernel size, do global pooling based on current input feature map.</li>
+<li><strong>cudnn_off</strong> (<em>boolean, optional, default=False</em>) – Turn off cudnn pooling and use MXNet pooling operator.</li>
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – pooling kernel size: (y, x) or (d, y, x)</li>
+<li><strong>pool_type</strong> (<em>{'avg', 'max', 'sum'}, required</em>) – Pooling type to be applied.</li>
+<li><strong>pooling_convention</strong> (<em>{'full', 'valid'},optional, default='valid'</em>) – Pooling convention to be applied.</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – stride: for pooling (y, x) or (d, y, x)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for pooling: (y, x) or (d, y, x)</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3900,7 +4320,7 @@ Perform pooling on the input.</p>
<li><p class="first"><strong>data</strong>: <em>(batch_size, channel, height, width)</em></p>
</li>
<li><p class="first"><strong>out</strong>: <em>(batch_size, num_filter, out_height, out_width)</em>, with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><s [...]
<span class="n">out_width</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
</pre></div>
</div>
@@ -3909,12 +4329,12 @@ Perform pooling on the input.</p>
<p>The definition of <em>f</em> depends on <code class="docutils literal"><span class="pre">pooling_convention</span></code>, which has two options:</p>
<ul>
<li><p class="first"><strong>valid</strong> (default):</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><s [...]
</pre></div>
</div>
</li>
<li><p class="first"><strong>full</strong>, which is compatible with Caffe:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><s [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><sp [...]
</pre></div>
</div>
</li>
@@ -3934,18 +4354,18 @@ Perform pooling on the input.</p>
height, width)</em>.</p>
<p>Defined in src/operator/pooling_v1.cc:L85</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator.</li>
-<li><strong>global_pool</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Ignore kernel size, do global pooling based on current input feature map.</li>
-<li><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – pooling kernel size: (y, x) or (d, y, x)</li>
-<li><strong>pool_type</strong> (<em>{'avg'</em><em>, </em><em>'max'</em><em>, </em><em>'sum'}</em><em>, </em><em>required</em>) – Pooling type to be applied.</li>
-<li><strong>pooling_convention</strong> (<em>{'full'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='valid'</em>) – Pooling convention to be applied.</li>
-<li><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – stride: for pooling (y, x) or (d, y, x)</li>
-<li><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – pad for pooling: (y, x) or (d, y, x)</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>global_pool</strong> (<em>boolean, optional, default=False</em>) – Ignore kernel size, do global pooling based on current input feature map.</li>
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – pooling kernel size: (y, x) or (d, y, x)</li>
+<li><strong>pool_type</strong> (<em>{'avg', 'max', 'sum'}, required</em>) – Pooling type to be applied.</li>
+<li><strong>pooling_convention</strong> (<em>{'full', 'valid'},optional, default='valid'</em>) – Pooling convention to be applied.</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – stride: for pooling (y, x) or (d, y, x)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for pooling: (y, x) or (d, y, x)</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -3963,21 +4383,21 @@ height, width)</em>.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">RNN</code><span class="sig-paren">(</span><em>data=None</em>, <em>parameters=None</em>, <em>state=None</em>, <em>state_cell=None</em>, <em>state_size=_Null</em>, <em>num_layers=_Null</em>, <em>bidirectional=_Null</em>, <em>mode=_Null</em>, <em>p=_Null</em>, <em>state_outputs=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" hre [...]
<dd><p>Applies a recurrent layer to input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to RNN</li>
<li><strong>parameters</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Vector of all RNN trainable parameters concatenated</li>
<li><strong>state</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – initial hidden state of the RNN</li>
<li><strong>state_cell</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – initial cell state for LSTM networks (only for LSTM)</li>
-<li><strong>state_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – size of the state for each layer</li>
-<li><strong>num_layers</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – number of stacked layers</li>
-<li><strong>bidirectional</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – whether to use bidirectional recurrent layers</li>
-<li><strong>mode</strong> (<em>{'gru'</em><em>, </em><em>'lstm'</em><em>, </em><em>'rnn_relu'</em><em>, </em><em>'rnn_tanh'}</em><em>, </em><em>required</em>) – the type of RNN to compute</li>
-<li><strong>p</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Dropout probability, fraction of the input that gets dropped out at training time</li>
-<li><strong>state_outputs</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to have the states as symbol outputs.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>state_size</strong> (<em>int (non-negative), required</em>) – size of the state for each layer</li>
+<li><strong>num_layers</strong> (<em>int (non-negative), required</em>) – number of stacked layers</li>
+<li><strong>bidirectional</strong> (<em>boolean, optional, default=False</em>) – whether to use bidirectional recurrent layers</li>
+<li><strong>mode</strong> (<em>{'gru', 'lstm', 'rnn_relu', 'rnn_tanh'}, required</em>) – the type of RNN to compute</li>
+<li><strong>p</strong> (<em>float, optional, default=0</em>) – Dropout probability, fraction of the input that gets dropped out at training time</li>
+<li><strong>state_outputs</strong> (<em>boolean, optional, default=False</em>) – Whether to have the states as symbol outputs.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4009,7 +4429,7 @@ bounding boxes after <cite>ROIPooling</cite>.</p>
<p>The size of each region of interest doesn’t have to be perfectly divisible by
the number of pooling sections(<cite>pooled_size</cite>).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>x = [[[[ 0., 1., 2., 3., 4., 5.],
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[[ 0., 1., 2., 3., 4., 5.],
[ 6., 7., 8., 9., 10., 11.],
[ 12., 13., 14., 15., 16., 17.],
[ 18., 19., 20., 21., 22., 23.],
@@ -4032,15 +4452,15 @@ ROIPooling(x, y, (2,2), 0.7) = [[[[ 7., 9.],
</div>
<p>Defined in src/operator/roi_pooling.cc:L269</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array to the pooling operator, a 4D Feature maps</li>
<li><strong>rois</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]], where (x1, y1) and (x2, y2) are top left and bottom right corners of designated region of interest. <cite>batch_index</cite> indicates the index of corresponding image in the input array</li>
-<li><strong>pooled_size</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – ROI pooling output shape (h,w)</li>
-<li><strong>spatial_scale</strong> (<em>float</em><em>, </em><em>required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>pooled_size</strong> (<em>Shape(tuple), required</em>) – ROI pooling output shape (h,w)</li>
+<li><strong>spatial_scale</strong> (<em>float, required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4064,15 +4484,15 @@ ROIPooling(x, y, (2,2), 0.7) = [[[[ 7., 9.],
<p>Given an array and a shape, this function returns a copy of the array in the new shape.
The shape is a tuple of integers such as (2,3,4).The size of the new shape should be same as the size of the input array.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">reshape</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">) [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">reshape</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)) [...]
</pre></div>
</div>
<p>Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}. The significance of each is explained below:</p>
<ul>
<li><p class="first"><code class="docutils literal"><span class="pre">0</span></code> copy this dimension from the input to the output shape.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
+- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
</pre></div>
</div>
</li>
@@ -4080,33 +4500,33 @@ The shape is a tuple of integers such as (2,3,4).The size of the new shape shoul
keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span clas [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,),</span> <span class="n">output</span> <span class="n">shape</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
+- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
+- input shape = (2,3,4), shape=(-1,), output shape = (24,)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-2</span></code> copy all/remainder of the input dimensions to the output shape.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span> <span cl [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
+- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
+- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-3</span></code> use the product of two consecutive dimensions of the input shape as the output dimension.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span clas [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">),</span> <span cl [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
+- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
+- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
+- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-4</span></code> split one dimension of the input into two dimensions passed subsequent to -4 in shape (can contain -1).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
+- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
</pre></div>
</div>
</li>
@@ -4114,23 +4534,23 @@ At most one dimension of shape can be -1.</p>
<p>If the argument <cite>reverse</cite> is set to 1, then the special values are inferred from right to left.</p>
<blockquote>
<div><p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="n">without</span> <span class="n">reverse</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="k">for</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span>< [...]
-<span class="o">-</span> <span class="k">with</span> <span class="n">reverse</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">output</span> <span class="n">shape</span> <span class="n">will</span> <span class="n">be</span> <span class="p">(</span><span class="mi">50</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>- without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be (40,5)
+- with reverse=1, output shape will be (50,4).
</pre></div>
</div>
</div></blockquote>
<p>Defined in src/operator/tensor/matrix_op.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to reshape.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The target shape</li>
-<li><strong>reverse</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then the special values are inferred from right to left</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Target new shape. One and only one dim can be 0, in which case it will be inferred from the rest of dims</li>
-<li><strong>keep_highest</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Whether keep the highest dim unchanged.If set to true, then the first dim in target_shape is ignored,and always fixed as input</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – The target shape</li>
+<li><strong>reverse</strong> (<em>boolean, optional, default=False</em>) – If true then the special values are inferred from right to left</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=()</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Target new shape. One and only one dim can be 0, in which case it will be inferred from the rest of dims</li>
+<li><strong>keep_highest</strong> (<em>boolean, optional, default=False</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Whether keep the highest dim unchanged.If set to true, then the first dim in target_shape is ignored,and always fixed as input</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4150,16 +4570,16 @@ At most one dimension of shape can be -1.</p>
<p>This tutorial demonstrates using SVM as output layer for classification instead of softmax:
<a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example/svm_mnist">https://github.com/dmlc/mxnet/tree/master/example/svm_mnist</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data for SVM transformation.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Class label for the input data.</li>
-<li><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – The loss function penalizes outputs that lie outside this margin. Default margin is 1.</li>
-<li><strong>regularization_coefficient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Regularization parameter for the SVM. This balances the tradeoff between coefficient size and error.</li>
-<li><strong>use_linear</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to use L1-SVM objective. L2-SVM objective is used by default.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>margin</strong> (<em>float, optional, default=1</em>) – The loss function penalizes outputs that lie outside this margin. Default margin is 1.</li>
+<li><strong>regularization_coefficient</strong> (<em>float, optional, default=1</em>) – Regularization parameter for the SVM. This balances the tradeoff between coefficient size and error.</li>
+<li><strong>use_linear</strong> (<em>boolean, optional, default=False</em>) – Whether to use L1-SVM objective. L2-SVM objective is used by default.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4188,46 +4608,46 @@ to have the max sequence length.</p>
<p class="last">Alternatively, you can also use <cite>take</cite> operator.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">]],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1., 2., 3.],
+ [ 4., 5., 6.],
+ [ 7., 8., 9.]],
- <span class="p">[[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]],</span>
+ [[ 10., 11., 12.],
+ [ 13., 14., 15.],
+ [ 16., 17., 18.]],
- <span class="p">[[</span> <span class="mf">19.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">,</span> <span class="mf">21.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">22.</span><span class="p">,</span> <span class="mf">23.</span><span class="p">,</span> <span class="mf">24.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">,</span> <span class="mf">27.</span><span class="p">]]]</span>
+ [[ 19., 20., 21.],
+ [ 22., 23., 24.],
+ [ 25., 26., 27.]]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">last</span> <span class="n">sequence</span> <span class="n">when</span> <span class="n">sequence_length</span> <span class="n">parameter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">used</span>
-<span class="n">SequenceLast</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">19.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">,</span> <span class="mf">21.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">22.</span><span class="p">,</span> <span class="mf">23.</span><span class="p">,</span> <span class="mf">24.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">,</span> <span class="mf">27.</span><span class="p">]]</span>
+// returns last sequence when sequence_length parameter is not used
+SequenceLast(x) = [[ 19., 20., 21.],
+ [ 22., 23., 24.],
+ [ 25., 26., 27.]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="n">y</span> <span class="ow">is</span> <span class="n">used</span>
-<span class="n">SequenceLast</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">]]</span>
+// sequence_length y is used
+SequenceLast(x, y=[1,1,1], use_sequence_length=True) =
+ [[ 1., 2., 3.],
+ [ 4., 5., 6.],
+ [ 7., 8., 9.]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="n">y</span> <span class="ow">is</span> <span class="n">used</span>
-<span class="n">SequenceLast</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">,</span> <span class="mf">27.</span><span class="p">]]</span>
+// sequence_length y is used
+SequenceLast(x, y=[1,2,3], use_sequence_length=True) =
+ [[ 1., 2., 3.],
+ [ 13., 14., 15.],
+ [ 25., 26., 27.]]
</pre></div>
</div>
<p>Defined in src/operator/sequence_last.cc:L73</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – n-dimensional input array of the form [max_sequence_length, batch_size, other_feature_dims] where n>2</li>
<li><strong>sequence_length</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – vector of sequence lengths of the form [batch_size]</li>
-<li><strong>use_sequence_length</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>use_sequence_length</strong> (<em>boolean, optional, default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4252,71 +4672,71 @@ To use this parameter, set <cite>use_sequence_length</cite> to <cite>True</cite>
otherwise each example in the batch is assumed to have the max sequence length and
this operator works as the <cite>identity</cite> operator.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 13., 14., 15.],
+ [ 16., 17., 18.]]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="mi">1</span>
-<span class="n">B1</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">]]</span>
+// Batch 1
+B1 = [[ 1., 2., 3.],
+ [ 7., 8., 9.],
+ [ 13., 14., 15.]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="mi">2</span>
-<span class="n">B2</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]</span>
+// Batch 2
+B2 = [[ 4., 5., 6.],
+ [ 10., 11., 12.],
+ [ 16., 17., 18.]]
-<span class="o">//</span> <span class="n">works</span> <span class="k">as</span> <span class="n">identity</span> <span class="n">operator</span> <span class="n">when</span> <span class="n">sequence_length</span> <span class="n">parameter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">used</span>
-<span class="n">SequenceMask</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+// works as identity operator when sequence_length parameter is not used
+SequenceMask(x) = [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 13., 14., 15.],
+ [ 16., 17., 18.]]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="n">means</span> <span class="mi">1</span> <span class="n">of</span> <span class="n">each</span> <span class="n">batch</span> <span class="n">will</span> <span class="n">be</span> <span class="n">kept</span>
-<span class="o">//</span> <span class="ow">and</span> <span class="n">other</span> <span class="n">rows</span> <span class="n">are</span> <span class="n">masked</span> <span class="k">with</span> <span class="n">default</span> <span class="n">mask</span> <span class="n">value</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">SequenceMask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+// sequence_length [1,1] means 1 of each batch will be kept
+// and other rows are masked with default mask value = 0
+SequenceMask(x, y=[1,1], use_sequence_length=True) =
+ [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]],</span>
+ [[ 0., 0., 0.],
+ [ 0., 0., 0.]],
- <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]]]</span>
+ [[ 0., 0., 0.],
+ [ 0., 0., 0.]]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> <span class="n">means</span> <span class="mi">2</span> <span class="n">of</span> <span class="n">batch</span> <span class="n">B1</span> <span class="ow">and</span> <span class="mi">3</span> <span class="n">of</span> <span class="n">batch</span> <span class="n">B2</span> <span class="n">will</span> <span [...]
-<span class="o">//</span> <span class="ow">and</span> <span class="n">other</span> <span class="n">rows</span> <span class="n">are</span> <span class="n">masked</span> <span class="k">with</span> <span class="n">value</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">SequenceMask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p"> [...]
- <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+// sequence_length [2,3] means 2 of batch B1 and 3 of batch B2 will be kept
+// and other rows are masked with value = 1
+SequenceMask(x, y=[2,3], use_sequence_length=True, value=1) =
+ [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 1., 1., 1.],
+ [ 16., 17., 18.]]]
</pre></div>
</div>
<p>Defined in src/operator/sequence_mask.cc:L108</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – n-dimensional input array of the form [max_sequence_length, batch_size, other_feature_dims] where n>2</li>
<li><strong>sequence_length</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – vector of sequence lengths of the form [batch_size]</li>
-<li><strong>use_sequence_length</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
-<li><strong>value</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The value to be used as a mask.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>use_sequence_length</strong> (<em>boolean, optional, default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
+<li><strong>value</strong> (<em>float, optional, default=0</em>) – The value to be used as a mask.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4340,70 +4760,70 @@ and returns an array of the same shape.</p>
To use this parameter, set <cite>use_sequence_length</cite> to <cite>True</cite>,
otherwise each example in the batch is assumed to have the max sequence length.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 13., 14., 15.],
+ [ 16., 17., 18.]]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="mi">1</span>
-<span class="n">B1</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">]]</span>
+// Batch 1
+B1 = [[ 1., 2., 3.],
+ [ 7., 8., 9.],
+ [ 13., 14., 15.]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="mi">2</span>
-<span class="n">B2</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]</span>
+// Batch 2
+B2 = [[ 4., 5., 6.],
+ [ 10., 11., 12.],
+ [ 16., 17., 18.]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">reverse</span> <span class="n">sequence</span> <span class="n">when</span> <span class="n">sequence_length</span> <span class="n">parameter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">used</span>
-<span class="n">SequenceReverse</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]],</span>
+// returns reverse sequence when sequence_length parameter is not used
+SequenceReverse(x) = [[[ 13., 14., 15.],
+ [ 16., 17., 18.]],
- <span class="p">[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[ 1., 2., 3.],
+ [ 4., 5., 6.]]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="n">means</span> <span class="mi">2</span> <span class="n">rows</span> <span class="n">of</span>
-<span class="o">//</span> <span class="n">both</span> <span class="n">batch</span> <span class="n">B1</span> <span class="ow">and</span> <span class="n">B2</span> <span class="n">will</span> <span class="n">be</span> <span class="nb">reversed</span><span class="o">.</span>
-<span class="n">SequenceReverse</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+// sequence_length [2,2] means 2 rows of
+// both batch B1 and B2 will be reversed.
+SequenceReverse(x, y=[2,2], use_sequence_length=True) =
+ [[[ 7., 8., 9.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]],</span>
+ [[ 1., 2., 3.],
+ [ 4., 5., 6.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]]]</span>
+ [[ 13., 14., 15.],
+ [ 16., 17., 18.]]]
-<span class="o">//</span> <span class="n">sequence_length</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> <span class="n">means</span> <span class="mi">2</span> <span class="n">of</span> <span class="n">batch</span> <span class="n">B2</span> <span class="ow">and</span> <span class="mi">3</span> <span class="n">of</span> <span class="n">batch</span> <span class="n">B3</span>
-<span class="o">//</span> <span class="n">will</span> <span class="n">be</span> <span class="nb">reversed</span><span class="o">.</span>
-<span class="n">SequenceReverse</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span>
- <span class="p">[[[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">17.</span><span class="p">,</span> <span class="mf">18.</span><span class="p">]],</span>
+// sequence_length [2,3] means 2 of batch B2 and 3 of batch B3
+// will be reversed.
+SequenceReverse(x, y=[2,3], use_sequence_length=True) =
+ [[[ 7., 8., 9.],
+ [ 16., 17., 18.]],
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]],</span>
+ [[ 1., 2., 3.],
+ [ 10., 11., 12.]],
- <span class="p">[[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[ 13., 14, 15.],
+ [ 4., 5., 6.]]]
</pre></div>
</div>
<p>Defined in src/operator/sequence_reverse.cc:L94</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – n-dimensional input array of the form [max_sequence_length, batch_size, other dims] where n>2</li>
<li><strong>sequence_length</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – vector of sequence lengths of the form [batch_size]</li>
-<li><strong>use_sequence_length</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>use_sequence_length</strong> (<em>boolean, optional, default=False</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4427,36 +4847,36 @@ otherwise each example in the batch is assumed to have the max sequence length.<
<p><strong>Note</strong> that <cite>num_outputs</cite> should evenly divide the length of the axis
along which to split the array.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]]</span>
-<span class="n">x</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1.]
+ [ 2.]]
+ [[ 3.]
+ [ 4.]]
+ [[ 5.]
+ [ 6.]]]
+x.shape = (3, 2, 1)
-<span class="n">y</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="n">a</span> <span class="nb">list</span> <span class="n">of</span> <span class="mi">2</span> [...]
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]]]</span>
+y = split(x, axis=1, num_outputs=2) // a list of 2 arrays with shape (3, 1, 1)
+y = [[[ 1.]]
+ [[ 3.]]
+ [[ 5.]]]
- <span class="p">[[[</span> <span class="mf">2.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">4.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[[ 2.]]
+ [[ 4.]]
+ [[ 6.]]]
-<span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+y[0].shape = (3, 1, 1)
-<span class="n">z</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">//</span> <span class="n">a</span> <span class="nb">list</span> <span class="n">of</span> <span class="mi">3</span> [...]
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]]</span>
+z = split(x, axis=0, num_outputs=3) // a list of 3 arrays with shape (1, 2, 1)
+z = [[[ 1.]
+ [ 2.]]]
- <span class="p">[[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]]</span>
+ [[[ 3.]
+ [ 4.]]]
- <span class="p">[[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[[ 5.]
+ [ 6.]]]
-<span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+z[0].shape = (1, 2, 1)
</pre></div>
</div>
<p><cite>squeeze_axis=1</cite> removes the axis with length 1 from the shapes of the output arrays.
@@ -4464,29 +4884,29 @@ along which to split the array.</p>
along the <cite>axis</cite> which it is split.
Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils literal"><span class="pre">input.shape[axis]</span> <span class="pre">==</span> <span class="pre">num_outputs</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">squeeze_axis</span><span class="o">=</span>< [...]
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>z = split(x, axis=0, num_outputs=3, squeeze_axis=1) // a list of 3 arrays with shape (2, 1)
+z = [[ 1.]
+ [ 2.]]
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
+ [[ 3.]
+ [ 4.]]
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]</span>
-<span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="p">,</span><span class="mi">1</span> <span class="p">)</span>
+ [[ 5.]
+ [ 6.]]
+z[0].shape = (2 ,1 )
</pre></div>
</div>
<p>Defined in src/operator/slice_channel.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>num_outputs</strong> (<em>int</em><em>, </em><em>required</em>) – Number of splits. Note that this should evenly divide the length of the <cite>axis</cite>.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Axis along which to split.</li>
-<li><strong>squeeze_axis</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true, Removes the axis with length 1 from the shapes of the output arrays. <strong>Note</strong> that setting <cite>squeeze_axis</cite> to <code class="docutils literal"><span class="pre">true</span></code> removes axis with length 1 only along the <cite>axis</cite> which it is split. Also <cite>squeeze_axis</cite> can be set to <code class="docutils literal"><span class [...]
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>num_outputs</strong> (<em>int, required</em>) – Number of splits. Note that this should evenly divide the length of the <cite>axis</cite>.</li>
+<li><strong>axis</strong> (<em>int, optional, default='1'</em>) – Axis along which to split.</li>
+<li><strong>squeeze_axis</strong> (<em>boolean, optional, default=False</em>) – If true, Removes the axis with length 1 from the shapes of the output arrays. <strong>Note</strong> that setting <cite>squeeze_axis</cite> to <code class="docutils literal"><span class="pre">true</span></code> removes axis with length 1 only along the <cite>axis</cite> which it is split. Also <cite>squeeze_axis</cite> can be set to <code class="docutils literal"><span class="pre">true</span></code> only if <c [...]
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4511,19 +4931,19 @@ To just compute softmax output, use the <cite>softmax</cite> operator.</p>
</div>
<p>Defined in src/operator/softmax_output.cc:L119</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scales the gradient by a float factor.</li>
-<li><strong>ignore_label</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – The instances whose <cite>labels</cite> == <cite>ignore_label</cite> will be ignored during backward, if <cite>use_ignore</cite> is set to <code class="docutils literal"><span class="pre">true</span></code>).</li>
-<li><strong>multi_output</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along axis <code class="docutils literal"><span class="pre">1</span></code>. This is applied when the shape of input array differs from the shape of label array.</li>
-<li><strong>use_ignore</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the <cite>ignore_label</cite> value will not contribute to the backward gradient.</li>
-<li><strong>preserve_shape</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along the last axis (<code class="docutils literal"><span class="pre">-1</span></code>).</li>
-<li><strong>normalization</strong> (<em>{'batch'</em><em>, </em><em>'null'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='null'</em>) – Normalizes the gradient.</li>
-<li><strong>out_grad</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiplies gradient with output gradient element-wise.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scales the gradient by a float factor.</li>
+<li><strong>ignore_label</strong> (<em>float, optional, default=-1</em>) – The instances whose <cite>labels</cite> == <cite>ignore_label</cite> will be ignored during backward, if <cite>use_ignore</cite> is set to <code class="docutils literal"><span class="pre">true</span></code>).</li>
+<li><strong>multi_output</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along axis <code class="docutils literal"><span class="pre">1</span></code>. This is applied when the shape of input array differs from the shape of label array.</li>
+<li><strong>use_ignore</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the <cite>ignore_label</cite> value will not contribute to the backward gradient.</li>
+<li><strong>preserve_shape</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along the last axis (<code class="docutils literal"><span class="pre">-1</span></code>).</li>
+<li><strong>normalization</strong> (<em>{'batch', 'null', 'valid'},optional, default='null'</em>) – Normalizes the gradient.</li>
+<li><strong>out_grad</strong> (<em>boolean, optional, default=False</em>) – Multiplies gradient with output gradient element-wise.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4551,23 +4971,23 @@ of each instance, where <cite>k</cite> = <code class="docutils literal"><span cl
has at least 3 dimensions.
This can be used for <cite>fully convolutional network</cite>, <cite>image segmentation</cite>, etc.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">input_array</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">3.</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> < [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">input_array</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">3.</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <s [...]
<span class="gp">>>> </span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="o">-.</span><span class="mi">4</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">softmax_act</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">SoftmaxActivation</span><span class="p">(</span><span class="n">input_array</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="nb">print</span> <span class="n">softmax_act</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="k">print</span> <span class="n">softmax_act</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">[[ 1.78322066e-02 1.46375655e-03 5.38485940e-04 6.56010211e-03 9.73605454e-01]</span>
<span class="go"> [ 6.56221947e-03 5.95310994e-04 9.73919690e-01 1.78379621e-02 1.08472735e-03]]</span>
</pre></div>
</div>
<p>Defined in src/operator/softmax_activation.cc:L48</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array to activation function.</li>
-<li><strong>mode</strong> (<em>{'channel'</em><em>, </em><em>'instance'}</em><em>,</em><em>optional</em><em>, </em><em>default='instance'</em>) – Specifies how to compute the softmax. If set to <code class="docutils literal"><span class="pre">instance</span></code>, it computes softmax for each instance. If set to <code class="docutils literal"><span class="pre">channel</span></code>, It computes cross channel softmax for each position of each instance.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>mode</strong> (<em>{'channel', 'instance'},optional, default='instance'</em>) – Specifies how to compute the softmax. If set to <code class="docutils literal"><span class="pre">instance</span></code>, it computes softmax for each instance. If set to <code class="docutils literal"><span class="pre">channel</span></code>, It computes cross channel softmax for each position of each instance.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4630,23 +5050,23 @@ The provided label can be a one-hot label array or a probability label array.</p
with a particular label to be ignored during backward propagation. <strong>This has no effect when
softmax `output` has same shape as `label`</strong>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</sp [...]
-<span class="n">label</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-<span class="n">ignore_label</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">SoftmaxOutput</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="p">,</span>\
- <span class="n">multi_output</span><span class="o">=</span><span class="n">true</span><span class="p">,</span> <span class="n">use_ignore</span><span class="o">=</span><span class="n">true</span><span class="p">,</span>\
- <span class="n">ignore_label</span><span class="o">=</span><span class="n">ignore_label</span><span class="p">)</span>
-<span class="c1">## forward softmax output</span>
-<span class="p">[[</span> <span class="mf">0.0320586</span> <span class="mf">0.08714432</span> <span class="mf">0.23688284</span> <span class="mf">0.64391428</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="p">]]</span>
-<span class="c1">## backward gradient output</span>
-<span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="p">]</span>
- <span class="p">[</span><span class="o">-</span><span class="mf">0.75</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="o">-</span><span class="mf">0.75</span> <span class="mf">0.25</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="mf">0.25</span> <span class="o">-</span><span class="mf">0.75</span><span class="p">]]</span>
-<span class="c1">## notice that the first row is all 0 because label[0] is 1, which is equal to ignore_label.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>data = [[1,2,3,4],[2,2,2,2],[3,3,3,3],[4,4,4,4]]
+label = [1,0,2,3]
+ignore_label = 1
+SoftmaxOutput(data=data, label = label,\
+ multi_output=true, use_ignore=true,\
+ ignore_label=ignore_label)
+## forward softmax output
+[[ 0.0320586 0.08714432 0.23688284 0.64391428]
+ [ 0.25 0.25 0.25 0.25 ]
+ [ 0.25 0.25 0.25 0.25 ]
+ [ 0.25 0.25 0.25 0.25 ]]
+## backward gradient output
+[[ 0. 0. 0. 0. ]
+ [-0.75 0.25 0.25 0.25]
+ [ 0.25 0.25 -0.75 0.25]
+ [ 0.25 0.25 0.25 -0.75]]
+## notice that the first row is all 0 because label[0] is 1, which is equal to ignore_label.
</pre></div>
</div>
</li>
@@ -4667,20 +5087,20 @@ The <cite>normalization</cite> mode can be set to the followings:</p>
</ul>
<p>Defined in src/operator/softmax_output.cc:L104</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array.</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Ground truth label.</li>
-<li><strong>grad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scales the gradient by a float factor.</li>
-<li><strong>ignore_label</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – The instances whose <cite>labels</cite> == <cite>ignore_label</cite> will be ignored during backward, if <cite>use_ignore</cite> is set to <code class="docutils literal"><span class="pre">true</span></code>).</li>
-<li><strong>multi_output</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along axis <code class="docutils literal"><span class="pre">1</span></code>. This is applied when the shape of input array differs from the shape of label array.</li>
-<li><strong>use_ignore</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the <cite>ignore_label</cite> value will not contribute to the backward gradient.</li>
-<li><strong>preserve_shape</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along the last axis (<code class="docutils literal"><span class="pre">-1</span></code>).</li>
-<li><strong>normalization</strong> (<em>{'batch'</em><em>, </em><em>'null'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='null'</em>) – Normalizes the gradient.</li>
-<li><strong>out_grad</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiplies gradient with output gradient element-wise.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>grad_scale</strong> (<em>float, optional, default=1</em>) – Scales the gradient by a float factor.</li>
+<li><strong>ignore_label</strong> (<em>float, optional, default=-1</em>) – The instances whose <cite>labels</cite> == <cite>ignore_label</cite> will be ignored during backward, if <cite>use_ignore</cite> is set to <code class="docutils literal"><span class="pre">true</span></code>).</li>
+<li><strong>multi_output</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along axis <code class="docutils literal"><span class="pre">1</span></code>. This is applied when the shape of input array differs from the shape of label array.</li>
+<li><strong>use_ignore</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the <cite>ignore_label</cite> value will not contribute to the backward gradient.</li>
+<li><strong>preserve_shape</strong> (<em>boolean, optional, default=False</em>) – If set to <code class="docutils literal"><span class="pre">true</span></code>, the softmax function will be computed along the last axis (<code class="docutils literal"><span class="pre">-1</span></code>).</li>
+<li><strong>normalization</strong> (<em>{'batch', 'null', 'valid'},optional, default='null'</em>) – Normalizes the gradient.</li>
+<li><strong>out_grad</strong> (<em>boolean, optional, default=False</em>) – Multiplies gradient with output gradient element-wise.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4698,16 +5118,16 @@ The <cite>normalization</cite> mode can be set to the followings:</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">SpatialTransformer</code><span class="sig-paren">(</span><em>data=None</em>, <em>loc=None</em>, <em>target_shape=_Null</em>, <em>transform_type=_Null</em>, <em>sampler_type=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.SpatialTransformer" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a spatial transformer to input feature map.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the SpatialTransformerOp.</li>
<li><strong>loc</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – localisation net, the output dim should be 6 when transform_type is affine. You shold initialize the weight and bias with identity tranform.</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em>0</em><em>,</em><em>0</em><em>)</em><em></em>) – output shape(h, w) of spatial transformer: (y, x)</li>
-<li><strong>transform_type</strong> (<em>{'affine'}</em><em>, </em><em>required</em>) – transformation type</li>
-<li><strong>sampler_type</strong> (<em>{'bilinear'}</em><em>, </em><em>required</em>) – sampling type</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=(0,0)</em>) – output shape(h, w) of spatial transformer: (y, x)</li>
+<li><strong>transform_type</strong> (<em>{'affine'}, required</em>) – transformation type</li>
+<li><strong>sampler_type</strong> (<em>{'bilinear'}, required</em>) – sampling type</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4725,32 +5145,32 @@ The <cite>normalization</cite> mode can be set to the followings:</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">SwapAxis</code><span class="sig-paren">(</span><em>data=None</em>, <em>dim1=_Null</em>, <em>dim2=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.SwapAxis" title="Permalink to this definition">¶</a></dt>
<dd><p>Interchanges two axes of an array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
- <span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> x = [[1, 2, 3]])
+ swapaxes(x, 0, 1) = [[ 1],
+ [ 2],
+ [ 3]]
- <span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]]</span> <span class="o">//</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="n">array</span>
+ x = [[[ 0, 1],
+ [ 2, 3]],
+ [[ 4, 5],
+ [ 6, 7]]] // (2,2,2) array
-<span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]]</span>
+swapaxes(x, 0, 2) = [[[ 0, 4],
+ [ 2, 6]],
+ [[ 1, 5],
+ [ 3, 7]]]
</pre></div>
</div>
<p>Defined in src/operator/swapaxis.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array.</li>
-<li><strong>dim1</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the first axis to be swapped.</li>
-<li><strong>dim2</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the second axis to be swapped.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>dim1</strong> (<em>int (non-negative), optional, default=0</em>) – the first axis to be swapped.</li>
+<li><strong>dim2</strong> (<em>int (non-negative), optional, default=0</em>) – the second axis to be swapped.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4769,17 +5189,17 @@ The <cite>normalization</cite> mode can be set to the followings:</p>
<dd><p>Performs nearest neighbor/bilinear up sampling to inputs.
This function support variable length of positional input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em></em><em>]</em><em></em>) – Array of tensors to upsample</li>
-<li><strong>scale</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>required</em>) – Up sampling scale</li>
-<li><strong>num_filter</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Input filter. Only used by bilinear sample_type.</li>
-<li><strong>sample_type</strong> (<em>{'bilinear'</em><em>, </em><em>'nearest'}</em><em>, </em><em>required</em>) – upsampling method</li>
-<li><strong>multi_input_mode</strong> (<em>{'concat'</em><em>, </em><em>'sum'}</em><em>,</em><em>optional</em><em>, </em><em>default='concat'</em>) – How to handle multiple input. concat means concatenate upsampled images along the channel dimension. sum means add all images together, only available for nearest neighbor upsampling.</li>
-<li><strong>workspace</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=512</em>) – Tmp workspace for deconvolution (MB)</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>data</strong> (<em>Symbol[]</em>) – Array of tensors to upsample</li>
+<li><strong>scale</strong> (<em>int (non-negative), required</em>) – Up sampling scale</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), optional, default=0</em>) – Input filter. Only used by bilinear sample_type.</li>
+<li><strong>sample_type</strong> (<em>{'bilinear', 'nearest'}, required</em>) – upsampling method</li>
+<li><strong>multi_input_mode</strong> (<em>{'concat', 'sum'},optional, default='concat'</em>) – How to handle multiple input. concat means concatenate upsampled images along the channel dimension. sum means add all images together, only available for nearest neighbor upsampling.</li>
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=512</em>) – Tmp workspace for deconvolution (MB)</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4797,17 +5217,17 @@ This function support variable length of positional input.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">abs</code><span class="sig-paren">(</span><em>data=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.abs" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise absolute value of the input.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="nb">abs</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">abs</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L236</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4833,29 +5253,29 @@ m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t\\
v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\
W_t = W_{t-1} - \alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon }\end{split}\]</div>
<p>It updates the weights using:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">beta1</span><span class="o">*</span><span class="n">m</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">beta1</span><span class="p">)</span><span class="o">*</span><span class="n">grad</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">beta1</span><span class="o">*</span><span class="n">m</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">beta1</span><span class="p">)</span><span class="o">*</span><span class="n">grad</span>
<span class="n">v</span> <span class="o">=</span> <span class="n">beta2</span><span class="o">*</span><span class="n">v</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">beta2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">grad</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="n">w</span> <span class="o">+=</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">m</span> <span class="o">/</span> <span class="p">(</span><span class="n">sqrt</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span>
</pre></div>
</div>
<p>Defined in src/operator/optimizer_op.cc:L126</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Gradient</li>
<li><strong>mean</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Moving mean</li>
<li><strong>var</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Moving variance</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>beta1</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – The decay rate for the 1st moment estimates.</li>
-<li><strong>beta2</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.999</em>) – The decay rate for the 2nd moment estimates.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e-08</em>) – A small constant for numerical stability.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>beta1</strong> (<em>float, optional, default=0.9</em>) – The decay rate for the 1st moment estimates.</li>
+<li><strong>beta2</strong> (<em>float, optional, default=0.999</em>) – The decay rate for the 2nd moment estimates.</li>
+<li><strong>epsilon</strong> (<em>float, optional, default=1e-08</em>) – A small constant for numerical stability.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4878,12 +5298,12 @@ W_t = W_{t-1} - \alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon }\end{split}\]</div
<p>Defined in src/operator/tensor/elemwise_sum.cc:L47
This function support variable length of positional input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>args</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em></em><em>]</em><em></em>) – Positional input arguments</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>args</strong> (<em>Symbol[]</em>) – Positional input arguments</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4906,12 +5326,12 @@ The output is in the closed interval <span class="math">\([0, \pi]\)</span></p>
\[arccos([-1, -.707, 0, .707, 1]) = [\pi, 3\pi/4, \pi/2, \pi/4, 0]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L541</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4930,12 +5350,12 @@ The output is in the closed interval <span class="math">\([0, \pi]\)</span></p>
<dd><p>Returns the element-wise inverse hyperbolic cosine of the input array, computed element-wise.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L647</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4958,12 +5378,12 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
\[arcsin([-1, -.707, 0, .707, 1]) = [-\pi/2, -\pi/4, 0, \pi/4, \pi/2]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L524</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -4982,12 +5402,12 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
<dd><p>Returns the element-wise inverse hyperbolic sine of the input array, computed element-wise.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L637</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5009,12 +5429,12 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
\[arctan([-1, 0, 1]) = [-\pi/4, 0, \pi/4]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L557</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5033,12 +5453,12 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
<dd><p>Returns the element-wise inverse hyperbolic tangent of the input array, computed element-wise.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L657</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5058,30 +5478,30 @@ The output is in the closed interval of [<span class="math">\(-\pi/2\)</span>, <
<p>In the case of multiple occurrences of maximum values, the indices corresponding to the first occurrence
are returned.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 0., 1., 2.],
+ [ 3., 4., 5.]]
-<span class="o">//</span> <span class="n">argmax</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">argmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]</span>
+// argmax along axis 0
+argmax(x, axis=0) = [ 1., 1., 1.]
-<span class="o">//</span> <span class="n">argmax</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span>
-<span class="n">argmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]</span>
+// argmax along axis 1
+argmax(x, axis=1) = [ 2., 2.]
-<span class="o">//</span> <span class="n">argmax</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span> <span class="n">keeping</span> <span class="n">same</span> <span class="n">dims</span> <span class="k">as</span> <span class="n">an</span> <span class="nb">input</span> <span class="n">array</span>
-<span class="n">argmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
+// argmax along axis 1 keeping same dims as an input array
+argmax(x, axis=1, keepdims=True) = [[ 2.],
+ [ 2.]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_index.cc:L33</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because [...]
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because</span> <span class="pre">global</span> <span class="p [...]
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5102,7 +5522,7 @@ are returned.</p>
<p>In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence
are returned.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">]]</span>
<span class="n">argmax_channel</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]</span>
@@ -5110,12 +5530,12 @@ are returned.</p>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_index.cc:L78</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5135,30 +5555,30 @@ are returned.</p>
<p>In the case of multiple occurrences of minimum values, the indices corresponding to the first occurrence
are returned.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 0., 1., 2.],
+ [ 3., 4., 5.]]
-<span class="o">//</span> <span class="n">argmin</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">argmin</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]</span>
+// argmin along axis 0
+argmin(x, axis=0) = [ 0., 0., 0.]
-<span class="o">//</span> <span class="n">argmin</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span>
-<span class="n">argmin</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]</span>
+// argmin along axis 1
+argmin(x, axis=1) = [ 0., 0.]
-<span class="o">//</span> <span class="n">argmin</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span> <span class="n">keeping</span> <span class="n">same</span> <span class="n">dims</span> <span class="k">as</span> <span class="n">an</span> <span class="nb">input</span> <span class="n">array</span>
-<span class="n">argmin</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">]]</span>
+// argmin along axis 1 keeping same dims as an input array
+argmin(x, axis=1, keepdims=True) = [[ 0.],
+ [ 0.]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_index.cc:L58</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because [...]
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because</span> <span class="pre">global</span> <span class="p [...]
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5178,31 +5598,31 @@ are returned.</p>
<p>This function performs sorting along the given axis and returns an array of indices having same shape
as an input array that index data in sorted order.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 0.3, 0.2, 0.4],
+ [ 0.1, 0.3, 0.2]]
-<span class="o">//</span> <span class="n">sort</span> <span class="n">along</span> <span class="n">axis</span> <span class="o">-</span><span class="mi">1</span>
-<span class="n">argsort</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
+// sort along axis -1
+argsort(x) = [[ 1., 0., 2.],
+ [ 0., 2., 1.]]
-<span class="o">//</span> <span class="n">sort</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">argsort</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]]</span>
+// sort along axis 0
+argsort(x, axis=0) = [[ 1., 0., 1.]
+ [ 0., 1., 0.]]
-<span class="o">//</span> <span class="n">flatten</span> <span class="ow">and</span> <span class="n">then</span> <span class="n">sort</span>
-<span class="n">argsort</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]</span>
+// flatten and then sort
+argsort(x) = [ 3., 1., 5., 0., 4., 2.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/ordering_op.cc:L157</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Axis along which to sort the input tensor. If not given, the flattened array is used. Default is -1.</li>
-<li><strong>is_ascend</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to sort in ascending or descending order.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='-1'</em>) – Axis along which to sort the input tensor. If not given, the flattened array is used. Default is -1.</li>
+<li><strong>is_ascend</strong> (<em>boolean, optional, default=True</em>) – Whether to sort in ascending or descending order.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5224,20 +5644,20 @@ as an input array that index data in sorted order.</p>
<p>For example, given <code class="docutils literal"><span class="pre">x</span></code> with shape <cite>(batch_size, n, m)</cite> and <code class="docutils literal"><span class="pre">y</span></code> with shape
<cite>(batch_size, m, k)</cite>, the result array will have shape <cite>(batch_size, n, k)</cite>,
which is computed by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">batch_dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="n">i</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:],</span> <span class="n">y</span><span class=" [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">batch_dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="n">i</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:],</span> <span class="n">y</span><span class="p [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L412</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The first input</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The second input</li>
-<li><strong>transpose_a</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then transpose the first input before dot.</li>
-<li><strong>transpose_b</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then transpose the second input before dot.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>transpose_a</strong> (<em>boolean, optional, default=False</em>) – If true then transpose the first input before dot.</li>
+<li><strong>transpose_b</strong> (<em>boolean, optional, default=False</em>) – If true then transpose the second input before dot.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5260,27 +5680,27 @@ which is computed by:</p>
</div>
<p>Given an input array of shape <code class="docutils literal"><span class="pre">(d0,</span> <span class="pre">d1)</span></code> and indices of shape <code class="docutils literal"><span class="pre">(i0,)</span></code>, the result will be
an output array of shape <code class="docutils literal"><span class="pre">(i0,)</span></code> with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
</pre></div>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1., 2.],
+ [ 3., 4.],
+ [ 5., 6.]]
-<span class="o">//</span> <span class="n">takes</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span>
-<span class="n">batch_take</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span> <span class="mf">4.</span> <span class="mf">5.</span><span class="p">]</span>
+// takes elements with specified indices
+batch_take(x, [0,1,0]) = [ 1. 4. 5.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/indexing_op.cc:L172</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>a</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array</li>
<li><strong>indices</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The index array</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5299,7 +5719,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<dd><p>Returns element-wise sum of the input arrays with broadcasting.</p>
<p><cite>broadcast_plus</cite> is an alias to the function <cite>broadcast_add</cite>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5314,13 +5734,13 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L32</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5340,30 +5760,30 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<p>Broadcasting is allowed on axes with size 1, such as from <cite>(2,1,3,1)</cite> to
<cite>(2,8,3,9)</cite>. Elements will be duplicated on the broadcasted axes.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">given</span> <span class="n">x</span> <span class="n">of</span> <span class="n">shape</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// given x of shape (1,2,1)
+x = [[[ 1.],
+ [ 2.]]]
-<span class="o">//</span> <span class="n">broadcast</span> <span class="n">x</span> <span class="n">on</span> <span class="n">on</span> <span class="n">axis</span> <span class="mi">2</span>
-<span class="n">broadcast_axis</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</spa [...]
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]]</span>
-<span class="o">//</span> <span class="n">broadcast</span> <span class="n">x</span> <span class="n">on</span> <span class="n">on</span> <span class="n">axes</span> <span class="mi">0</span> <span class="ow">and</span> <span class="mi">2</span>
-<span class="n">broadcast_axis</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span c [...]
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]]</span>
+// broadcast x on on axis 2
+broadcast_axis(x, axis=2, size=3) = [[[ 1., 1., 1.],
+ [ 2., 2., 2.]]]
+// broadcast x on on axes 0 and 2
+broadcast_axis(x, axis=(0,2), size=(2,3)) = [[[ 1., 1., 1.],
+ [ 2., 2., 2.]],
+ [[ 1., 1., 1.],
+ [ 2., 2., 2.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L168</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The axes to perform the broadcasting.</li>
-<li><strong>size</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Target sizes of the broadcasting axes.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – The axes to perform the broadcasting.</li>
+<li><strong>size</strong> (<em>Shape(tuple), optional, default=()</em>) – Target sizes of the broadcasting axes.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5383,30 +5803,30 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<p>Broadcasting is allowed on axes with size 1, such as from <cite>(2,1,3,1)</cite> to
<cite>(2,8,3,9)</cite>. Elements will be duplicated on the broadcasted axes.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">given</span> <span class="n">x</span> <span class="n">of</span> <span class="n">shape</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// given x of shape (1,2,1)
+x = [[[ 1.],
+ [ 2.]]]
-<span class="o">//</span> <span class="n">broadcast</span> <span class="n">x</span> <span class="n">on</span> <span class="n">on</span> <span class="n">axis</span> <span class="mi">2</span>
-<span class="n">broadcast_axis</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</spa [...]
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]]</span>
-<span class="o">//</span> <span class="n">broadcast</span> <span class="n">x</span> <span class="n">on</span> <span class="n">on</span> <span class="n">axes</span> <span class="mi">0</span> <span class="ow">and</span> <span class="mi">2</span>
-<span class="n">broadcast_axis</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span c [...]
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]]]</span>
+// broadcast x on on axis 2
+broadcast_axis(x, axis=2, size=3) = [[[ 1., 1., 1.],
+ [ 2., 2., 2.]]]
+// broadcast x on on axes 0 and 2
+broadcast_axis(x, axis=(0,2), size=(2,3)) = [[[ 1., 1., 1.],
+ [ 2., 2., 2.]],
+ [[ 1., 1., 1.],
+ [ 2., 2., 2.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L168</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The axes to perform the broadcasting.</li>
-<li><strong>size</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Target sizes of the broadcasting axes.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – The axes to perform the broadcasting.</li>
+<li><strong>size</strong> (<em>Shape(tuple), optional, default=()</em>) – Target sizes of the broadcasting axes.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5424,7 +5844,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_div</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_div" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise division of the input arrays with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
@@ -5436,13 +5856,13 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L137</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5460,7 +5880,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_equal</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_equal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>equal to</strong> (==) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5472,13 +5892,13 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L27</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5496,7 +5916,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_greater</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_greater" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>greater than</strong> (>) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5508,13 +5928,13 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L63</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5532,7 +5952,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_greater_equal</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_greater_equal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>greater than or equal to</strong> (>=) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5544,13 +5964,13 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L81</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5570,7 +5990,7 @@ an output array of shape <code class="docutils literal"><span class="pre">(i0,)<
with broadcasting.</p>
<p>It is equivalent to doing <span class="math">\(sqrt(x_1^2 + x_2^2)\)</span>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
@@ -5587,13 +6007,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_extended.cc:L137</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5611,7 +6031,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_lesser</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_lesser" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>lesser than</strong> (<) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5623,13 +6043,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L99</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5647,7 +6067,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_lesser_equal</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_lesser_equal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>lesser than or equal to</strong> (<=) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5659,13 +6079,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L117</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5684,7 +6104,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise maximum of the input arrays with broadcasting.</p>
<p>This function compares two input arrays and returns a new array having the element-wise maxima.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5696,13 +6116,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_extended.cc:L61</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5721,7 +6141,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise minimum of the input arrays with broadcasting.</p>
<p>This function compares two input arrays and returns a new array having the element-wise minima.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5733,13 +6153,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_extended.cc:L96</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5758,7 +6178,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise difference of the input arrays with broadcasting.</p>
<p><cite>broadcast_minus</cite> is an alias to the function <cite>broadcast_sub</cite>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5773,13 +6193,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L71</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
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+<col class="field-body"/>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5797,7 +6217,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_mod</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_mod" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise modulo of the input arrays with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
@@ -5809,13 +6229,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L170</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5833,7 +6253,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_mul</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_mul" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise product of the input arrays with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5845,13 +6265,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L104</p>
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-<col class="field-body"></col>
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+<col class="field-body"/>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5869,7 +6289,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_not_equal</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_not_equal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the result of element-wise <strong>not equal to</strong> (!=) comparison operation with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5881,13 +6301,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_logic.cc:L45</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
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+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5906,7 +6326,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise sum of the input arrays with broadcasting.</p>
<p><cite>broadcast_plus</cite> is an alias to the function <cite>broadcast_add</cite>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5921,13 +6341,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L32</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5945,7 +6365,7 @@ with broadcasting.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">broadcast_power</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.broadcast_power" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns result of first array elements raised to powers from second array, element-wise with broadcasting.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5957,13 +6377,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_extended.cc:L26</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -5982,7 +6402,7 @@ with broadcasting.</p>
<dd><p>Returns element-wise difference of the input arrays with broadcasting.</p>
<p><cite>broadcast_minus</cite> is an alias to the function <cite>broadcast_sub</cite>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">],</span>
@@ -5997,13 +6417,13 @@ with broadcasting.</p>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L71</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First input to the function</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second input to the function</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6026,21 +6446,21 @@ Also see, <a class="reference external" href="https://docs.scipy.org/doc/numpy/u
<p>Broadcasting is allowed on axes with size 1, such as from <cite>(2,1,3,1)</cite> to
<cite>(2,8,3,9)</cite>. Elements will be duplicated on the broadcasted axes.</p>
<p>For example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">broadcast_to</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span cl [...]
- <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]])</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>broadcast_to([[1,2,3]], shape=(2,3)) = [[ 1., 2., 3.],
+ [ 1., 2., 3.]])
</pre></div>
</div>
<p>The dimension which you do not want to change can also be kept as <cite>0</cite> which means copy the original value.
So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above example.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L192</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The shape of the desired array. We can set the dim to zero if it’s same as the original. E.g <cite>A = broadcast_to(B, shape=(10, 0, 0))</cite> has the same meaning as <cite>A = broadcast_axis(B, axis=0, size=10)</cite>.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – The shape of the desired array. We can set the dim to zero if it’s same as the original. E.g <cite>A = broadcast_to(B, shape=(10, 0, 0))</cite> has the same meaning as <cite>A = broadcast_axis(B, axis=0, size=10)</cite>.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6062,20 +6482,20 @@ So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above
<p class="last"><code class="docutils literal"><span class="pre">Cast</span></code> is deprecated. Use <code class="docutils literal"><span class="pre">cast</span></code> instead.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">cast</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cast</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">cast</span><span class="p">([</span><span class="mf">1e20</span><span class="p">,</span> <span class="mf">11.1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float16'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="n">inf</span><span class="p">,</span> <span class="mf">11.09375</span><span class="p">]</span>
<span class="n">cast</span><span class="p">([</span><span class="mi">300</span><span class="p">,</span> <span class="mf">11.1</span><span class="p">,</span> <span class="mf">10.9</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'uint8'</span><span class="p">)</span> <span class="o">=</span> [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L175</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input.</li>
-<li><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>, </em><em>required</em>) – Output data type.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>dtype</strong> (<em>{'float16', 'float32', 'float64', 'int32', 'uint8'}, required</em>) – Output data type.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6094,17 +6514,17 @@ So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above
<dd><p>Returns element-wise ceiling of the input.</p>
<p>The ceil of the scalar x is the smallest integer i, such that i >= x.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">ceil</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ceil</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L295</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6122,13 +6542,13 @@ So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above
<code class="descclassname">mxnet.symbol.</code><code class="descname">choose_element_0index</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.choose_element_0index" title="Permalink to this definition">¶</a></dt>
<dd><p>Choose one element from each line(row for python, column for R/Julia) in lhs according to index indicated by rhs. This function assume rhs uses 0-based index.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Left operand to the function.</li>
<li><strong>rhs</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Right operand to the function.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6147,25 +6567,25 @@ So with <cite>shape=(2,0)</cite>, we will obtain the same result as in the above
<dd><p>Clips (limits) the values in an array.</p>
<p>Given an interval, values outside the interval are clipped to the interval edges.
Clipping <code class="docutils literal"><span class="pre">x</span></code> between <cite>a_min</cite> and <cite>a_x</cite> would be:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">clip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">a_min</span><span class="p">,</span> <span class="n">a_max</span><span class="p">)</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">a_max</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>clip(x, a_min, a_max) = max(min(x, a_max), a_min))
</pre></div>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</sp [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</spa [...]
<span class="n">clip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span clas [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L457</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array.</li>
-<li><strong>a_min</strong> (<em>float</em><em>, </em><em>required</em>) – Minimum value</li>
-<li><strong>a_max</strong> (<em>float</em><em>, </em><em>required</em>) – Maximum value</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>a_min</strong> (<em>float, required</em>) – Minimum value</li>
+<li><strong>a_max</strong> (<em>float, required</em>) – Maximum value</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6191,37 +6611,37 @@ which they will be concatenated.
The dimension of the output array along the concatenated axis will be equal
to the sum of the corresponding dimensions of the input arrays.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">]]</span>
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">]]</span>
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">6</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">],[</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[1,1],[2,2]]
+y = [[3,3],[4,4],[5,5]]
+z = [[6,6], [7,7],[8,8]]
-<span class="n">concat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+concat(x,y,z,dim=0) = [[ 1., 1.],
+ [ 2., 2.],
+ [ 3., 3.],
+ [ 4., 4.],
+ [ 5., 5.],
+ [ 6., 6.],
+ [ 7., 7.],
+ [ 8., 8.]]
-<span class="n">Note</span> <span class="n">that</span> <span class="n">you</span> <span class="n">cannot</span> <span class="n">concat</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span> <span class="n">along</span> <span class="n">dimension</span> <span class="mi">1</span> <span class="n">since</span> <span class="n">dimension</span>
-<span class="mi">0</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">the</span> <span class="n">same</span> <span class="k">for</span> <span class="nb">all</span> <span class="n">the</span> <span class="nb">input</span> <span class="n">arrays</span><span class="o">.</span>
+Note that you cannot concat x,y,z along dimension 1 since dimension
+0 is not the same for all the input arrays.
-<span class="n">concat</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">6.</span><span clas [...]
- <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+concat(y,z,dim=1) = [[ 3., 3., 6., 6.],
+ [ 4., 4., 7., 7.],
+ [ 5., 5., 8., 8.]]
</pre></div>
</div>
<p>Defined in src/operator/concat.cc:L80
This function support variable length of positional input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em></em><em>]</em><em></em>) – List of arrays to concatenate</li>
-<li><strong>dim</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – the dimension to be concated.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>data</strong> (<em>Symbol[]</em>) – List of arrays to concatenate</li>
+<li><strong>dim</strong> (<em>int, optional, default='1'</em>) – the dimension to be concated.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6243,12 +6663,12 @@ This function support variable length of positional input.</p>
\[cos([0, \pi/4, \pi/2]) = [1, 0.707, 0]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L491</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6269,12 +6689,12 @@ This function support variable length of positional input.</p>
\[cosh(x) = 0.5\times(exp(x) + exp(-x))\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L613</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6303,7 +6723,7 @@ and <code class="docutils literal"><span class="pre">end=(e_1,</span> <span clas
<p>The resulting array’s <em>k</em>-th dimension contains elements
from the <em>k</em>-th dimension of the input array with the open range <code class="docutils literal"><span class="pre">[b_k,</span> <span class="pre">e_k)</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">9.</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]]</span>
@@ -6313,14 +6733,14 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L257</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Source input</li>
-<li><strong>begin</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – starting indices for the slice operation, supports negative indices.</li>
-<li><strong>end</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – ending indices for the slice operation, supports negative indices.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>begin</strong> (<em>Shape(tuple), required</em>) – starting indices for the slice operation, supports negative indices.</li>
+<li><strong>end</strong> (<em>Shape(tuple), required</em>) – ending indices for the slice operation, supports negative indices.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6341,12 +6761,12 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
\[degrees([0, \pi/2, \pi, 3\pi/2, 2\pi]) = [0, 90, 180, 270, 360]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L571</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6373,11 +6793,11 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
axis of the second input</p>
<p>For example, given 3-D <code class="docutils literal"><span class="pre">x</span></code> with shape <cite>(n,m,k)</cite> and <code class="docutils literal"><span class="pre">y</span></code> with shape <cite>(k,r,s)</cite>, the
result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span [...]
</pre></div>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">reshape</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</s [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">reshape</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</sp [...]
<span class="n">y</span> <span class="o">=</span> <span class="n">reshape</span><span class="p">([</span><span class="mi">7</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],< [...]
<span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,:]</span><span class="o">*</span><span class="n">y</span><span class="p">[:,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span> <span class="o">=</span> <span class="mi">0</span>
@@ -6387,15 +6807,15 @@ result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
</ul>
<p>Defined in src/operator/tensor/matrix_op.cc:L376</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The first input</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The second input</li>
-<li><strong>transpose_a</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then transpose the first input before dot.</li>
-<li><strong>transpose_b</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then transpose the second input before dot.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>transpose_a</strong> (<em>boolean, optional, default=False</em>) – If true then transpose the first input before dot.</li>
+<li><strong>transpose_b</strong> (<em>boolean, optional, default=False</em>) – If true then transpose the second input before dot.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6413,13 +6833,13 @@ result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">elemwise_add</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.elemwise_add" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds arguments element-wise.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – first input</li>
<li><strong>rhs</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – second input</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6439,17 +6859,17 @@ result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
<div class="math">
\[exp(x) = e^x \approx 2.718^x\]</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">exp</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="n">inf</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.707</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">exp</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="n">inf</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.707</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L402</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6470,13 +6890,13 @@ result array will have shape <cite>(n,m,r,s)</cite>. It is computed by:</p>
will return a new array with shape <code class="docutils literal"><span class="pre">(2,1,3,4)</span></code>.</p>
<p>Defined in src/operator/tensor/matrix_op.cc:L213</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Source input</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>required</em>) – Position where new axis is to be inserted. Suppose that the input <cite>NDArray</cite>‘s dimension is <cite>ndim</cite>, the range of the inserted axis is <cite>[-ndim, ndim]</cite></li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int, required</em>) – Position where new axis is to be inserted. Suppose that the input <cite>NDArray</cite>‘s dimension is <cite>ndim</cite>, the range of the inserted axis is <cite>[-ndim, ndim]</cite></li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6496,12 +6916,12 @@ will return a new array with shape <code class="docutils literal"><span class="p
<p>This function provides greater precision than <code class="docutils literal"><span class="pre">exp(x)</span> <span class="pre">-</span> <span class="pre">1</span></code> for small values of <code class="docutils literal"><span class="pre">x</span></code>.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L475</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6519,14 +6939,14 @@ will return a new array with shape <code class="docutils literal"><span class="p
<code class="descclassname">mxnet.symbol.</code><code class="descname">fill_element_0index</code><span class="sig-paren">(</span><em>lhs=None</em>, <em>mhs=None</em>, <em>rhs=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.fill_element_0index" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill one element of each line(row for python, column for R/Julia) in lhs according to index indicated by rhs and values indicated by mhs. This function assume rhs uses 0-based index.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lhs</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Left operand to the function.</li>
<li><strong>mhs</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Middle operand to the function.</li>
<li><strong>rhs</strong> (<a class="reference internal" href="ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Right operand to the function.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6544,17 +6964,17 @@ will return a new array with shape <code class="docutils literal"><span class="p
<code class="descclassname">mxnet.symbol.</code><code class="descname">fix</code><span class="sig-paren">(</span><em>data=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.fix" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise rounded value to the nearest integer towards zero of the input.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">fix</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">2.</span><span class=" [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">fix</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">2.</span><span class="p [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L333</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6578,7 +6998,7 @@ will return a new array with shape <code class="docutils literal"><span class="p
<p>For an input array with shape <code class="docutils literal"><span class="pre">(d1,</span> <span class="pre">d2,</span> <span class="pre">...,</span> <span class="pre">dk)</span></code>, <cite>flatten</cite> operation reshapes
the input array into an output array of shape <code class="docutils literal"><span class="pre">(d1,</span> <span class="pre">d2*...*dk)</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span>
<span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span>
<span class="p">[</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">]</span>
@@ -6594,12 +7014,12 @@ the input array into an output array of shape <code class="docutils literal"><sp
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L132</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6618,7 +7038,7 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Reverses the order of elements along given axis while preserving array shape.</p>
<p>Note: reverse and flip are equivalent. We use reverse in the following examples.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">]]</span>
<span class="n">reverse</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><sp [...]
@@ -6630,13 +7050,13 @@ the input array into an output array of shape <code class="docutils literal"><sp
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L601</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data array</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The axis which to reverse elements.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>Shape(tuple), required</em>) – The axis which to reverse elements.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6655,17 +7075,17 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Returns element-wise floor of the input.</p>
<p>The floor of the scalar x is the largest integer i, such that i <= x.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">floor</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">floor</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span clas [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L308</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6684,12 +7104,12 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Returns the gamma function (extension of the factorial function to the reals) , computed element-wise on the input array.</p>
<p>From:src/operator/tensor/elemwise_unary_op.cc:667</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6708,12 +7128,12 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Returns element-wise log of the absolute value of the gamma function of the input.</p>
<p>From:src/operator/tensor/elemwise_unary_op.cc:677</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6732,12 +7152,12 @@ the input array into an output array of shape <code class="docutils literal"><sp
<dd><p>Returns a copy of the input.</p>
<p>From:src/operator/tensor/elemwise_unary_op.cc:49</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6769,35 +7189,35 @@ is either the identity or the matrix transposition.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">multiply</span><span class="o">-</span><span class="n">add</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">C</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">linalg_gemm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">transpose_b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf"> [...]
- <span class="o">=</span> <span class="p">[[</span><span class="mf">14.0</span><span class="p">,</span> <span class="mf">14.0</span><span class="p">,</span> <span class="mf">14.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">14.0</span><span class="p">,</span> <span class="mf">14.0</span><span class="p">,</span> <span class="mf">14.0</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix multiply-add
+A = [[1.0, 1.0], [1.0, 1.0]]
+B = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]
+C = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
+linalg_gemm(A, B, C, transpose_b = 1, alpha = 2.0 , beta = 10.0)
+ = [[14.0, 14.0, 14.0], [14.0, 14.0, 14.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">multiply</span><span class="o">-</span><span class="n">add</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]]</span>
-<span class="n">C</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">10.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.01</span><span class="p">]]]</span>
-<span class="n">linalg_gemm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">transpose_b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf"> [...]
- <span class="o">=</span> <span class="p">[[[</span><span class="mf">104.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.14</span><span class="p">]]]</span>
+// Batch matrix multiply-add
+A = [[[1.0, 1.0]], [[0.1, 0.1]]]
+B = [[[1.0, 1.0]], [[0.1, 0.1]]]
+C = [[[10.0]], [[0.01]]]
+linalg_gemm(A, B, C, transpose_b = 1, alpha = 2.0 , beta = 10.0)
+ = [[[104.0]], [[0.14]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L48</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of input matrices</li>
<li><strong>B</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of input matrices</li>
<li><strong>C</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of input matrices</li>
-<li><strong>transpose_a</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply with transposed of first input (A).</li>
-<li><strong>transpose_b</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply with transposed of second input (B).</li>
-<li><strong>alpha</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor multiplied with A*B.</li>
-<li><strong>beta</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor multiplied with C.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>transpose_a</strong> (<em>boolean, optional, default=False</em>) – Multiply with transposed of first input (A).</li>
+<li><strong>transpose_b</strong> (<em>boolean, optional, default=False</em>) – Multiply with transposed of second input (B).</li>
+<li><strong>alpha</strong> (<em>double, optional, default=1</em>) – Scalar factor multiplied with A*B.</li>
+<li><strong>beta</strong> (<em>double, optional, default=1</em>) – Scalar factor multiplied with C.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6829,31 +7249,31 @@ the identity or the matrix transposition.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">multiply</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">linalg_gemm2</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">transpose_b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span>
- <span class="o">=</span> <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix multiply
+A = [[1.0, 1.0], [1.0, 1.0]]
+B = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]
+linalg_gemm2(A, B, transpose_b = 1, alpha = 2.0)
+ = [[4.0, 4.0, 4.0], [4.0, 4.0, 4.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">multiply</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]]</span>
-<span class="n">linalg_gemm2</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">transpose_b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="p">)</span>
- <span class="o">=</span> <span class="p">[[[</span><span class="mf">4.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.04</span> <span class="p">]]]</span>
+// Batch matrix multiply
+A = [[[1.0, 1.0]], [[0.1, 0.1]]]
+B = [[[1.0, 1.0]], [[0.1, 0.1]]]
+linalg_gemm2(A, B, transpose_b = 1, alpha = 2.0 )
+ = [[[4.0]], [[0.04 ]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L106</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of input matrices</li>
<li><strong>B</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of input matrices</li>
-<li><strong>transpose_a</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply with transposed of first input (A).</li>
-<li><strong>transpose_b</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply with transposed of second input (B).</li>
-<li><strong>alpha</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor multiplied with A*B.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>transpose_a</strong> (<em>boolean, optional, default=False</em>) – Multiply with transposed of first input (A).</li>
+<li><strong>transpose_b</strong> (<em>boolean, optional, default=False</em>) – Multiply with transposed of second input (B).</li>
+<li><strong>alpha</strong> (<em>double, optional, default=1</em>) – Scalar factor multiplied with A*B.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6887,23 +7307,23 @@ apart from the diagonal.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">factorization</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">4.25</span><span class="p">]]</span>
-<span class="n">linalg_potrf</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix factorization
+A = [[4.0, 1.0], [1.0, 4.25]]
+linalg_potrf(A) = [[2.0, 0], [0.5, 2.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">factorization</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">4.25</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">16.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</spa [...]
-<span class="n">linalg_potrf</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mi">0</spa [...]
+// Batch matrix factorization
+A = [[[4.0, 1.0], [1.0, 4.25]], [[16.0, 4.0], [4.0, 17.0]]]
+linalg_potrf(A) = [[[2.0, 0], [0.5, 2.0]], [[4.0, 0], [1.0, 4.0]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L159</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of input matrices to be decomposed</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6936,24 +7356,24 @@ inverse of each <em>B</em><sub>i</sub> from this decomposition, i.e</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">inverse</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]]</span>
-<span class="n">linalg_potri</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.26563</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0625</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.0625</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix inverse
+A = [[2.0, 0], [0.5, 2.0]]
+linalg_potri(A) = [[0.26563, -0.0625], [-0.0625, 0.25]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">inverse</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><spa [...]
-<span class="n">linalg_potri</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">0.26563</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0625</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.0625</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mf">0.06641</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.01562</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.01562</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0625</span><span class="p">]]]</span>
+// Batch matrix inverse
+A = [[[2.0, 0], [0.5, 2.0]], [[4.0, 0], [1.0, 4.0]]]
+linalg_potri(A) = [[[0.26563, -0.0625], [-0.0625, 0.25]],
+ [[0.06641, -0.01562], [-0.01562, 0,0625]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L211</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of lower triangular matrices</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -6981,23 +7401,23 @@ of all diagonal elements. All matrices must be square and all diagonal elements
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">reduction</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">]]</span>
-<span class="n">linalg_sumlogdiag</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.9459</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix reduction
+A = [[1.0, 1.0], [1.0, 7.0]]
+linalg_sumlogdiag(A) = [1.9459]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">reduction</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><spa [...]
-<span class="n">linalg_sumlogdiag</span><span class="p">(</span><span class="n">A</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.9459</span><span class="p">,</span> <span class="mf">3.9318</span><span class="p">]</span>
+// Batch matrix reduction
+A = [[[1.0, 1.0], [1.0, 7.0]], [[3.0, 0], [0, 17.0]]]
+linalg_sumlogdiag(A) = [1.9459, 3.9318]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L379</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of square matrices</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7034,30 +7454,30 @@ All matrices <em>A</em><sub>i</sub> must be lower triangular.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">multiply</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">linalg_trmm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix multiply
+A = [[1.0, 0], [1.0, 1.0]]
+B = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
+linalg_trmm(A, B, alpha = 2.0) = [[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">multiply</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><spa [...]
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> [...]
-<span class="n">linalg_trmm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</sp [...]
- <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]]]</span>
+// Batch matrix multiply
+A = [[[1.0, 0], [1.0, 1.0]], [[1.0, 0], [1.0, 1.0]]]
+B = [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]]
+linalg_trmm(A, B, alpha = 2.0 ) = [[[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]],
+ [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L268</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of lower triangular matrices</li>
<li><strong>B</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of matrices</li>
-<li><strong>transpose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Use transposed of the triangular matrix</li>
-<li><strong>rightside</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply triangular matrix from the right to non-triangular one.</li>
-<li><strong>alpha</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor to be applied to the result.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>transpose</strong> (<em>boolean, optional, default=False</em>) – Use transposed of the triangular matrix</li>
+<li><strong>rightside</strong> (<em>boolean, optional, default=False</em>) – Multiply triangular matrix from the right to non-triangular one.</li>
+<li><strong>alpha</strong> (<em>double, optional, default=1</em>) – Scalar factor to be applied to the result.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7094,31 +7514,31 @@ All matrices <em>A</em><sub>i</sub> must be lower triangular.</p>
proper backward gradients.</p>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">//</span> <span class="n">Single</span> <span class="n">matrix</span> <span class="n">solve</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]</span>
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]]</span>
-<span class="n">linalg_trsm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>// Single matrix solve
+A = [[1.0, 0], [1.0, 1.0]]
+B = [[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]]
+linalg_trsm(A, B, alpha = 0.5) = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
-<span class="o">//</span> <span class="n">Batch</span> <span class="n">matrix</span> <span class="n">solve</span>
-<span class="n">A</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><spa [...]
-<span class="n">B</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">]]]</span>
-<span class="n">linalg_trsm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</sp [...]
- <span class="p">[[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span> <span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]]]</span>
+// Batch matrix solve
+A = [[[1.0, 0], [1.0, 1.0]], [[1.0, 0], [1.0, 1.0]]]
+B = [[[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]],
+ [[4.0, 4.0, 4.0], [8.0, 8.0, 8.0]]]
+linalg_trsm(A, B, alpha = 0.5 ) = [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
+ [[2.0, 2.0, 2.0 ], [2.0, 2.0, 2.0]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/la_op.cc:L331</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>A</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of lower triangular matrices</li>
<li><strong>B</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor of matrices</li>
-<li><strong>transpose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Use transposed of the triangular matrix</li>
-<li><strong>rightside</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Multiply triangular matrix from the right to non-triangular one.</li>
-<li><strong>alpha</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar factor to be applied to the result.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>transpose</strong> (<em>boolean, optional, default=False</em>) – Use transposed of the triangular matrix</li>
+<li><strong>rightside</strong> (<em>boolean, optional, default=False</em>) – Multiply triangular matrix from the right to non-triangular one.</li>
+<li><strong>alpha</strong> (<em>double, optional, default=1</em>) – Scalar factor to be applied to the result.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7138,12 +7558,12 @@ proper backward gradients.</p>
<p>The natural logarithm is logarithm in base <em>e</em>, so that <code class="docutils literal"><span class="pre">log(exp(x))</span> <span class="pre">=</span> <span class="pre">x</span></code></p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L412</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7163,12 +7583,12 @@ proper backward gradients.</p>
<p><code class="docutils literal"><span class="pre">10**log10(x)</span> <span class="pre">=</span> <span class="pre">x</span></code></p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L422</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7189,12 +7609,12 @@ proper backward gradients.</p>
<span class="math">\(1+x\approx 1\)</span></p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L462</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7214,12 +7634,12 @@ proper backward gradients.</p>
<p><code class="docutils literal"><span class="pre">2**log2(x)</span> <span class="pre">=</span> <span class="pre">x</span></code></p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L432</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7238,7 +7658,7 @@ proper backward gradients.</p>
<dd><p>Computes the log softmax of the input.
This is equivalent to computing softmax followed by log.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">.</span><span class="mi">1</span><span class="p">])</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">.</span><span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="go">array([-1.41702998, -0.41702995, -2.31702995], dtype=float32)</span>
@@ -7249,13 +7669,13 @@ This is equivalent to computing softmax followed by log.</p>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – The axis along which to compute softmax.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int, optional, default='-1'</em>) – The axis along which to compute softmax.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7275,12 +7695,12 @@ This is equivalent to computing softmax followed by log.</p>
.. note:: <code class="docutils literal"><span class="pre">make_loss</span></code> is deprecated, use <code class="docutils literal"><span class="pre">MakeLoss</span></code>.</p>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L110</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7299,25 +7719,24 @@ This is equivalent to computing softmax followed by log.</p>
<dd><p>Computes the max of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L121</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7336,25 +7755,24 @@ NOT in axis instead.</p>
<dd><p>Computes the max of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L121</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7373,25 +7791,24 @@ NOT in axis instead.</p>
<dd><p>Computes the mean of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L64</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7410,25 +7827,24 @@ NOT in axis instead.</p>
<dd><p>Computes the min of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L135</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7447,25 +7863,24 @@ NOT in axis instead.</p>
<dd><p>Computes the min of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L135</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7483,20 +7898,20 @@ NOT in axis instead.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">mp_sgd_mom_update</code><span class="sig-paren">(</span><em>weight=None</em>, <em>grad=None</em>, <em>mom=None</em>, <em>weight32=None</em>, <em>lr=_Null</em>, <em>momentum=_Null</em>, <em>wd=_Null</em>, <em>rescale_grad=_Null</em>, <em>clip_gradient=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.mp_sgd_m [...]
<dd><p>Updater function for multi-precision sgd optimizer</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Gradient</li>
<li><strong>mom</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Momentum</li>
<li><strong>weight32</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight32</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The decay rate of momentum estimates at each epoch.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0</em>) – The decay rate of momentum estimates at each epoch.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7514,18 +7929,18 @@ NOT in axis instead.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">mp_sgd_update</code><span class="sig-paren">(</span><em>weight=None</em>, <em>grad=None</em>, <em>weight32=None</em>, <em>lr=_Null</em>, <em>wd=_Null</em>, <em>rescale_grad=_Null</em>, <em>clip_gradient=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.mp_sgd_update" title="Permalink to this definition">¶</a></dt>
<dd><p>Updater function for multi-precision sgd optimizer</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – gradient</li>
<li><strong>weight32</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight32</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7544,25 +7959,24 @@ NOT in axis instead.</p>
<dd><p>Computes the product of array elements over given axes treating Not a Numbers (<code class="docutils literal"><span class="pre">NaN</span></code>) as one.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L107</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7581,25 +7995,24 @@ NOT in axis instead.</p>
<dd><p>Computes the sum of array elements over given axes treating Not a Numbers (<code class="docutils literal"><span class="pre">NaN</span></code>) as zero.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L92</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7618,12 +8031,12 @@ NOT in axis instead.</p>
<dd><p>Numerical negative of the argument, element-wise.</p>
<p>From:src/operator/tensor/elemwise_unary_op.cc:206</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7641,7 +8054,7 @@ NOT in axis instead.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">norm</code><span class="sig-paren">(</span><em>data=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.norm" title="Permalink to this definition">¶</a></dt>
<dd><p>Flattens the input array and then computes the l2 norm.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
<span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mf">5.47722578</span><span class="p">]</span>
@@ -7649,12 +8062,12 @@ NOT in axis instead.</p>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L218</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Source input</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7677,22 +8090,22 @@ NOT in axis instead.</p>
</div>
<p>Samples are distributed according to a normal distribution parametrized by <em>loc</em> (mean) and <em>scale</em> (standard deviation).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cla [...]
<span class="p">[</span><span class="o">-</span><span class="mf">1.23474145</span><span class="p">,</span> <span class="mf">1.55807114</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L62</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>loc</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Mean of the distribution.</li>
-<li><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Standard deviation of the distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>loc</strong> (<em>float, optional, default=0</em>) – Mean of the distribution.</li>
+<li><strong>scale</strong> (<em>float, optional, default=1</em>) – Standard deviation of the distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7713,44 +8126,44 @@ NOT in axis instead.</p>
other locations take value <cite>off_value</cite>.</p>
<p><cite>one_hot</cite> operation with <cite>indices</cite> of shape <code class="docutils literal"><span class="pre">(i0,</span> <span class="pre">i1)</span></code> and <cite>depth</cite> of <code class="docutils literal"><span class="pre">d</span></code> would result
in an output array of shape <code class="docutils literal"><span class="pre">(i0,</span> <span class="pre">i1,</span> <span class="pre">d)</span></code> with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">off_value</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">off_value</span>
<span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]]</span> <span class="o">=</span> <span class="n">on_value</span>
</pre></div>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">one_hot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">1.</span> <span clas [...]
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>one_hot([1,0,2,0], 3) = [[ 0. 1. 0.]
+ [ 1. 0. 0.]
+ [ 0. 0. 1.]
+ [ 1. 0. 0.]]
-<span class="n">one_hot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">,</span> <span class="n">on_value</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">off_value</span><span class="o">=</span><span class="mi">1</span><sp [...]
- <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span> <span class="mi">8</span> <span class="mi">1</span><span class="p">]</span>
- <span class="p">[</span><span class="mi">8</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]</span>
- <span class="p">[</span><span class="mi">1</span> <span class="mi">1</span> <span class="mi">8</span><span class="p">]</span>
- <span class="p">[</span><span class="mi">8</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]]</span>
+one_hot([1,0,2,0], 3, on_value=8, off_value=1,
+ dtype='int32') = [[1 8 1]
+ [8 1 1]
+ [1 1 8]
+ [8 1 1]]
-<span class="n">one_hot</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">]],</span> <span class="mi">3</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">0.</span> <span [...]
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+one_hot([[1,0],[1,0],[2,0]], 3) = [[[ 0. 1. 0.]
+ [ 1. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[ 0. 1. 0.]
+ [ 1. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]</span>
+ [[ 0. 0. 1.]
+ [ 1. 0. 0.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/indexing_op.cc:L218</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>indices</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – array of locations where to set on_value</li>
-<li><strong>depth</strong> (<em>int</em><em>, </em><em>required</em>) – Depth of the one hot dimension.</li>
-<li><strong>on_value</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – The value assigned to the locations represented by indices.</li>
-<li><strong>off_value</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The value assigned to the locations not represented by indices.</li>
-<li><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='float32'</em>) – DType of the output</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>depth</strong> (<em>int, required</em>) – Depth of the one hot dimension.</li>
+<li><strong>on_value</strong> (<em>double, optional, default=1</em>) – The value assigned to the locations represented by indices.</li>
+<li><strong>off_value</strong> (<em>double, optional, default=0</em>) – The value assigned to the locations not represented by indices.</li>
+<li><strong>dtype</strong> (<em>{'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='float32'</em>) – DType of the output</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7769,7 +8182,7 @@ in an output array of shape <code class="docutils literal"><span class="pre">(i0
<dd><p>Return an array of ones with the same shape and type
as the input array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]]</span>
<span class="n">ones_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
@@ -7777,12 +8190,12 @@ as the input array.</p>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7818,77 +8231,77 @@ to add before and after the elements of the array along dimension <code class="d
The widths of the higher two dimensions <code class="docutils literal"><span class="pre">before_1</span></code>, <code class="docutils literal"><span class="pre">after_1</span></code>, <code class="docutils literal"><span class="pre">before_2</span></code>,
<code class="docutils literal"><span class="pre">after_2</span></code> must be 0.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[[</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[[ 1. 2. 3.]
+ [ 4. 5. 6.]]
- <span class="p">[[</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span><span class="p">]]]</span>
+ [[ 7. 8. 9.]
+ [ 10. 11. 12.]]]
- <span class="p">[[[</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span><span class="p">]]</span>
+ [[[ 11. 12. 13.]
+ [ 14. 15. 16.]]
- <span class="p">[[</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span><span class="p">]]]]</span>
+ [[ 17. 18. 19.]
+ [ 20. 21. 22.]]]]
-<span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">mode</span><span class="o">=</span><span class="s2">"edge"</span><span class="p">,</span> <span class="n">pad_width</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class= [...]
+pad(x,mode="edge", pad_width=(0,0,0,0,1,1,1,1)) =
- <span class="p">[[[[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">6.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">6.</span><span class="p">]]</span>
+ [[[[ 1. 1. 2. 3. 3.]
+ [ 1. 1. 2. 3. 3.]
+ [ 4. 4. 5. 6. 6.]
+ [ 4. 4. 5. 6. 6.]]
- <span class="p">[[</span> <span class="mf">7.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">7.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">12.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">12.</span><span class="p">]]]</span>
+ [[ 7. 7. 8. 9. 9.]
+ [ 7. 7. 8. 9. 9.]
+ [ 10. 10. 11. 12. 12.]
+ [ 10. 10. 11. 12. 12.]]]
- <span class="p">[[[</span> <span class="mf">11.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">11.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">13.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">16.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">14.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">16.</span><span class="p">]]</span>
+ [[[ 11. 11. 12. 13. 13.]
+ [ 11. 11. 12. 13. 13.]
+ [ 14. 14. 15. 16. 16.]
+ [ 14. 14. 15. 16. 16.]]
- <span class="p">[[</span> <span class="mf">17.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">17.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">19.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">22.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">20.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">22.</span><span class="p">]]]]</span>
+ [[ 17. 17. 18. 19. 19.]
+ [ 17. 17. 18. 19. 19.]
+ [ 20. 20. 21. 22. 22.]
+ [ 20. 20. 21. 22. 22.]]]]
-<span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"constant"</span><span class="p">,</span> <span class="n">constant_value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">pad_width</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,< [...]
+pad(x, mode="constant", constant_value=0, pad_width=(0,0,0,0,1,1,1,1)) =
- <span class="p">[[[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[[[ 0. 0. 0. 0. 0.]
+ [ 0. 1. 2. 3. 0.]
+ [ 0. 4. 5. 6. 0.]
+ [ 0. 0. 0. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">10.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]</span>
+ [[ 0. 0. 0. 0. 0.]
+ [ 0. 7. 8. 9. 0.]
+ [ 0. 10. 11. 12. 0.]
+ [ 0. 0. 0. 0. 0.]]]
- <span class="p">[[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">11.</span> <span class="mf">12.</span> <span class="mf">13.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">14.</span> <span class="mf">15.</span> <span class="mf">16.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
+ [[[ 0. 0. 0. 0. 0.]
+ [ 0. 11. 12. 13. 0.]
+ [ 0. 14. 15. 16. 0.]
+ [ 0. 0. 0. 0. 0.]]
- <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">17.</span> <span class="mf">18.</span> <span class="mf">19.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">20.</span> <span class="mf">21.</span> <span class="mf">22.</span> <span class="mf">0.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]]]</span>
+ [[ 0. 0. 0. 0. 0.]
+ [ 0. 17. 18. 19. 0.]
+ [ 0. 20. 21. 22. 0.]
+ [ 0. 0. 0. 0. 0.]]]]
</pre></div>
</div>
<p>Defined in src/operator/pad.cc:L747</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – An n-dimensional input array.</li>
-<li><strong>mode</strong> (<em>{'constant'</em><em>, </em><em>'edge'</em><em>, </em><em>'reflect'}</em><em>, </em><em>required</em>) – Padding type to use. “constant” pads with <cite>constant_value</cite> “edge” pads using the edge values of the input array “reflect” pads by reflecting values with respect to the edges.</li>
-<li><strong>pad_width</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – Widths of the padding regions applied to the edges of each axis. It is a tuple of integer padding widths for each axis of the format <code class="docutils literal"><span class="pre">(before_1,</span> <span class="pre">after_1,</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">before_N,</span> <span class="pre">after_N)</span></code>. It s [...]
-<li><strong>constant_value</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The value used for padding when <cite>mode</cite> is “constant”.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>mode</strong> (<em>{'constant', 'edge', 'reflect'}, required</em>) – Padding type to use. “constant” pads with <cite>constant_value</cite> “edge” pads using the edge values of the input array “reflect” pads by reflecting values with respect to the edges.</li>
+<li><strong>pad_width</strong> (<em>Shape(tuple), required</em>) – Widths of the padding regions applied to the edges of each axis. It is a tuple of integer padding widths for each axis of the format <code class="docutils literal"><span class="pre">(before_1,</span> <span class="pre">after_1,</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">before_N,</span> <span class="pre">after_N)</span></code>. It should be of length <code class="docutils literal"><spa [...]
+<li><strong>constant_value</strong> (<em>double, optional, default=0</em>) – The value used for padding when <cite>mode</cite> is “constant”.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7907,44 +8320,44 @@ The widths of the higher two dimensions <code class="docutils literal"><span cla
<dd><p>Picks elements from an input array according to the input indices along the given axis.</p>
<p>Given an input array of shape <code class="docutils literal"><span class="pre">(d0,</span> <span class="pre">d1)</span></code> and indices of shape <code class="docutils literal"><span class="pre">(i0,)</span></code>, the result will be
an output array of shape <code class="docutils literal"><span class="pre">(i0,)</span></code> with:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
</pre></div>
</div>
<p>By default, if any index mentioned is too large, it is replaced by the index that addresses
the last element along an axis (the <cite>clip</cite> mode).</p>
<p>This function supports n-dimensional input and (n-1)-dimensional indices arrays.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1., 2.],
+ [ 3., 4.],
+ [ 5., 6.]]
-<span class="o">//</span> <span class="n">picks</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">pick</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]</span>
+// picks elements with specified indices along axis 0
+pick(x, y=[0,1], 0) = [ 1., 4.]
-<span class="o">//</span> <span class="n">picks</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span>
-<span class="n">pick</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">4. [...]
+// picks elements with specified indices along axis 1
+pick(x, y=[0,1,0], 1) = [ 1., 4., 5.]
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
+y = [[ 1.],
+ [ 0.],
+ [ 2.]]
-<span class="o">//</span> <span class="n">picks</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">dims</span> <span class="n">are</span> <span class="n">maintained</span>
-<span class="n">pick</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]</span>
+// picks elements with specified indices along axis 1 and dims are maintained
+pick(x,y, 1, keepdims=True) = [[ 2.],
+ [ 3.],
+ [ 6.]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_index.cc:L126</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array</li>
<li><strong>index</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The index array</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because [...]
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='None'</em>) – The axis along which to perform the reduction. Negative values means indexing from right to left. <code class="docutils literal"><span class="pre">Requires</span> <span class="pre">axis</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">set</span> <span class="pre">as</span> <span class="pre">int,</span> <span class="pre">because</span> <span class="pre">global</span> <span class="p [...]
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axis is left in the result as dimension with size one.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -7963,25 +8376,24 @@ the last element along an axis (the <cite>clip</cite> mode).</p>
<dd><p>Computes the product of array elements over given axes.</p>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L77</p>
<table class="docutils field-list" frame="void" rules="none">
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+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8002,12 +8414,12 @@ NOT in axis instead.</p>
\[radians([0, 90, 180, 270, 360]) = [0, \pi/2, \pi, 3\pi/2, 2\pi]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L585</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8026,21 +8438,21 @@ NOT in axis instead.</p>
<dd><p>Draw random samples from an exponential distribution.</p>
<p>Samples are distributed according to an exponential distribution parametrized by <em>lambda</em> (rate).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_exponential</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.0097189</ [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_exponential</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.0097189</s [...]
<span class="p">[</span> <span class="mf">0.04146638</span><span class="p">,</span> <span class="mf">0.31715935</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lam</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Lambda parameter (rate) of the exponential distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>lam</strong> (<em>float, optional, default=1</em>) – Lambda parameter (rate) of the exponential distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8059,22 +8471,22 @@ NOT in axis instead.</p>
<dd><p>Draw random samples from a gamma distribution.</p>
<p>Samples are distributed according to a gamma distribution parametrized by <em>alpha</em> (shape) and <em>beta</em> (scale).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_gamma</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_gamma</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span c [...]
<span class="p">[</span> <span class="mf">3.91697288</span><span class="p">,</span> <span class="mf">3.65933681</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L75</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
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+<col class="field-body"/>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Alpha parameter (shape) of the gamma distribution.</li>
-<li><strong>beta</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Beta parameter (scale) of the gamma distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>alpha</strong> (<em>float, optional, default=1</em>) – Alpha parameter (shape) of the gamma distribution.</li>
+<li><strong>beta</strong> (<em>float, optional, default=1</em>) – Beta parameter (scale) of the gamma distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8096,22 +8508,22 @@ NOT in axis instead.</p>
number of unsuccessful experiments (generalized to real numbers).
Samples will always be returned as a floating point data type.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span c [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span cl [...]
<span class="p">[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L133</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
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+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>mu</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Mean of the negative binomial distribution.</li>
-<li><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Alpha (dispersion) parameter of the negative binomial distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>mu</strong> (<em>float, optional, default=1</em>) – Mean of the negative binomial distribution.</li>
+<li><strong>alpha</strong> (<em>float, optional, default=1</em>) – Alpha (dispersion) parameter of the negative binomial distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8132,22 +8544,22 @@ Samples will always be returned as a floating point data type.</p>
<em>k</em> (limit of unsuccessful experiments) and <em>p</em> (failure probability in each experiment).
Samples will always be returned as a floating point data type.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span>< [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><s [...]
<span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L117</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>k</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Limit of unsuccessful experiments.</li>
-<li><strong>p</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Failure probability in each experiment.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>k</strong> (<em>int, optional, default='1'</em>) – Limit of unsuccessful experiments.</li>
+<li><strong>p</strong> (<em>float, optional, default=1</em>) – Failure probability in each experiment.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8170,22 +8582,22 @@ Samples will always be returned as a floating point data type.</p>
</div>
<p>Samples are distributed according to a normal distribution parametrized by <em>loc</em> (mean) and <em>scale</em> (standard deviation).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cla [...]
<span class="p">[</span><span class="o">-</span><span class="mf">1.23474145</span><span class="p">,</span> <span class="mf">1.55807114</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L62</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>loc</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Mean of the distribution.</li>
-<li><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Standard deviation of the distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>loc</strong> (<em>float, optional, default=0</em>) – Mean of the distribution.</li>
+<li><strong>scale</strong> (<em>float, optional, default=1</em>) – Standard deviation of the distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8205,21 +8617,21 @@ Samples will always be returned as a floating point data type.</p>
<p>Samples are distributed according to a Poisson distribution parametrized by <em>lambda</em> (rate).
Samples will always be returned as a floating point data type.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">5.</span><span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">5.</span><span c [...]
<span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L102</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>lam</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Lambda parameter (rate) of the Poisson distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>lam</strong> (<em>float, optional, default=1</em>) – Lambda parameter (rate) of the Poisson distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8243,22 +8655,22 @@ Samples will always be returned as a floating point data type.</p>
<p>Samples are uniformly distributed over the half-open interval <em>[low, high)</em>
(includes <em>low</em>, but excludes <em>high</em>).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cla [...]
<span class="p">[</span> <span class="mf">0.54488319</span><span class="p">,</span> <span class="mf">0.84725171</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L45</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>low</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Lower bound of the distribution.</li>
-<li><strong>high</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Upper bound of the distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>low</strong> (<em>float, optional, default=0</em>) – Lower bound of the distribution.</li>
+<li><strong>high</strong> (<em>float, optional, default=1</em>) – Upper bound of the distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8277,17 +8689,17 @@ Samples will always be returned as a floating point data type.</p>
<dd><p>Returns the reciprocal of the argument, element-wise.</p>
<p>Calculates 1/x.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">reciprocal</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mf">1.6</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">reciprocal</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mf">1.6</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L220</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8308,12 +8720,12 @@ Samples will always be returned as a floating point data type.</p>
\[max(features, 0)\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L18</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8332,14 +8744,14 @@ Samples will always be returned as a floating point data type.</p>
<dd><p>Repeats elements of an array.</p>
<p>By default, <code class="docutils literal"><span class="pre">repeat</span></code> flattens the input array into 1-D and then repeats the
elements:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
<span class="n">repeat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><s [...]
</pre></div>
</div>
<p>The parameter <code class="docutils literal"><span class="pre">axis</span></code> specifies the axis along which to perform repeat:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">repeat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">repeat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class [...]
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]]</span>
<span class="n">repeat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
@@ -8353,14 +8765,14 @@ elements:</p>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L499</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data array</li>
-<li><strong>repeats</strong> (<em>int</em><em>, </em><em>required</em>) – The number of repetitions for each element.</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – The axis along which to repeat values. The negative numbers are interpreted counting from the backward. By default, use the flattened input array, and return a flat output array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>repeats</strong> (<em>int, required</em>) – The number of repetitions for each element.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='None'</em>) – The axis along which to repeat values. The negative numbers are interpreted counting from the backward. By default, use the flattened input array, and return a flat output array.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8384,15 +8796,15 @@ elements:</p>
<p>Given an array and a shape, this function returns a copy of the array in the new shape.
The shape is a tuple of integers such as (2,3,4).The size of the new shape should be same as the size of the input array.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">reshape</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">) [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">reshape</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)) [...]
</pre></div>
</div>
<p>Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}. The significance of each is explained below:</p>
<ul>
<li><p class="first"><code class="docutils literal"><span class="pre">0</span></code> copy this dimension from the input to the output shape.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
+- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
</pre></div>
</div>
</li>
@@ -8400,33 +8812,33 @@ The shape is a tuple of integers such as (2,3,4).The size of the new shape shoul
keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span clas [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,),</span> <span class="n">output</span> <span class="n">shape</span> [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
+- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
+- input shape = (2,3,4), shape=(-1,), output shape = (24,)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-2</span></code> copy all/remainder of the input dimensions to the output shape.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span> <span cl [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
+- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
+- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-3</span></code> use the product of two consecutive dimensions of the input shape as the output dimension.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span clas [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">),</span> <span cl [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
+- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
+- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
+- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">-4</span></code> split one dimension of the input into two dimensions passed subsequent to -4 in shape (can contain -1).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class [...]
-<span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>- input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
+- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
</pre></div>
</div>
</li>
@@ -8434,23 +8846,23 @@ At most one dimension of shape can be -1.</p>
<p>If the argument <cite>reverse</cite> is set to 1, then the special values are inferred from right to left.</p>
<blockquote>
<div><p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="n">without</span> <span class="n">reverse</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="k">for</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span>< [...]
-<span class="o">-</span> <span class="k">with</span> <span class="n">reverse</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">output</span> <span class="n">shape</span> <span class="n">will</span> <span class="n">be</span> <span class="p">(</span><span class="mi">50</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>- without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be (40,5)
+- with reverse=1, output shape will be (50,4).
</pre></div>
</div>
</div></blockquote>
<p>Defined in src/operator/tensor/matrix_op.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to reshape.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – The target shape</li>
-<li><strong>reverse</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true then the special values are inferred from right to left</li>
-<li><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Target new shape. One and only one dim can be 0, in which case it will be inferred from the rest of dims</li>
-<li><strong>keep_highest</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Whether keep the highest dim unchanged.If set to true, then the first dim in target_shape is ignored,and always fixed as input</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – The target shape</li>
+<li><strong>reverse</strong> (<em>boolean, optional, default=False</em>) – If true then the special values are inferred from right to left</li>
+<li><strong>target_shape</strong> (<em>Shape(tuple), optional, default=()</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Target new shape. One and only one dim can be 0, in which case it will be inferred from the rest of dims</li>
+<li><strong>keep_highest</strong> (<em>boolean, optional, default=False</em>) – (Deprecated! Use <code class="docutils literal"><span class="pre">shape</span></code> instead.) Whether keep the highest dim unchanged.If set to true, then the first dim in target_shape is ignored,and always fixed as input</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8469,7 +8881,7 @@ At most one dimension of shape can be -1.</p>
<dd><p>Reverses the order of elements along given axis while preserving array shape.</p>
<p>Note: reverse and flip are equivalent. We use reverse in the following examples.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">]]</span>
<span class="n">reverse</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">,</span> <span class="mf">9.</span><sp [...]
@@ -8481,13 +8893,13 @@ At most one dimension of shape can be -1.</p>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L601</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data array</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The axis which to reverse elements.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>Shape(tuple), required</em>) – The axis which to reverse elements.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8512,17 +8924,17 @@ At most one dimension of shape can be -1.</p>
</ul>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">rint</span><span class="p">([</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span clas [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">rint</span><span class="p">([</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L282</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8563,21 +8975,21 @@ Tieleman & Hinton, 2012.</p>
<span class="math">\(\eta\)</span> to be 0.001.</p>
<p>Defined in src/operator/optimizer_op.cc:L178</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Gradient</li>
<li><strong>n</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – n</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>gamma1</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.95</em>) – The decay rate of momentum estimates.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e-08</em>) – A small constant for numerical stability.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>clip_weights</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip weights to the range of [-clip_weights, clip_weights] If clip_weights <= 0, weight clipping is turned off. weights = max(min(weights, clip_weights), -clip_weights).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>gamma1</strong> (<em>float, optional, default=0.95</em>) – The decay rate of momentum estimates.</li>
+<li><strong>epsilon</strong> (<em>float, optional, default=1e-08</em>) – A small constant for numerical stability.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>clip_weights</strong> (<em>float, optional, default=-1</em>) – Clip weights to the range of [-clip_weights, clip_weights] If clip_weights <= 0, weight clipping is turned off. weights = max(min(weights, clip_weights), -clip_weights).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8610,8 +9022,8 @@ E[g]_t = \gamma_1 * E[g]_{t-1} + (1 - \gamma_1) * g_t\\
to be 0.9 and the learning rate <span class="math">\(\eta\)</span> to be 0.0001.</p>
<p>Defined in src/operator/optimizer_op.cc:L217</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight</li>
@@ -8619,15 +9031,15 @@ to be 0.9 and the learning rate <span class="math">\(\eta\)</span> to be 0.0001.
<li><strong>n</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – n</li>
<li><strong>g</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – g</li>
<li><strong>delta</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – delta</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>gamma1</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.95</em>) – Decay rate.</li>
-<li><strong>gamma2</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.9</em>) – Decay rate.</li>
-<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e-08</em>) – A small constant for numerical stability.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>clip_weights</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip weights to the range of [-clip_weights, clip_weights] If clip_weights <= 0, weight clipping is turned off. weights = max(min(weights, clip_weights), -clip_weights).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>gamma1</strong> (<em>float, optional, default=0.95</em>) – Decay rate.</li>
+<li><strong>gamma2</strong> (<em>float, optional, default=0.9</em>) – Decay rate.</li>
+<li><strong>epsilon</strong> (<em>float, optional, default=1e-08</em>) – A small constant for numerical stability.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>clip_weights</strong> (<em>float, optional, default=-1</em>) – Clip weights to the range of [-clip_weights, clip_weights] If clip_weights <= 0, weight clipping is turned off. weights = max(min(weights, clip_weights), -clip_weights).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8645,17 +9057,17 @@ to be 0.9 and the learning rate <span class="math">\(\eta\)</span> to be 0.0001.
<code class="descclassname">mxnet.symbol.</code><code class="descname">round</code><span class="sig-paren">(</span><em>data=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.round" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise rounded value to the nearest integer of the input.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="nb">round</span><span class="p">([</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">round</span><span class="p">([</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span cla [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L266</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8675,17 +9087,17 @@ to be 0.9 and the learning rate <span class="math">\(\eta\)</span> to be 0.0001.
<div class="math">
\[rsqrt(x) = 1/\sqrt{x}\]</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">rsqrt</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">16</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">rsqrt</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">16</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L383</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8713,26 +9125,26 @@ which is parameterized by the input value at index <em>i</em>. If the shape para
operator is not set, then one sample will be drawn per distribution and the output array
has the same shape as the input array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">lam</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">8.5</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>lam = [ 1.0, 8.5 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_exponential</span><span class="p">(</span><span class="n">lam</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.51837951</span><span class="p">,</span> <span class="mf">0.09994757</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_exponential(lam) = [ 0.51837951, 0.09994757]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_exponential</span><span class="p">(</span><span class="n">lam</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.51837951</span><span class="p">,</span> <span class="mf">0.19866663</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.09994757</span><span class="p">,</span> <span class="mf">0.50447971</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_exponential(lam, shape=(2)) = [[ 0.51837951, 0.19866663],
+ [ 0.09994757, 0.50447971]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L370</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lam</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Lambda (rate) parameters of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8760,28 +9172,28 @@ which is parameterized by the input values at index <em>i</em>. If the shape par
operator is not set, then one sample will be drawn per distribution and the output array
has the same shape as the input arrays.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">alpha</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.5</span> <span class="p">]</span>
-<span class="n">beta</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.7</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>alpha = [ 0.0, 2.5 ]
+beta = [ 1.0, 0.7 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_gamma</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">beta</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span> <span class="p">,</span> <span class="mf">2.25797319</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_gamma(alpha, beta) = [ 0. , 2.25797319]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_gamma</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">beta</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span> <span class="p">,</span> <span class="mf">0.</span> <span class="p">],</span>
- <span class="p">[</span> <span class="mf">2.25797319</span><span class="p">,</span> <span class="mf">1.70734084</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_gamma(alpha, beta, shape=(2)) = [[ 0. , 0. ],
+ [ 2.25797319, 1.70734084]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L368</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>alpha</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Alpha (shape) parameters of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>beta</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Beta (scale) parameters of the distributions.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8810,28 +9222,28 @@ operator is not set, then one sample will be drawn per distribution and the outp
has the same shape as the input arrays.</p>
<p>Samples will always be returned as a floating point data type.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">mu</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.5</span> <span class="p">]</span>
-<span class="n">alpha</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.1</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>mu = [ 2.0, 2.5 ]
+alpha = [ 1.0, 0.1 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_generalized_negative_binomial(mu, alpha) = [ 0., 3.]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_generalized_negative_binomial(mu, alpha, shape=(2)) = [[ 0., 3.],
+ [ 3., 1.]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L379</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>mu</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Means of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>alpha</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Alpha (dispersion) parameters of the distributions.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8859,29 +9271,29 @@ gradient.</p>
<p>Note that the input distribution must be normalized, i.e. <em>data</em> must sum to
1 along its last axis.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">probs</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>probs = [[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_multinomial</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_multinomial(probs) = [3, 0]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_multinomial</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_multinomial(probs, shape=(2)) = [[4, 2],
+ [0, 0]]
-<span class="o">//</span> <span class="n">requests</span> <span class="n">log</span> <span class="n">likelihood</span>
-<span class="n">sample_multinomial</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">get_prob</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class=" [...]
+// requests log likelihood
+sample_multinomial(probs, get_prob=True) = [2, 1], [0.2, 0.3]
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Distribution probabilities. Must sum to one on the last axis.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>get_prob</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to also return the log probability of sampled result. This is usually used for differentiating through stochastic variables, e.g. in reinforcement learning.</li>
-<li><strong>dtype</strong> (<em>{'int32'}</em><em>,</em><em>optional</em><em>, </em><em>default='int32'</em>) – DType of the output in case this can’t be inferred. Only support int32 for now.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>get_prob</strong> (<em>boolean, optional, default=False</em>) – Whether to also return the log probability of sampled result. This is usually used for differentiating through stochastic variables, e.g. in reinforcement learning.</li>
+<li><strong>dtype</strong> (<em>{'int32'},optional, default='int32'</em>) – DType of the output in case this can’t be inferred. Only support int32 for now.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8910,28 +9322,28 @@ operator is not set, then one sample will be drawn per distribution and the outp
has the same shape as the input arrays.</p>
<p>Samples will always be returned as a floating point data type.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">k</span> <span class="o">=</span> <span class="p">[</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">49</span> <span class="p">]</span>
-<span class="n">p</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.4</span> <span class="p">,</span> <span class="mf">0.77</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>k = [ 20, 49 ]
+p = [ 0.4 , 0.77 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">16.</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_negative_binomial(k, p) = [ 15., 16.]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">50.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">16.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_negative_binomial(k, p, shape=(2)) = [[ 15., 50.],
+ [ 16., 12.]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L375</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>k</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Limits of unsuccessful experiments.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>p</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Failure probabilities in each experiment.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -8959,28 +9371,28 @@ which is parameterized by the input values at index <em>i</em>. If the shape par
operator is not set, then one sample will be drawn per distribution and the output array
has the same shape as the input arrays.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">mu</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.5</span> <span class="p">]</span>
-<span class="n">sigma</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.7</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>mu = [ 0.0, 2.5 ]
+sigma = [ 1.0, 3.7 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.56410581</span><span class="p">,</span> <span class="mf">0.95934606</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_normal(mu, sigma) = [-0.56410581, 0.95934606]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span><span class="o">-</span><span class="mf">0.56410581</span><span class="p">,</span> <span class="mf">0.2928229</span> <span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.95934606</span><span class="p">,</span> <span class="mf">4.48287058</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_normal(mu, sigma, shape=(2)) = [[-0.56410581, 0.2928229 ],
+ [ 0.95934606, 4.48287058]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L365</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>mu</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Means of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>sigma</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Standard deviations of the distributions.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9009,26 +9421,26 @@ operator is not set, then one sample will be drawn per distribution and the outp
has the same shape as the input array.</p>
<p>Samples will always be returned as a floating point data type.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">lam</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">8.5</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>lam = [ 1.0, 8.5 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_poisson</span><span class="p">(</span><span class="n">lam</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">13.</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_poisson(lam) = [ 0., 13.]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_poisson</span><span class="p">(</span><span class="n">lam</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_poisson(lam, shape=(2)) = [[ 0., 4.],
+ [ 13., 8.]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L372</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>lam</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Lambda (rate) parameters of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9056,28 +9468,28 @@ which is parameterized by the input values at index <em>i</em>. If the shape par
operator is not set, then one sample will be drawn per distribution and the output array
has the same shape as the input arrays.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">low</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.5</span> <span class="p">]</span>
-<span class="n">high</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.7</span> <span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>low = [ 0.0, 2.5 ]
+high = [ 1.0, 3.7 ]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">single</span> <span class="n">sample</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_uniform</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.40451524</span><span class="p">,</span> <span class="mf">3.18687344</span><span class="p">]</span>
+// Draw a single sample for each distribution
+sample_uniform(low, high) = [ 0.40451524, 3.18687344]
-<span class="o">//</span> <span class="n">Draw</span> <span class="n">a</span> <span class="n">vector</span> <span class="n">containing</span> <span class="n">two</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">each</span> <span class="n">distribution</span>
-<span class="n">sample_uniform</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.40451524</span><span class="p">,</span> <span class="mf">0.18017688</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.18687344</span><span class="p">,</span> <span class="mf">3.68352246</span><span class="p">]]</span>
+// Draw a vector containing two samples for each distribution
+sample_uniform(low, high, shape=(2)) = [[ 0.40451524, 0.18017688],
+ [ 3.18687344, 3.68352246]]
</pre></div>
</div>
<p>Defined in src/operator/random/multisample_op.cc:L363</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>low</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Lower bounds of the distributions.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape to be sampled from each random distribution.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape to be sampled from each random distribution.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
<li><strong>high</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Upper bounds of the distributions.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9101,26 +9513,26 @@ like below:</p>
v_t = \gamma v_{t-1} - \alpha * \nabla J(W_{t-1})\\
W_t = W_{t-1} + v_t\end{split}\]</div>
<p>It updates the weights using:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">v</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">v</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">v</span> <span class="o">=</span> <span class="n">momentum</span> <span class="o">*</span> <span class="n">v</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
<span class="n">weight</span> <span class="o">+=</span> <span class="n">v</span>
</pre></div>
</div>
<p>Where the parameter <code class="docutils literal"><span class="pre">momentum</span></code> is the decay rate of momentum estimates at each epoch.</p>
<p>Defined in src/operator/optimizer_op.cc:L55</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Gradient</li>
<li><strong>mom</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Momentum</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The decay rate of momentum estimates at each epoch.</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>momentum</strong> (<em>float, optional, default=0</em>) – The decay rate of momentum estimates at each epoch.</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9138,22 +9550,22 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
<code class="descclassname">mxnet.symbol.</code><code class="descname">sgd_update</code><span class="sig-paren">(</span><em>weight=None</em>, <em>grad=None</em>, <em>lr=_Null</em>, <em>wd=_Null</em>, <em>rescale_grad=_Null</em>, <em>clip_gradient=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.sgd_update" title="Permalink to this definition">¶</a></dt>
<dd><p>Update function for Stochastic Gradient Descent (SDG) optimizer.</p>
<p>It updates the weights using:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">-</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
</pre></div>
</div>
<p>Defined in src/operator/optimizer_op.cc:L25</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight</li>
<li><strong>grad</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Gradient</li>
-<li><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</li>
-<li><strong>wd</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
-<li><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
-<li><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>lr</strong> (<em>float, required</em>) – Learning rate</li>
+<li><strong>wd</strong> (<em>float, optional, default=0</em>) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.</li>
+<li><strong>rescale_grad</strong> (<em>float, optional, default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</li>
+<li><strong>clip_gradient</strong> (<em>float, optional, default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9174,12 +9586,12 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
\[y = 1 / (1 + exp(-x))\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L36</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9197,17 +9609,17 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
<code class="descclassname">mxnet.symbol.</code><code class="descname">sign</code><span class="sig-paren">(</span><em>data=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.sign" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns element-wise sign of the input.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">sign</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</ [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sign</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</s [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L251</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9229,12 +9641,12 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
\[sin([0, \pi/4, \pi/2]) = [0, 0.707, 1]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L448</p>
<table class="docutils field-list" frame="void" rules="none">
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-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9255,12 +9667,12 @@ W_t = W_{t-1} + v_t\end{split}\]</div>
\[sinh(x) = 0.5\times(exp(x) - exp(-x))\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L599</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9289,7 +9701,7 @@ and <code class="docutils literal"><span class="pre">end=(e_1,</span> <span clas
<p>The resulting array’s <em>k</em>-th dimension contains elements
from the <em>k</em>-th dimension of the input array with the open range <code class="docutils literal"><span class="pre">[b_k,</span> <span class="pre">e_k)</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">9.</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]]</span>
@@ -9299,14 +9711,14 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L257</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Source input</li>
-<li><strong>begin</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – starting indices for the slice operation, supports negative indices.</li>
-<li><strong>end</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – ending indices for the slice operation, supports negative indices.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>begin</strong> (<em>Shape(tuple), required</em>) – starting indices for the slice operation, supports negative indices.</li>
+<li><strong>end</strong> (<em>Shape(tuple), required</em>) – ending indices for the slice operation, supports negative indices.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9326,7 +9738,7 @@ from the <em>k</em>-th dimension of the input array with the open range <code cl
<p>Returns an array slice along a given <cite>axis</cite> starting from the <cite>begin</cite> index
to the <cite>end</cite> index.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">9.</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">]]</span>
@@ -9344,15 +9756,15 @@ to the <cite>end</cite> index.</p>
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L337</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Source input</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>required</em>) – Axis along which to be sliced, supports negative indexes.</li>
-<li><strong>begin</strong> (<em>int</em><em>, </em><em>required</em>) – The beginning index along the axis to be sliced, supports negative indexes.</li>
-<li><strong>end</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>required</em>) – The ending index along the axis to be sliced, supports negative indexes.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int, required</em>) – Axis along which to be sliced, supports negative indexes.</li>
+<li><strong>begin</strong> (<em>int, required</em>) – The beginning index along the axis to be sliced, supports negative indexes.</li>
+<li><strong>end</strong> (<em>int or None, required</em>) – The ending index along the axis to be sliced, supports negative indexes.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9377,18 +9789,18 @@ to the <cite>end</cite> index.</p>
\end{cases}\end{split}\]</div>
<p>where <span class="math">\(x\)</span> is an element of the tensor <em>lhs</em> and <span class="math">\(\sigma\)</span> is the scalar.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">smooth_l1</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">smooth_l1</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span><span class= [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_binary_scalar_op_extended.cc:L79</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – source input</li>
<li><strong>scalar</strong> (<em>float</em>) – scalar input</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9410,25 +9822,25 @@ to the <cite>end</cite> index.</p>
\[softmax(\mathbf{z})_j = \frac{e^{z_j}}{\sum_{k=1}^K e^{z_k}}\]</div>
<p>for <span class="math">\(j = 1, ..., K\)</span></p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1. 1. 1.]
+ [ 1. 1. 1.]]
-<span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.5</span> <span class="mf">0.5</span> <span class="mf">0.5</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">0.5</span> <span class="mf">0.5</span> <span class="mf">0.5</span><span class="p">]]</span>
+softmax(x,axis=0) = [[ 0.5 0.5 0.5]
+ [ 0.5 0.5 0.5]]
-<span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">,</span> <span class="mf">0.33333334</span><span class="p">]]</span>
+softmax(x,axis=1) = [[ 0.33333334, 0.33333334, 0.33333334],
+ [ 0.33333334, 0.33333334, 0.33333334]]
</pre></div>
</div>
<p>Defined in src/operator/nn/softmax.cc:L35</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – The axis along which to compute softmax.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int, optional, default='-1'</em>) – The axis along which to compute softmax.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9464,7 +9876,7 @@ to the <cite>end</cite> index.</p>
</li>
</ul>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
<span class="p">[</span><span class="mi">11</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
<span class="n">label</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
@@ -9477,13 +9889,13 @@ to the <cite>end</cite> index.</p>
</div>
<p>Defined in src/operator/loss_binary_op.cc:L40</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data</li>
<li><strong>label</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input label</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9501,35 +9913,35 @@ to the <cite>end</cite> index.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">sort</code><span class="sig-paren">(</span><em>data=None</em>, <em>axis=_Null</em>, <em>is_ascend=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.sort" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a sorted copy of an input array along the given axis.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1, 4],
+ [ 3, 1]]
-<span class="o">//</span> <span class="n">sorts</span> <span class="n">along</span> <span class="n">the</span> <span class="n">last</span> <span class="n">axis</span>
-<span class="n">sort</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]]</span>
+// sorts along the last axis
+sort(x) = [[ 1., 4.],
+ [ 1., 3.]]
-<span class="o">//</span> <span class="n">flattens</span> <span class="ow">and</span> <span class="n">then</span> <span class="n">sorts</span>
-<span class="n">sort</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]</span>
+// flattens and then sorts
+sort(x) = [ 1., 1., 3., 4.]
-<span class="o">//</span> <span class="n">sorts</span> <span class="n">along</span> <span class="n">the</span> <span class="n">first</span> <span class="n">axis</span>
-<span class="n">sort</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]]</span>
+// sorts along the first axis
+sort(x, axis=0) = [[ 1., 1.],
+ [ 3., 4.]]
-<span class="o">//</span> <span class="ow">in</span> <span class="n">a</span> <span class="n">descend</span> <span class="n">order</span>
-<span class="n">sort</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">is_ascend</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
+// in a descend order
+sort(x, is_ascend=0) = [[ 4., 1.],
+ [ 3., 1.]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/ordering_op.cc:L107</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Axis along which to choose sort the input tensor. If not given, the flattened array is used. Default is -1.</li>
-<li><strong>is_ascend</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=True</em>) – Whether to sort in ascending or descending order.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int or None, optional, default='-1'</em>) – Axis along which to choose sort the input tensor. If not given, the flattened array is used. Default is -1.</li>
+<li><strong>is_ascend</strong> (<em>boolean, optional, default=True</em>) – Whether to sort in ascending or descending order.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9553,36 +9965,36 @@ to the <cite>end</cite> index.</p>
<p><strong>Note</strong> that <cite>num_outputs</cite> should evenly divide the length of the axis
along which to split the array.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]]</span>
-<span class="n">x</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[[ 1.]
+ [ 2.]]
+ [[ 3.]
+ [ 4.]]
+ [[ 5.]
+ [ 6.]]]
+x.shape = (3, 2, 1)
-<span class="n">y</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="n">a</span> <span class="nb">list</span> <span class="n">of</span> <span class="mi">2</span> [...]
-<span class="n">y</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]]]</span>
+y = split(x, axis=1, num_outputs=2) // a list of 2 arrays with shape (3, 1, 1)
+y = [[[ 1.]]
+ [[ 3.]]
+ [[ 5.]]]
- <span class="p">[[[</span> <span class="mf">2.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">4.</span><span class="p">]]</span>
- <span class="p">[[</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[[ 2.]]
+ [[ 4.]]
+ [[ 6.]]]
-<span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+y[0].shape = (3, 1, 1)
-<span class="n">z</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">//</span> <span class="n">a</span> <span class="nb">list</span> <span class="n">of</span> <span class="mi">3</span> [...]
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]]</span>
+z = split(x, axis=0, num_outputs=3) // a list of 3 arrays with shape (1, 2, 1)
+z = [[[ 1.]
+ [ 2.]]]
- <span class="p">[[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]]</span>
+ [[[ 3.]
+ [ 4.]]]
- <span class="p">[[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[[ 5.]
+ [ 6.]]]
-<span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+z[0].shape = (1, 2, 1)
</pre></div>
</div>
<p><cite>squeeze_axis=1</cite> removes the axis with length 1 from the shapes of the output arrays.
@@ -9590,29 +10002,29 @@ along which to split the array.</p>
along the <cite>axis</cite> which it is split.
Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils literal"><span class="pre">input.shape[axis]</span> <span class="pre">==</span> <span class="pre">num_outputs</span></code>.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">squeeze_axis</span><span class="o">=</span>< [...]
-<span class="n">z</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">2.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>z = split(x, axis=0, num_outputs=3, squeeze_axis=1) // a list of 3 arrays with shape (2, 1)
+z = [[ 1.]
+ [ 2.]]
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">4.</span><span class="p">]]</span>
+ [[ 3.]
+ [ 4.]]
- <span class="p">[[</span> <span class="mf">5.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">6.</span><span class="p">]]</span>
-<span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="p">,</span><span class="mi">1</span> <span class="p">)</span>
+ [[ 5.]
+ [ 6.]]
+z[0].shape = (2 ,1 )
</pre></div>
</div>
<p>Defined in src/operator/slice_channel.cc:L88</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>num_outputs</strong> (<em>int</em><em>, </em><em>required</em>) – Number of splits. Note that this should evenly divide the length of the <cite>axis</cite>.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Axis along which to split.</li>
-<li><strong>squeeze_axis</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If true, Removes the axis with length 1 from the shapes of the output arrays. <strong>Note</strong> that setting <cite>squeeze_axis</cite> to <code class="docutils literal"><span class="pre">true</span></code> removes axis with length 1 only along the <cite>axis</cite> which it is split. Also <cite>squeeze_axis</cite> can be set to <code class="docutils literal"><span class [...]
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>num_outputs</strong> (<em>int, required</em>) – Number of splits. Note that this should evenly divide the length of the <cite>axis</cite>.</li>
+<li><strong>axis</strong> (<em>int, optional, default='1'</em>) – Axis along which to split.</li>
+<li><strong>squeeze_axis</strong> (<em>boolean, optional, default=False</em>) – If true, Removes the axis with length 1 from the shapes of the output arrays. <strong>Note</strong> that setting <cite>squeeze_axis</cite> to <code class="docutils literal"><span class="pre">true</span></code> removes axis with length 1 only along the <cite>axis</cite> which it is split. Also <cite>squeeze_axis</cite> can be set to <code class="docutils literal"><span class="pre">true</span></code> only if <c [...]
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9632,17 +10044,17 @@ Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils
<div class="math">
\[\textrm{sqrt}(x) = \sqrt{x}\]</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">sqrt</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sqrt</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L365</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9662,17 +10074,17 @@ Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils
<div class="math">
\[square(x) = x^2\]</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">square</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">square</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L347</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9693,7 +10105,7 @@ Also <cite>squeeze_axis</cite> can be set to true only if <code class="docutils
result. For example, if axis=0 it will be the first dimension and if axis=-1 it
will be the last dimension.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
<span class="n">stack</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
@@ -9704,13 +10116,13 @@ will be the last dimension.</p>
</div>
<p>This function support variable length of positional input.</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em></em><em>]</em><em></em>) – List of arrays to stack</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The axis in the result array along which the input arrays are stacked.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>data</strong> (<em>Symbol[]</em>) – List of arrays to stack</li>
+<li><strong>axis</strong> (<em>int, optional, default='0'</em>) – The axis in the result array along which the input arrays are stacked.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9731,32 +10143,32 @@ will be the last dimension.</p>
in the backward direction. In other words, this operator prevents the contribution
of its inputs to be taken into account for computing gradients.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">v1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-<span class="n">v2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
-<span class="n">b</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
-<span class="n">b_stop_grad</span> <span class="o">=</span> <span class="n">stop_gradient</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">b</span><span class="p">)</span>
-<span class="n">loss</span> <span class="o">=</span> <span class="n">MakeLoss</span><span class="p">(</span><span class="n">b_stop_grad</span> <span class="o">+</span> <span class="n">a</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>v1 = [1, 2]
+v2 = [0, 1]
+a = Variable('a')
+b = Variable('b')
+b_stop_grad = stop_gradient(3 * b)
+loss = MakeLoss(b_stop_grad + a)
-<span class="n">executor</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">simple_bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">a</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">b</span><span class="o">=</span><sp [...]
-<span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="n">v1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">v2</span><span class="p">)</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">outputs</span>
-<span class="p">[</span> <span class="mf">1.</span> <span class="mf">5.</span><span class="p">]</span>
+executor = loss.simple_bind(ctx=cpu(), a=(1,2), b=(1,2))
+executor.forward(is_train=True, a=v1, b=v2)
+executor.outputs
+[ 1. 5.]
-<span class="n">executor</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-<span class="n">executor</span><span class="o">.</span><span class="n">grad_arrays</span>
-<span class="p">[</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
+executor.backward()
+executor.grad_arrays
+[ 0. 0.]
+[ 1. 1.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L99</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9778,40 +10190,39 @@ of its inputs to be taken into account for computing gradients.</p>
<p class="last"><cite>sum</cite> and <cite>sum_axis</cite> are equivalent.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">7</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">3</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>data = [[[1,2],[2,3],[1,3]],
+ [[1,4],[4,3],[5,2]],
+ [[7,1],[7,2],[7,3]]]
-<span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="p">[[</span> <span class="mf">4.</span> <span class="mf">8.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">21.</span> <span class="mf">6.</span><span class="p">]]</span>
+sum(data, axis=1)
+[[ 4. 8.]
+ [ 10. 9.]
+ [ 21. 6.]]
-<span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
-<span class="p">[</span> <span class="mf">12.</span> <span class="mf">19.</span> <span class="mf">27.</span><span class="p">]</span>
+sum(data, axis=[1,2])
+[ 12. 19. 27.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9833,40 +10244,39 @@ NOT in axis instead.</p>
<p class="last"><cite>sum</cite> and <cite>sum_axis</cite> are equivalent.</p>
</div>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">]],</span>
- <span class="p">[[</span><span class="mi">7</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">3</span><span class="p">]]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>data = [[[1,2],[2,3],[1,3]],
+ [[1,4],[4,3],[5,2]],
+ [[7,1],[7,2],[7,3]]]
-<span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="p">[[</span> <span class="mf">4.</span> <span class="mf">8.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">10.</span> <span class="mf">9.</span><span class="p">]</span>
- <span class="p">[</span> <span class="mf">21.</span> <span class="mf">6.</span><span class="p">]]</span>
+sum(data, axis=1)
+[[ 4. 8.]
+ [ 10. 9.]
+ [ 21. 6.]]
-<span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
-<span class="p">[</span> <span class="mf">12.</span> <span class="mf">19.</span> <span class="mf">27.</span><span class="p">]</span>
+sum(data, axis=[1,2])
+[ 12. 19. 27.]
</pre></div>
</div>
<p>Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>axis</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – <p>The axis or axes along which to perform the reduction.</p>
-<blockquote>
-<div>The default, <cite>axis=()</cite>, will compute over all elements into a
-scalar array with shape <cite>(1,)</cite>.<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
+<li><strong>axis</strong> (<em>Shape(tuple), optional, default=()</em>) – <p>The axis or axes along which to perform the reduction.</p>
+<p>The default, <cite>axis=()</cite>, will compute over all elements into a
+scalar array with shape <cite>(1,)</cite>.</p>
+<p>If <cite>axis</cite> is int, a reduction is performed on a particular axis.</p>
<p>If <cite>axis</cite> is a tuple of ints, a reduction is performed on all the axes
specified in the tuple.</p>
<p>If <cite>exclude</cite> is true, reduction will be performed on the axes that are
NOT in axis instead.</p>
<p>Negative values means indexing from right to left.</p>
-</div></blockquote>
</li>
-<li><strong>keepdims</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
-<li><strong>exclude</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>keepdims</strong> (<em>boolean, optional, default=False</em>) – If this is set to <cite>True</cite>, the reduced axes are left in the result as dimension with size one.</li>
+<li><strong>exclude</strong> (<em>boolean, optional, default=False</em>) – Whether to perform reduction on axis that are NOT in axis instead.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9884,32 +10294,32 @@ NOT in axis instead.</p>
<code class="descclassname">mxnet.symbol.</code><code class="descname">swapaxes</code><span class="sig-paren">(</span><em>data=None</em>, <em>dim1=_Null</em>, <em>dim2=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.swapaxes" title="Permalink to this definition">¶</a></dt>
<dd><p>Interchanges two axes of an array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
- <span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> x = [[1, 2, 3]])
+ swapaxes(x, 0, 1) = [[ 1],
+ [ 2],
+ [ 3]]
- <span class="n">x</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]]</span> <span class="o">//</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="n">array</span>
+ x = [[[ 0, 1],
+ [ 2, 3]],
+ [[ 4, 5],
+ [ 6, 7]]] // (2,2,2) array
-<span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span>
- <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
- <span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]]</span>
+swapaxes(x, 0, 2) = [[[ 0, 4],
+ [ 2, 6]],
+ [[ 1, 5],
+ [ 3, 7]]]
</pre></div>
</div>
<p>Defined in src/operator/swapaxis.cc:L51</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array.</li>
-<li><strong>dim1</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the first axis to be swapped.</li>
-<li><strong>dim2</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the second axis to be swapped.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>dim1</strong> (<em>int (non-negative), optional, default=0</em>) – the first axis to be swapped.</li>
+<li><strong>dim2</strong> (<em>int (non-negative), optional, default=0</em>) – the second axis to be swapped.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9929,7 +10339,7 @@ NOT in axis instead.</p>
<p>This function slices the input array along a particular axis with the provided indices.</p>
<p>Given an input array with shape <code class="docutils literal"><span class="pre">(d0,</span> <span class="pre">d1,</span> <span class="pre">d2)</span></code> and indices with shape <code class="docutils literal"><span class="pre">(i0,</span> <span class="pre">i1)</span></code>, the output
will have shape <code class="docutils literal"><span class="pre">(i0,</span> <span class="pre">i1,</span> <span class="pre">d1,</span> <span class="pre">d2)</span></code>, computed by:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">],:,:]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">],:,:]</span>
</pre></div>
</div>
<div class="admonition note">
@@ -9940,29 +10350,29 @@ will have shape <code class="docutils literal"><span class="pre">(i0,</span> <sp
</ul>
</div>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 1., 2.],
+ [ 3., 4.],
+ [ 5., 6.]]
-<span class="o">//</span> <span class="n">takes</span> <span class="n">elements</span> <span class="k">with</span> <span class="n">specified</span> <span class="n">indices</span> <span class="n">along</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">take</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]])</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]],</span>
+// takes elements with specified indices along axis 0
+take(x, [[0,1],[1,2]]) = [[[ 1., 2.],
+ [ 3., 4.]],
- <span class="p">[[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]]</span>
+ [[ 3., 4.],
+ [ 5., 6.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/indexing_op.cc:L117</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>a</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
<li><strong>indices</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The indices of the values to be extracted.</li>
-<li><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The axis of input array to be taken.</li>
-<li><strong>mode</strong> (<em>{'clip'</em><em>, </em><em>'raise'</em><em>, </em><em>'wrap'}</em><em>,</em><em>optional</em><em>, </em><em>default='clip'</em>) – Specify how out-of-bound indices bahave. “clip” means clip to the range. So, if all indices mentioned are too large, they are replaced by the index that addresses the last element along an axis. “wrap” means to wrap around. “raise” means to raise an error.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axis</strong> (<em>int, optional, default='0'</em>) – The axis of input array to be taken.</li>
+<li><strong>mode</strong> (<em>{'clip', 'raise', 'wrap'},optional, default='clip'</em>) – Specify how out-of-bound indices bahave. “clip” means clip to the range. So, if all indices mentioned are too large, they are replaced by the index that addresses the last element along an axis. “wrap” means to wrap around. “raise” means to raise an error.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -9984,12 +10394,12 @@ will have shape <code class="docutils literal"><span class="pre">(i0,</span> <sp
\[tan([0, \pi/4, \pi/2]) = [0, 1, -inf]\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L507</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -10010,12 +10420,12 @@ will have shape <code class="docutils literal"><span class="pre">(i0,</span> <sp
\[tanh(x) = sinh(x) / cosh(x)\]</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L627</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -10036,7 +10446,7 @@ will have shape <code class="docutils literal"><span class="pre">(i0,</span> <sp
there cases:</p>
<ul>
<li><p class="first"><strong>n=d</strong>. Repeat <em>i</em>-th dimension of the input by <code class="docutils literal"><span class="pre">reps[i]</span></code> times:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
<span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span clas [...]
@@ -10048,14 +10458,14 @@ there cases:</p>
</li>
<li><p class="first"><strong>n>d</strong>. <code class="docutils literal"><span class="pre">reps</span></code> is promoted to length <em>n</em> by pre-pending 1’s to it. Thus for
an input shape <code class="docutils literal"><span class="pre">(2,3)</span></code>, <code class="docutils literal"><span class="pre">repos=(2,)</span></code> is treated as <code class="docutils literal"><span class="pre">(1,2)</span></code>:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class= [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class=" [...]
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]]</span>
</pre></div>
</div>
</li>
-<li><p class="first"><strong>n<d< strong="">. The input is promoted to be d-dimensional by prepending new axes. So a
-shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array is promoted to <code class="docutils literal"><span class="pre">(1,2,2)</span></code> for 3-D replication:</d<></strong></p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,< [...]
+<li><p class="first"><strong>n<d</strong>. The input is promoted to be d-dimensional by prepending new axes. So a
+shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array is promoted to <code class="docutils literal"><span class="pre">(1,2,2)</span></code> for 3-D replication:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">tile</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">1.</span><span class="p">,</ [...]
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]],</span>
@@ -10070,13 +10480,13 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
</ul>
<p>Defined in src/operator/tensor/matrix_op.cc:L560</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data array</li>
-<li><strong>reps</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>required</em>) – The number of times for repeating the tensor a. If reps has length d, the result will have dimension of max(d, a.ndim); If a.ndim < d, a is promoted to be d-dimensional by prepending new axes. If a.ndim > d, reps is promoted to a.ndim by pre-pending 1’s to it.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>reps</strong> (<em>Shape(tuple), required</em>) – The number of times for repeating the tensor a. If reps has length d, the result will have dimension of max(d, a.ndim); If a.ndim < d, a is promoted to be d-dimensional by prepending new axes. If a.ndim > d, reps is promoted to a.ndim by pre-pending 1’s to it.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -10094,42 +10504,42 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
<code class="descclassname">mxnet.symbol.</code><code class="descname">topk</code><span class="sig-paren">(</span><em>data=None</em>, <em>axis=_Null</em>, <em>k=_Null</em>, <em>ret_typ=_Null</em>, <em>is_ascend=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.topk" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the top <em>k</em> elements in an input array along the given axis.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>x = [[ 0.3, 0.2, 0.4],
+ [ 0.1, 0.3, 0.2]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">an</span> <span class="n">index</span> <span class="n">of</span> <span class="n">the</span> <span class="n">largest</span> <span class="n">element</span> <span class="n">on</span> <span class="n">last</span> <span class="n">axis</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">1.</span><span class="p">]]</span>
+// returns an index of the largest element on last axis
+topk(x) = [[ 2.],
+ [ 1.]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">the</span> <span class="n">value</span> <span class="n">of</span> <span class="n">top</span><span class="o">-</span><span class="mi">2</span> <span class="n">largest</span> <span class="n">elements</span> <span class="n">on</span> <span class="n">last</span> <span class="n">axis</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">ret_typ</span><span class="o">=</span><span class="s1">'value'</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span>
- <span class="p">[</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+// returns the value of top-2 largest elements on last axis
+topk(x, ret_typ='value', k=2) = [[ 0.4, 0.3],
+ [ 0.3, 0.2]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">the</span> <span class="n">value</span> <span class="n">of</span> <span class="n">top</span><span class="o">-</span><span class="mi">2</span> <span class="n">smallest</span> <span class="n">elements</span> <span class="n">on</span> <span class="n">last</span> <span class="n">axis</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">ret_typ</span><span class="o">=</span><span class="s1">'value'</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">is_ascend</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.2</span> [...]
- <span class="p">[</span> <span class="mf">0.1</span> <span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+// returns the value of top-2 smallest elements on last axis
+topk(x, ret_typ='value', k=2, is_ascend=1) = [[ 0.2 , 0.3],
+ [ 0.1 , 0.2]]
-<span class="o">//</span> <span class="n">returns</span> <span class="n">the</span> <span class="n">value</span> <span class="n">of</span> <span class="n">top</span><span class="o">-</span><span class="mi">2</span> <span class="n">largest</span> <span class="n">elements</span> <span class="n">on</span> <span class="n">axis</span> <span class="mi">0</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ret_typ</span><span class="o">=</span><span class="s1">'value'</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.3</span><span [...]
- <span class="p">[</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]]</span>
+// returns the value of top-2 largest elements on axis 0
+topk(x, axis=0, ret_typ='value', k=2) = [[ 0.3, 0.3, 0.4],
+ [ 0.1, 0.2, 0.2]]
-<span class="o">//</span> <span class="n">flattens</span> <span class="ow">and</span> <span class="n">then</span> <span class="n">returns</span> <span class="nb">list</span> <span class="n">of</span> <span class="n">both</span> <span class="n">values</span> <span class="ow">and</span> <span class="n">indices</span>
-<span class="n">topk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">ret_typ</span><span class="o">=</span><span class="s1">'both'</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="p">[</span> <s [...]
+// flattens and then returns list of both values and indices
+topk(x, ret_typ='both', k=2) = [[[ 0.4, 0.3], [ 0.3, 0.2]] , [[ 2., 0.], [ 1., 2.]]]
</pre></div>
</div>
<p>Defined in src/operator/tensor/ordering_op.cc:L44</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array</li>
-<li><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Axis along which to choose the top k indices. If not given, the flattened array is used. Default is -1.</li>
-<li><strong>k</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Number of top elements to select, should be always smaller than or equal to the element number in the given axis. A global sort is performed if set k < 1.</li>
-<li><strong>ret_typ</strong> (<em>{'both'</em><em>, </em><em>'indices'</em><em>, </em><em>'mask'</em><em>, </em><em>'value'}</em><em>,</em><em>optional</em><em>, </em><em>default='indices'</em>) – The return type.
+<li><strong>axis</strong> (<em>int or None, optional, default='-1'</em>) – Axis along which to choose the top k indices. If not given, the flattened array is used. Default is -1.</li>
+<li><strong>k</strong> (<em>int, optional, default='1'</em>) – Number of top elements to select, should be always smaller than or equal to the element number in the given axis. A global sort is performed if set k < 1.</li>
+<li><strong>ret_typ</strong> (<em>{'both', 'indices', 'mask', 'value'},optional, default='indices'</em>) – The return type.
“value” means to return the top k values, “indices” means to return the indices of the top k values, “mask” means to return a mask array containing 0 and 1. 1 means the top k values. “both” means to return a list of both values and indices of top k elements.</li>
-<li><strong>is_ascend</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=False</em>) – Whether to choose k largest or k smallest elements. Top K largest elements will be chosen if set to false.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>is_ascend</strong> (<em>boolean, optional, default=False</em>) – Whether to choose k largest or k smallest elements. Top K largest elements will be chosen if set to false.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -10147,7 +10557,7 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
<code class="descclassname">mxnet.symbol.</code><code class="descname">transpose</code><span class="sig-paren">(</span><em>data=None</em>, <em>axes=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.transpose" title="Permalink to this definition">¶</a></dt>
<dd><p>Permutes the dimensions of an array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
<span class="n">transpose</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
@@ -10174,13 +10584,13 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
</div>
<p>Defined in src/operator/tensor/matrix_op.cc:L177</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Source input</li>
-<li><strong>axes</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Target axis order. By default the axes will be inverted.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>axes</strong> (<em>Shape(tuple), optional, default=()</em>) – Target axis order. By default the axes will be inverted.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -10200,17 +10610,17 @@ shape <code class="docutils literal"><span class="pre">(2,2)</span></code> array
<p>The truncated value of the scalar x is the nearest integer i which is closer to
zero than x is. In short, the fractional part of the signed number x is discarded.</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">trunc</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span cla [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">trunc</span><span class="p">([</span><span class="o">-</span><span class="mf">2.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">])</span> <span class="o">=</span> <span class="p">[</span><span clas [...]
</pre></div>
</div>
<p>Defined in src/operator/tensor/elemwise_unary_op.cc:L322</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -10234,22 +10644,22 @@ zero than x is. In short, the fractional part of the signed number x is discarde
<p>Samples are uniformly distributed over the half-open interval <em>[low, high)</em>
(includes <em>low</em>, but excludes <em>high</em>).</p>
<p>Example:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">random_uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cl [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">random_uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span cla [...]
<span class="p">[</span> <span class="mf">0.54488319</span><span class="p">,</span> <span class="mf">0.84725171</span><span class="p">]]</span>
</pre></div>
</div>
<p>Defined in src/operator/random/sample_op.cc:L45</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>low</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Lower bound of the distribution.</li>
-<li><strong>high</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Upper bound of the distribution.</li>
-<li><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>(</em><em></em><em>)</em><em></em>) – Shape of the output.</li>
-<li><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
-<li><strong>dtype</strong> (<em>{'None'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>low</strong> (<em>float, optional, default=0</em>) – Lower bound of the distribution.</li>
+<li><strong>high</strong> (<em>float, optional, default=1</em>) – Upper bound of the distribution.</li>
+<li><strong>shape</strong> (<em>Shape(tuple), optional, default=()</em>) – Shape of the output.</li>
+<li><strong>ctx</strong> (<em>string, optional, default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</li>
+<li><strong>dtype</strong> (<em>{'None', 'float16', 'float32', 'float64'},optional, default='None'</em>) – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -10268,14 +10678,14 @@ zero than x is. In short, the fractional part of the signed number x is discarde
<dd><p>Given three ndarrays, condition, x, and y, return an ndarray with the elements from x or y, depending on the elements from condition are true or false. x and y must have the same shape. If condition has the same shape as x, each element in the output array is from x if the corresponding element in the condition is true, and from y if false. If condition does not have the same shape as x, it must be a 1D array whose size is the same as x’s first dimension size. Each row of the outp [...]
<p>From:src/operator/tensor/control_flow_op.cc:21</p>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>condition</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – condition array</li>
<li><strong>x</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – </li>
<li><strong>y</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – </li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -10294,7 +10704,7 @@ zero than x is. In short, the fractional part of the signed number x is discarde
<dd><p>Return an array of zeros with the same shape and type
as the input array.</p>
<p>Examples:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="n">zeros_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
@@ -10302,12 +10712,585 @@ as the input array.</p>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name"></col>
-<col class="field-body"></col>
+<col class="field-name"/>
+<col class="field-body"/>
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</li>
-<li><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<span class="target" id="module-mxnet.contrib.symbol"></span><p>Symbol namespace used to register contrib functions</p>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.CTCLoss">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">CTCLoss</code><span class="sig-paren">(</span><em>data=None</em>, <em>label=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.symbol.CTCLoss" title="Permalink to this definition">¶</a></dt>
+<dd><p>Connectionist Temporal Classification Loss.</p>
+<p>The shapes of the inputs and outputs:</p>
+<ul class="simple">
+<li><strong>data</strong>: <em>(sequence_length, batch_size, alphabet_size + 1)</em></li>
+<li><strong>label</strong>: <em>(batch_size, label_sequence_length)</em></li>
+<li><strong>out</strong>: <em>(batch_size)</em>.</li>
+</ul>
+<p><code class="docutils literal"><span class="pre">label</span></code> is a tensor of integers between 1 and <em>alphabet_size</em>. If a
+sequence of labels is shorter than <em>label_sequence_length</em>, use the special
+padding character 0 at the end of the sequence to conform it to the correct
+length. For example, if <em>label_sequence_length</em> = 4, and one has two sequences
+of labels [2, 1] and [3, 2, 2], the resulting <code class="docutils literal"><span class="pre">`label`</span></code> tensor should be
+padded to be:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</s [...]
+</pre></div>
+</div>
+<p>The <code class="docutils literal"><span class="pre">data</span></code> tensor consists of sequences of activation vectors. The layer
+applies a softmax to each vector, which then becomes a vector of probabilities
+over the alphabet. Note that the 0th element of this vector is reserved for the
+special blank character.</p>
+<p><code class="docutils literal"><span class="pre">out</span></code> is a list of CTC loss values, one per example in the batch.</p>
+<p>See <em>Connectionist Temporal Classification: Labelling Unsegmented
+Sequence Data with Recurrent Neural Networks</em>, A. Graves <em>et al</em>. for more
+information.</p>
+<p>Defined in src/operator/contrib/ctc_loss.cc:L81</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the ctc_loss op.</li>
+<li><strong>label</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Ground-truth labels for the loss.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.DeformableConvolution">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">DeformableConvolution</code><span class="sig-paren">(</span><em>data=None</em>, <em>offset=None</em>, <em>weight=None</em>, <em>bias=None</em>, <em>kernel=_Null</em>, <em>stride=_Null</em>, <em>dilate=_Null</em>, <em>pad=_Null</em>, <em>num_filter=_Null</em>, <em>num_group=_Null</em>, <em>num_deformable_group=_Null</em>, <em>workspace=_Null</em>, <em>no_bias=_Null</em>, <em>layout=_Null</em>, <em>name=None</em [...]
+<dd><p>Compute 2-D deformable convolution on 4-D input.</p>
+<p>The deformable convolution operation is described in <a class="reference external" href="https://arxiv.org/abs/1703.06211">https://arxiv.org/abs/1703.06211</a></p>
+<p>For 2-D deformable convolution, the shapes are</p>
+<ul class="simple">
+<li><strong>data</strong>: <em>(batch_size, channel, height, width)</em></li>
+<li><strong>offset</strong>: <em>(batch_size, num_deformable_group * kernel[0] * kernel[1], height, width)</em></li>
+<li><strong>weight</strong>: <em>(num_filter, channel, kernel[0], kernel[1])</em></li>
+<li><strong>bias</strong>: <em>(num_filter,)</em></li>
+<li><strong>out</strong>: <em>(batch_size, num_filter, out_height, out_width)</em>.</li>
+</ul>
+<p>Define:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">k</span><span class="p">,</span><span class="n">p</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">d</span><span class="p">)</span> <span class="o">=</span> <span class="n">floor</span><span class="p">((</span><span class="n">x</span><span class="o">+</span><span [...]
+</pre></div>
+</div>
+<p>then we have:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out_height</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><spa [...]
+<span class="n">out_width</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">pad</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">stride</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dilate</sp [...]
+</pre></div>
+</div>
+<p>If <code class="docutils literal"><span class="pre">no_bias</span></code> is set to be true, then the <code class="docutils literal"><span class="pre">bias</span></code> term is ignored.</p>
+<p>The default data <code class="docutils literal"><span class="pre">layout</span></code> is <em>NCHW</em>, namely <em>(batch_size, channle, height,
+width)</em>.</p>
+<p>If <code class="docutils literal"><span class="pre">num_group</span></code> is larger than 1, denoted by <em>g</em>, then split the input <code class="docutils literal"><span class="pre">data</span></code>
+evenly into <em>g</em> parts along the channel axis, and also evenly split <code class="docutils literal"><span class="pre">weight</span></code>
+along the first dimension. Next compute the convolution on the <em>i</em>-th part of
+the data with the <em>i</em>-th weight part. The output is obtained by concating all
+the <em>g</em> results.</p>
+<p>If <code class="docutils literal"><span class="pre">num_deformable_group</span></code> is larger than 1, denoted by <em>dg</em>, then split the
+input <code class="docutils literal"><span class="pre">offset</span></code> evenly into <em>dg</em> parts along the channel axis, and also evenly
+split <code class="docutils literal"><span class="pre">out</span></code> evenly into <em>dg</em> parts along the channel axis. Next compute the
+deformable convolution, apply the <em>i</em>-th part of the offset part on the <em>i</em>-th
+out.</p>
+<p>Both <code class="docutils literal"><span class="pre">weight</span></code> and <code class="docutils literal"><span class="pre">bias</span></code> are learnable parameters.</p>
+<p>Defined in src/operator/contrib/deformable_convolution.cc:L81</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the DeformableConvolutionOp.</li>
+<li><strong>offset</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input offset to the DeformableConvolutionOp.</li>
+<li><strong>weight</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight matrix.</li>
+<li><strong>bias</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bias parameter.</li>
+<li><strong>kernel</strong> (<em>Shape(tuple), required</em>) – convolution kernel size: (h, w) or (d, h, w)</li>
+<li><strong>stride</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution stride: (h, w) or (d, h, w)</li>
+<li><strong>dilate</strong> (<em>Shape(tuple), optional, default=()</em>) – convolution dilate: (h, w) or (d, h, w)</li>
+<li><strong>pad</strong> (<em>Shape(tuple), optional, default=()</em>) – pad for convolution: (h, w) or (d, h, w)</li>
+<li><strong>num_filter</strong> (<em>int (non-negative), required</em>) – convolution filter(channel) number</li>
+<li><strong>num_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of group partitions.</li>
+<li><strong>num_deformable_group</strong> (<em>int (non-negative), optional, default=1</em>) – Number of deformable group partitions.</li>
+<li><strong>workspace</strong> (<em>long (non-negative), optional, default=1024</em>) – Maximum temperal workspace allowed for convolution (MB).</li>
+<li><strong>no_bias</strong> (<em>boolean, optional, default=False</em>) – Whether to disable bias parameter.</li>
+<li><strong>layout</strong> (<em>{None, 'NCDHW', 'NCHW', 'NCW'},optional, default='None'</em>) – Set layout for input, output and weight. Empty for
+default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.DeformablePSROIPooling">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">DeformablePSROIPooling</code><span class="sig-paren">(</span><em>data=None</em>, <em>rois=None</em>, <em>trans=None</em>, <em>spatial_scale=_Null</em>, <em>output_dim=_Null</em>, <em>group_size=_Null</em>, <em>pooled_size=_Null</em>, <em>part_size=_Null</em>, <em>sample_per_part=_Null</em>, <em>trans_std=_Null</em>, <em>no_trans=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwarg [...]
+<dd><p>Performs deformable position-sensitive region-of-interest pooling on inputs.The DeformablePSROIPooling operation is described in <a class="reference external" href="https://arxiv.org/abs/1703.06211.batch_size">https://arxiv.org/abs/1703.06211.batch_size</a> will change to the number of region bounding boxes after DeformablePSROIPooling</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator, a 4D Feature maps</li>
+<li><strong>rois</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data</li>
+<li><strong>trans</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – transition parameter</li>
+<li><strong>spatial_scale</strong> (<em>float, required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</li>
+<li><strong>output_dim</strong> (<em>int, required</em>) – fix output dim</li>
+<li><strong>group_size</strong> (<em>int, required</em>) – fix group size</li>
+<li><strong>pooled_size</strong> (<em>int, required</em>) – fix pooled size</li>
+<li><strong>part_size</strong> (<em>int, optional, default='0'</em>) – fix part size</li>
+<li><strong>sample_per_part</strong> (<em>int, optional, default='1'</em>) – fix samples per part</li>
+<li><strong>trans_std</strong> (<em>float, optional, default=0</em>) – fix transition std</li>
+<li><strong>no_trans</strong> (<em>boolean, optional, default=False</em>) – Whether to disable trans parameter.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.MultiBoxDetection">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">MultiBoxDetection</code><span class="sig-paren">(</span><em>cls_prob=None</em>, <em>loc_pred=None</em>, <em>anchor=None</em>, <em>clip=_Null</em>, <em>threshold=_Null</em>, <em>background_id=_Null</em>, <em>nms_threshold=_Null</em>, <em>force_suppress=_Null</em>, <em>variances=_Null</em>, <em>nms_topk=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren [...]
+<dd><p>Convert multibox detection predictions.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>cls_prob</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Class probabilities.</li>
+<li><strong>loc_pred</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Location regression predictions.</li>
+<li><strong>anchor</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Multibox prior anchor boxes</li>
+<li><strong>clip</strong> (<em>boolean, optional, default=True</em>) – Clip out-of-boundary boxes.</li>
+<li><strong>threshold</strong> (<em>float, optional, default=0.01</em>) – Threshold to be a positive prediction.</li>
+<li><strong>background_id</strong> (<em>int, optional, default='0'</em>) – Background id.</li>
+<li><strong>nms_threshold</strong> (<em>float, optional, default=0.5</em>) – Non-maximum suppression threshold.</li>
+<li><strong>force_suppress</strong> (<em>boolean, optional, default=False</em>) – Suppress all detections regardless of class_id.</li>
+<li><strong>variances</strong> (<em>, optional, default=(0.1,0.1,0.2,0.2)</em>) – Variances to be decoded from box regression output.</li>
+<li><strong>nms_topk</strong> (<em>int, optional, default='-1'</em>) – Keep maximum top k detections before nms, -1 for no limit.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.MultiBoxPrior">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">MultiBoxPrior</code><span class="sig-paren">(</span><em>data=None</em>, <em>sizes=_Null</em>, <em>ratios=_Null</em>, <em>clip=_Null</em>, <em>steps=_Null</em>, <em>offsets=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.symbol.MultiBoxPrior" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate prior(anchor) boxes from data, sizes and ratios.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</li>
+<li><strong>sizes</strong> (<em>, optional, default=(1,)</em>) – List of sizes of generated MultiBoxPriores.</li>
+<li><strong>ratios</strong> (<em>, optional, default=(1,)</em>) – List of aspect ratios of generated MultiBoxPriores.</li>
+<li><strong>clip</strong> (<em>boolean, optional, default=False</em>) – Whether to clip out-of-boundary boxes.</li>
+<li><strong>steps</strong> (<em>, optional, default=(-1,-1)</em>) – Priorbox step across y and x, -1 for auto calculation.</li>
+<li><strong>offsets</strong> (<em>, optional, default=(0.5,0.5)</em>) – Priorbox center offsets, y and x respectively</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.MultiBoxTarget">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">MultiBoxTarget</code><span class="sig-paren">(</span><em>anchor=None</em>, <em>label=None</em>, <em>cls_pred=None</em>, <em>overlap_threshold=_Null</em>, <em>ignore_label=_Null</em>, <em>negative_mining_ratio=_Null</em>, <em>negative_mining_thresh=_Null</em>, <em>minimum_negative_samples=_Null</em>, <em>variances=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span clas [...]
+<dd><p>Compute Multibox training targets</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>anchor</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Generated anchor boxes.</li>
+<li><strong>label</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Object detection labels.</li>
+<li><strong>cls_pred</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Class predictions.</li>
+<li><strong>overlap_threshold</strong> (<em>float, optional, default=0.5</em>) – Anchor-GT overlap threshold to be regarded as a possitive match.</li>
+<li><strong>ignore_label</strong> (<em>float, optional, default=-1</em>) – Label for ignored anchors.</li>
+<li><strong>negative_mining_ratio</strong> (<em>float, optional, default=-1</em>) – Max negative to positive samples ratio, use -1 to disable mining</li>
+<li><strong>negative_mining_thresh</strong> (<em>float, optional, default=0.5</em>) – Threshold used for negative mining.</li>
+<li><strong>minimum_negative_samples</strong> (<em>int, optional, default='0'</em>) – Minimum number of negative samples.</li>
+<li><strong>variances</strong> (<em>, optional, default=(0.1,0.1,0.2,0.2)</em>) – Variances to be encoded in box regression target.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.MultiProposal">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">MultiProposal</code><span class="sig-paren">(</span><em>cls_score=None</em>, <em>bbox_pred=None</em>, <em>im_info=None</em>, <em>rpn_pre_nms_top_n=_Null</em>, <em>rpn_post_nms_top_n=_Null</em>, <em>threshold=_Null</em>, <em>rpn_min_size=_Null</em>, <em>scales=_Null</em>, <em>ratios=_Null</em>, <em>feature_stride=_Null</em>, <em>output_score=_Null</em>, <em>iou_loss=_Null</em>, <em>name=None</em>, <em>attr=None [...]
+<dd><p>Generate region proposals via RPN</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>cls_score</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Score of how likely proposal is object.</li>
+<li><strong>bbox_pred</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – BBox Predicted deltas from anchors for proposals</li>
+<li><strong>im_info</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Image size and scale.</li>
+<li><strong>rpn_pre_nms_top_n</strong> (<em>int, optional, default='6000'</em>) – Number of top scoring boxes to keep after applying NMS to RPN proposals</li>
+<li><strong>rpn_post_nms_top_n</strong> (<em>int, optional, default='300'</em>) – Overlap threshold used for non-maximumsuppresion(suppress boxes with IoU >= this threshold</li>
+<li><strong>threshold</strong> (<em>float, optional, default=0.7</em>) – NMS value, below which to suppress.</li>
+<li><strong>rpn_min_size</strong> (<em>int, optional, default='16'</em>) – Minimum height or width in proposal</li>
+<li><strong>scales</strong> (<em>, optional, default=(4,8,16,32)</em>) – Used to generate anchor windows by enumerating scales</li>
+<li><strong>ratios</strong> (<em>, optional, default=(0.5,1,2)</em>) – Used to generate anchor windows by enumerating ratios</li>
+<li><strong>feature_stride</strong> (<em>int, optional, default='16'</em>) – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.</li>
+<li><strong>output_score</strong> (<em>boolean, optional, default=False</em>) – Add score to outputs</li>
+<li><strong>iou_loss</strong> (<em>boolean, optional, default=False</em>) – Usage of IoU Loss</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.PSROIPooling">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">PSROIPooling</code><span class="sig-paren">(</span><em>data=None</em>, <em>rois=None</em>, <em>spatial_scale=_Null</em>, <em>output_dim=_Null</em>, <em>pooled_size=_Null</em>, <em>group_size=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.symbol.PSROIPooling" title="Permalink to this definition">¶ [...]
+<dd><p>Performs region-of-interest pooling on inputs. Resize bounding box coordinates by spatial_scale and crop input feature maps accordingly. The cropped feature maps are pooled by max pooling to a fixed size output indicated by pooled_size. batch_size will change to the number of region bounding boxes after PSROIPooling</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator, a 4D Feature maps</li>
+<li><strong>rois</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data</li>
+<li><strong>spatial_scale</strong> (<em>float, required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</li>
+<li><strong>output_dim</strong> (<em>int, required</em>) – fix output dim</li>
+<li><strong>pooled_size</strong> (<em>int, required</em>) – fix pooled size</li>
+<li><strong>group_size</strong> (<em>int, optional, default='0'</em>) – fix group size</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.Proposal">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">Proposal</code><span class="sig-paren">(</span><em>cls_score=None</em>, <em>bbox_pred=None</em>, <em>im_info=None</em>, <em>rpn_pre_nms_top_n=_Null</em>, <em>rpn_post_nms_top_n=_Null</em>, <em>threshold=_Null</em>, <em>rpn_min_size=_Null</em>, <em>scales=_Null</em>, <em>ratios=_Null</em>, <em>feature_stride=_Null</em>, <em>output_score=_Null</em>, <em>iou_loss=_Null</em>, <em>name=None</em>, <em>attr=None</em> [...]
+<dd><p>Generate region proposals via RPN</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>cls_score</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Score of how likely proposal is object.</li>
+<li><strong>bbox_pred</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – BBox Predicted deltas from anchors for proposals</li>
+<li><strong>im_info</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Image size and scale.</li>
+<li><strong>rpn_pre_nms_top_n</strong> (<em>int, optional, default='6000'</em>) – Number of top scoring boxes to keep after applying NMS to RPN proposals</li>
+<li><strong>rpn_post_nms_top_n</strong> (<em>int, optional, default='300'</em>) – Overlap threshold used for non-maximumsuppresion(suppress boxes with IoU >= this threshold</li>
+<li><strong>threshold</strong> (<em>float, optional, default=0.7</em>) – NMS value, below which to suppress.</li>
+<li><strong>rpn_min_size</strong> (<em>int, optional, default='16'</em>) – Minimum height or width in proposal</li>
+<li><strong>scales</strong> (<em>, optional, default=(4,8,16,32)</em>) – Used to generate anchor windows by enumerating scales</li>
+<li><strong>ratios</strong> (<em>, optional, default=(0.5,1,2)</em>) – Used to generate anchor windows by enumerating ratios</li>
+<li><strong>feature_stride</strong> (<em>int, optional, default='16'</em>) – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.</li>
+<li><strong>output_score</strong> (<em>boolean, optional, default=False</em>) – Add score to outputs</li>
+<li><strong>iou_loss</strong> (<em>boolean, optional, default=False</em>) – Usage of IoU Loss</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.count_sketch">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">count_sketch</code><span class="sig-paren">(</span><em>data=None</em>, <em>h=None</em>, <em>s=None</em>, <em>out_dim=_Null</em>, <em>processing_batch_size=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.symbol.count_sketch" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply CountSketch to input: map a d-dimension data to k-dimension data”</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last"><cite>count_sketch</cite> is only available on GPU.</p>
+</div>
+<p>Assume input data has shape (N, d), sign hash table s has shape (N, d),
+index hash table h has shape (N, d) and mapping dimension out_dim = k,
+each element in s is either +1 or -1, each element in h is random integer from 0 to k-1.
+Then the operator computs:</p>
+<div class="math">
+\[out[h[i]] += data[i] * s[i]\]</div>
+<dl class="docutils">
+<dt>Example::</dt>
+<dd><p class="first">out_dim = 5
+x = [[1.2, 2.5, 3.4],[3.2, 5.7, 6.6]]
+h = [0, 3, 4]
+s = [1, -1, 1]
+mx.contrib.ndarray.count_sketch(data=x, h=h, s=s, out_dim = 5) = [[1.2, 0, 0, -2.5, 3.4],</p>
+<blockquote class="last">
+<div>[3.2, 0, 0, -5.7, 6.6]]</div></blockquote>
+</dd>
+</dl>
+<p>Defined in src/operator/contrib/count_sketch.cc:L47</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the CountSketchOp.</li>
+<li><strong>h</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The index vector</li>
+<li><strong>s</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The sign vector</li>
+<li><strong>out_dim</strong> (<em>int, required</em>) – The output dimension.</li>
+<li><strong>processing_batch_size</strong> (<em>int, optional, default='32'</em>) – How many sketch vectors to process at one time.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.ctc_loss">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">ctc_loss</code><span class="sig-paren">(</span><em>data=None</em>, <em>label=None</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.symbol.ctc_loss" title="Permalink to this definition">¶</a></dt>
+<dd><p>Connectionist Temporal Classification Loss.</p>
+<p>The shapes of the inputs and outputs:</p>
+<ul class="simple">
+<li><strong>data</strong>: <em>(sequence_length, batch_size, alphabet_size + 1)</em></li>
+<li><strong>label</strong>: <em>(batch_size, label_sequence_length)</em></li>
+<li><strong>out</strong>: <em>(batch_size)</em>.</li>
+</ul>
+<p><code class="docutils literal"><span class="pre">label</span></code> is a tensor of integers between 1 and <em>alphabet_size</em>. If a
+sequence of labels is shorter than <em>label_sequence_length</em>, use the special
+padding character 0 at the end of the sequence to conform it to the correct
+length. For example, if <em>label_sequence_length</em> = 4, and one has two sequences
+of labels [2, 1] and [3, 2, 2], the resulting <code class="docutils literal"><span class="pre">`label`</span></code> tensor should be
+padded to be:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</s [...]
+</pre></div>
+</div>
+<p>The <code class="docutils literal"><span class="pre">data</span></code> tensor consists of sequences of activation vectors. The layer
+applies a softmax to each vector, which then becomes a vector of probabilities
+over the alphabet. Note that the 0th element of this vector is reserved for the
+special blank character.</p>
+<p><code class="docutils literal"><span class="pre">out</span></code> is a list of CTC loss values, one per example in the batch.</p>
+<p>See <em>Connectionist Temporal Classification: Labelling Unsegmented
+Sequence Data with Recurrent Neural Networks</em>, A. Graves <em>et al</em>. for more
+information.</p>
+<p>Defined in src/operator/contrib/ctc_loss.cc:L81</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the ctc_loss op.</li>
+<li><strong>label</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Ground-truth labels for the loss.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.dequantize">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">dequantize</code><span class="sig-paren">(</span><em>input=None</em>, <em>min_range=None</em>, <em>max_range=None</em>, <em>out_type=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.symbol.dequantize" title="Permalink to this definition">¶</a></dt>
+<dd><p>Dequantize the input tensor into a float tensor.
+[min_range, max_range] are scalar floats that spcify the range for
+the output data.</p>
+<p>Each value of the tensor will undergo the following:</p>
+<p><cite>out[i] = min_range + (in[i] * (max_range - min_range) / range(INPUT_TYPE))</cite></p>
+<p>here <cite>range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()</cite></p>
+<p>Defined in src/operator/contrib/dequantize.cc:L22</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>uint8</cite></li>
+<li><strong>min_range</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The minimum scalar value possibly produced for the input</li>
+<li><strong>max_range</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The maximum scalar value possibly produced for the input</li>
+<li><strong>out_type</strong> (<em>{'float32'}, required</em>) – Output data type.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.fft">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">fft</code><span class="sig-paren">(</span><em>data=None</em>, <em>compute_size=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.symbol.fft" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply 1D FFT to input”</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last"><cite>fft</cite> is only available on GPU.</p>
+</div>
+<p>Currently accept 2 input data shapes: (N, d) or (N1, N2, N3, d), data can only be real numbers.
+The output data has shape: (N, 2*d) or (N1, N2, N3, 2*d). The format is: [real0, imag0, real1, imag1, ...].</p>
+<dl class="docutils">
+<dt>Example::</dt>
+<dd>data = np.random.normal(0,1,(3,4))
+out = mx.contrib.ndarray.fft(data = mx.nd.array(data,ctx = mx.gpu(0)))</dd>
+</dl>
+<p>Defined in src/operator/contrib/fft.cc:L40</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the FFTOp.</li>
+<li><strong>compute_size</strong> (<em>int, optional, default='128'</em>) – Maximum size of sub-batch to be forwarded at one time</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.ifft">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">ifft</code><span class="sig-paren">(</span><em>data=None</em>, <em>compute_size=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.symbol.ifft" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply 1D ifft to input”</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last"><cite>ifft</cite> is only available on GPU.</p>
+</div>
+<p>Currently accept 2 input data shapes: (N, d) or (N1, N2, N3, d). Data is in format: [real0, imag0, real1, imag1, ...].
+Last dimension must be an even number.
+The output data has shape: (N, d/2) or (N1, N2, N3, d/2). It is only the real part of the result.</p>
+<dl class="docutils">
+<dt>Example::</dt>
+<dd>data = np.random.normal(0,1,(3,4))
+out = mx.contrib.ndarray.ifft(data = mx.nd.array(data,ctx = mx.gpu(0)))</dd>
+</dl>
+<p>Defined in src/operator/contrib/ifft.cc:L42</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the IFFTOp.</li>
+<li><strong>compute_size</strong> (<em>int, optional, default='128'</em>) – Maximum size of sub-batch to be forwarded at one time</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result symbol.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+<dl class="function">
+<dt id="mxnet.contrib.symbol.quantize">
+<code class="descclassname">mxnet.contrib.symbol.</code><code class="descname">quantize</code><span class="sig-paren">(</span><em>input=None</em>, <em>min_range=None</em>, <em>max_range=None</em>, <em>out_type=_Null</em>, <em>name=None</em>, <em>attr=None</em>, <em>out=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.contrib.symbol.quantize" title="Permalink to this definition">¶</a></dt>
+<dd><p>Quantize a input tensor from float to <cite>out_type</cite>,
+with user-specified <cite>min_range</cite> and <cite>max_range</cite>.</p>
+<p>[min_range, max_range] are scalar floats that spcify the range for
+the input data. Each value of the tensor will undergo the following:</p>
+<p><cite>out[i] = (in[i] - min_range) * range(OUTPUT_TYPE) / (max_range - min_range)</cite></p>
+<p>here <cite>range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()</cite></p>
+<p>Defined in src/operator/contrib/quantize.cc:L22</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name"/>
+<col class="field-body"/>
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>float32</cite></li>
+<li><strong>min_range</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The minimum scalar value possibly produced for the input</li>
+<li><strong>max_range</strong> (<a class="reference internal" href="#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The maximum scalar value possibly produced for the input</li>
+<li><strong>out_type</strong> (<em>{'uint8'},optional, default='uint8'</em>) – Output data type.</li>
+<li><strong>name</strong> (<em>string, optional.</em>) – Name of the resulting symbol.</li>
</ul>
</td>
</tr>
@@ -10376,6 +11359,7 @@ as the input array.</p>
<li><a class="reference internal" href="#more">More</a></li>
</ul>
</li>
+<li><a class="reference internal" href="#contrib">Contrib</a></li>
<li><a class="reference internal" href="#api-reference">API Reference</a></li>
</ul>
</li>
diff --git a/versions/master/api/python/symbol_in_pictures.html b/versions/master/api/python/symbol_in_pictures.html
index 1ec3d29..0314f3f 100644
--- a/versions/master/api/python/symbol_in_pictures.html
+++ b/versions/master/api/python/symbol_in_pictures.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Symbolic Configuration and Execution in Pictures — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
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var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
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-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
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@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
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-</input></div>
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+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
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-</form>
+</input></form>
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@@ -237,7 +235,7 @@ Previous Navbar Layout End -->
<div class="section" id="symbolic-configuration-and-execution-in-pictures">
<span id="symbolic-configuration-and-execution-in-pictures"></span><h1>Symbolic Configuration and Execution in Pictures<a class="headerlink" href="#symbolic-configuration-and-execution-in-pictures" title="Permalink to this headline">¶</a></h1>
<p>This topic explains symbolic construction and execution in pictures.
-We recommend that you also read <a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a>.</p>
+We recommend that you also read <a class="reference internal" href="symbol.html"><em>Symbolic API</em></a>.</p>
<div class="section" id="compose-symbols">
<span id="compose-symbols"></span><h2>Compose Symbols<a class="headerlink" href="#compose-symbols" title="Permalink to this headline">¶</a></h2>
<p>Symbols are a description of the computation that you want to perform. The symbolic construction API generates the computation
@@ -294,7 +292,7 @@ the Executor for you.</p>
</div>
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
-<p>See <a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a> and <a class="reference internal" href="index.html"><span class="doc">Python Documentation</span></a>.</p>
+<p>See <a class="reference internal" href="symbol.html"><em>Symbolic API</em></a> and <a class="reference internal" href="index.html"><em>Python Documentation</em></a>.</p>
</div>
</div>
<div class="container">
diff --git a/versions/master/api/r/index.html b/versions/master/api/r/index.html
index 8458a1e..b99d726 100644
--- a/versions/master/api/r/index.html
+++ b/versions/master/api/r/index.html
@@ -2,15 +2,15 @@
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<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
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-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
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@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
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@@ -48,12 +48,10 @@
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-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../julia/index.html" rel="next" title="MXNet - Julia API"/>
+<link href="../julia/index.html" rel="next" title="MXNet - Julia API">
<link href="../python/metric.html" rel="prev" title="Evaluation Metric API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
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+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
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<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
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<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -224,7 +222,7 @@ Previous Navbar Layout End -->
<div class="sphinxsidebarwrapper">
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../python/index.html">Python Documents</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">R Documents</a><ul>
+<li class="toctree-l1 current"><a class="current reference internal" href="">R Documents</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#resources">Resources</a><ul>
<li class="toctree-l3"><a class="reference external" href="https://media.readthedocs.org/pdf/mxnet-test/latest/mxnet-test.pdf">MXNet R Reference Manual</a></li>
<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/tutorials/index.html#R-Tutorials">MXNet for R Tutorials</a></li>
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index a7b29f0..e493706 100644
--- a/versions/master/api/scala/docs/index/index-g.html
+++ b/versions/master/api/scala/docs/index/index-g.html
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<div class="name">getParams</div>
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</div><div class="entry">
+ <div class="name">getSymbol</div>
+ <div class="occurrences"><a href="../ml/dmlc/mxnet/module/BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a> </div>
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</li><li name="scala.collection.Iterator#contains" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
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<h4 class="signature">
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- <a id="corresponds[B<:<?>](that:<?>)(p:<?>):Boolean"></a>
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<h4 class="signature">
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<h4 class="signature">
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<h4 class="signature">
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<h4 class="signature">
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<a id="grouped[B>:DataBatch](Int):GroupedIterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef → Any</dd></dl></div>
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<h4 class="signature">
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#indexWhere" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="indexWhere(p:<?>):Int"></a>
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<a id="indexWhere((DataBatch)⇒Boolean):Int"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1373,7 +1373,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef</dd></dl></div>
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<a id="padTo[A1>:DataBatch](Int,A1):Iterator[A1]"></a>
<h4 class="signature">
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@@ -1390,7 +1390,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
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- <a id="partition(p:<?>):(Iterator[A],Iterator[A])"></a>
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<a id="partition((DataBatch)⇒Boolean):(Iterator[DataBatch],Iterator[DataBatch])"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1407,7 +1407,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#patch" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="patch[B<:<?>](from:<?>,patchElems:<?>,replaced:<?>):Iterator[B]"></a>
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<a id="patch[B>:DataBatch](Int,Iterator[B],Int):Iterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1560,7 +1560,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Attributes</dt><dd>protected[this] </dd><dt>Definition Classes</dt><dd>TraversableOnce</dd></dl></div>
</li><li name="scala.collection.Iterator#sameElements" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="sameElements(that:<?>):Boolean"></a>
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<a id="sameElements(Iterator[_]):Boolean"></a>
<h4 class="signature">
<span class="modifier_kind">
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
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<a id="scanLeft[B](B)((B,DataBatch)⇒B):Iterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1594,7 +1594,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#scanRight" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="scanRight[B<:<?>](z:<?>)(op:<?>):Iterator[B]"></a>
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<a id="scanRight[B](B)((DataBatch,B)⇒B):Iterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1645,7 +1645,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>TraversableOnce → GenTraversableOnce</dd></dl></div>
</li><li name="scala.collection.Iterator#slice" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="slice(from:<?>,until:<?>):Iterator[A]"></a>
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<a id="slice(Int,Int):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1662,7 +1662,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
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<a id="sliding[B>:DataBatch](Int,Int):GroupedIterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1679,7 +1679,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
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- <a id="span(p:<?>):(Iterator[A],Iterator[A])"></a>
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<a id="span((DataBatch)⇒Boolean):(Iterator[DataBatch],Iterator[DataBatch])"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1730,7 +1730,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef</dd></dl></div>
</li><li name="scala.collection.Iterator#take" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
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<a id="take(Int):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1747,7 +1747,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#takeWhile" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
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<a id="takeWhile((DataBatch)⇒Boolean):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -2071,7 +2071,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<h4 class="signature">
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#zip" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="zip[B<:<?>](that:<?>):Iterator[(A,B)]"></a>
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<a id="zip[B](Iterator[B]):Iterator[(DataBatch,B)]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -2105,7 +2105,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#zipAll" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="zipAll[B<:<?>,A1<:<?>,B1<:<?>](that:<?>,thisElem:<?>,thatElem:<?>):Iterator[(A1,B1)]"></a>
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<a id="zipAll[B,A1>:DataBatch,B1>:B](Iterator[B],A1,B1):Iterator[(A1,B1)]"></a>
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diff --git a/versions/master/api/scala/docs/ml/dmlc/mxnet/DataPack.html b/versions/master/api/scala/docs/ml/dmlc/mxnet/DataPack.html
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>TraversableLike → GenTraversableLike</dd></dl></div>
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<a id="collectFirst[B](PartialFunction[DataBatch,B]):Option[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
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<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>TraversableOnce → GenTraversableOnce</dd></dl></div>
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<a id="copyToBuffer[B>:DataBatch](Buffer[B]):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
diff --git a/versions/master/api/scala/docs/ml/dmlc/mxnet/io/PrefetchingIter.html b/versions/master/api/scala/docs/ml/dmlc/mxnet/io/PrefetchingIter.html
index 9f08fba..1df461a 100644
--- a/versions/master/api/scala/docs/ml/dmlc/mxnet/io/PrefetchingIter.html
+++ b/versions/master/api/scala/docs/ml/dmlc/mxnet/io/PrefetchingIter.html
@@ -113,7 +113,7 @@ and combine them with prefetching.
<div id="types" class="types members">
<h3>Type Members</h3>
<ol><li name="scala.collection.Iterator.GroupedIterator" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="GroupedIterator[B>:A]extendsAbstractIterator[Seq[B]]withIterator[Seq[B]]"></a>
+ <a id="GroupedIterator[B<:<?>]extendsAbstractIterator[Seq[B]]withIterator[Seq[B]]"></a>
<a id="GroupedIterator[B>:A]:GroupedIterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -171,7 +171,7 @@ and combine them with prefetching.
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef → Any</dd></dl></div>
</li><li name="scala.collection.Iterator#++" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="++[B>:A](that:=>scala.collection.GenTraversableOnce[B]):Iterator[B]"></a>
+ <a id="++[B<:<?>](that:<?>):Iterator[B]"></a>
<a id="++[B>:DataBatch](⇒GenTraversableOnce[B]):Iterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -381,7 +381,7 @@ and combine them with prefetching.
</dd></dl></div>
</li><li name="scala.collection.Iterator#collect" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="collect[B](pf:PartialFunction[A,B]):Iterator[B]"></a>
+ <a id="collect[B<:<?>](pf:<?>):Iterator[B]"></a>
<a id="collect[B](PartialFunction[DataBatch,B]):Iterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -401,7 +401,7 @@ and combine them with prefetching.
</dd><dt>Migration</dt><dd class="cmt"><p><i>(Changed in version 2.8.0)</i> <code>collect</code> has changed. The previous behavior can be reproduced with <code>toSeq</code>.</p></dd></dl></div>
</li><li name="scala.collection.TraversableOnce#collectFirst" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="collectFirst[B](pf:PartialFunction[A,B]):Option[B]"></a>
+ <a id="collectFirst[B<:<?>](pf:<?>):Option[B]"></a>
<a id="collectFirst[B](PartialFunction[DataBatch,B]):Option[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -418,7 +418,7 @@ and combine them with prefetching.
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>TraversableOnce</dd></dl></div>
</li><li name="scala.collection.Iterator#contains" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="contains(elem:Any):Boolean"></a>
+ <a id="contains(elem:<?>):Boolean"></a>
<a id="contains(Any):Boolean"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -486,7 +486,7 @@ and combine them with prefetching.
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>TraversableOnce → GenTraversableOnce</dd></dl></div>
</li><li name="scala.collection.TraversableOnce#copyToBuffer" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="copyToBuffer[B>:A](dest:scala.collection.mutable.Buffer[B]):Unit"></a>
+ <a id="copyToBuffer[B<:<?>](dest:<?>):Unit"></a>
<a id="copyToBuffer[B>:DataBatch](Buffer[B]):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -503,7 +503,7 @@ and combine them with prefetching.
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>TraversableOnce</dd></dl></div>
</li><li name="scala.collection.Iterator#corresponds" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="corresponds[B](that:scala.collection.GenTraversableOnce[B])(p:(A,B)=>Boolean):Boolean"></a>
+ <a id="corresponds[B<:<?>](that:<?>)(p:<?>):Boolean"></a>
<a id="corresponds[B](GenTraversableOnce[B])((DataBatch,B)⇒Boolean):Boolean"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -573,7 +573,7 @@ and combine them with prefetching.
The object shall never be used after it is disposed.
</p></div></div>
</li><li name="scala.collection.Iterator#drop" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="drop(n:Int):Iterator[A]"></a>
+ <a id="drop(n:<?>):Iterator[A]"></a>
<a id="drop(Int):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -590,7 +590,7 @@ The object shall never be used after it is disposed.
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#dropWhile" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="dropWhile(p:A=>Boolean):Iterator[A]"></a>
+ <a id="dropWhile(p:<?>):Iterator[A]"></a>
<a id="dropWhile((DataBatch)⇒Boolean):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -675,7 +675,7 @@ The object shall never be used after it is disposed.
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator → TraversableOnce → GenTraversableOnce</dd></dl></div>
</li><li name="scala.collection.Iterator#filter" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="filter(p:A=>Boolean):Iterator[A]"></a>
+ <a id="filter(p:<?>):Iterator[A]"></a>
<a id="filter((DataBatch)⇒Boolean):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -692,7 +692,7 @@ The object shall never be used after it is disposed.
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#filterNot" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="filterNot(p:A=>Boolean):Iterator[A]"></a>
+ <a id="filterNot(p:<?>):Iterator[A]"></a>
<a id="filterNot((DataBatch)⇒Boolean):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -749,7 +749,7 @@ The object shall never be used after it is disposed.
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator → TraversableOnce → GenTraversableOnce</dd></dl></div>
</li><li name="scala.collection.Iterator#flatMap" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="flatMap[B](f:A=>scala.collection.GenTraversableOnce[B]):Iterator[B]"></a>
+ <a id="flatMap[B<:<?>](f:<?>):Iterator[B]"></a>
<a id="flatMap[B]((DataBatch)⇒GenTraversableOnce[B]):Iterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -938,7 +938,7 @@ The object shall never be used after it is disposed.
in current batch</p><div class="fullcomment"><div class="comment cmt"><p>get the number of padding examples
in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class="cmt"><p>number of padding examples in current batch</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.io.PrefetchingIter">PrefetchingIter</a> → <a href="../DataIter.html" class="extype" name="ml.dmlc.mxnet.DataIter">DataIter</a></dd></dl></div>
</li><li name="scala.collection.Iterator#grouped" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="grouped[B>:A](size:Int):Iterator.this.GroupedIterator[B]"></a>
+ <a id="grouped[B<:<?>](size:<?>):Iterator.this.GroupedIterator[B]"></a>
<a id="grouped[B>:DataBatch](Int):GroupedIterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1006,7 +1006,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef → Any</dd></dl></div>
</li><li name="scala.collection.Iterator#indexOf" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="indexOf[B>:A](elem:B):Int"></a>
+ <a id="indexOf[B<:<?>](elem:<?>):Int"></a>
<a id="indexOf[B>:DataBatch](B):Int"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1023,7 +1023,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#indexWhere" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="indexWhere(p:A=>Boolean):Int"></a>
+ <a id="indexWhere(p:<?>):Int"></a>
<a id="indexWhere((DataBatch)⇒Boolean):Int"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1329,7 +1329,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef</dd></dl></div>
</li><li name="scala.collection.Iterator#padTo" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="padTo[A1>:A](len:Int,elem:A1):Iterator[A1]"></a>
+ <a id="padTo[A1<:<?>](len:<?>,elem:<?>):Iterator[A1]"></a>
<a id="padTo[A1>:DataBatch](Int,A1):Iterator[A1]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1346,7 +1346,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#partition" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="partition(p:A=>Boolean):(Iterator[A],Iterator[A])"></a>
+ <a id="partition(p:<?>):(Iterator[A],Iterator[A])"></a>
<a id="partition((DataBatch)⇒Boolean):(Iterator[DataBatch],Iterator[DataBatch])"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1363,7 +1363,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#patch" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="patch[B>:A](from:Int,patchElems:Iterator[B],replaced:Int):Iterator[B]"></a>
+ <a id="patch[B<:<?>](from:<?>,patchElems:<?>,replaced:<?>):Iterator[B]"></a>
<a id="patch[B>:DataBatch](Int,Iterator[B],Int):Iterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1586,7 +1586,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Attributes</dt><dd>protected[this] </dd><dt>Definition Classes</dt><dd>TraversableOnce</dd></dl></div>
</li><li name="scala.collection.Iterator#sameElements" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="sameElements(that:Iterator[_]):Boolean"></a>
+ <a id="sameElements(that:<?>):Boolean"></a>
<a id="sameElements(Iterator[_]):Boolean"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1603,7 +1603,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#scanLeft" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="scanLeft[B](z:B)(op:(B,A)=>B):Iterator[B]"></a>
+ <a id="scanLeft[B<:<?>](z:<?>)(op:<?>):Iterator[B]"></a>
<a id="scanLeft[B](B)((B,DataBatch)⇒B):Iterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1620,7 +1620,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#scanRight" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="scanRight[B](z:B)(op:(A,B)=>B):Iterator[B]"></a>
+ <a id="scanRight[B<:<?>](z:<?>)(op:<?>):Iterator[B]"></a>
<a id="scanRight[B](B)((DataBatch,B)⇒B):Iterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1671,7 +1671,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>TraversableOnce → GenTraversableOnce</dd></dl></div>
</li><li name="scala.collection.Iterator#slice" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="slice(from:Int,until:Int):Iterator[A]"></a>
+ <a id="slice(from:<?>,until:<?>):Iterator[A]"></a>
<a id="slice(Int,Int):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1688,7 +1688,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#sliding" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="sliding[B>:A](size:Int,step:Int):Iterator.this.GroupedIterator[B]"></a>
+ <a id="sliding[B<:<?>](size:<?>,step:<?>):Iterator.this.GroupedIterator[B]"></a>
<a id="sliding[B>:DataBatch](Int,Int):GroupedIterator[B]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1705,7 +1705,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#span" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="span(p:A=>Boolean):(Iterator[A],Iterator[A])"></a>
+ <a id="span(p:<?>):(Iterator[A],Iterator[A])"></a>
<a id="span((DataBatch)⇒Boolean):(Iterator[DataBatch],Iterator[DataBatch])"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1756,7 +1756,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef</dd></dl></div>
</li><li name="scala.collection.Iterator#take" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="take(n:Int):Iterator[A]"></a>
+ <a id="take(n:<?>):Iterator[A]"></a>
<a id="take(Int):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -1773,7 +1773,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#takeWhile" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="takeWhile(p:A=>Boolean):Iterator[A]"></a>
+ <a id="takeWhile(p:<?>):Iterator[A]"></a>
<a id="takeWhile((DataBatch)⇒Boolean):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -2097,7 +2097,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</dd></dl></div>
</li><li name="scala.collection.Iterator#withFilter" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="withFilter(p:A=>Boolean):Iterator[A]"></a>
+ <a id="withFilter(p:<?>):Iterator[A]"></a>
<a id="withFilter((DataBatch)⇒Boolean):Iterator[DataBatch]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -2114,7 +2114,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#zip" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="zip[B](that:Iterator[B]):Iterator[(A,B)]"></a>
+ <a id="zip[B<:<?>](that:<?>):Iterator[(A,B)]"></a>
<a id="zip[B](Iterator[B]):Iterator[(DataBatch,B)]"></a>
<h4 class="signature">
<span class="modifier_kind">
@@ -2131,7 +2131,7 @@ in current batch</p></div><dl class="paramcmts block"><dt>returns</dt><dd class=
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Iterator</dd></dl></div>
</li><li name="scala.collection.Iterator#zipAll" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="zipAll[B,A1>:A,B1>:B](that:Iterator[B],thisElem:A1,thatElem:B1):Iterator[(A1,B1)]"></a>
+ <a id="zipAll[B<:<?>,A1<:<?>,B1<:<?>](that:<?>,thisElem:<?>,thatElem:<?>):Iterator[(A1,B1)]"></a>
<a id="zipAll[B,A1>:DataBatch,B1>:B](Iterator[B],A1,B1):Iterator[(A1,B1)]"></a>
<h4 class="signature">
<span class="modifier_kind">
diff --git a/versions/master/api/scala/docs/ml/dmlc/mxnet/module/BaseModule.html b/versions/master/api/scala/docs/ml/dmlc/mxnet/module/BaseModule.html
index 4040331..43e861b 100644
--- a/versions/master/api/scala/docs/ml/dmlc/mxnet/module/BaseModule.html
+++ b/versions/master/api/scala/docs/ml/dmlc/mxnet/module/BaseModule.html
@@ -341,7 +341,7 @@ This is necessary before one can perform computation with the module.</p></div><
</span>
<p class="shortcomment cmt">Get parameters, those are potentially copies of the the actual parameters used
to do computation on the device.</p><div class="fullcomment"><div class="comment cmt"><p>Get parameters, those are potentially copies of the the actual parameters used
-to do computation on the device.</p></div><dl class="paramcmts block"><dt>returns</dt><dd class="cmt"><p><code>(arg_params, aux_params)</code>, a pair of dictionary of name to value mapping.</p></dd></dl></div>
+to do computation on the device.</p></div><dl class="paramcmts block"><dt>returns</dt><dd class="cmt"><p><code>(argParams, auxParams)</code>, a pair of dictionary of name to value mapping.</p></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.BaseModule#initOptimizer" visbl="pub" data-isabs="true" fullComment="no" group="Ungrouped">
<a id="initOptimizer(kvstore:String,optimizer:ml.dmlc.mxnet.Optimizer,resetOptimizer:Boolean,forceInit:Boolean):Unit"></a>
<a id="initOptimizer(String,Optimizer,Boolean,Boolean):Unit"></a>
@@ -360,34 +360,38 @@ to do computation on the device.</p></div><dl class="paramcmts block"><dt>return
</span>
</li><li name="ml.dmlc.mxnet.module.BaseModule#initParams" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped">
- <a id="initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit"></a>
- <a id="initParams(Initializer,Map[String,NDArray],Map[String,NDArray],Boolean,Boolean):Unit"></a>
+ <a id="initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit"></a>
+ <a id="initParams(Initializer,Map[String,NDArray],Map[String,NDArray],Boolean,Boolean,Boolean):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
<span class="modifier">abstract </span>
<span class="kind">def</span>
</span>
<span class="symbol">
- <span class="name">initParams</span><span class="params">(<span name="initializer">initializer: <a href="../Initializer.html" class="extype" name="ml.dmlc.mxnet.Initializer">Initializer</a> = <span class="symbol"><span class="name"><a href="../../../package.html">new Uniform(0.01f)</a></span></span></span>, <span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.ht [...]
+ <span class="name">initParams</span><span class="params">(<span name="initializer">initializer: <a href="../Initializer.html" class="extype" name="ml.dmlc.mxnet.Initializer">Initializer</a> = <span class="symbol"><span class="name"><a href="../../../package.html">new Uniform(0.01f)</a></span></span></span>, <span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.ht [...]
</span>
</h4><span class="permalink">
- <a href="../../../../index.html#ml.dmlc.mxnet.module.BaseModule@initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit" title="Permalink" target="_top">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.BaseModule@initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit" title="Permalink" target="_top">
<img src="../../../../lib/permalink.png" alt="Permalink" />
</a>
</span>
<p class="shortcomment cmt">Initialize the parameters and auxiliary states.</p><div class="fullcomment"><div class="comment cmt"><p>Initialize the parameters and auxiliary states.</p></div><dl class="paramcmts block"><dt class="param">initializer</dt><dd class="cmt"><p>: Initializer
Called to initialize parameters if needed.
- arg_params : dict
+ argParams : dict
If not None, should be a dictionary of existing arg_params. Initialization
will be copied from that.
- aux_params : dict
+ auxParams : dict
If not None, should be a dictionary of existing aux_params. Initialization
will be copied from that.
- allow_missing : bool
+ allowMissing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
- force_init : bool
- If true, will force re-initialize even if already initialized.</p></dd></dl></div>
+ forceInit : bool
+ If true, will force re-initialize even if already initialized.
+ allowExtra : bool
+ Whether allow extra parameters that are not needed by symbol.
+ If this is True, no error will be thrown when argParams or auxParams
+ contain extra parameters that is not needed by the executor.</p></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.BaseModule#installMonitor" visbl="pub" data-isabs="true" fullComment="no" group="Ungrouped">
<a id="installMonitor(monitor:ml.dmlc.mxnet.Monitor):Unit"></a>
<a id="installMonitor(Monitor):Unit"></a>
@@ -699,6 +703,23 @@ list <code>[]</code>.
</a>
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef → Any</dd></dl></div>
+ </li><li name="ml.dmlc.mxnet.module.BaseModule#getSymbol" visbl="pub" data-isabs="false" fullComment="no" group="Ungrouped">
+ <a id="getSymbol:ml.dmlc.mxnet.Symbol"></a>
+ <a id="getSymbol:Symbol"></a>
+ <h4 class="signature">
+ <span class="modifier_kind">
+ <span class="modifier"></span>
+ <span class="kind">def</span>
+ </span>
+ <span class="symbol">
+ <span class="name">getSymbol</span><span class="result">: <a href="../Symbol.html" class="extype" name="ml.dmlc.mxnet.Symbol">Symbol</a></span>
+ </span>
+ </h4><span class="permalink">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.BaseModule@getSymbol:ml.dmlc.mxnet.Symbol" title="Permalink" target="_top">
+ <img src="../../../../lib/permalink.png" alt="Permalink" />
+ </a>
+ </span>
+
</li><li name="scala.AnyRef#hashCode" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="hashCode():Int"></a>
<a id="hashCode():Int"></a>
@@ -902,31 +923,35 @@ list <code>[]</code>.
indicating whether we should reset <code>eval_data</code> before starting evaluating.</p></dd><dt class="param">epoch</dt><dd class="cmt"><p>Default 0. For compatibility, this will be passed to callbacks (if any).
During training, this will correspond to the training epoch number.</p></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.BaseModule#setParams" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit"></a>
- <a id="setParams(Map[String,NDArray],Map[String,NDArray],Boolean,Boolean):Unit"></a>
+ <a id="setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit"></a>
+ <a id="setParams(Map[String,NDArray],Map[String,NDArray],Boolean,Boolean,Boolean):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
<span class="modifier"></span>
<span class="kind">def</span>
</span>
<span class="symbol">
- <span class="name">setParams</span><span class="params">(<span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" class="extype" name="ml.dmlc.mxnet.NDArray">NDArray</a>]</span>, <span name="auxParams">auxParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" clas [...]
+ <span class="name">setParams</span><span class="params">(<span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" class="extype" name="ml.dmlc.mxnet.NDArray">NDArray</a>]</span>, <span name="auxParams">auxParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" clas [...]
</span>
</h4><span class="permalink">
- <a href="../../../../index.html#ml.dmlc.mxnet.module.BaseModule@setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit" title="Permalink" target="_top">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.BaseModule@setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit" title="Permalink" target="_top">
<img src="../../../../lib/permalink.png" alt="Permalink" />
</a>
</span>
<p class="shortcomment cmt">Assign parameter and aux state values.</p><div class="fullcomment"><div class="comment cmt"><p>Assign parameter and aux state values.
- arg_params : dict
+ argParams : dict
Dictionary of name to value (<code>NDArray</code>) mapping.
- aux_params : dict
+ auxParams : dict
Dictionary of name to value (<code>NDArray</code>) mapping.
- allow_missing : bool
+ allowMissing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
- force_init : bool
+ forceInit : bool
If true, will force re-initialize even if already initialized.
+ allowExtra : bool
+ Whether allow extra parameters that are not needed by symbol.
+ If this is True, no error will be thrown when argParams or auxParams
+ contain extra parameters that is not needed by the executor.
</p></div></div>
</li><li name="scala.AnyRef#synchronized" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="synchronized[T0](x$1:=>T0):T0"></a>
diff --git a/versions/master/api/scala/docs/ml/dmlc/mxnet/module/DataParallelExecutorGroup.html b/versions/master/api/scala/docs/ml/dmlc/mxnet/module/DataParallelExecutorGroup.html
index e5911b5..15291c8 100644
--- a/versions/master/api/scala/docs/ml/dmlc/mxnet/module/DataParallelExecutorGroup.html
+++ b/versions/master/api/scala/docs/ml/dmlc/mxnet/module/DataParallelExecutorGroup.html
@@ -186,18 +186,18 @@ a call to the forward function. Backward cannot be called unless
This parameter is only needed when bind is called
on outputs that are not a loss function.</p></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.DataParallelExecutorGroup#bindExec" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="bindExec(dataShapes:Seq[ml.dmlc.mxnet.DataDesc],labelShapes:Option[Seq[ml.dmlc.mxnet.DataDesc]],sharedGroup:Option[ml.dmlc.mxnet.module.DataParallelExecutorGroup]):Unit"></a>
- <a id="bindExec(Seq[DataDesc],Option[Seq[DataDesc]],Option[DataParallelExecutorGroup]):Unit"></a>
+ <a id="bindExec(dataShapes:Seq[ml.dmlc.mxnet.DataDesc],labelShapes:Option[Seq[ml.dmlc.mxnet.DataDesc]],sharedGroup:Option[ml.dmlc.mxnet.module.DataParallelExecutorGroup],reshape:Boolean):Unit"></a>
+ <a id="bindExec(Seq[DataDesc],Option[Seq[DataDesc]],Option[DataParallelExecutorGroup],Boolean):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
<span class="modifier"></span>
<span class="kind">def</span>
</span>
<span class="symbol">
- <span class="name">bindExec</span><span class="params">(<span name="dataShapes">dataShapes: <span class="extype" name="scala.Seq">Seq</span>[<a href="../DataDesc.html" class="extype" name="ml.dmlc.mxnet.DataDesc">DataDesc</a>]</span>, <span name="labelShapes">labelShapes: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Seq">Seq</span>[<a href="../DataDesc.html" class="extype" name="ml.dmlc.mxnet.DataDesc">DataDesc</a>]]</span>, <span name=" [...]
+ <span class="name">bindExec</span><span class="params">(<span name="dataShapes">dataShapes: <span class="extype" name="scala.Seq">Seq</span>[<a href="../DataDesc.html" class="extype" name="ml.dmlc.mxnet.DataDesc">DataDesc</a>]</span>, <span name="labelShapes">labelShapes: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Seq">Seq</span>[<a href="../DataDesc.html" class="extype" name="ml.dmlc.mxnet.DataDesc">DataDesc</a>]]</span>, <span name=" [...]
</span>
</h4><span class="permalink">
- <a href="../../../../index.html#ml.dmlc.mxnet.module.DataParallelExecutorGroup@bindExec(dataShapes:Seq[ml.dmlc.mxnet.DataDesc],labelShapes:Option[Seq[ml.dmlc.mxnet.DataDesc]],sharedGroup:Option[ml.dmlc.mxnet.module.DataParallelExecutorGroup]):Unit" title="Permalink" target="_top">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.DataParallelExecutorGroup@bindExec(dataShapes:Seq[ml.dmlc.mxnet.DataDesc],labelShapes:Option[Seq[ml.dmlc.mxnet.DataDesc]],sharedGroup:Option[ml.dmlc.mxnet.module.DataParallelExecutorGroup],reshape:Boolean):Unit" title="Permalink" target="_top">
<img src="../../../../lib/permalink.png" alt="Permalink" />
</a>
</span>
@@ -551,23 +551,42 @@ Note this function will inplace update the NDArrays in arg_params and aux_params
</a>
</span>
<div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef</dd></dl></div>
+ </li><li name="ml.dmlc.mxnet.module.DataParallelExecutorGroup#reshape" visbl="pub" data-isabs="false" fullComment="no" group="Ungrouped">
+ <a id="reshape(dataShapes:Seq[ml.dmlc.mxnet.DataDesc],labelShapes:Option[Seq[ml.dmlc.mxnet.DataDesc]]):Unit"></a>
+ <a id="reshape(Seq[DataDesc],Option[Seq[DataDesc]]):Unit"></a>
+ <h4 class="signature">
+ <span class="modifier_kind">
+ <span class="modifier"></span>
+ <span class="kind">def</span>
+ </span>
+ <span class="symbol">
+ <span class="name">reshape</span><span class="params">(<span name="dataShapes">dataShapes: <span class="extype" name="scala.Seq">Seq</span>[<a href="../DataDesc.html" class="extype" name="ml.dmlc.mxnet.DataDesc">DataDesc</a>]</span>, <span name="labelShapes">labelShapes: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Seq">Seq</span>[<a href="../DataDesc.html" class="extype" name="ml.dmlc.mxnet.DataDesc">DataDesc</a>]]</span>)</span><span c [...]
+ </span>
+ </h4><span class="permalink">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.DataParallelExecutorGroup@reshape(dataShapes:Seq[ml.dmlc.mxnet.DataDesc],labelShapes:Option[Seq[ml.dmlc.mxnet.DataDesc]]):Unit" title="Permalink" target="_top">
+ <img src="../../../../lib/permalink.png" alt="Permalink" />
+ </a>
+ </span>
+ <p class="shortcomment cmt">Reshape executors.</p>
</li><li name="ml.dmlc.mxnet.module.DataParallelExecutorGroup#setParams" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray]):Unit"></a>
- <a id="setParams(Map[String,NDArray],Map[String,NDArray]):Unit"></a>
+ <a id="setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowExtra:Boolean):Unit"></a>
+ <a id="setParams(Map[String,NDArray],Map[String,NDArray],Boolean):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
<span class="modifier"></span>
<span class="kind">def</span>
</span>
<span class="symbol">
- <span class="name">setParams</span><span class="params">(<span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" class="extype" name="ml.dmlc.mxnet.NDArray">NDArray</a>]</span>, <span name="auxParams">auxParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" clas [...]
+ <span class="name">setParams</span><span class="params">(<span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" class="extype" name="ml.dmlc.mxnet.NDArray">NDArray</a>]</span>, <span name="auxParams">auxParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" clas [...]
</span>
</h4><span class="permalink">
- <a href="../../../../index.html#ml.dmlc.mxnet.module.DataParallelExecutorGroup@setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray]):Unit" title="Permalink" target="_top">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.DataParallelExecutorGroup@setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowExtra:Boolean):Unit" title="Permalink" target="_top">
<img src="../../../../lib/permalink.png" alt="Permalink" />
</a>
</span>
- <p class="shortcomment cmt">Assign, i.e.</p><div class="fullcomment"><div class="comment cmt"><p>Assign, i.e. copy parameters to all the executors.</p></div><dl class="paramcmts block"><dt class="param">argParams</dt><dd class="cmt"><p>A dictionary of name to <code>NDArray</code> parameter mapping.</p></dd><dt class="param">auxParams</dt><dd class="cmt"><p>A dictionary of name to <code>NDArray</code> auxiliary variable mapping.</p></dd></dl></div>
+ <p class="shortcomment cmt">Assign, i.e.</p><div class="fullcomment"><div class="comment cmt"><p>Assign, i.e. copy parameters to all the executors.</p></div><dl class="paramcmts block"><dt class="param">argParams</dt><dd class="cmt"><p>A dictionary of name to <code>NDArray</code> parameter mapping.</p></dd><dt class="param">auxParams</dt><dd class="cmt"><p>A dictionary of name to <code>NDArray</code> auxiliary variable mapping.</p></dd><dt class="param">allowExtra</dt><dd class="cm [...]
+ If this is True, no error will be thrown when argParams or auxParams
+ contain extra parameters that is not needed by the executor.</p></dd></dl></div>
</li><li name="scala.AnyRef#synchronized" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="synchronized[T0](x$1:=>T0):T0"></a>
<a id="synchronized[T0](⇒T0):T0"></a>
diff --git a/versions/master/api/scala/docs/ml/dmlc/mxnet/module/Module.html b/versions/master/api/scala/docs/ml/dmlc/mxnet/module/Module.html
index 9b7cc0e..58b4960 100644
--- a/versions/master/api/scala/docs/ml/dmlc/mxnet/module/Module.html
+++ b/versions/master/api/scala/docs/ml/dmlc/mxnet/module/Module.html
@@ -531,7 +531,24 @@ optimizer (esp. kvstore) is used.</p></div></div>
</span>
<p class="shortcomment cmt">Get current parameters.</p><div class="fullcomment"><div class="comment cmt"><p>Get current parameters.
<code>(arg_params, aux_params)</code>, each a dictionary of name to parameters (in
-<code>NDArray</code>) mapping.</p></div><dl class="paramcmts block"><dt>returns</dt><dd class="cmt"><p><code>(arg_params, aux_params)</code>, a pair of dictionary of name to value mapping.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.module.Module">Module</a> → <a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
+<code>NDArray</code>) mapping.</p></div><dl class="paramcmts block"><dt>returns</dt><dd class="cmt"><p><code>(argParams, auxParams)</code>, a pair of dictionary of name to value mapping.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.module.Module">Module</a> → <a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
+ </li><li name="ml.dmlc.mxnet.module.BaseModule#getSymbol" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
+ <a id="getSymbol:ml.dmlc.mxnet.Symbol"></a>
+ <a id="getSymbol:Symbol"></a>
+ <h4 class="signature">
+ <span class="modifier_kind">
+ <span class="modifier"></span>
+ <span class="kind">def</span>
+ </span>
+ <span class="symbol">
+ <span class="name">getSymbol</span><span class="result">: <a href="../Symbol.html" class="extype" name="ml.dmlc.mxnet.Symbol">Symbol</a></span>
+ </span>
+ </h4><span class="permalink">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.Module@getSymbol:ml.dmlc.mxnet.Symbol" title="Permalink" target="_top">
+ <img src="../../../../lib/permalink.png" alt="Permalink" />
+ </a>
+ </span>
+ <div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="scala.AnyRef#hashCode" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="hashCode():Int"></a>
<a id="hashCode():Int"></a>
@@ -569,25 +586,27 @@ optimizer (esp. kvstore) is used.</p></div></div>
& <code>idx2name</code> for optimizer according to executorGroup</p></dd><dt class="param">forceInit</dt><dd class="cmt"><p>Default <code>False</code>, indicating whether we should force re-initializing
the optimizer in the case an optimizer is already installed.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.module.Module">Module</a> → <a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.Module#initParams" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit"></a>
- <a id="initParams(Initializer,Map[String,NDArray],Map[String,NDArray],Boolean,Boolean):Unit"></a>
+ <a id="initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit"></a>
+ <a id="initParams(Initializer,Map[String,NDArray],Map[String,NDArray],Boolean,Boolean,Boolean):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
<span class="modifier"></span>
<span class="kind">def</span>
</span>
<span class="symbol">
- <span class="name">initParams</span><span class="params">(<span name="initializer">initializer: <a href="../Initializer.html" class="extype" name="ml.dmlc.mxnet.Initializer">Initializer</a> = <span class="symbol"><span class="name"><a href="../../../package.html">new Uniform(0.01f)</a></span></span></span>, <span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.ht [...]
+ <span class="name">initParams</span><span class="params">(<span name="initializer">initializer: <a href="../Initializer.html" class="extype" name="ml.dmlc.mxnet.Initializer">Initializer</a> = <span class="symbol"><span class="name"><a href="../../../package.html">new Uniform(0.01f)</a></span></span></span>, <span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.ht [...]
</span>
</h4><span class="permalink">
- <a href="../../../../index.html#ml.dmlc.mxnet.module.Module@initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit" title="Permalink" target="_top">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.Module@initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit" title="Permalink" target="_top">
<img src="../../../../lib/permalink.png" alt="Permalink" />
</a>
</span>
<p class="shortcomment cmt">Initialize the parameters and auxiliary states.</p><div class="fullcomment"><div class="comment cmt"><p>Initialize the parameters and auxiliary states.</p></div><dl class="paramcmts block"><dt class="param">initializer</dt><dd class="cmt"><p>Called to initialize parameters if needed.</p></dd><dt class="param">argParams</dt><dd class="cmt"><p>If not None, should be a dictionary of existing arg_params.
Initialization will be copied from that.</p></dd><dt class="param">auxParams</dt><dd class="cmt"><p>If not None, should be a dictionary of existing aux_params.
Initialization will be copied from that.</p></dd><dt class="param">allowMissing</dt><dd class="cmt"><p>If true, params could contain missing values,
- and the initializer will be called to fill those missing params.</p></dd><dt class="param">forceInit</dt><dd class="cmt"><p>If true, will force re-initialize even if already initialized.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.module.Module">Module</a> → <a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
+ and the initializer will be called to fill those missing params.</p></dd><dt class="param">forceInit</dt><dd class="cmt"><p>If true, will force re-initialize even if already initialized.</p></dd><dt class="param">allowExtra</dt><dd class="cmt"><p>Whether allow extra parameters that are not needed by symbol.
+ If this is True, no error will be thrown when argParams or auxParams
+ contain extra parameters that is not needed by the executor.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.module.Module">Module</a> → <a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.Module#installMonitor" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="installMonitor(monitor:ml.dmlc.mxnet.Monitor):Unit"></a>
<a id="installMonitor(Monitor):Unit"></a>
@@ -825,6 +844,23 @@ list <code>[]</code>.
<code><span class="extype" name="out1_batch1,">out2_batch1, ...], [out1_batch2, out2_batch2, ...</span></code>
This mode is useful because in some cases (e.g. bucketing),
the module does not necessarily produce the same number of outputs.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
+ </li><li name="ml.dmlc.mxnet.module.Module#reshape" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
+ <a id="reshape(dataShapes:IndexedSeq[ml.dmlc.mxnet.DataDesc],labelShapes:Option[IndexedSeq[ml.dmlc.mxnet.DataDesc]]):Unit"></a>
+ <a id="reshape(IndexedSeq[DataDesc],Option[IndexedSeq[DataDesc]]):Unit"></a>
+ <h4 class="signature">
+ <span class="modifier_kind">
+ <span class="modifier"></span>
+ <span class="kind">def</span>
+ </span>
+ <span class="symbol">
+ <span class="name">reshape</span><span class="params">(<span name="dataShapes">dataShapes: <span class="extype" name="scala.IndexedSeq">IndexedSeq</span>[<a href="../DataDesc.html" class="extype" name="ml.dmlc.mxnet.DataDesc">DataDesc</a>]</span>, <span name="labelShapes">labelShapes: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.IndexedSeq">IndexedSeq</span>[<a href="../DataDesc.html" class="extype" name="ml.dmlc.mxnet.DataDesc">DataDesc [...]
+ </span>
+ </h4><span class="permalink">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.Module@reshape(dataShapes:IndexedSeq[ml.dmlc.mxnet.DataDesc],labelShapes:Option[IndexedSeq[ml.dmlc.mxnet.DataDesc]]):Unit" title="Permalink" target="_top">
+ <img src="../../../../lib/permalink.png" alt="Permalink" />
+ </a>
+ </span>
+ <p class="shortcomment cmt">Reshapes the module for new input shapes.</p><div class="fullcomment"><div class="comment cmt"><p>Reshapes the module for new input shapes.</p></div><dl class="paramcmts block"><dt class="param">dataShapes</dt><dd class="cmt"><p>Typically is <code>dataIter.provideData</code>.</p></dd><dt class="param">labelShapes</dt><dd class="cmt"><p>Typically is <code>dataIter.provideLabel</code>.</p></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.Module#saveCheckpoint" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="saveCheckpoint(prefix:String,epoch:Int,saveOptStates:Boolean):Unit"></a>
<a id="saveCheckpoint(String,Int,Boolean):Unit"></a>
@@ -898,31 +934,35 @@ Use mx.callback.module_checkpoint as epoch_end_callback to save during training.
indicating whether we should reset <code>eval_data</code> before starting evaluating.</p></dd><dt class="param">epoch</dt><dd class="cmt"><p>Default 0. For compatibility, this will be passed to callbacks (if any).
During training, this will correspond to the training epoch number.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.BaseModule#setParams" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit"></a>
- <a id="setParams(Map[String,NDArray],Map[String,NDArray],Boolean,Boolean):Unit"></a>
+ <a id="setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit"></a>
+ <a id="setParams(Map[String,NDArray],Map[String,NDArray],Boolean,Boolean,Boolean):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
<span class="modifier"></span>
<span class="kind">def</span>
</span>
<span class="symbol">
- <span class="name">setParams</span><span class="params">(<span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" class="extype" name="ml.dmlc.mxnet.NDArray">NDArray</a>]</span>, <span name="auxParams">auxParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" clas [...]
+ <span class="name">setParams</span><span class="params">(<span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" class="extype" name="ml.dmlc.mxnet.NDArray">NDArray</a>]</span>, <span name="auxParams">auxParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" clas [...]
</span>
</h4><span class="permalink">
- <a href="../../../../index.html#ml.dmlc.mxnet.module.Module@setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit" title="Permalink" target="_top">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.Module@setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit" title="Permalink" target="_top">
<img src="../../../../lib/permalink.png" alt="Permalink" />
</a>
</span>
<p class="shortcomment cmt">Assign parameter and aux state values.</p><div class="fullcomment"><div class="comment cmt"><p>Assign parameter and aux state values.
- arg_params : dict
+ argParams : dict
Dictionary of name to value (<code>NDArray</code>) mapping.
- aux_params : dict
+ auxParams : dict
Dictionary of name to value (<code>NDArray</code>) mapping.
- allow_missing : bool
+ allowMissing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
- force_init : bool
+ forceInit : bool
If true, will force re-initialize even if already initialized.
+ allowExtra : bool
+ Whether allow extra parameters that are not needed by symbol.
+ If this is True, no error will be thrown when argParams or auxParams
+ contain extra parameters that is not needed by the executor.
</p></div><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="scala.AnyRef#synchronized" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="synchronized[T0](x$1:=>T0):T0"></a>
diff --git a/versions/master/api/scala/docs/ml/dmlc/mxnet/module/SequentialModule.html b/versions/master/api/scala/docs/ml/dmlc/mxnet/module/SequentialModule.html
index 9fbb356..60770b0 100644
--- a/versions/master/api/scala/docs/ml/dmlc/mxnet/module/SequentialModule.html
+++ b/versions/master/api/scala/docs/ml/dmlc/mxnet/module/SequentialModule.html
@@ -539,6 +539,23 @@ can perform computation with the module.</p></div><dl class="paramcmts block"><d
</span>
<p class="shortcomment cmt">Get current parameters.</p><div class="fullcomment"><div class="comment cmt"><p>Get current parameters.</p></div><dl class="paramcmts block"><dt>returns</dt><dd class="cmt"><p>(argParams, auxParams),
each a Map of name to parameters (in NDArray) mapping.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.module.SequentialModule">SequentialModule</a> → <a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
+ </li><li name="ml.dmlc.mxnet.module.BaseModule#getSymbol" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
+ <a id="getSymbol:ml.dmlc.mxnet.Symbol"></a>
+ <a id="getSymbol:Symbol"></a>
+ <h4 class="signature">
+ <span class="modifier_kind">
+ <span class="modifier"></span>
+ <span class="kind">def</span>
+ </span>
+ <span class="symbol">
+ <span class="name">getSymbol</span><span class="result">: <a href="../Symbol.html" class="extype" name="ml.dmlc.mxnet.Symbol">Symbol</a></span>
+ </span>
+ </h4><span class="permalink">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.SequentialModule@getSymbol:ml.dmlc.mxnet.Symbol" title="Permalink" target="_top">
+ <img src="../../../../lib/permalink.png" alt="Permalink" />
+ </a>
+ </span>
+ <div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="scala.AnyRef#hashCode" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="hashCode():Int"></a>
<a id="hashCode():Int"></a>
@@ -576,25 +593,27 @@ each a Map of name to parameters (in NDArray) mapping.</p></dd></dl><dl class="a
& <code>idx2name</code> for optimizer according to executorGroup</p></dd><dt class="param">forceInit</dt><dd class="cmt"><p>Default <code>False</code>, indicating whether we should force re-initializing
the optimizer in the case an optimizer is already installed.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.module.SequentialModule">SequentialModule</a> → <a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.SequentialModule#initParams" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit"></a>
- <a id="initParams(Initializer,Map[String,NDArray],Map[String,NDArray],Boolean,Boolean):Unit"></a>
+ <a id="initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit"></a>
+ <a id="initParams(Initializer,Map[String,NDArray],Map[String,NDArray],Boolean,Boolean,Boolean):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
<span class="modifier"></span>
<span class="kind">def</span>
</span>
<span class="symbol">
- <span class="name">initParams</span><span class="params">(<span name="initializer">initializer: <a href="../Initializer.html" class="extype" name="ml.dmlc.mxnet.Initializer">Initializer</a> = <span class="symbol"><span class="name"><a href="../../../package.html">new Uniform(0.01f)</a></span></span></span>, <span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.ht [...]
+ <span class="name">initParams</span><span class="params">(<span name="initializer">initializer: <a href="../Initializer.html" class="extype" name="ml.dmlc.mxnet.Initializer">Initializer</a> = <span class="symbol"><span class="name"><a href="../../../package.html">new Uniform(0.01f)</a></span></span></span>, <span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.ht [...]
</span>
</h4><span class="permalink">
- <a href="../../../../index.html#ml.dmlc.mxnet.module.SequentialModule@initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit" title="Permalink" target="_top">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.SequentialModule@initParams(initializer:ml.dmlc.mxnet.Initializer,argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit" title="Permalink" target="_top">
<img src="../../../../lib/permalink.png" alt="Permalink" />
</a>
</span>
<p class="shortcomment cmt">Initialize the parameters and auxiliary states.</p><div class="fullcomment"><div class="comment cmt"><p>Initialize the parameters and auxiliary states.</p></div><dl class="paramcmts block"><dt class="param">initializer</dt><dd class="cmt"><p>Called to initialize parameters if needed.</p></dd><dt class="param">argParams</dt><dd class="cmt"><p>If not None, should be a dictionary of existing arg_params.
Initialization will be copied from that.</p></dd><dt class="param">auxParams</dt><dd class="cmt"><p>If not None, should be a dictionary of existing aux_params.
Initialization will be copied from that.</p></dd><dt class="param">allowMissing</dt><dd class="cmt"><p>If true, params could contain missing values,
- and the initializer will be called to fill those missing params.</p></dd><dt class="param">forceInit</dt><dd class="cmt"><p>If true, will force re-initialize even if already initialized.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.module.SequentialModule">SequentialModule</a> → <a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
+ and the initializer will be called to fill those missing params.</p></dd><dt class="param">forceInit</dt><dd class="cmt"><p>If true, will force re-initialize even if already initialized.</p></dd><dt class="param">allowExtra</dt><dd class="cmt"><p>Whether allow extra parameters that are not needed by symbol.
+ If this is True, no error will be thrown when argParams or auxParams
+ contain extra parameters that is not needed by the executor.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="ml.dmlc.mxnet.module.SequentialModule">SequentialModule</a> → <a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.SequentialModule#installMonitor" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="installMonitor(monitor:ml.dmlc.mxnet.Monitor):Unit"></a>
<a id="installMonitor(Monitor):Unit"></a>
@@ -852,31 +871,35 @@ module is the output shape of a SequentialModule.</p></dd></dl><dl class="attrib
indicating whether we should reset <code>eval_data</code> before starting evaluating.</p></dd><dt class="param">epoch</dt><dd class="cmt"><p>Default 0. For compatibility, this will be passed to callbacks (if any).
During training, this will correspond to the training epoch number.</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="ml.dmlc.mxnet.module.BaseModule#setParams" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
- <a id="setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit"></a>
- <a id="setParams(Map[String,NDArray],Map[String,NDArray],Boolean,Boolean):Unit"></a>
+ <a id="setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit"></a>
+ <a id="setParams(Map[String,NDArray],Map[String,NDArray],Boolean,Boolean,Boolean):Unit"></a>
<h4 class="signature">
<span class="modifier_kind">
<span class="modifier"></span>
<span class="kind">def</span>
</span>
<span class="symbol">
- <span class="name">setParams</span><span class="params">(<span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" class="extype" name="ml.dmlc.mxnet.NDArray">NDArray</a>]</span>, <span name="auxParams">auxParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" clas [...]
+ <span class="name">setParams</span><span class="params">(<span name="argParams">argParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" class="extype" name="ml.dmlc.mxnet.NDArray">NDArray</a>]</span>, <span name="auxParams">auxParams: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <a href="../NDArray.html" clas [...]
</span>
</h4><span class="permalink">
- <a href="../../../../index.html#ml.dmlc.mxnet.module.SequentialModule@setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean):Unit" title="Permalink" target="_top">
+ <a href="../../../../index.html#ml.dmlc.mxnet.module.SequentialModule@setParams(argParams:Map[String,ml.dmlc.mxnet.NDArray],auxParams:Map[String,ml.dmlc.mxnet.NDArray],allowMissing:Boolean,forceInit:Boolean,allowExtra:Boolean):Unit" title="Permalink" target="_top">
<img src="../../../../lib/permalink.png" alt="Permalink" />
</a>
</span>
<p class="shortcomment cmt">Assign parameter and aux state values.</p><div class="fullcomment"><div class="comment cmt"><p>Assign parameter and aux state values.
- arg_params : dict
+ argParams : dict
Dictionary of name to value (<code>NDArray</code>) mapping.
- aux_params : dict
+ auxParams : dict
Dictionary of name to value (<code>NDArray</code>) mapping.
- allow_missing : bool
+ allowMissing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
- force_init : bool
+ forceInit : bool
If true, will force re-initialize even if already initialized.
+ allowExtra : bool
+ Whether allow extra parameters that are not needed by symbol.
+ If this is True, no error will be thrown when argParams or auxParams
+ contain extra parameters that is not needed by the executor.
</p></div><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="BaseModule.html" class="extype" name="ml.dmlc.mxnet.module.BaseModule">BaseModule</a></dd></dl></div>
</li><li name="scala.AnyRef#synchronized" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped">
<a id="synchronized[T0](x$1:=>T0):T0"></a>
diff --git a/versions/master/api/scala/index.html b/versions/master/api/scala/index.html
index 89a93e7..bced6c4 100644
--- a/versions/master/api/scala/index.html
+++ b/versions/master/api/scala/index.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet - Scala API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../perl/index.html" rel="next" title="MXNet - Perl API"/>
+<link href="../perl/index.html" rel="next" title="MXNet - Perl API">
<link href="../c++/index.html" rel="prev" title="MXNet - C++ API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -153,7 +151,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -180,7 +178,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -190,11 +188,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -227,7 +225,7 @@ Previous Navbar Layout End -->
<li class="toctree-l1"><a class="reference internal" href="../r/index.html">R Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../julia/index.html">Julia Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../c++/index.html">C++ Documents</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Scala Documents</a><ul>
+<li class="toctree-l1 current"><a class="current reference internal" href="">Scala Documents</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#scala-api-reference">Scala API Reference</a></li>
<li class="toctree-l2"><a class="reference internal" href="#resources">Resources</a><ul>
<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/api/scala/docs/index.html">MXNet Scala API Documentation</a></li>
@@ -272,12 +270,12 @@ and apply them to tasks, such as image classification and data science challenge
<div class="section" id="scala-api-reference">
<span id="scala-api-reference"></span><h2>Scala API Reference<a class="headerlink" href="#scala-api-reference" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
-<li><a class="reference internal" href="module.html"><span class="doc">Module API</span></a> is a flexible high-level interface for training neural networks.</li>
-<li><a class="reference internal" href="model.html"><span class="doc">Model API</span></a> is an alternate simple high-level interface for training neural networks.</li>
-<li><a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a> performs operations on NDArrays to assemble neural networks from layers.</li>
-<li><a class="reference internal" href="io.html"><span class="doc">IO Data Loading API</span></a> performs parsing and data loading.</li>
-<li><a class="reference internal" href="ndarray.html"><span class="doc">NDArray API</span></a> performs vector/matrix/tensor operations.</li>
-<li><a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> performs multi-GPU and multi-host distributed training.</li>
+<li><a class="reference internal" href="module.html"><em>Module API</em></a> is a flexible high-level interface for training neural networks.</li>
+<li><a class="reference internal" href="model.html"><em>Model API</em></a> is an alternate simple high-level interface for training neural networks.</li>
+<li><a class="reference internal" href="symbol.html"><em>Symbolic API</em></a> performs operations on NDArrays to assemble neural networks from layers.</li>
+<li><a class="reference internal" href="io.html"><em>IO Data Loading API</em></a> performs parsing and data loading.</li>
+<li><a class="reference internal" href="ndarray.html"><em>NDArray API</em></a> performs vector/matrix/tensor operations.</li>
+<li><a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> performs multi-GPU and multi-host distributed training.</li>
</ul>
</div>
<div class="section" id="resources">
diff --git a/versions/master/api/scala/io.html b/versions/master/api/scala/io.html
index 186d1ec..5032c62 100644
--- a/versions/master/api/scala/io.html
+++ b/versions/master/api/scala/io.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet Scala Data Loading API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -332,7 +330,7 @@ The following example code shows how to create a CIFAR data iterator.</p>
<div class="section" id="step-1-make-an-image-list-file">
<span id="step-1-make-an-image-list-file"></span><h3>Step 1. Make an Image List File<a class="headerlink" href="#step-1-make-an-image-list-file" title="Permalink to this headline">¶</a></h3>
<p>After you download the data, you need to make an image list file. The format is:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">integer_image_index</span> \<span class="n">t</span> <span class="n">label_index</span> \<span class="n">t</span> <span class="n">path_to_image</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> integer_image_index \t label_index \t path_to_image
</pre></div>
</div>
<p>Typically, the program takes the list of names of all of the images, shuffles them, then separates them into two lists: a training filename list and a testing filename list. Write the list in the right format.</p>
@@ -365,7 +363,7 @@ The following example code shows how to create a CIFAR data iterator.</p>
For example, if you have four labels for a single image, you can use the following procedure to use the RecordIO tools.</p>
<ol>
<li><p class="first">Write the image list files as follows:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">integer_image_index</span> \<span class="n">t</span> <span class="n">label_1</span> \<span class="n">t</span> <span class="n">label_2</span> \<span class="n">t</span> <span class="n">label_3</span> \<span class="n">t</span> <span class="n">label_4</span> \<span class="n">t</span> <span class="n">path_to_image</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> integer_image_index \t label_1 \t label_2 \t label_3 \t label_4 \t path_to_image
</pre></div>
</div>
</li>
@@ -376,7 +374,7 @@ For example, if you have four labels for a single image, you can use the followi
</pre></div>
</div>
<ol class="simple">
-<li>In the iterator generation code, set <code class="docutils literal"><span class="pre">label_width=4</span></code> and <code class="docutils literal"><span class="pre">path_imglist=<<the< span=""> <span class="pre">PATH</span> <span class="pre">TO</span> <span class="pre">YOUR</span> <span class="pre">image.lst>></span></the<></span></code>, for example:</li>
+<li>In the iterator generation code, set <code class="docutils literal"><span class="pre">label_width=4</span></code> and <code class="docutils literal"><span class="pre">path_imglist=<<The</span> <span class="pre">PATH</span> <span class="pre">TO</span> <span class="pre">YOUR</span> <span class="pre">image.lst>></span></code>, for example:</li>
</ol>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="k">val</span> <span class="n">dataiter</span> <span class="k">=</span> <span class="nc">IO</span><span class="o">.</span><span class="nc">ImageRecordIter</span><span class="o">(</span><span class="nc">Map</span><span class="o">(</span>
<span class="s">"path_imgrec"</span> <span class="o">-></span> <span class="s">"data/cifar/train.rec"</span><span class="o">,</span>
@@ -391,8 +389,8 @@ For example, if you have four labels for a single image, you can use the followi
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
-<li><a class="reference internal" href="ndarray.html"><span class="doc">NDArray API</span></a> for vector/matrix/tensor operations</li>
-<li><a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> for multi-GPU and multi-host distributed training</li>
+<li><a class="reference internal" href="ndarray.html"><em>NDArray API</em></a> for vector/matrix/tensor operations</li>
+<li><a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> for multi-GPU and multi-host distributed training</li>
</ul>
</div>
</div>
diff --git a/versions/master/api/scala/kvstore.html b/versions/master/api/scala/kvstore.html
index 02f536f..e1416c7 100644
--- a/versions/master/api/scala/kvstore.html
+++ b/versions/master/api/scala/kvstore.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>KVStore API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/api/scala/model.html b/versions/master/api/scala/model.html
index 489eec6..ea92c89 100644
--- a/versions/master/api/scala/model.html
+++ b/versions/master/api/scala/model.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet Scala Model API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -237,7 +235,7 @@ Previous Navbar Layout End -->
<div class="section" id="mxnet-scala-model-api">
<span id="mxnet-scala-model-api"></span><h1>MXNet Scala Model API<a class="headerlink" href="#mxnet-scala-model-api" title="Permalink to this headline">¶</a></h1>
<p>The model API provides a simplified way to train neural networks using common best practices.
-It’s a thin wrapper built on top of the <a class="reference internal" href="ndarray.html"><span class="doc">ndarray</span></a> and <a class="reference internal" href="symbol.html"><span class="doc">symbolic</span></a>
+It’s a thin wrapper built on top of the <a class="reference internal" href="ndarray.html"><em>ndarray</em></a> and <a class="reference internal" href="symbol.html"><em>symbolic</em></a>
modules that make neural network training easy.</p>
<p>Topics:</p>
<ul class="simple">
@@ -303,7 +301,7 @@ You should be able to save and load directly into cloud storage, such as Amazon
<div class="section" id="periodic-checkpointing">
<span id="periodic-checkpointing"></span><h2>Periodic Checkpointing<a class="headerlink" href="#periodic-checkpointing" title="Permalink to this headline">¶</a></h2>
<p>We recommend checkpointing your model after each iteration.
-To do this, use <code class="docutils literal"><span class="pre">EpochEndCallback</span></code> to add a <code class="docutils literal"><span class="pre">Model.saveCheckpoint(<parameters>)</parameters></span></code> checkpoint callback to the function after each iteration .</p>
+To do this, use <code class="docutils literal"><span class="pre">EpochEndCallback</span></code> to add a <code class="docutils literal"><span class="pre">Model.saveCheckpoint(<parameters>)</span></code> checkpoint callback to the function after each iteration .</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="c1">// modelPrefix-symbol.json will be saved for symbol.</span>
<span class="c1">// modelPrefix-epoch.params will be saved for parameters.</span>
<span class="c1">// Checkpoint the model into file. Can specify parameters.</span>
@@ -343,10 +341,10 @@ To do this, use <code class="docutils literal"><span class="pre">EpochEndCallbac
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
-<li>See <a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a> for operations on NDArrays that assemble neural networks from layers.</li>
-<li>See <a class="reference internal" href="io.html"><span class="doc">IO Data Loading API</span></a> for parsing and loading data.</li>
-<li>See <a class="reference internal" href="ndarray.html"><span class="doc">NDArray API</span></a> for vector/matrix/tensor operations.</li>
-<li>See <a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> for multi-GPU and multi-host distributed training.</li>
+<li>See <a class="reference internal" href="symbol.html"><em>Symbolic API</em></a> for operations on NDArrays that assemble neural networks from layers.</li>
+<li>See <a class="reference internal" href="io.html"><em>IO Data Loading API</em></a> for parsing and loading data.</li>
+<li>See <a class="reference internal" href="ndarray.html"><em>NDArray API</em></a> for vector/matrix/tensor operations.</li>
+<li>See <a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> for multi-GPU and multi-host distributed training.</li>
</ul>
</div>
</div>
diff --git a/versions/master/api/scala/module.html b/versions/master/api/scala/module.html
index 3640216..899713d 100644
--- a/versions/master/api/scala/module.html
+++ b/versions/master/api/scala/module.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Module API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -343,11 +341,11 @@ If you simply want to fit a module, you don’t need to call <code class="docuti
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
-<li>See <a class="reference internal" href="model.html"><span class="doc">Model API</span></a> for an alternative simple high-level interface for training neural networks.</li>
-<li>See <a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a> for operations on NDArrays that assemble neural networks from layers.</li>
-<li>See <a class="reference internal" href="io.html"><span class="doc">IO Data Loading API</span></a> for parsing and loading data.</li>
-<li>See <a class="reference internal" href="ndarray.html"><span class="doc">NDArray API</span></a> for vector/matrix/tensor operations.</li>
-<li>See <a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> for multi-GPU and multi-host distributed training.</li>
+<li>See <a class="reference internal" href="model.html"><em>Model API</em></a> for an alternative simple high-level interface for training neural networks.</li>
+<li>See <a class="reference internal" href="symbol.html"><em>Symbolic API</em></a> for operations on NDArrays that assemble neural networks from layers.</li>
+<li>See <a class="reference internal" href="io.html"><em>IO Data Loading API</em></a> for parsing and loading data.</li>
+<li>See <a class="reference internal" href="ndarray.html"><em>NDArray API</em></a> for vector/matrix/tensor operations.</li>
+<li>See <a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> for multi-GPU and multi-host distributed training.</li>
</ul>
</div>
</div>
diff --git a/versions/master/api/scala/ndarray.html b/versions/master/api/scala/ndarray.html
index c1f6788..c3f1f32 100644
--- a/versions/master/api/scala/ndarray.html
+++ b/versions/master/api/scala/ndarray.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>NDArray API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -386,7 +384,7 @@ Previous Navbar Layout End -->
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
-<li>See <a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> for multi-GPU and multi-host distributed training.</li>
+<li>See <a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> for multi-GPU and multi-host distributed training.</li>
</ul>
</div>
</div>
diff --git a/versions/master/api/scala/symbol.html b/versions/master/api/scala/symbol.html
index 26e4328..a5bc954 100644
--- a/versions/master/api/scala/symbol.html
+++ b/versions/master/api/scala/symbol.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet Scala Symbolic API — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -246,7 +244,7 @@ Previous Navbar Layout End -->
<li><a class="reference external" href="#multiple-outputs">Multiple Outputs</a> explains how to configure multiple outputs.</li>
<li><a class="reference external" href="http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.Symbol">Symbol Creation API Reference</a> documents functions.</li>
</ul>
-<p>We also highly encourage you to read <a class="reference internal" href="symbol_in_pictures.html"><span class="doc">Symbolic Configuration and Execution in Pictures</span></a>.</p>
+<p>We also highly encourage you to read <a class="reference internal" href="symbol_in_pictures.html"><em>Symbolic Configuration and Execution in Pictures</em></a>.</p>
<div class="section" id="how-to-compose-symbols">
<span id="how-to-compose-symbols"></span><h2>How to Compose Symbols<a class="headerlink" href="#how-to-compose-symbols" title="Permalink to this headline">¶</a></h2>
<p>The symbolic API provides a way to configure computation graphs.
@@ -281,8 +279,8 @@ You can configure the graphs either at the level of neural network layer operati
</pre></div>
</div>
<p>For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use <code class="docutils literal"><span class="pre">attr(key)</span></code>:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">data</span><span class="o">.</span><span class="n">attr</span><span class="p">(</span><span class="s2">"mood"</span><span class="p">)</span>
- <span class="n">res6</span><span class="p">:</span> <span class="n">Option</span><span class="p">[</span><span class="n">String</span><span class="p">]</span> <span class="o">=</span> <span class="n">Some</span><span class="p">(</span><span class="n">angry</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> data.attr("mood")
+ res6: Option[String] = Some(angry)
</pre></div>
</div>
<p>To attach attributes, you can use <code class="docutils literal"><span class="pre">AttrScope</span></code>. <code class="docutils literal"><span class="pre">AttrScope</span></code> automatically adds the specified attributes to all of the symbols created within that scope. The user can also inherit this object to change naming behavior. For example:</p>
@@ -322,12 +320,12 @@ Refer to <a class="reference external" href="http://mxnet.io/api/scala/docs/inde
<span id="executing-symbols"></span><h2>Executing Symbols<a class="headerlink" href="#executing-symbols" title="Permalink to this headline">¶</a></h2>
<p>After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them.
If you are training a neural network, this is typically
-handled by the high-level <a class="reference internal" href="model.html"><span class="doc">Model class</span></a> and the [<code class="docutils literal"><span class="pre">fit()</span></code>] function.</p>
+handled by the high-level <a class="reference internal" href="model.html"><em>Model class</em></a> and the [<code class="docutils literal"><span class="pre">fit()</span></code>] function.</p>
<p>For neural networks used in “feed-forward”, “prediction”, or “inference” mode (all terms for the same
thing: running a trained network), the input arguments are the
input data, and the weights of the neural network that were learned during training.</p>
<p>To manually execute a set of symbols, you need to create an [<code class="docutils literal"><span class="pre">Executor</span></code>] object,
-which is typically constructed by calling the [<code class="docutils literal"><span class="pre">simpleBind(<parameters>)</parameters></span></code>] method on a symbol.</p>
+which is typically constructed by calling the [<code class="docutils literal"><span class="pre">simpleBind(<parameters>)</span></code>] method on a symbol.</p>
</div>
<div class="section" id="multiple-outputs">
<span id="multiple-outputs"></span><h2>Multiple Outputs<a class="headerlink" href="#multiple-outputs" title="Permalink to this headline">¶</a></h2>
@@ -349,9 +347,9 @@ The resulting executor will have two outputs, one for fc1_output and one for sof
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
-<li>See <a class="reference internal" href="io.html"><span class="doc">IO Data Loading API</span></a> for parsing and loading data.</li>
-<li>See <a class="reference internal" href="ndarray.html"><span class="doc">NDArray API</span></a> for vector/matrix/tensor operations.</li>
-<li>See <a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> for multi-GPU and multi-host distributed training.</li>
+<li>See <a class="reference internal" href="io.html"><em>IO Data Loading API</em></a> for parsing and loading data.</li>
+<li>See <a class="reference internal" href="ndarray.html"><em>NDArray API</em></a> for vector/matrix/tensor operations.</li>
+<li>See <a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> for multi-GPU and multi-host distributed training.</li>
</ul>
</div>
</div>
diff --git a/versions/master/api/scala/symbol_in_pictures.html b/versions/master/api/scala/symbol_in_pictures.html
index 94887fd..e22504d 100644
--- a/versions/master/api/scala/symbol_in_pictures.html
+++ b/versions/master/api/scala/symbol_in_pictures.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Symbolic Configuration and Execution in Pictures — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -237,7 +235,7 @@ Previous Navbar Layout End -->
<div class="section" id="symbolic-configuration-and-execution-in-pictures">
<span id="symbolic-configuration-and-execution-in-pictures"></span><h1>Symbolic Configuration and Execution in Pictures<a class="headerlink" href="#symbolic-configuration-and-execution-in-pictures" title="Permalink to this headline">¶</a></h1>
<p>This topic explains symbolic construction and execution in pictures.
-We recommend that you also read <a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a>.</p>
+We recommend that you also read <a class="reference internal" href="symbol.html"><em>Symbolic API</em></a>.</p>
<div class="section" id="compose-symbols">
<span id="compose-symbols"></span><h2>Compose Symbols<a class="headerlink" href="#compose-symbols" title="Permalink to this headline">¶</a></h2>
<p>Symbols are a description of the computation that you want to perform. The symbolic construction API generates the computation
@@ -293,7 +291,7 @@ the Executor for you.</p>
</div>
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
-<p>See <a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a> and <a class="reference internal" href="index.html"><span class="doc">Python Documentation</span></a>.</p>
+<p>See <a class="reference internal" href="symbol.html"><em>Symbolic API</em></a> and <a class="reference internal" href="index.html"><em>Python Documentation</em></a>.</p>
</div>
</div>
<div class="container">
diff --git a/versions/master/architecture/index.html b/versions/master/architecture/index.html
index d91f7ba..9bd119d 100644
--- a/versions/master/architecture/index.html
+++ b/versions/master/architecture/index.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet Architecture — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="../tutorials/index.html" rel="next" title="Tutorials"/>
+<link href="../tutorials/index.html" rel="next" title="Tutorials">
<link href="../get_started/index.html" rel="prev" title="<no title>"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -153,7 +151,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -180,7 +178,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -190,11 +188,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -230,7 +228,7 @@ Previous Navbar Layout End -->
<li class="toctree-l1"><a class="reference internal" href="../api/scala/index.html">Scala Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../api/perl/index.html">Perl Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/index.html">HowTo Documents</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">System Documents</a><ul>
+<li class="toctree-l1 current"><a class="current reference internal" href="">System Documents</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#deep-learning-system-design-concepts">Deep Learning System Design Concepts</a><ul>
<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/architecture/overview.html">MXNet System Overview</a></li>
<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/architecture/program_model.html">Deep Learning Programming Style: Symbolic vs Imperative</a></li>
diff --git a/versions/master/architecture/note_data_loading.html b/versions/master/architecture/note_data_loading.html
index e0ed38b..c8dbbcf 100644
--- a/versions/master/architecture/note_data_loading.html
+++ b/versions/master/architecture/note_data_loading.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Designing Efficient Data Loaders for Deep Learning — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -302,11 +300,11 @@ To achieve our goals, we need to pack binary data into a splittable format.
In MXNet, we rely on the binary recordIO format implemented in dmlc-core.</p>
<div class="section" id="binary-record">
<span id="binary-record"></span><h3>Binary Record<a class="headerlink" href="#binary-record" title="Permalink to this headline">¶</a></h3>
-<p><img alt="baserecordio" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/baserecordio.jpg"/>
+<p><img alt="baserecordio" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/io/baserecordio.jpg">
In MXNet’s binary RecordIO, we store each data instance as a record.
<strong>kMagic</strong> is a <em>magic number</em> indicating the start of a record.
<strong>Lrecord</strong> encodes length and a continue flag.
-In lrecord,</p>
+In lrecord,</img></p>
<ul class="simple">
<li>cflag == 0: this is a complete record</li>
<li>cflag == 1: start of a multiple-records</li>
diff --git a/versions/master/architecture/note_engine.html b/versions/master/architecture/note_engine.html
index c0b84db..75d16db 100644
--- a/versions/master/architecture/note_engine.html
+++ b/versions/master/architecture/note_engine.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Dependency Engine for Deep Learning — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -455,7 +453,7 @@ using <code class="docutils literal"><span class="pre">read_vars</span></code> a
Any execution function can be processed.
This interface is generic for the operations and resources we want to schedule.</p>
<p>For fun, let’s look at how the engine internals work with the tags by considering the following code snippet:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">B</span> <span class="o">=</span> <span class="n">A</span> <span class="o">+</span> <span class="mi">1</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="n">B</span> <span class="o">=</span> <span class="n">A</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">C</span> <span class="o">=</span> <span class="n">A</span> <span class="o">+</span> <span class="mi">2</span>
<span class="n">A</span> <span class="o">=</span> <span class="n">C</span> <span class="o">*</span> <span class="mi">2</span>
<span class="n">D</span> <span class="o">=</span> <span class="n">A</span> <span class="o">+</span> <span class="mi">3</span>
diff --git a/versions/master/architecture/note_memory.html b/versions/master/architecture/note_memory.html
index 34301e9..403c17b 100644
--- a/versions/master/architecture/note_memory.html
+++ b/versions/master/architecture/note_memory.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Optimizing Memory Consumption in Deep Learning — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/architecture/overview.html b/versions/master/architecture/overview.html
index f15f1b2..8031741 100644
--- a/versions/master/architecture/overview.html
+++ b/versions/master/architecture/overview.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet System Architecture — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -270,7 +268,7 @@ execute a bunch of functions following their dependencies.
Execution of any two functions with dependencies should be serialized.
To boost performance, functions with no dependencies <em>can</em> be executed in parallel.
For a general discussion of this topic,
-see our <a class="reference internal" href="note_engine.html"><span class="doc">notes on the dependency engine</span></a>.</p>
+see our <a class="reference internal" href="note_engine.html"><em>notes on the dependency engine</em></a>.</p>
<div class="section" id="interface">
<span id="interface"></span><h3>Interface<a class="headerlink" href="#interface" title="Permalink to this headline">¶</a></h3>
<p>The following API is the core interface for the execution engine:</p>
@@ -396,33 +394,33 @@ Ensure that the operator has finished computing before calling this API.</p>
<blockquote>
<div><dl class="class">
<dt id="_CPPv2N5mxnet6EngineE">
-<span id="mxnet::Engine"></span><span class="target" id="mxnetclassmxnet_1_1Engine"></span><em class="property">class </em></dt>
+<span id="mxnet::Engine"></span><span class="target" id="mxnetclassmxnet_1_1Engine"></span><em class="property">class </em><code class="descclassname">mxnet::</code><code class="descname">Engine</code><a class="headerlink" href="#_CPPv2N5mxnet6EngineE" title="Permalink to this definition">¶</a></dt>
<dd><p>Dependency engine that schedules operations. </p>
<div class="breathe-sectiondef docutils container">
<p class="breathe-sectiondef-title rubric">Public Types</p>
<dl class="type">
<dt id="_CPPv2N5mxnet6Engine18CallbackOnCompleteE">
-<span id="mxnet::Engine::CallbackOnComplete"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a16b757432556f835d27f1b5e1dbe1b06"></span><em class="property">typedef </em></dt>
+<span id="mxnet::Engine::CallbackOnComplete"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a16b757432556f835d27f1b5e1dbe1b06"></span><em class="property">typedef </em>engine::CallbackOnComplete <code class="descname">CallbackOnComplete</code><a class="headerlink" href="#_CPPv2N5mxnet6Engine18CallbackOnCompleteE" title="Permalink to this definition">¶</a></dt>
<dd><p>callback on complete </p>
</dd></dl>
<dl class="type">
<dt id="_CPPv2N5mxnet6Engine6SyncFnE">
-<span id="mxnet::Engine::SyncFn"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a07f30ab85fca436e1bbcc72cd4d8bb35"></span><em class="property">typedef </em></dt>
+<span id="mxnet::Engine::SyncFn"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a07f30ab85fca436e1bbcc72cd4d8bb35"></span><em class="property">typedef </em>std::function<void<span class="sig-paren">(</span>RunContext<span class="sig-paren">)</span>> <code class="descname">SyncFn</code><a class="headerlink" href="#_CPPv2N5mxnet6Engine6SyncFnE" title="Permalink to this definition">¶</a></dt>
<dd><p>Synchronous operation to pass to engine. </p>
</dd></dl>
<dl class="type">
<dt id="_CPPv2N5mxnet6Engine7AsyncFnE">
-<span id="mxnet::Engine::AsyncFn"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ad41feff70bba0f29fc24f60b5381984c"></span><em class="property">typedef </em></dt>
+<span id="mxnet::Engine::AsyncFn"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ad41feff70bba0f29fc24f60b5381984c"></span><em class="property">typedef </em>std::function<void<span class="sig-paren">(</span>RunContext, <a class="reference internal" href="#_CPPv2N5mxnet6Engine18CallbackOnCompleteE" title="mxnet::Engine::CallbackOnComplete">CallbackOnComplete</a><span class="sig-paren">)</span>> <code class="descname">AsyncFn</code><a class="headerlink" href="#_CPPv2N5mxnet6Eng [...]
<dd><p>Asynchronous operation to pass to engine. </p>
</dd></dl>
<dl class="type">
<dt id="_CPPv2N5mxnet6Engine9VarHandleE">
-<span id="mxnet::Engine::VarHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1aac31510c793a12944c33f9cac6150491"></span><em class="property">typedef </em></dt>
+<span id="mxnet::Engine::VarHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1aac31510c793a12944c33f9cac6150491"></span><em class="property">typedef </em>engine::VarHandle <code class="descname">VarHandle</code><a class="headerlink" href="#_CPPv2N5mxnet6Engine9VarHandleE" title="Permalink to this definition">¶</a></dt>
<dd><p>Variable pointer. </p>
</dd></dl>
<dl class="type">
<dt id="_CPPv2N5mxnet6Engine9OprHandleE">
-<span id="mxnet::Engine::OprHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a832436e413a075291aa1a631942c3f01"></span><em class="property">typedef </em></dt>
+<span id="mxnet::Engine::OprHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a832436e413a075291aa1a631942c3f01"></span><em class="property">typedef </em>engine::OprHandle <code class="descname">OprHandle</code><a class="headerlink" href="#_CPPv2N5mxnet6Engine9OprHandleE" title="Permalink to this definition">¶</a></dt>
<dd><p>Operator pointer. </p>
</dd></dl>
</div>
@@ -430,7 +428,7 @@ Ensure that the operator has finished computing before calling this API.</p>
<p class="breathe-sectiondef-title rubric">Public Functions</p>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine14NotifyShutdownEv">
-<span id="mxnet::Engine::NotifyShutdown"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a3c0e2989538b5369c1592eddbcf0181c"></span><em class="property">virtual</em> void <code class="descclassname">mxnet::Engine::</code><code class="descname">NotifyShutdown</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv2N5mxnet6Engine14NotifyShutdownEv" title="Permalink to this definition">¶</a><br/> = 0</dt>
+<span id="mxnet::Engine::NotifyShutdown"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a3c0e2989538b5369c1592eddbcf0181c"></span><em class="property">virtual</em> void <code class="descname">NotifyShutdown</code><span class="sig-paren">(</span><span class="sig-paren">)</span> = 0<a class="headerlink" href="#_CPPv2N5mxnet6Engine14NotifyShutdownEv" title="Permalink to this definition">¶</a></dt>
<dd><p>Notify the engine about a shutdown, This can help engine to print less messages into display. </p>
<p>User do not have to call this function. <dl class="docutils">
<dt><strong>Return</strong></dt>
@@ -440,7 +438,7 @@ Ensure that the operator has finished computing before calling this API.</p>
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine11NewVariableEv">
-<span id="mxnet::Engine::NewVariable"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a6e141b188f018d5d933ab99868631d5e"></span><em class="property">virtual</em> <a class="reference internal" href="#_CPPv2N5mxnet6Engine9VarHandleE" title="mxnet::Engine::VarHandle">VarHandle</a> <code class="descclassname">mxnet::Engine::</code><code class="descname">NewVariable</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv2N5mxnet6Engi [...]
+<span id="mxnet::Engine::NewVariable"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a6e141b188f018d5d933ab99868631d5e"></span><em class="property">virtual</em> <a class="reference internal" href="#_CPPv2N5mxnet6Engine9VarHandleE" title="mxnet::Engine::VarHandle">VarHandle</a> <code class="descname">NewVariable</code><span class="sig-paren">(</span><span class="sig-paren">)</span> = 0<a class="headerlink" href="#_CPPv2N5mxnet6Engine11NewVariableEv" title="Permalink to this de [...]
<dd><p>Allocate a new variable, the variable can then be used to schedule the operation concurrently via dependency patterns. </p>
<p><dl class="docutils">
<dt><strong>Return</strong></dt>
@@ -450,7 +448,7 @@ Ensure that the operator has finished computing before calling this API.</p>
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine11NewOperatorE7AsyncFnRNSt6vectorI9VarHandleEERNSt6vectorI9VarHandleEE10FnPropertyPKc">
-<span id="mxnet::Engine::NewOperator__AsyncFn.std::vector:VarHandle:CR.std::vector:VarHandle:CR.FnProperty.cCP"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1aff3332258a158ef33a9a4b7bcdc2fe6f"></span><em class="property">virtual</em> <a class="reference internal" href="#_CPPv2N5mxnet6Engine9OprHandleE" title="mxnet::Engine::OprHandle">OprHandle</a> <code class="descclassname">mxnet::Engine::</code><code class="descname">NewOperator</code><span class="sig-paren">(</span><a cl [...]
+<span id="mxnet::Engine::NewOperator__AsyncFn.std::vector:VarHandle:CR.std::vector:VarHandle:CR.FnProperty.cCP"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1aff3332258a158ef33a9a4b7bcdc2fe6f"></span><em class="property">virtual</em> <a class="reference internal" href="#_CPPv2N5mxnet6Engine9OprHandleE" title="mxnet::Engine::OprHandle">OprHandle</a> <code class="descname">NewOperator</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engi [...]
<dd><p>Create a new operator. The returned operator could be saved externally so that it could be resued for scheduling. </p>
<p><dl class="docutils">
<dt><strong>Return</strong></dt>
@@ -469,7 +467,7 @@ Ensure that the operator has finished computing before calling this API.</p>
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine14DeleteOperatorE9OprHandle">
-<span id="mxnet::Engine::DeleteOperator__OprHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a3fd7c8b35a2f52805506cc242bf82ca7"></span><em class="property">virtual</em> void <code class="descclassname">mxnet::Engine::</code><code class="descname">DeleteOperator</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine9OprHandleE" title="mxnet::Engine::OprHandle">OprHandle</a> <em>op</em><span class="sig-paren">)</span><a class="head [...]
+<span id="mxnet::Engine::DeleteOperator__OprHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a3fd7c8b35a2f52805506cc242bf82ca7"></span><em class="property">virtual</em> void <code class="descname">DeleteOperator</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine9OprHandleE" title="mxnet::Engine::OprHandle">OprHandle</a> <em>op</em><span class="sig-paren">)</span> = 0<a class="headerlink" href="#_CPPv2N5mxnet6Engine14DeleteOpe [...]
<dd><p>Delete the given operator. </p>
<p>
The delete will not happen immediately, but will wait until all the operations using this operator are completed. <dl class="docutils">
@@ -483,7 +481,7 @@ The delete will not happen immediately, but will wait until all the operations u
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine4PushE9OprHandle7Contextib">
-<span id="mxnet::Engine::Push__OprHandle.Context.i.b"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ad213d6b1a7c1e0d4d41275b9efe5f097"></span><em class="property">virtual</em> void <code class="descclassname">mxnet::Engine::</code><code class="descname">Push</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine9OprHandleE" title="mxnet::Engine::OprHandle">OprHandle</a> <em>op</em>, Context <em>exec_ctx</em>, int <em>priority</em> = 0 [...]
+<span id="mxnet::Engine::Push__OprHandle.Context.i.b"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ad213d6b1a7c1e0d4d41275b9efe5f097"></span><em class="property">virtual</em> void <code class="descname">Push</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine9OprHandleE" title="mxnet::Engine::OprHandle">OprHandle</a> <em>op</em>, Context <em>exec_ctx</em>, int <em>priority</em> = 0, bool <em>profiling</em> = false<span class="sig- [...]
<dd><p>Push an operator to the engine. </p>
<p><dl class="docutils">
<dt><strong>Parameters</strong></dt>
@@ -499,7 +497,7 @@ The delete will not happen immediately, but will wait until all the operations u
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine9PushAsyncE7AsyncFn7ContextRNSt6vectorI9VarHandleEERNSt6vectorI9VarHandleEE10FnPropertyiPKc">
-<span id="mxnet::Engine::PushAsync__AsyncFn.Context.std::vector:VarHandle:CR.std::vector:VarHandle:CR.FnProperty.i.cCP"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ac71feb4f966cd4573452bd148b850c82"></span><em class="property">virtual</em> void <code class="descclassname">mxnet::Engine::</code><code class="descname">PushAsync</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine7AsyncFnE" title="mxnet::Engine::AsyncFn">AsyncFn</a> [...]
+<span id="mxnet::Engine::PushAsync__AsyncFn.Context.std::vector:VarHandle:CR.std::vector:VarHandle:CR.FnProperty.i.cCP"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ac71feb4f966cd4573452bd148b850c82"></span><em class="property">virtual</em> void <code class="descname">PushAsync</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine7AsyncFnE" title="mxnet::Engine::AsyncFn">AsyncFn</a> <em>exec_fun</em>, Context <em>exec_ctx</em>, std: [...]
<dd><p>Push an asynchronous operation to the engine. </p>
<p><dl class="docutils">
<dt><strong>Parameters</strong></dt>
@@ -518,7 +516,7 @@ The delete will not happen immediately, but will wait until all the operations u
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine14DeleteVariableE6SyncFn7Context9VarHandle">
-<span id="mxnet::Engine::DeleteVariable__SyncFn.Context.VarHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a738e5192dab345ab0ec9888b095903cf"></span><em class="property">virtual</em> void <code class="descclassname">mxnet::Engine::</code><code class="descname">DeleteVariable</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine6SyncFnE" title="mxnet::Engine::SyncFn">SyncFn</a> <em>delete_fn</em>, Context <em>exec_ctx</em>, <a c [...]
+<span id="mxnet::Engine::DeleteVariable__SyncFn.Context.VarHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a738e5192dab345ab0ec9888b095903cf"></span><em class="property">virtual</em> void <code class="descname">DeleteVariable</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine6SyncFnE" title="mxnet::Engine::SyncFn">SyncFn</a> <em>delete_fn</em>, Context <em>exec_ctx</em>, <a class="reference internal" href="#_CPPv2N5mxnet6Eng [...]
<dd><p>Schedule the deletion of a variable. </p>
<p>The delete will not happen immediately, but will wait until all the operations depending on var are completed.</p>
<p><dl class="docutils">
@@ -534,7 +532,7 @@ The delete will not happen immediately, but will wait until all the operations u
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine10WaitForVarE9VarHandle">
-<span id="mxnet::Engine::WaitForVar__VarHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1aed51bd7f294d9f2b569764a0c151d883"></span><em class="property">virtual</em> void <code class="descclassname">mxnet::Engine::</code><code class="descname">WaitForVar</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine9VarHandleE" title="mxnet::Engine::VarHandle">VarHandle</a> <em>var</em><span class="sig-paren">)</span><a class="headerlink" [...]
+<span id="mxnet::Engine::WaitForVar__VarHandle"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1aed51bd7f294d9f2b569764a0c151d883"></span><em class="property">virtual</em> void <code class="descname">WaitForVar</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine9VarHandleE" title="mxnet::Engine::VarHandle">VarHandle</a> <em>var</em><span class="sig-paren">)</span> = 0<a class="headerlink" href="#_CPPv2N5mxnet6Engine10WaitForVarE9VarH [...]
<dd><p>Wait for a variable. </p>
<p><dl class="docutils">
<dt><strong>Parameters</strong></dt>
@@ -547,17 +545,17 @@ The delete will not happen immediately, but will wait until all the operations u
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine10WaitForAllEv">
-<span id="mxnet::Engine::WaitForAll"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a64483aecce780e96056be89d6289e782"></span><em class="property">virtual</em> void <code class="descclassname">mxnet::Engine::</code><code class="descname">WaitForAll</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv2N5mxnet6Engine10WaitForAllEv" title="Permalink to this definition">¶</a><br/> = 0</dt>
+<span id="mxnet::Engine::WaitForAll"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a64483aecce780e96056be89d6289e782"></span><em class="property">virtual</em> void <code class="descname">WaitForAll</code><span class="sig-paren">(</span><span class="sig-paren">)</span> = 0<a class="headerlink" href="#_CPPv2N5mxnet6Engine10WaitForAllEv" title="Permalink to this definition">¶</a></dt>
<dd><p>Wait until all the activity of engine finishes. </p>
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6EngineD0Ev">
-<span id="mxnet::Engine::~Engine"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1aff025321827e15096c02342225f2395b"></span><em class="property">virtual</em> <code class="descclassname">mxnet::Engine::</code><code class="descname">~Engine</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv2N5mxnet6EngineD0Ev" title="Permalink to this definition">¶</a><br/></dt>
+<span id="mxnet::Engine::~Engine"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1aff025321827e15096c02342225f2395b"></span><em class="property">virtual</em> <code class="descname">~Engine</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv2N5mxnet6EngineD0Ev" title="Permalink to this definition">¶</a></dt>
<dd><p>virtual destructor </p>
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine8PushSyncE6SyncFn7ContextRNSt6vectorI9VarHandleEERNSt6vectorI9VarHandleEE10FnPropertyiPKc">
-<span id="mxnet::Engine::PushSync__SyncFn.Context.std::vector:VarHandle:CR.std::vector:VarHandle:CR.FnProperty.i.cCP"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a1c2f38927e4bf7a62e23353b0bd3d619"></span>void <code class="descclassname">mxnet::Engine::</code><code class="descname">PushSync</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine6SyncFnE" title="mxnet::Engine::SyncFn">SyncFn</a> <em>exec_fn</em>, Context <em>exec_ctx</ [...]
+<span id="mxnet::Engine::PushSync__SyncFn.Context.std::vector:VarHandle:CR.std::vector:VarHandle:CR.FnProperty.i.cCP"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1a1c2f38927e4bf7a62e23353b0bd3d619"></span>void <code class="descname">PushSync</code><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6Engine6SyncFnE" title="mxnet::Engine::SyncFn">SyncFn</a> <em>exec_fn</em>, Context <em>exec_ctx</em>, std::vector<<a class="reference internal" hre [...]
<dd><p>Push an synchronous operation to the engine. </p>
<p><dl class="docutils">
<dt><strong>Parameters</strong></dt>
@@ -581,7 +579,7 @@ The delete will not happen immediately, but will wait until all the operations u
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine14CreateCallbackEPFvP6EnginePvEPv">
-<span class="target" id="mxnetclassmxnet_1_1Engine_1acf25be815b4200b48ee6e4e169bf95b8"></span><a class="reference internal" href="#_CPPv2N5mxnet6Engine18CallbackOnCompleteE" title="mxnet::Engine::CallbackOnComplete">CallbackOnComplete</a> <code class="descclassname">mxnet::Engine::</code><code class="descname">CreateCallback</code><span class="sig-paren">(</span>void (*<em>callback</em>)<span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6EngineE" title="mxn [...]
+<span class="target" id="mxnetclassmxnet_1_1Engine_1acf25be815b4200b48ee6e4e169bf95b8"></span><a class="reference internal" href="#_CPPv2N5mxnet6Engine18CallbackOnCompleteE" title="mxnet::Engine::CallbackOnComplete">CallbackOnComplete</a> <code class="descname">CreateCallback</code><span class="sig-paren">(</span>void (*<em>callback</em>)<span class="sig-paren">(</span><a class="reference internal" href="#_CPPv2N5mxnet6EngineE" title="mxnet::Engine">Engine</a> *, void *<span class="sig-p [...]
<dd><p>factory function to create OnComplete callback. </p>
<p><dl class="docutils">
<dt><strong>Parameters</strong></dt>
@@ -598,20 +596,20 @@ The delete will not happen immediately, but will wait until all the operations u
<p class="breathe-sectiondef-title rubric">Public Static Functions</p>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine3GetEv">
-<span id="mxnet::Engine::Get"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ae0a23da15ef63d9479c7468e1f2f825f"></span><em class="property">static</em> <a class="reference internal" href="#_CPPv2N5mxnet6EngineE" title="mxnet::Engine">Engine</a> *<code class="descclassname">mxnet::Engine::</code><code class="descname">Get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv2N5mxnet6Engine3GetEv" title="Permalink to this defin [...]
+<span id="mxnet::Engine::Get"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ae0a23da15ef63d9479c7468e1f2f825f"></span><em class="property">static</em> <a class="reference internal" href="#_CPPv2N5mxnet6EngineE" title="mxnet::Engine">Engine</a> *<code class="descname">Get</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv2N5mxnet6Engine3GetEv" title="Permalink to this definition">¶</a></dt>
<dd><p><dl class="docutils">
<dt><strong>Return</strong></dt>
-<dd><a class="reference internal" href="#mxnetclassmxnet_1_1Engine"><span class="std std-ref">Engine</span></a> singleton. </dd>
+<dd><a class="reference internal" href="#mxnetclassmxnet_1_1Engine"><span>Engine</span></a> singleton. </dd>
</dl>
</p>
</dd></dl>
<dl class="function">
<dt id="_CPPv2N5mxnet6Engine13_GetSharedRefEv">
-<span id="mxnet::Engine::_GetSharedRef"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ab6417f2ae519b946c104f975d84d55d5"></span><em class="property">static</em> std::shared_ptr<<a class="reference internal" href="#_CPPv2N5mxnet6EngineE" title="mxnet::Engine">Engine</a>> <code class="descclassname">mxnet::Engine::</code><code class="descname">_GetSharedRef</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv2N5mxnet6Engine13 [...]
+<span id="mxnet::Engine::_GetSharedRef"></span><span class="target" id="mxnetclassmxnet_1_1Engine_1ab6417f2ae519b946c104f975d84d55d5"></span><em class="property">static</em> std::shared_ptr<<a class="reference internal" href="#_CPPv2N5mxnet6EngineE" title="mxnet::Engine">Engine</a>> <code class="descname">_GetSharedRef</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv2N5mxnet6Engine13_GetSharedRefEv" title="Permalink to this definiti [...]
<dd><p>Get shared pointer reference to engine singleton. Most user should not call this function. This function is called by another singleton X who requires engine to be destructed after X. </p>
<p><dl class="docutils">
<dt><strong>Return</strong></dt>
-<dd>A shared pointer to <a class="reference internal" href="#mxnetclassmxnet_1_1Engine"><span class="std std-ref">Engine</span></a> singleton. </dd>
+<dd>A shared pointer to <a class="reference internal" href="#mxnetclassmxnet_1_1Engine"><span>Engine</span></a> singleton. </dd>
</dl>
</p>
</dd></dl>
@@ -746,7 +744,7 @@ MXNet defines two interfaces to achieve this:</li>
<p>The <code class="docutils literal"><span class="pre">ResourceRequest</span></code> structure (in <code class="docutils literal"><span class="pre">resource.h</span></code>) currently contains only a type flag:</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span> <span class="k">struct</span> <span class="n">ResourceRequest</span> <span class="p">{</span>
<span class="k">enum</span> <span class="n">Type</span> <span class="p">{</span>
- <span class="n">kRandom</span><span class="p">,</span> <span class="c1">// get a mshadow::Random<xpu> object</xpu></span>
+ <span class="n">kRandom</span><span class="p">,</span> <span class="c1">// get a mshadow::Random<xpu> object</span>
<span class="n">kTempSpace</span><span class="p">,</span> <span class="c1">// request temporary space</span>
<span class="p">};</span>
<span class="n">Type</span> <span class="n">type</span><span class="p">;</span>
@@ -881,7 +879,7 @@ the stride size, padding size, and so on.
These parameters should be passed to the operator
before any <code class="docutils literal"><span class="pre">Forward</span></code> or <code class="docutils literal"><span class="pre">Backward</span></code> interface is called.
To do so, you could define a <code class="docutils literal"><span class="pre">ConvolutionParam</span></code> structure, as follows:</p>
-<div class="highlight-c++"><div class="highlight"><pre><span></span> <span class="cp">#include</span> <span class="cpf"><dmlc parameter.h=""></dmlc></span><span class="cp"></span>
+<div class="highlight-c++"><div class="highlight"><pre><span></span> <span class="cp">#include</span> <span class="cpf"><dmlc/parameter.h></span><span class="cp"></span>
<span class="k">struct</span> <span class="nl">ConvolutionParam</span> <span class="p">:</span> <span class="k">public</span> <span class="n">dmlc</span><span class="o">::</span><span class="n">Parameter</span><span class="o"><</span><span class="n">ConvolutionParam</span><span class="o">></span> <span class="p">{</span>
<span class="n">TShape</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">pad</span><span class="p">;</span>
<span class="kt">uint32_t</span> <span class="n">num_filter</span><span class="p">,</span> <span class="n">num_group</span><span class="p">,</span> <span class="n">workspace</span><span class="p">;</span>
@@ -972,8 +970,8 @@ because calculations will be done in the <code class="docutils literal"><span c
<p>In the following example, we’ll create an operator
functioning as a smooth l1 loss,
which is a mixture of l1 loss and l2 loss. The loss itself can be written as:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">loss</span> <span class="o">=</span> <span class="n">outside_weight</span> <span class="o">.*</span> <span class="n">f</span><span class="p">(</span><span class="n">inside_weight</span> <span class="o">.*</span> <span class="p">(</span><span class="n">data</span> <span class="o">-</span> <span class="n">label</span><span class="p">))</span>
- <span class="n">grad</span> <span class="o">=</span> <span class="n">outside_weight</span> <span class="o">.*</span> <span class="n">inside_weight</span> <span class="o">.*</span> <span class="n">f</span><span class="s1">'(inside_weight .* (data - label))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> loss = outside_weight .* f(inside_weight .* (data - label))
+ grad = outside_weight .* inside_weight .* f'(inside_weight .* (data - label))
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">.*</span></code> stands for element-wise multiplication, and <code class="docutils literal"><span class="pre">f</span></code>, <code class="docutils literal"><span class="pre">f'</span></code> is the smooth l1 loss function,
@@ -1009,7 +1007,7 @@ Refer to the additional usages of <code class="docutils literal"><span class="pr
<p>Before we start on our smooth l1 loss example, we define a <code class="docutils literal"><span class="pre">XPU</span></code> to <code class="docutils literal"><span class="pre">cpu</span></code> or <code class="docutils literal"><span class="pre">gpu</span></code> in the header
<code class="docutils literal"><span class="pre">smooth_l1_unary-inl.h</span></code> implementation so that we reuse the same code in <code class="docutils literal"><span class="pre">smooth_l1_unary.cc</span></code> and
<code class="docutils literal"><span class="pre">smooth_l1_unary.cu</span></code>.</p>
-<div class="highlight-cpp"><div class="highlight"><pre><span></span> <span class="cp">#include</span> <span class="cpf"><mxnet operator_util.h=""></mxnet></span><span class="cp"></span>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span> <span class="cp">#include</span> <span class="cpf"><mxnet/operator_util.h></span><span class="cp"></span>
<span class="cp">#if defined(__CUDACC__)</span>
<span class="cp">#define XPU gpu</span>
<span class="cp">#else</span>
@@ -1046,12 +1044,12 @@ Written explicitly, it is:</p>
<li><code class="docutils literal"><span class="pre">RunContext</span> <span class="pre">ctx</span></code> contains information needed during runtime for execution.</li>
</ul>
<div class="highlight-cpp"><div class="highlight"><pre><span></span> <span class="k">struct</span> <span class="n">RunContext</span> <span class="p">{</span>
- <span class="kt">void</span> <span class="o">*</span><span class="n">stream</span><span class="p">;</span> <span class="c1">// the stream of the device, can be NULL or Stream<gpu>* in GPU mode</gpu></span>
+ <span class="kt">void</span> <span class="o">*</span><span class="n">stream</span><span class="p">;</span> <span class="c1">// the stream of the device, can be NULL or Stream<gpu>* in GPU mode</span>
<span class="k">template</span><span class="o"><</span><span class="k">typename</span> <span class="n">xpu</span><span class="o">></span> <span class="kr">inline</span> <span class="n">mshadow</span><span class="o">::</span><span class="n">Stream</span><span class="o"><</span><span class="n">xpu</span><span class="o">>*</span> <span class="n">get_stream</span><span class="p">()</span> <span class="c1">// get mshadow stream from Context</span>
<span class="p">}</span> <span class="c1">// namespace mxnet</span>
</pre></div>
</div>
-<p><code class="docutils literal"><span class="pre">mshadow::stream<xpu></xpu></span> <span class="pre">*s</span> <span class="pre">=</span> <span class="pre">ctx.get_stream<xpu>();</xpu></span></code> is an example of obtaining a stream from <code class="docutils literal"><span class="pre">ctx</span></code>.</p>
+<p><code class="docutils literal"><span class="pre">mshadow::stream<xpu></span> <span class="pre">*s</span> <span class="pre">=</span> <span class="pre">ctx.get_stream<xpu>();</span></code> is an example of obtaining a stream from <code class="docutils literal"><span class="pre">ctx</span></code>.</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">OpReqType</span> <span class="pre">req</span></code> denotes how computation results are written into <code class="docutils literal"><span class="pre">ret</span></code>.</li>
</ul>
@@ -1214,14 +1212,14 @@ can’t be present at the same time. To enable <code class="docutils literal"><s
<code class="docutils literal"><span class="pre">set_enable_scalar(bool</span> <span class="pre">enable_scalar)</span></code> in registration. Then, in forward functions and gradients, the <code class="docutils literal"><span class="pre">scalar</span></code> can be accessed from <code class="docutils literal"><span class="pre">env.scalar</span></code> as in the function parameter <code class="docutils literal"><span class="pre">EnvArguments</span> <span class="pre">env</span></code>.</p>
<p>To enable <code class="docutils literal"><span class="pre">kwargs</span></code>, use <code class="docutils literal"><span class="pre">set_enable_kwargs(bool</span> <span class="pre">enable_kwargs)</span></code> in registration. Then, in forward
functions and gradients, additional arguments are contained in <code class="docutils literal"><span class="pre">env.kwarg</span></code>, which is defined as
-<code class="docutils literal"><span class="pre">std::vector<std::pair<std::string,< span=""> <span class="pre">std::string></span> <span class="pre">></span></std::pair<std::string,<></span></code>. Use the DMLC parameter structure to
+<code class="docutils literal"><span class="pre">std::vector<std::pair<std::string,</span> <span class="pre">std::string></span> <span class="pre">></span></code>. Use the DMLC parameter structure to
simplify parsing keyword arguments. For more details, see the <a class="reference external" href="https://github.com/dmlc/dmlc-core/blob/master/doc/parameter.md">guide on parameter structure</a>.</p>
-<p>Additional resources like <code class="docutils literal"><span class="pre">mshadow::Random<xpu></xpu></span></code> and temporary memory space can also be requested and
+<p>Additional resources like <code class="docutils literal"><span class="pre">mshadow::Random<xpu></span></code> and temporary memory space can also be requested and
accessed from <code class="docutils literal"><span class="pre">EnvArguments.resource</span></code>. The registration routine is <code class="docutils literal"><span class="pre">set_resource_request(ResourceRequest</span> <span class="pre">req)</span></code>
-or <code class="docutils literal"><span class="pre">set_resource_request(const</span> <span class="pre">std::vector<resourcerequest>)</resourcerequest></span></code>, where <code class="docutils literal"><span class="pre">mxnet::ResourceRequest</span></code> is defined as:</p>
+or <code class="docutils literal"><span class="pre">set_resource_request(const</span> <span class="pre">std::vector<ResourceRequest>)</span></code>, where <code class="docutils literal"><span class="pre">mxnet::ResourceRequest</span></code> is defined as:</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span> <span class="k">struct</span> <span class="n">ResourceRequest</span> <span class="p">{</span>
<span class="k">enum</span> <span class="n">Type</span> <span class="p">{</span> <span class="c1">// Resource type, indicating what the pointer type is</span>
- <span class="n">kRandom</span><span class="p">,</span> <span class="c1">// mshadow::Random<xpu> object</xpu></span>
+ <span class="n">kRandom</span><span class="p">,</span> <span class="c1">// mshadow::Random<xpu> object</span>
<span class="n">kTempSpace</span> <span class="c1">// A dynamic temp space that can be arbitrary size</span>
<span class="p">};</span>
<span class="n">Type</span> <span class="n">type</span><span class="p">;</span> <span class="c1">// type of resources</span>
@@ -1229,7 +1227,7 @@ or <code class="docutils literal"><span class="pre">set_resource_request(const</
</pre></div>
</div>
<p>Registration will request the declared resource requests from <code class="docutils literal"><span class="pre">mxnet::ResourceManager</span></code>, and place resources
-in <code class="docutils literal"><span class="pre">std::vector<resource></resource></span> <span class="pre">resource</span></code> in <code class="docutils literal"><span class="pre">EnvArguments</span></code>. To access resources, use the following:</p>
+in <code class="docutils literal"><span class="pre">std::vector<Resource></span> <span class="pre">resource</span></code> in <code class="docutils literal"><span class="pre">EnvArguments</span></code>. To access resources, use the following:</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span> <span class="k">auto</span> <span class="n">tmp_space_res</span> <span class="o">=</span> <span class="n">env</span><span class="p">.</span><span class="n">resources</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">get_space</span><span class="p">(</span><span class="n">some_shape</span><span class="p">,</span> <span class="n">some_stream</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">rand_res</span> <span class="o">=</span> <span class="n">env</span><span class="p">.</span><span class="n">resources</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">get_random</span><span class="p">(</span><span class="n">some_stream</span><span class="p">);</span>
</pre></div>
diff --git a/versions/master/architecture/program_model.html b/versions/master/architecture/program_model.html
index 2621655..1939138 100644
--- a/versions/master/architecture/program_model.html
+++ b/versions/master/architecture/program_model.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Deep Learning Programming Style — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -762,7 +760,7 @@ to create more interesting and intelligent deep learning libraries.</p>
</div>
<div class="section" id="contribute-to-mxnet">
<span id="contribute-to-mxnet"></span><h2>Contribute to MXNet<a class="headerlink" href="#contribute-to-mxnet" title="Permalink to this headline">¶</a></h2>
-<p>This document is part of our effort to provide <a class="reference internal" href="index.html"><span class="doc">open-source system design notes</span></a>
+<p>This document is part of our effort to provide <a class="reference internal" href="index.html"><em>open-source system design notes</em></a>
for deep learning libraries. If you’re interested in contributing to <em>MXNet</em> or its
documentation, <a class="reference external" href="http://github.com/dmlc/mxnet">fork us on GitHub</a>.</p>
</div>
diff --git a/versions/master/architecture/release_note_0_9.html b/versions/master/architecture/release_note_0_9.html
index 87be3fa..b24cea3 100644
--- a/versions/master/architecture/release_note_0_9.html
+++ b/versions/master/architecture/release_note_0_9.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet 0.9 (NNVM) Release Note — mxnet documentation</title>
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-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
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+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
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<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
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</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -239,7 +237,7 @@ Previous Navbar Layout End -->
<p>Version 0.9 brings a number of important features and changes, including a back-end refactor to adopt the <a class="reference external" href="https://github.com/dmlc/nnvm">NNVM</a> framework, a profiler for analyzing performance, a fast image IO and augmentation module that bypasses GIL, and various other changes.</p>
<div class="section" id="nnvm-refactor">
<span id="nnvm-refactor"></span><h2>NNVM Refactor<a class="headerlink" href="#nnvm-refactor" title="Permalink to this headline">¶</a></h2>
-<p>NNVM is a library for neural network graph construction, optimization, and operator registration. It serves as an intermediary layer between the front-end (MXNet user API) and the back-end (computation on the device). After version 0.9, MXNet fully adopts the NNVM framework. Now it’s easier to create operators. You can also register “pass”es that process and optimizes the graph when <code class="docutils literal"><span class="pre">bind</span></code> is called on the symbol. For more d [...]
+<p>NNVM is a library for neural network graph construction, optimization, and operator registration. It serves as an intermediary layer between the front-end (MXNet user API) and the back-end (computation on the device). After version 0.9, MXNet fully adopts the NNVM framework. Now it’s easier to create operators. You can also register “pass”es that process and optimizes the graph when <code class="docutils literal"><span class="pre">bind</span></code> is called on the symbol. For more d [...]
<p>Other changes brought by NNVM include:</p>
<ul class="simple">
<li>Backward shape inference is now supported</li>
diff --git a/versions/master/architecture/rnn_interface.html b/versions/master/architecture/rnn_interface.html
index 18eb526..95bf05c 100644
--- a/versions/master/architecture/rnn_interface.html
+++ b/versions/master/architecture/rnn_interface.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
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-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Survey of Existing Interfaces and Implementations — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/community/contribute.html b/versions/master/community/contribute.html
index a623e62..0eeeb0b 100644
--- a/versions/master/community/contribute.html
+++ b/versions/master/community/contribute.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Contribute to MXNet — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/community/index.html b/versions/master/community/index.html
index 4eb35d3..cd96b9c 100644
--- a/versions/master/community/index.html
+++ b/versions/master/community/index.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet Community — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/community/mxnet_channels.html b/versions/master/community/mxnet_channels.html
index bf7da26..0db1a94 100644
--- a/versions/master/community/mxnet_channels.html
+++ b/versions/master/community/mxnet_channels.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Join MXNet Development Discussion — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/doxygen/annotated.html b/versions/master/doxygen/annotated.html
index 3f2616f..366a5d9 100644
--- a/versions/master/doxygen/annotated.html
+++ b/versions/master/doxygen/annotated.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,49 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -64,64 +88,65 @@ $(function() {
<div class="contents">
<div class="textblock">Here are the classes, structs, unions and interfaces with brief descriptions:</div><div class="directory">
<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span><span onclick="javascript:toggleLevel(4);">4</span>]</div><table class="directory">
-<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacedmlc.html" target="_self">dmlc</a></td><td class="desc"></td></tr>
-<tr id="row_0_0_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_0_0_" class="arrow" onclick="toggleFolder('0_0_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacedmlc_1_1parameter.html" target="_self">parameter</a></td><td class="desc"></td></tr>
-<tr id="row_0_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html" target="_self">FieldEntry< mxnet::TShape ></a></td><td class="desc"></td></tr>
-<tr id="row_1_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_1_" class="arrow" onclick="toggleFolder('1_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet.html" target="_self">mxnet</a></td><td class="desc">Namespace of mxnet </td></tr>
-<tr id="row_1_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_0_" class="arrow" onclick="toggleFolder('1_0_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet_1_1autograd.html" target="_self">autograd</a></td><td class="desc"></td></tr>
-<tr id="row_1_0_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html" target="_self">AGNodeEntry</a></td><td class="desc"></td></tr>
-<tr id="row_1_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_1_" class="arrow" onclick="toggleFolder('1_1_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet_1_1common.html" target="_self">common</a></td><td class="desc"></td></tr>
-<tr id="row_1_1_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_1_1_0_" class="arrow" onclick="toggleFolder('1_1_0_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet_1_1common_1_1helper.html" target="_self">helper</a></td><td class="desc">Helper functions </td></tr>
-<tr id="row_1_1_0_0_" class="even"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html" target="_self">UniqueIf</a></td><td class="desc">Helper for non-array type <code>T</code> </td></tr>
-<tr id="row_1_1_0_1_"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html" target="_self">UniqueIf< T[]></a></td><td class="desc">Helper for an array of unknown bound <code>T</code> </td></tr>
-<tr id="row_1_1_0_2_" class="even"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html" target="_self">UniqueIf< T[kSize]></a></td><td class="desc">Helper for an array of known bound <code>T</code> </td></tr>
-<tr id="row_1_1_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html" target="_self">LazyAllocArray</a></td><td class="desc"></td></tr>
-<tr id="row_1_1_2_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html" target="_self">ObjectPool</a></td><td class="desc">Object pool for fast allocation and deallocation </td></tr>
-<tr id="row_1_1_3_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html" target="_self">ObjectPoolAllocatable</a></td><td class="desc">Helper trait class for easy allocation and deallocation </td></tr>
-<tr id="row_1_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_2_" class="arrow" onclick="toggleFolder('1_2_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet_1_1engine.html" target="_self">engine</a></td><td class="desc">Namespace of engine internal types </td></tr>
-<tr id="row_1_2_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html" target="_self">CallbackOnComplete</a></td><td class="desc">OnComplete Callback to the engine, called by AsyncFn when action completes </td></tr>
-<tr id="row_1_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_3_" class="arrow" onclick="toggleFolder('1_3_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet_1_1op.html" target="_self">op</a></td><td class="desc">Namespace of arguments </td></tr>
-<tr id="row_1_3_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html" target="_self">EnvArguments</a></td><td class="desc">Environment arguments that is used by the function. These can be things like scalar arguments when add a value with scalar </td></tr>
-<tr id="row_1_3_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html" target="_self">GradFunctionArgument</a></td><td class="desc">Super class of all gradient function argument </td></tr>
-<tr id="row_1_3_2_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1Input0.html" target="_self">Input0</a></td><td class="desc">First input to the function </td></tr>
-<tr id="row_1_3_3_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1Input1.html" target="_self">Input1</a></td><td class="desc">Second input to the function </td></tr>
-<tr id="row_1_3_4_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1OutputGrad.html" target="_self">OutputGrad</a></td><td class="desc">Gradient of output value </td></tr>
-<tr id="row_1_3_5_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1OutputValue.html" target="_self">OutputValue</a></td><td class="desc">Ouput value of the function to the function </td></tr>
-<tr id="row_1_3_6_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html" target="_self">SimpleOpRegEntry</a></td><td class="desc">Registry entry to register simple operators via functions </td></tr>
-<tr id="row_1_3_7_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html" target="_self">SimpleOpRegistry</a></td><td class="desc">Registry for <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> functions </td></tr>
-<tr id="row_1_4_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1Context.html" target="_self">Context</a></td><td class="desc"><a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> information about the execution environment </td></tr>
-<tr id="row_1_5_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1DataBatch.html" target="_self">DataBatch</a></td><td class="desc"><a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, returned by Iterato [...]
-<tr id="row_1_6_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1DataInst.html" target="_self">DataInst</a></td><td class="desc">Single data instance </td></tr>
-<tr id="row_1_7_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1DataIteratorReg.html" target="_self">DataIteratorReg</a></td><td class="desc">Registry entry for DataIterator factory functions </td></tr>
-<tr id="row_1_8_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1Engine.html" target="_self">Engine</a></td><td class="desc">Dependency engine that schedules operations </td></tr>
-<tr id="row_1_9_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1Executor.html" target="_self">Executor</a></td><td class="desc"><a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> of a computation graph. <a class="el" href="classmxnet_1_1Executor.html" title="Ex [...]
-<tr id="row_1_10_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1IIterator.html" target="_self">IIterator</a></td><td class="desc">Iterator type </td></tr>
-<tr id="row_1_11_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1KVStore.html" target="_self">KVStore</a></td><td class="desc">Distributed key-value store </td></tr>
-<tr id="row_1_12_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1NDArray.html" target="_self">NDArray</a></td><td class="desc">Ndarray interface </td></tr>
-<tr id="row_1_13_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html" target="_self">NDArrayFunctionReg</a></td><td class="desc">Registry entry for NDArrayFunction </td></tr>
-<tr id="row_1_14_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1OpContext.html" target="_self">OpContext</a></td><td class="desc">All the possible information needed by <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd" title="perform a forward operation of Operator, save the output to TBlob. ">Operator.Forward</a> and Backward This is [...]
-<tr id="row_1_15_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1Operator.html" target="_self">Operator</a></td><td class="desc"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> interface. <a class="el" href="classmxnet_1_1Operator.html" [...]
-<tr id="row_1_16_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1OperatorProperty.html" target="_self">OperatorProperty</a></td><td class="desc"><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> is a object that stores all informat [...]
-<tr id="row_1_17_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1OperatorPropertyReg.html" target="_self">OperatorPropertyReg</a></td><td class="desc">Registry entry for <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</ [...]
-<tr id="row_1_18_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1OpStatePtr.html" target="_self">OpStatePtr</a></td><td class="desc"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> state. This is a pointer type, its content is mutable even if <a cla [...]
-<tr id="row_1_19_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1Resource.html" target="_self">Resource</a></td><td class="desc">Resources used by mxnet operations. A resource is something special other than <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, but will still participate </td></tr>
-<tr id="row_1_20_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1ResourceManager.html" target="_self">ResourceManager</a></td><td class="desc">Global resource manager </td></tr>
-<tr id="row_1_21_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1ResourceRequest.html" target="_self">ResourceRequest</a></td><td class="desc">The resources that can be requested by <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> </td></tr>
-<tr id="row_1_22_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1RunContext.html" target="_self">RunContext</a></td><td class="desc">Execution time context. The information needed in runtime for actual execution </td></tr>
-<tr id="row_1_23_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_23_" class="arrow" onclick="toggleFolder('1_23_')">▼</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1Storage.html" target="_self">Storage</a></td><td class="desc"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> manager across multiple devices </td></tr>
-<tr id="row_1_23_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1Storage_1_1Handle.html" target="_self">Handle</a></td><td class="desc"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> handle </td></tr>
-<tr id="row_1_24_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1TBlob.html" target="_self">TBlob</a></td><td class="desc">Tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob c [...]
-<tr id="row_2_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structMXCallbackList.html" target="_self">MXCallbackList</a></td><td class="desc"></td></tr>
-<tr id="row_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structNativeOpInfo.html" target="_self">NativeOpInfo</a></td><td class="desc"></td></tr>
-<tr id="row_4_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structNDArrayOpInfo.html" target="_self">NDArrayOpInfo</a></td><td class="desc"></td></tr>
+<tr id="row_0_" class="even"><td class="entry"><img id="arr_0_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('0_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacedmlc.html" target="_self">dmlc</a></td><td class="desc"></td></tr>
+<tr id="row_0_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img id="arr_0_0_" src="ftv2mlastnode.png" alt="\" width="16" height="22" onclick="toggleFolder('0_0_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacedmlc_1_1parameter.html" target="_self">parameter</a></td><td class="desc"></td></tr>
+<tr id="row_0_0_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html" target="_self">FieldEntry< mxnet::TShape ></a></td><td class="desc"></td></tr>
+<tr id="row_1_"><td class="entry"><img id="arr_1_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('1_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet.html" target="_self">mxnet</a></td><td class="desc">Namespace of mxnet </td></tr>
+<tr id="row_1_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img id="arr_1_0_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('1_0_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1autograd.html" target="_self">autograd</a></td><td class="desc"></td></tr>
+<tr id="row_1_0_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html" target="_self">AGNodeEntry</a></td><td class="desc"></td></tr>
+<tr id="row_1_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img id="arr_1_1_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('1_1_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1common.html" target="_self">common</a></td><td class="desc"></td></tr>
+<tr id="row_1_1_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1common_1_1cuda.html" target="_self">cuda</a></td><td class="desc">Common utils for cuda </td></tr>
+<tr id="row_1_1_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img id="arr_1_1_1_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('1_1_1_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1common_1_1helper.html" target="_self">helper</a></td><td class="desc">Helper functions </td></tr>
+<tr id="row_1_1_1_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html" target="_self">UniqueIf</a></td><td class="desc">Helper for non-array type <code>T</cod [...]
+<tr id="row_1_1_1_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html" target="_self">UniqueIf< T[]></a></td><td class="desc">Hel [...]
+<tr id="row_1_1_1_2_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html" target="_self">UniqueIf< T[kSize]></a></td><td class="desc">He [...]
+<tr id="row_1_1_2_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html" target="_self">LazyAllocArray</a></td><td class="desc"></td></tr>
+<tr id="row_1_1_3_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html" target="_self">ObjectPool</a></td><td class="desc">Object pool for fast allocation and deallocation </td></tr>
+<tr id="row_1_1_4_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html" target="_self">ObjectPoolAllocatable</a></td><td class="desc">Helper trait class for easy allocation and deallocation </td></tr>
+<tr id="row_1_2_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img id="arr_1_2_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('1_2_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1engine.html" target="_self">engine</a></td><td class="desc">Namespace of engine internal types </td></tr>
+<tr id="row_1_2_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html" target="_self">CallbackOnComplete</a></td><td class="desc">OnComplete Callback to the engine, called by AsyncFn when action completes [...]
+<tr id="row_1_3_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img id="arr_1_3_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('1_3_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1op.html" target="_self">op</a></td><td class="desc">Namespace of arguments </td></tr>
+<tr id="row_1_3_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html" target="_self">GradFunctionArgument</a></td><td class="desc">Super class of all gradient function argument </td></tr>
+<tr id="row_1_3_1_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1Input0.html" target="_self">Input0</a></td><td class="desc">First input to the function </td></tr>
+<tr id="row_1_3_2_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1Input1.html" target="_self">Input1</a></td><td class="desc">Second input to the function </td></tr>
+<tr id="row_1_3_3_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1OutputValue.html" target="_self">OutputValue</a></td><td class="desc">Ouput value of the function to the function </td></tr>
+<tr id="row_1_3_4_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1OutputGrad.html" target="_self">OutputGrad</a></td><td class="desc">Gradient of output value </td></tr>
+<tr id="row_1_3_5_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html" target="_self">EnvArguments</a></td><td class="desc">Environment arguments that is used by the function. These can be things like scalar arguments when add a va [...]
+<tr id="row_1_3_6_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html" target="_self">SimpleOpRegEntry</a></td><td class="desc">Registry entry to register simple operators via functions </td></tr>
+<tr id="row_1_3_7_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html" target="_self">SimpleOpRegistry</a></td><td class="desc">Registry for <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be [...]
+<tr id="row_1_4_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1Context.html" target="_self">Context</a></td><td class="desc"><a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> information about the execution environment </td></tr>
+<tr id="row_1_5_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1RunContext.html" target="_self">RunContext</a></td><td class="desc">Execution time context. The information needed in runtime for actual execution </td></tr>
+<tr id="row_1_6_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1Engine.html" target="_self">Engine</a></td><td class="desc">Dependency engine that schedules operations </td></tr>
+<tr id="row_1_7_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1Executor.html" target="_self">Executor</a></td><td class="desc"><a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> of a computation graph. <a class="el [...]
+<tr id="row_1_8_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1IIterator.html" target="_self">IIterator</a></td><td class="desc">Iterator type </td></tr>
+<tr id="row_1_9_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1DataInst.html" target="_self">DataInst</a></td><td class="desc">Single data instance </td></tr>
+<tr id="row_1_10_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1DataBatch.html" target="_self">DataBatch</a></td><td class="desc"><a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> of <a class="el" href="classmxnet_1_1NDArray.html" [...]
+<tr id="row_1_11_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1DataIteratorReg.html" target="_self">DataIteratorReg</a></td><td class="desc">Registry entry for DataIterator factory functions </td></tr>
+<tr id="row_1_12_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1KVStore.html" target="_self">KVStore</a></td><td class="desc">Distributed key-value store </td></tr>
+<tr id="row_1_13_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1NDArray.html" target="_self">NDArray</a></td><td class="desc">Ndarray interface </td></tr>
+<tr id="row_1_14_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html" target="_self">NDArrayFunctionReg</a></td><td class="desc">Registry entry for NDArrayFunction </td></tr>
+<tr id="row_1_15_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1OpContext.html" target="_self">OpContext</a></td><td class="desc">All the possible information needed by <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd" title="perform a forward operation of Operator, save the ou [...]
+<tr id="row_1_16_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1OpStatePtr.html" target="_self">OpStatePtr</a></td><td class="desc"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator< [...]
+<tr id="row_1_17_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1Operator.html" target="_self">Operator</a></td><td class="desc"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> interface. <a [...]
+<tr id="row_1_18_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1OperatorProperty.html" target="_self">OperatorProperty</a></td><td class="desc"><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains m [...]
+<tr id="row_1_19_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1OperatorPropertyReg.html" target="_self">OperatorPropertyReg</a></td><td class="desc">Registry entry for <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It al [...]
+<tr id="row_1_20_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1ResourceRequest.html" target="_self">ResourceRequest</a></td><td class="desc">The resources that can be requested by <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of [...]
+<tr id="row_1_21_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1Resource.html" target="_self">Resource</a></td><td class="desc">Resources used by mxnet operations. A resource is something special other than <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, but will still p [...]
+<tr id="row_1_22_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1ResourceManager.html" target="_self">ResourceManager</a></td><td class="desc">Global resource manager </td></tr>
+<tr id="row_1_23_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img id="arr_1_23_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('1_23_')"/><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1Storage.html" target="_self">Storage</a></td><td class="desc"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> manager across multiple devic [...]
+<tr id="row_1_23_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1Storage_1_1Handle.html" target="_self">Handle</a></td><td class="desc"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. [...]
+<tr id="row_1_24_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1TBlob.html" target="_self">TBlob</a></td><td class="desc">Tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface <a class="el" hr [...]
+<tr id="row_2_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structMXCallbackList.html" target="_self">MXCallbackList</a></td><td class="desc"></td></tr>
+<tr id="row_3_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structNativeOpInfo.html" target="_self">NativeOpInfo</a></td><td class="desc"></td></tr>
+<tr id="row_4_" class="even"><td class="entry"><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structNDArrayOpInfo.html" target="_self">NDArrayOpInfo</a></td><td class="desc"></td></tr>
</table>
</div><!-- directory -->
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/base_8h.html b/versions/master/doxygen/base_8h.html
index a05d40c..b851dff 100644
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@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/base.h File Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
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-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ </span>
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+ </li>
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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@@ -74,39 +96,28 @@ $(function() {
<p>configuation of mxnet as well as basic data structure.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/base.h></code><br />
-<code>#include <dmlc/io.h></code><br />
-<code>#include <dmlc/type_traits.h></code><br />
-<code>#include <dmlc/parameter.h></code><br />
-<code>#include <mshadow/tensor.h></code><br />
-<code>#include <nnvm/op.h></code><br />
-<code>#include <nnvm/tuple.h></code><br />
-<code>#include <nnvm/symbolic.h></code><br />
-<code>#include <string></code><br />
+<div class="textblock"><code>#include <dmlc/base.h></code><br/>
+<code>#include <dmlc/io.h></code><br/>
+<code>#include <dmlc/type_traits.h></code><br/>
+<code>#include <dmlc/parameter.h></code><br/>
+<code>#include <mshadow/tensor.h></code><br/>
+<code>#include <nnvm/op.h></code><br/>
+<code>#include <nnvm/tuple.h></code><br/>
+<code>#include <nnvm/symbolic.h></code><br/>
+<code>#include <string></code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for base.h:</div>
<div class="dyncontent">
<div class="center"><img src="base_8h__incl.png" border="0" usemap="#include_2mxnet_2base_8h" alt=""/></div>
+<map name="include_2mxnet_2base_8h" id="include_2mxnet_2base_8h">
+</map>
</div>
</div><div class="textblock"><div class="dynheader">
This graph shows which files directly or indirectly include this file:</div>
<div class="dyncontent">
<div class="center"><img src="base_8h__dep__incl.png" border="0" usemap="#include_2mxnet_2base_8hdep" alt=""/></div>
<map name="include_2mxnet_2base_8hdep" id="include_2mxnet_2base_8hdep">
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<p><a href="base_8h_source.html">Go to the source code of this file.</a></p>
@@ -114,85 +125,83 @@ This graph shows which files directly or indirectly include this file:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> information about the execution environment. <a href="structmxnet_1_1Context.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> information about the execution environment. <a href="structmxnet_1_1Context.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">execution time context. The information needed in runtime for actual execution. <a href="structmxnet_1_1RunContext.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">execution time context. The information needed in runtime for actual execution. <a href="structmxnet_1_1RunContext.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
+<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
+<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="define-members"></a>
Macros</h2></td></tr>
<tr class="memitem:ae475a2ee335d3e9dd2cb395e4c202226"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#ae475a2ee335d3e9dd2cb395e4c202226">MXNET_USE_OPENCV</a>   1</td></tr>
-<tr class="memdesc:ae475a2ee335d3e9dd2cb395e4c202226"><td class="mdescLeft"> </td><td class="mdescRight">whether to use opencv support <a href="#ae475a2ee335d3e9dd2cb395e4c202226">More...</a><br /></td></tr>
+<tr class="memdesc:ae475a2ee335d3e9dd2cb395e4c202226"><td class="mdescLeft"> </td><td class="mdescRight">whether to use opencv support <a href="#ae475a2ee335d3e9dd2cb395e4c202226">More...</a><br/></td></tr>
<tr class="separator:ae475a2ee335d3e9dd2cb395e4c202226"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a11c5ff746d300f8e69a6030ee9902347"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#a11c5ff746d300f8e69a6030ee9902347">MXNET_USE_CUDNN</a>   MSHADOW_USE_CUDNN</td></tr>
-<tr class="memdesc:a11c5ff746d300f8e69a6030ee9902347"><td class="mdescLeft"> </td><td class="mdescRight">whether to use cuda support <a href="#a11c5ff746d300f8e69a6030ee9902347">More...</a><br /></td></tr>
+<tr class="memdesc:a11c5ff746d300f8e69a6030ee9902347"><td class="mdescLeft"> </td><td class="mdescRight">whether to use cuda support <a href="#a11c5ff746d300f8e69a6030ee9902347">More...</a><br/></td></tr>
<tr class="separator:a11c5ff746d300f8e69a6030ee9902347"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8e6134b0829b92e4ba36e3ef1ac506ec"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#a8e6134b0829b92e4ba36e3ef1ac506ec">MXNET_GPU_NOT_ENABLED_ERROR</a>   "GPU is not enabled"</td></tr>
-<tr class="memdesc:a8e6134b0829b92e4ba36e3ef1ac506ec"><td class="mdescLeft"> </td><td class="mdescRight">Error message for using gpu when MXNET_USE_CUDA==0. <a href="#a8e6134b0829b92e4ba36e3ef1ac506ec">More...</a><br /></td></tr>
+<tr class="memdesc:a8e6134b0829b92e4ba36e3ef1ac506ec"><td class="mdescLeft"> </td><td class="mdescRight">Error message for using gpu when MXNET_USE_CUDA==0. <a href="#a8e6134b0829b92e4ba36e3ef1ac506ec">More...</a><br/></td></tr>
<tr class="separator:a8e6134b0829b92e4ba36e3ef1ac506ec"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a91a09948aaaffa1eb64508db79009a05"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#a91a09948aaaffa1eb64508db79009a05">MXNET_API</a></td></tr>
-<tr class="memdesc:a91a09948aaaffa1eb64508db79009a05"><td class="mdescLeft"> </td><td class="mdescRight">define compatible keywords in g++ Used to support g++-4.6 and g++4.7 <a href="#a91a09948aaaffa1eb64508db79009a05">More...</a><br /></td></tr>
+<tr class="memdesc:a91a09948aaaffa1eb64508db79009a05"><td class="mdescLeft"> </td><td class="mdescRight">define compatible keywords in g++ Used to support g++-4.6 and g++4.7 <a href="#a91a09948aaaffa1eb64508db79009a05">More...</a><br/></td></tr>
<tr class="separator:a91a09948aaaffa1eb64508db79009a05"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a966330f8fb9e6e7bd65ab3603beb49db"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#a966330f8fb9e6e7bd65ab3603beb49db">MXNET_PREDICT_ONLY</a>   0</td></tr>
-<tr class="memdesc:a966330f8fb9e6e7bd65ab3603beb49db"><td class="mdescLeft"> </td><td class="mdescRight">define prediction only <a href="#a966330f8fb9e6e7bd65ab3603beb49db">More...</a><br /></td></tr>
+<tr class="memdesc:a966330f8fb9e6e7bd65ab3603beb49db"><td class="mdescLeft"> </td><td class="mdescRight">define prediction only <a href="#a966330f8fb9e6e7bd65ab3603beb49db">More...</a><br/></td></tr>
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<tr class="memitem:aa14c0c7d2ea15e79c6edc864f5ecdc54"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#aa14c0c7d2ea15e79c6edc864f5ecdc54">PROFILER_MESSAGE</a>(msg)   nullptr</td></tr>
-<tr class="memdesc:aa14c0c7d2ea15e79c6edc864f5ecdc54"><td class="mdescLeft"> </td><td class="mdescRight">define operator message for profiler <a href="#aa14c0c7d2ea15e79c6edc864f5ecdc54">More...</a><br /></td></tr>
+<tr class="memdesc:aa14c0c7d2ea15e79c6edc864f5ecdc54"><td class="mdescLeft"> </td><td class="mdescRight">define operator message for profiler <a href="#aa14c0c7d2ea15e79c6edc864f5ecdc54">More...</a><br/></td></tr>
<tr class="separator:aa14c0c7d2ea15e79c6edc864f5ecdc54"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aee5302444d57230419d0cd77bf72770d"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#aee5302444d57230419d0cd77bf72770d">MXNET_MAJOR</a>   0</td></tr>
-<tr class="memdesc:aee5302444d57230419d0cd77bf72770d"><td class="mdescLeft"> </td><td class="mdescRight">major version <a href="#aee5302444d57230419d0cd77bf72770d">More...</a><br /></td></tr>
+<tr class="memdesc:aee5302444d57230419d0cd77bf72770d"><td class="mdescLeft"> </td><td class="mdescRight">major version <a href="#aee5302444d57230419d0cd77bf72770d">More...</a><br/></td></tr>
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<tr class="memitem:aea13dab0ddd61ec142feb0b6759c6acf"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#aea13dab0ddd61ec142feb0b6759c6acf">MXNET_MINOR</a>   10</td></tr>
-<tr class="memdesc:aea13dab0ddd61ec142feb0b6759c6acf"><td class="mdescLeft"> </td><td class="mdescRight">minor version <a href="#aea13dab0ddd61ec142feb0b6759c6acf">More...</a><br /></td></tr>
+<tr class="memdesc:aea13dab0ddd61ec142feb0b6759c6acf"><td class="mdescLeft"> </td><td class="mdescRight">minor version <a href="#aea13dab0ddd61ec142feb0b6759c6acf">More...</a><br/></td></tr>
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<tr class="memitem:a76b95738a0fa0478cd508f324d2ac49d"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#a76b95738a0fa0478cd508f324d2ac49d">MXNET_PATCH</a>   1</td></tr>
-<tr class="memdesc:a76b95738a0fa0478cd508f324d2ac49d"><td class="mdescLeft"> </td><td class="mdescRight">patch version <a href="#a76b95738a0fa0478cd508f324d2ac49d">More...</a><br /></td></tr>
+<tr class="memdesc:a76b95738a0fa0478cd508f324d2ac49d"><td class="mdescLeft"> </td><td class="mdescRight">patch version <a href="#a76b95738a0fa0478cd508f324d2ac49d">More...</a><br/></td></tr>
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<tr class="memitem:a18d0da52a992a3360eaa481b43094e80"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#a18d0da52a992a3360eaa481b43094e80">MXNET_VERSION</a>   (<a class="el" href="base_8h.html#aee5302444d57230419d0cd77bf72770d">MXNET_MAJOR</a>*10000 + <a class="el" href="base_8h.html#aea13dab0ddd61ec142feb0b6759c6acf">MXNET_MINOR</a>*100 + <a class="el" href="base_8h.html#a76b95738 [...]
-<tr class="memdesc:a18d0da52a992a3360eaa481b43094e80"><td class="mdescLeft"> </td><td class="mdescRight">mxnet version <a href="#a18d0da52a992a3360eaa481b43094e80">More...</a><br /></td></tr>
+<tr class="memdesc:a18d0da52a992a3360eaa481b43094e80"><td class="mdescLeft"> </td><td class="mdescRight">mxnet version <a href="#a18d0da52a992a3360eaa481b43094e80">More...</a><br/></td></tr>
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-<tr class="memitem:a4a85852d8252d23300a8364d48a1c8ea"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#a4a85852d8252d23300a8364d48a1c8ea">MXNET_MAKE_VERSION</a>(major, minor, patch)   ((major)*10000 + (minor)*100 + patch)</td></tr>
-<tr class="memdesc:a4a85852d8252d23300a8364d48a1c8ea"><td class="mdescLeft"> </td><td class="mdescRight">helper for making version number <a href="#a4a85852d8252d23300a8364d48a1c8ea">More...</a><br /></td></tr>
+<tr class="memitem:a4a85852d8252d23300a8364d48a1c8ea"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#a4a85852d8252d23300a8364d48a1c8ea">MXNET_MAKE_VERSION</a>(major, minor, patch)   ((major)*10000 + (minor)*100 + patch)</td></tr>
+<tr class="memdesc:a4a85852d8252d23300a8364d48a1c8ea"><td class="mdescLeft"> </td><td class="mdescRight">helper for making version number <a href="#a4a85852d8252d23300a8364d48a1c8ea">More...</a><br/></td></tr>
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<tr class="memitem:ae34be2bf889657a1452b87fd36f034c3"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html#ae34be2bf889657a1452b87fd36f034c3">PROFILER_MESSAGE_FUNCNAME</a>   <a class="el" href="base_8h.html#aa14c0c7d2ea15e79c6edc864f5ecdc54">PROFILER_MESSAGE</a>(__FUNCTION__)</td></tr>
-<tr class="memdesc:ae34be2bf889657a1452b87fd36f034c3"><td class="mdescLeft"> </td><td class="mdescRight">define function name as profiler message <a href="#ae34be2bf889657a1452b87fd36f034c3">More...</a><br /></td></tr>
+<tr class="memdesc:ae34be2bf889657a1452b87fd36f034c3"><td class="mdescLeft"> </td><td class="mdescRight">define function name as profiler message <a href="#ae34be2bf889657a1452b87fd36f034c3">More...</a><br/></td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
<tr class="memitem:a850af9d76d9d4beaeb5234c1434973e3"><td class="memItemLeft" align="right" valign="top">typedef mshadow::cpu </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">mxnet::cpu</a></td></tr>
-<tr class="memdesc:a850af9d76d9d4beaeb5234c1434973e3"><td class="mdescLeft"> </td><td class="mdescRight">mxnet cpu <a href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">More...</a><br /></td></tr>
+<tr class="memdesc:a850af9d76d9d4beaeb5234c1434973e3"><td class="mdescLeft"> </td><td class="mdescRight">mxnet cpu <a href="#a850af9d76d9d4beaeb5234c1434973e3">More...</a><br/></td></tr>
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<tr class="memitem:a31ef33571a4f6ba8847eeee1f4907141"><td class="memItemLeft" align="right" valign="top">typedef mshadow::gpu </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a31ef33571a4f6ba8847eeee1f4907141">mxnet::gpu</a></td></tr>
-<tr class="memdesc:a31ef33571a4f6ba8847eeee1f4907141"><td class="mdescLeft"> </td><td class="mdescRight">mxnet gpu <a href="namespacemxnet.html#a31ef33571a4f6ba8847eeee1f4907141">More...</a><br /></td></tr>
+<tr class="memdesc:a31ef33571a4f6ba8847eeee1f4907141"><td class="mdescLeft"> </td><td class="mdescRight">mxnet gpu <a href="#a31ef33571a4f6ba8847eeee1f4907141">More...</a><br/></td></tr>
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<tr class="memitem:abadedc1b42ee3e5b8852e63783b982ef"><td class="memItemLeft" align="right" valign="top">typedef mshadow::index_t </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">mxnet::index_t</a></td></tr>
-<tr class="memdesc:abadedc1b42ee3e5b8852e63783b982ef"><td class="mdescLeft"> </td><td class="mdescRight">index type usually use unsigned <a href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">More...</a><br /></td></tr>
+<tr class="memdesc:abadedc1b42ee3e5b8852e63783b982ef"><td class="mdescLeft"> </td><td class="mdescRight">index type usually use unsigned <a href="#abadedc1b42ee3e5b8852e63783b982ef">More...</a><br/></td></tr>
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<tr class="memitem:a4421326e33f2a848cb2fa3e70ab24ff4"><td class="memItemLeft" align="right" valign="top">typedef mshadow::default_real_t </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">mxnet::real_t</a></td></tr>
-<tr class="memdesc:a4421326e33f2a848cb2fa3e70ab24ff4"><td class="mdescLeft"> </td><td class="mdescRight">data type that will be used to store ndarray <a href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">More...</a><br /></td></tr>
+<tr class="memdesc:a4421326e33f2a848cb2fa3e70ab24ff4"><td class="mdescLeft"> </td><td class="mdescRight">data type that will be used to store ndarray <a href="#a4421326e33f2a848cb2fa3e70ab24ff4">More...</a><br/></td></tr>
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<tr class="memitem:a6e14bb5f6ffc81892feca8d1da658687"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">mxnet::TShape</a> = nnvm::TShape</td></tr>
-<tr class="memdesc:a6e14bb5f6ffc81892feca8d1da658687"><td class="mdescLeft"> </td><td class="mdescRight">Shape data structure used to record shape information. <a href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">More...</a><br /></td></tr>
+<tr class="memdesc:a6e14bb5f6ffc81892feca8d1da658687"><td class="mdescLeft"> </td><td class="mdescRight">Shape data structure used to record shape information. <a href="#a6e14bb5f6ffc81892feca8d1da658687">More...</a><br/></td></tr>
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<tr class="memitem:ac2fb286fd7ddfbc7af1456f6c7a7ee52"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52">mxnet::Op</a> = nnvm::Op</td></tr>
-<tr class="memdesc:ac2fb286fd7ddfbc7af1456f6c7a7ee52"><td class="mdescLeft"> </td><td class="mdescRight">operator structure from NNVM <a href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52">More...</a><br /></td></tr>
+<tr class="memdesc:ac2fb286fd7ddfbc7af1456f6c7a7ee52"><td class="mdescLeft"> </td><td class="mdescRight">operator structure from NNVM <a href="#ac2fb286fd7ddfbc7af1456f6c7a7ee52">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>configuation of mxnet as well as basic data structure. </p>
<p>Copyright (c) 2015 by Contributors </p>
</div><h2 class="groupheader">Macro Definition Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#aa14c0c7d2ea15e79c6edc864f5ecdc54">◆ </a></span>PROFILER_MESSAGE</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#ae34be2bf889657a1452b87fd36f034c3">◆ </a></span>PROFILER_MESSAGE_FUNCNAME</h2>
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@@ -411,9 +398,9 @@ Typedefs</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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+ <ul class="tablist">
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+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
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@@ -66,46 +88,260 @@ $(function() {
<div class="title">base.h</div> </div>
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<div class="contents">
-<a href="base_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_BASE_H_</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#define MXNET_BASE_H_</span></div><div class="line"><a name="l00008"></a><span [...]
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
-<div class="ttc" id="structmxnet_1_1Context_html_a8b8d414a429c12ab55cfa9ed13539393"><div class="ttname"><a href="structmxnet_1_1Context.html#a8b8d414a429c12ab55cfa9ed13539393">mxnet::Context::operator==</a></div><div class="ttdeci">bool operator==(const Context &b) const</div><div class="ttdoc">check if current context equals another one </div><div class="ttdef"><b>Definition:</b> base.h:148</div></div>
+<a href="base_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_BASE_H_</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_BASE_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <dmlc/base.h></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <dmlc/io.h></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <dmlc/type_traits.h></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <dmlc/parameter.h></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <mshadow/tensor.h></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment">// nnvm headers for symbolic construction.</span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <nnvm/op.h></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <nnvm/tuple.h></span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <nnvm/symbolic.h></span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <string></span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#ifndef MXNET_USE_OPENCV</span></div>
+<div class="line"><a name="l00024"></a><span class="lineno"><a class="line" href="base_8h.html#ae475a2ee335d3e9dd2cb395e4c202226"> 24</a></span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_USE_OPENCV 1</span></div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#ifndef MXNET_USE_CUDA</span></div>
+<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_USE_CUDA MSHADOW_USE_CUDA</span></div>
+<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#ifndef MXNET_USE_CUDNN</span></div>
+<div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="base_8h.html#a11c5ff746d300f8e69a6030ee9902347"> 38</a></span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_USE_CUDNN MSHADOW_USE_CUDNN</span></div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="base_8h.html#a8e6134b0829b92e4ba36e3ef1ac506ec"> 42</a></span> <span class="preprocessor">#define MXNET_GPU_NOT_ENABLED_ERROR "GPU is not enabled"</span></div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="preprocessor">#if DMLC_USE_CXX11 && defined(__GNUC__) && !defined(__clang_version__)</span></div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="preprocessor"></span><span class="preprocessor">#if __GNUC__ == 4 && __GNUC_MINOR__ < 8</span></div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="preprocessor"></span><span class="preprocessor">#error "Currently we need g++ 4.8 or higher to fully support c++11 features"</span></div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="preprocessor"></span><span class="preprocessor">#define override</span></div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="preprocessor"></span><span class="preprocessor">#define final</span></div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="preprocessor">#ifdef _MSC_VER</span></div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="preprocessor"></span><span class="preprocessor">#ifdef MXNET_EXPORTS</span></div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_API __declspec(dllexport)</span></div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="preprocessor"></span><span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_API __declspec(dllimport)</span></div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="preprocessor"></span><span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00066"></a><span class="lineno"><a class="line" href="base_8h.html#a91a09948aaaffa1eb64508db79009a05"> 66</a></span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_API</span></div>
+<div class="line"><a name="l00067"></a><span class="lineno"> 67</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span> <span class="preprocessor">#ifndef MXNET_PREDICT_ONLY</span></div>
+<div class="line"><a name="l00073"></a><span class="lineno"><a class="line" href="base_8h.html#a966330f8fb9e6e7bd65ab3603beb49db"> 73</a></span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_PREDICT_ONLY 0</span></div>
+<div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="preprocessor">#if MXNET_USE_PROFILER</span></div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="preprocessor"></span><span class="preprocessor">#define PROFILER_MESSAGE(msg) msg</span></div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="preprocessor"></span><span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00082"></a><span class="lineno"><a class="line" href="base_8h.html#aa14c0c7d2ea15e79c6edc864f5ecdc54"> 82</a></span> <span class="preprocessor"></span><span class="preprocessor">#define PROFILER_MESSAGE(msg) nullptr</span></div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00086"></a><span class="lineno"><a class="line" href="base_8h.html#aee5302444d57230419d0cd77bf72770d"> 86</a></span> <span class="preprocessor">#define MXNET_MAJOR 0</span></div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00088"></a><span class="lineno"><a class="line" href="base_8h.html#aea13dab0ddd61ec142feb0b6759c6acf"> 88</a></span> <span class="preprocessor">#define MXNET_MINOR 10</span></div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="base_8h.html#a76b95738a0fa0478cd508f324d2ac49d"> 90</a></span> <span class="preprocessor">#define MXNET_PATCH 1</span></div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="base_8h.html#a18d0da52a992a3360eaa481b43094e80"> 92</a></span> <span class="preprocessor">#define MXNET_VERSION (MXNET_MAJOR*10000 + MXNET_MINOR*100 + MXNET_PATCH)</span></div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00094"></a><span class="lineno"><a class="line" href="base_8h.html#a4a85852d8252d23300a8364d48a1c8ea"> 94</a></span> <span class="preprocessor">#define MXNET_MAKE_VERSION(major, minor, patch) ((major)*10000 + (minor)*100 + patch)</span></div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00098"></a><span class="lineno"><a class="line" href="base_8h.html#ae34be2bf889657a1452b87fd36f034c3"> 98</a></span> <span class="preprocessor">#define PROFILER_MESSAGE_FUNCNAME PROFILER_MESSAGE(__FUNCTION__)</span></div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00101"></a><span class="lineno"><a class="line" href="namespacemxnet.html"> 101</a></span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00103"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3"> 103</a></span> <span class="keyword">typedef</span> <a class="code" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">mshadow::cpu</a> <a class="code" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">cpu</a>;</div>
+<div class="line"><a name="l00105"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a31ef33571a4f6ba8847eeee1f4907141"> 105</a></span> <span class="keyword">typedef</span> <a class="code" href="namespacemxnet.html#a31ef33571a4f6ba8847eeee1f4907141">mshadow::gpu</a> <a class="code" href="namespacemxnet.html#a31ef33571a4f6ba8847eeee1f4907141">gpu</a>;</div>
+<div class="line"><a name="l00107"></a><span class="lineno"><a class="line" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef"> 107</a></span> <span class="keyword">typedef</span> <a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">mshadow::index_t</a> <a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a>;</div>
+<div class="line"><a name="l00109"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4"> 109</a></span> <span class="keyword">typedef</span> mshadow::default_real_t <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a>;</div>
+<div class="line"><a name="l00111"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687"> 111</a></span> <span class="keyword">using</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> = <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">nnvm::TShape</a>;</div>
+<div class="line"><a name="l00113"></a><span class="lineno"><a class="line" href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52"> 113</a></span> <span class="keyword">using</span> <a class="code" href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52">Op</a> = <a class="code" href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52">nnvm::Op</a>;</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div>
+<div class="line"><a name="l00116"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html"> 116</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1Context.html">Context</a> {</div>
+<div class="line"><a name="l00118"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e"> 118</a></span>  <span class="keyword">enum</span> <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">DeviceType</a> {</div>
+<div class="line"><a name="l00119"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140"> 119</a></span>  <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140">kCPU</a> = cpu::kDevMask,</div>
+<div class="line"><a name="l00120"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eab21bed4d5cee411db15283d7529f28cf"> 120</a></span>  <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eab21bed4d5cee411db15283d7529f28cf">kGPU</a> = gpu::kDevMask,</div>
+<div class="line"><a name="l00121"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34"> 121</a></span>  <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34">kCPUPinned</a> = 3</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  };</div>
+<div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3"> 124</a></span>  <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">DeviceType</a> <a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a>;</div>
+<div class="line"><a name="l00126"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9"> 126</a></span>  int32_t <a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a>;</div>
+<div class="line"><a name="l00128"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a290642a21363bef82bcb32cf7f98b884"> 128</a></span>  <a class="code" href="structmxnet_1_1Context.html#a290642a21363bef82bcb32cf7f98b884">Context</a>() : <a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a>(<a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140">kCPU</ [...]
+<div class="line"><a name="l00133"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe"> 133</a></span>  <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe">dev_mask</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">if</span> (<a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a> == <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34">kCPUPinned</a>) <span class="keywordflow">return</span> cpu::kDevMask;</div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">return</span> <a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a>;</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  }</div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="structmxnet_1_1Context.html#a1862b54dcfd20332eb2365451f47c678">operator<</a>(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1Context.html">Context</a> &b) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00148"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#ad59267de7b56e3aebe86a6d0e9fca2f0"> 148</a></span>  <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="structmxnet_1_1Context.html#ad59267de7b56e3aebe86a6d0e9fca2f0">operator==</a>(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1Context.html">Context</a> &b)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordflow">return</span> <a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a> == b.<a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a> && <a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a> == b.<a class="code" href="structmxnet_1_1Context.html#a8486dc [...]
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div>
+<div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a10fded12664afacb95f75c07bb77bfe6"> 156</a></span>  <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="structmxnet_1_1Context.html#a10fded12664afacb95f75c07bb77bfe6">operator!=</a>(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1Context.html">Context</a> &b)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">return</span> !(*<span class="keyword">this</span> == b);</div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  }</div>
+<div class="line"><a name="l00163"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a90542e0930244392f9a81ed0f206b612"> 163</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="structmxnet_1_1Context.html#a90542e0930244392f9a81ed0f206b612">Save</a>(dmlc::Stream *strm)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  strm->Write(&<a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a>, <span class="keyword">sizeof</span>(<a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a>));</div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  strm->Write(&<a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a>, <span class="keyword">sizeof</span>(<a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a>));</div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  }</div>
+<div class="line"><a name="l00172"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a882305327a53b9b9a82f5802b4b013e3"> 172</a></span>  <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="structmxnet_1_1Context.html#a882305327a53b9b9a82f5802b4b013e3">Load</a>(dmlc::Stream *strm) {</div>
+<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">if</span> (strm->Read(&<a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a>, <span class="keyword">sizeof</span>(<a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a>)) != <span class="keyword">sizeof</span>(<a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bba [...]
+<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordflow">if</span> (strm->Read(&<a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a>, <span class="keyword">sizeof</span>(int32_t)) != <span class="keyword">sizeof</span>(int32_t)) <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
+<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
+<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  }</div>
+<div class="line"><a name="l00178"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a7433936d1daf38f7dced0de49fbe6a4b"> 178</a></span>  <span class="keyword">static</span> <span class="keyword">const</span> int32_t <a class="code" href="structmxnet_1_1Context.html#a7433936d1daf38f7dced0de49fbe6a4b">kMaxDevType</a> = 4;</div>
+<div class="line"><a name="l00180"></a><span class="lineno"><a class="line" href="structmxnet_1_1Context.html#a958219ae412395685ef1d82af0dbbe67"> 180</a></span>  <span class="keyword">static</span> <span class="keyword">const</span> int32_t <a class="code" href="structmxnet_1_1Context.html#a958219ae412395685ef1d82af0dbbe67">kMaxDevID</a> = 16;</div>
+<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keyword">inline</span> <span class="keyword">static</span> <a class="code" href="structmxnet_1_1Context.html">Context</a> <a class="code" href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">Create</a>(<a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">DeviceType</a> <a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbac [...]
+<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">inline</span> <span class="keyword">static</span> <a class="code" href="structmxnet_1_1Context.html">Context</a> <a class="code" href="structmxnet_1_1Context.html#a4bb231d70623fde216de51589347e513">CPU</a>(int32_t <a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a> = 0);</div>
+<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keyword">inline</span> <span class="keyword">static</span> <a class="code" href="structmxnet_1_1Context.html">Context</a> <a class="code" href="structmxnet_1_1Context.html#a39d1a2b59bce135da127475995a58671">GPU</a>(int32_t <a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a> = -1);</div>
+<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">inline</span> <span class="keyword">static</span> <a class="code" href="structmxnet_1_1Context.html">Context</a> <a class="code" href="structmxnet_1_1Context.html#a420d4e662a5129b202507f42c991391c">CPUPinned</a>(int32_t <a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a> = -1);</div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keyword">inline</span> <span class="keyword">static</span> <a class="code" href="structmxnet_1_1Context.html">Context</a> <a class="code" href="structmxnet_1_1Context.html#ac58071fc1c0647bcab7aca4a39ae6a29">FromString</a>(std::string str);</div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span> };</div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div>
+<div class="line"><a name="l00213"></a><span class="lineno"><a class="line" href="structmxnet_1_1RunContext.html"> 213</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> {</div>
+<div class="line"><a name="l00215"></a><span class="lineno"><a class="line" href="structmxnet_1_1RunContext.html#ace5970ed7c25ccc98ffd0dcf35fd3c88"> 215</a></span>  <a class="code" href="structmxnet_1_1Context.html">Context</a> <a class="code" href="structmxnet_1_1RunContext.html#ace5970ed7c25ccc98ffd0dcf35fd3c88">ctx</a>;</div>
+<div class="line"><a name="l00219"></a><span class="lineno"><a class="line" href="structmxnet_1_1RunContext.html#a5c4294a44199f4056915e32a7f70fe0e"> 219</a></span>  <span class="keywordtype">void</span> *<a class="code" href="structmxnet_1_1RunContext.html#a5c4294a44199f4056915e32a7f70fe0e">stream</a>;</div>
+<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keyword">template</span><<span class="keyword">typename</span> xpu></div>
+<div class="line"><a name="l00226"></a><span class="lineno"><a class="line" href="structmxnet_1_1RunContext.html#a8bcd2fc6392f95a1fdc3d98f74dbfcb9"> 226</a></span>  <span class="keyword">inline</span> mshadow::Stream<xpu>* <a class="code" href="structmxnet_1_1RunContext.html#a8bcd2fc6392f95a1fdc3d98f74dbfcb9">get_stream</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span>mshadow::Stream<xpu>*<span class="keyword">></span>(<a class="code" href="structmxnet_1_1RunContext.html#a5c4294a44199f4056915e32a7f70fe0e">stream</a>);</div>
+<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  }</div>
+<div class="line"><a name="l00230"></a><span class="lineno"><a class="line" href="structmxnet_1_1RunContext.html#a5da6c1a11d0e6ea09dbe718cf6a4ef8d"> 230</a></span>  <span class="keyword">inline</span> <span class="keyword">const</span> <a class="code" href="structmxnet_1_1Context.html">Context</a>& <a class="code" href="structmxnet_1_1RunContext.html#a5da6c1a11d0e6ea09dbe718cf6a4ef8d">get_ctx</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">return</span> <a class="code" href="structmxnet_1_1RunContext.html#ace5970ed7c25ccc98ffd0dcf35fd3c88">ctx</a>;</div>
+<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  }</div>
+<div class="line"><a name="l00233"></a><span class="lineno"> 233</span> };</div>
+<div class="line"><a name="l00234"></a><span class="lineno"> 234</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div>
+<div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00238"></a><span class="lineno"> 238</span> <span class="comment">// implementing Context</span></div>
+<div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="structmxnet_1_1Context.html#a1862b54dcfd20332eb2365451f47c678">Context::operator<</a>(<span class="keyword">const</span> Context &b)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">if</span> (<a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a> == b.dev_type) {</div>
+<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">return</span> <a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a> < b.dev_id;</div>
+<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  } <span class="keywordflow">else</span> {</div>
+<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordflow">return</span> <a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a> < b.dev_type;</div>
+<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  }</div>
+<div class="line"><a name="l00245"></a><span class="lineno"> 245</span> }</div>
+<div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="keyword">inline</span> Context <a class="code" href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">Context::Create</a>(DeviceType dev_type, int32_t dev_id) {</div>
+<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <a class="code" href="structmxnet_1_1Context.html#a290642a21363bef82bcb32cf7f98b884">Context</a> ctx;</div>
+<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  ctx.dev_type = <a class="code" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a>;</div>
+<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordflow">if</span> (dev_id < 0) {</div>
+<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  ctx.dev_id = 0;</div>
+<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">if</span> (dev_type != <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140">kCPU</a>) {</div>
+<div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="preprocessor">#if MXNET_USE_CUDA</span></div>
+<div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="preprocessor"></span> CHECK_EQ(cudaGetDevice(&ctx.dev_id), cudaSuccess);</div>
+<div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00255"></a><span class="lineno"> 255</span> <span class="preprocessor"></span> LOG(FATAL) << <span class="stringliteral">"Please compile with CUDA enabled for cuda features"</span>;</div>
+<div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  } <span class="keywordflow">else</span> {</div>
+<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  ctx.dev_id = <a class="code" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a>;</div>
+<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  }</div>
+<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keywordflow">return</span> ctx;</div>
+<div class="line"><a name="l00262"></a><span class="lineno"> 262</span> }</div>
+<div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="keyword">inline</span> Context <a class="code" href="structmxnet_1_1Context.html#a4bb231d70623fde216de51589347e513">Context::CPU</a>(int32_t dev_id) {</div>
+<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">return</span> <a class="code" href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">Create</a>(<a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140">kCPU</a>, dev_id);</div>
+<div class="line"><a name="l00265"></a><span class="lineno"> 265</span> }</div>
+<div class="line"><a name="l00266"></a><span class="lineno"> 266</span> </div>
+<div class="line"><a name="l00267"></a><span class="lineno"> 267</span> <span class="keyword">inline</span> Context <a class="code" href="structmxnet_1_1Context.html#a420d4e662a5129b202507f42c991391c">Context::CPUPinned</a>(int32_t dev_id) {</div>
+<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keywordflow">return</span> <a class="code" href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">Create</a>(<a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34">kCPUPinned</a>, dev_id);</div>
+<div class="line"><a name="l00269"></a><span class="lineno"> 269</span> }</div>
+<div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div>
+<div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="keyword">inline</span> Context <a class="code" href="structmxnet_1_1Context.html#a39d1a2b59bce135da127475995a58671">Context::GPU</a>(int32_t dev_id) {</div>
+<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keywordflow">return</span> <a class="code" href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">Create</a>(<a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eab21bed4d5cee411db15283d7529f28cf">kGPU</a>, dev_id);</div>
+<div class="line"><a name="l00273"></a><span class="lineno"> 273</span> }</div>
+<div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div>
+<div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="keyword">inline</span> Context <a class="code" href="structmxnet_1_1Context.html#ac58071fc1c0647bcab7aca4a39ae6a29">Context::FromString</a>(std::string str) {</div>
+<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <a class="code" href="structmxnet_1_1Context.html#a290642a21363bef82bcb32cf7f98b884">Context</a> ret;</div>
+<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keywordflow">try</span> {</div>
+<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  std::string::size_type l = str.find(<span class="charliteral">'('</span>);</div>
+<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  CHECK_NE(l, std::string::npos);</div>
+<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  std::string::size_type r = str.find(<span class="charliteral">')'</span>);</div>
+<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  CHECK_EQ(r, str.length()-1);</div>
+<div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div>
+<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  std::string type = str.substr(0, l);</div>
+<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordtype">int</span> <span class="keywordtype">id</span> = std::stoi(str.substr(l+1, r-l-1));</div>
+<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keywordflow">if</span> (type == <span class="stringliteral">"cpu"</span>) {</div>
+<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  ret = <a class="code" href="structmxnet_1_1Context.html#a4bb231d70623fde216de51589347e513">CPU</a>(<span class="keywordtype">id</span>);</div>
+<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (type == <span class="stringliteral">"gpu"</span>) {</div>
+<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  ret = <a class="code" href="structmxnet_1_1Context.html#a39d1a2b59bce135da127475995a58671">GPU</a>(<span class="keywordtype">id</span>);</div>
+<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (type == <span class="stringliteral">"cpu_pinned"</span>) {</div>
+<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  ret = <a class="code" href="structmxnet_1_1Context.html#a420d4e662a5129b202507f42c991391c">CPUPinned</a>(<span class="keywordtype">id</span>);</div>
+<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  } <span class="keywordflow">else</span> {</div>
+<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  LOG(FATAL) << <span class="stringliteral">"Invalid context string "</span> << str;</div>
+<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  }</div>
+<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  } <span class="keywordflow">catch</span> (...) {</div>
+<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  LOG(FATAL) << <span class="stringliteral">"Invalid context string "</span> << str;</div>
+<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  }</div>
+<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keywordflow">return</span> ret;</div>
+<div class="line"><a name="l00298"></a><span class="lineno"> 298</span> }</div>
+<div class="line"><a name="l00299"></a><span class="lineno"> 299</span> </div>
+<div class="line"><a name="l00300"></a><span class="lineno"> 300</span> <span class="keyword">inline</span> std::ostream& operator<<(std::ostream &out, <span class="keyword">const</span> Context &ctx) {</div>
+<div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keywordflow">if</span> (ctx.dev_type == <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140">Context::kCPU</a>) {</div>
+<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  out << <span class="stringliteral">"cpu("</span>;</div>
+<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (ctx.dev_type == <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eab21bed4d5cee411db15283d7529f28cf">Context::kGPU</a>) {</div>
+<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  out << <span class="stringliteral">"gpu("</span>;</div>
+<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (ctx.dev_type == <a class="code" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34">Context::kCPUPinned</a>) {</div>
+<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  out << <span class="stringliteral">"cpu_pinned("</span>;</div>
+<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  } <span class="keywordflow">else</span> {</div>
+<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  out << <span class="stringliteral">"unknown("</span>;</div>
+<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  }</div>
+<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  out << ctx.dev_id << <span class="stringliteral">")"</span>;</div>
+<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordflow">return</span> out;</div>
+<div class="line"><a name="l00312"></a><span class="lineno"> 312</span> }</div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div>
+<div class="line"><a name="l00314"></a><span class="lineno"> 314</span> <span class="comment">// describe op registration point</span></div>
+<div class="line"><a name="l00315"></a><span class="lineno"> 315</span> <span class="preprocessor">#define STRINGIZE_DETAIL(x) #x</span></div>
+<div class="line"><a name="l00316"></a><span class="lineno"> 316</span> <span class="preprocessor"></span><span class="preprocessor">#define STRINGIZE(x) STRINGIZE_DETAIL(x)</span></div>
+<div class="line"><a name="l00317"></a><span class="lineno"> 317</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_DESCRIBE(...) describe(__VA_ARGS__ "\n\nFrom:" __FILE__ ":" STRINGIZE(__LINE__))</span></div>
+<div class="line"><a name="l00318"></a><span class="lineno"> 318</span> <span class="preprocessor"></span><span class="preprocessor">#define ADD_FILELINE "\n\nDefined in " __FILE__ ":L" STRINGIZE(__LINE__)</span></div>
+<div class="line"><a name="l00319"></a><span class="lineno"> 319</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00320"></a><span class="lineno"> 320</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div>
+<div class="line"><a name="l00322"></a><span class="lineno"> 322</span> <span class="preprocessor">#include "<a class="code" href="tensor__blob_8h.html">./tensor_blob.h</a>"</span></div>
+<div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="preprocessor">#endif // MXNET_BASE_H_</span></div>
+<div class="ttc" id="structmxnet_1_1Context_html_a958219ae412395685ef1d82af0dbbe67"><div class="ttname"><a href="structmxnet_1_1Context.html#a958219ae412395685ef1d82af0dbbe67">mxnet::Context::kMaxDevID</a></div><div class="ttdeci">static const int32_t kMaxDevID</div><div class="ttdoc">the maximal device index </div><div class="ttdef"><b>Definition:</b> base.h:180</div></div>
+<div class="ttc" id="structmxnet_1_1RunContext_html_a8bcd2fc6392f95a1fdc3d98f74dbfcb9"><div class="ttname"><a href="structmxnet_1_1RunContext.html#a8bcd2fc6392f95a1fdc3d98f74dbfcb9">mxnet::RunContext::get_stream</a></div><div class="ttdeci">mshadow::Stream< xpu > * get_stream() const </div><div class="ttdoc">get mshadow stream from Context </div><div class="ttdef"><b>Definition:</b> base.h:226</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a882305327a53b9b9a82f5802b4b013e3"><div class="ttname"><a href="structmxnet_1_1Context.html#a882305327a53b9b9a82f5802b4b013e3">mxnet::Context::Load</a></div><div class="ttdeci">bool Load(dmlc::Stream *strm)</div><div class="ttdoc">load the content from binary stream </div><div class="ttdef"><b>Definition:</b> base.h:172</div></div>
<div class="ttc" id="namespacemxnet_html_a4421326e33f2a848cb2fa3e70ab24ff4"><div class="ttname"><a href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">mxnet::real_t</a></div><div class="ttdeci">mshadow::default_real_t real_t</div><div class="ttdoc">data type that will be used to store ndarray </div><div class="ttdef"><b>Definition:</b> base.h:109</div></div>
-<div class="ttc" id="structmxnet_1_1Context_html_afd07473e20810635cb3fffc216d76494"><div class="ttname"><a href="structmxnet_1_1Context.html#afd07473e20810635cb3fffc216d76494">mxnet::Context::operator<</a></div><div class="ttdeci">bool operator<(const Context &b) const</div><div class="ttdoc">Comparator, used to enable Context as std::map key. </div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a39d1a2b59bce135da127475995a58671"><div class="ttname"><a href="structmxnet_1_1Context.html#a39d1a2b59bce135da127475995a58671">mxnet::Context::GPU</a></div><div class="ttdeci">static Context GPU(int32_t dev_id=-1)</div></div>
<div class="ttc" id="namespacemxnet_html_a6e14bb5f6ffc81892feca8d1da658687"><div class="ttname"><a href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">mxnet::TShape</a></div><div class="ttdeci">nnvm::TShape TShape</div><div class="ttdoc">Shape data structure used to record shape information. </div><div class="ttdef"><b>Definition:</b> base.h:111</div></div>
<div class="ttc" id="structmxnet_1_1RunContext_html_ace5970ed7c25ccc98ffd0dcf35fd3c88"><div class="ttname"><a href="structmxnet_1_1RunContext.html#ace5970ed7c25ccc98ffd0dcf35fd3c88">mxnet::RunContext::ctx</a></div><div class="ttdeci">Context ctx</div><div class="ttdoc">base Context </div><div class="ttdef"><b>Definition:</b> base.h:215</div></div>
+<div class="ttc" id="structmxnet_1_1Context_html_a1862b54dcfd20332eb2365451f47c678"><div class="ttname"><a href="structmxnet_1_1Context.html#a1862b54dcfd20332eb2365451f47c678">mxnet::Context::operator<</a></div><div class="ttdeci">bool operator<(const Context &b) const </div><div class="ttdoc">Comparator, used to enable Context as std::map key. </div></div>
+<div class="ttc" id="structmxnet_1_1Context_html_a4f6fd8608a4abc9673cb6da4654f84fe"><div class="ttname"><a href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe">mxnet::Context::dev_mask</a></div><div class="ttdeci">int dev_mask() const </div><div class="ttdoc">Get corresponding device mask. </div><div class="ttdef"><b>Definition:</b> base.h:133</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a7433936d1daf38f7dced0de49fbe6a4b"><div class="ttname"><a href="structmxnet_1_1Context.html#a7433936d1daf38f7dced0de49fbe6a4b">mxnet::Context::kMaxDevType</a></div><div class="ttdeci">static const int32_t kMaxDevType</div><div class="ttdoc">the maximal device type </div><div class="ttdef"><b>Definition:</b> base.h:178</div></div>
<div class="ttc" id="structmxnet_1_1RunContext_html"><div class="ttname"><a href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></div><div class="ttdoc">execution time context. The information needed in runtime for actual execution. </div><div class="ttdef"><b>Definition:</b> base.h:213</div></div>
-<div class="ttc" id="structmxnet_1_1RunContext_html_a9901db00632774aef42081ceda0d23f1"><div class="ttname"><a href="structmxnet_1_1RunContext.html#a9901db00632774aef42081ceda0d23f1">mxnet::RunContext::get_stream</a></div><div class="ttdeci">mshadow::Stream< xpu > * get_stream() const</div><div class="ttdoc">get mshadow stream from Context </div><div class="ttdef"><b>Definition:</b> base.h:226</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a66040df23c2a5663f29bc50aa0bbace3"><div class="ttname"><a href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">mxnet::Context::dev_type</a></div><div class="ttdeci">DeviceType dev_type</div><div class="ttdoc">the device type we run the op on </div><div class="ttdef"><b>Definition:</b> base.h:124</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140"><div class="ttname"><a href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140">mxnet::Context::kCPU</a></div><div class="ttdef"><b>Definition:</b> base.h:119</div></div>
-<div class="ttc" id="structmxnet_1_1Context_html_a4182c1a2f910064d058832c57489d8f5"><div class="ttname"><a href="structmxnet_1_1Context.html#a4182c1a2f910064d058832c57489d8f5">mxnet::Context::dev_mask</a></div><div class="ttdeci">int dev_mask() const</div><div class="ttdoc">Get corresponding device mask. </div><div class="ttdef"><b>Definition:</b> base.h:133</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a8486dcc599dffe447e2f098b170d3db9"><div class="ttname"><a href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">mxnet::Context::dev_id</a></div><div class="ttdeci">int32_t dev_id</div><div class="ttdoc">device id we are going to run it on </div><div class="ttdef"><b>Definition:</b> base.h:126</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34"><div class="ttname"><a href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34">mxnet::Context::kCPUPinned</a></div><div class="ttdef"><b>Definition:</b> base.h:121</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_ac58071fc1c0647bcab7aca4a39ae6a29"><div class="ttname"><a href="structmxnet_1_1Context.html#ac58071fc1c0647bcab7aca4a39ae6a29">mxnet::Context::FromString</a></div><div class="ttdeci">static Context FromString(std::string str)</div></div>
<div class="ttc" id="structmxnet_1_1RunContext_html_a5c4294a44199f4056915e32a7f70fe0e"><div class="ttname"><a href="structmxnet_1_1RunContext.html#a5c4294a44199f4056915e32a7f70fe0e">mxnet::RunContext::stream</a></div><div class="ttdeci">void * stream</div><div class="ttdoc">the stream of the device, can be NULL or Stream<gpu>* in GPU mode </div><div class="ttdef"><b>Definition:</b> base.h:219</div></div>
+<div class="ttc" id="structmxnet_1_1Context_html_a90542e0930244392f9a81ed0f206b612"><div class="ttname"><a href="structmxnet_1_1Context.html#a90542e0930244392f9a81ed0f206b612">mxnet::Context::Save</a></div><div class="ttdeci">void Save(dmlc::Stream *strm) const </div><div class="ttdoc">save the content into binary stream </div><div class="ttdef"><b>Definition:</b> base.h:163</div></div>
<div class="ttc" id="namespacemxnet_html_a31ef33571a4f6ba8847eeee1f4907141"><div class="ttname"><a href="namespacemxnet.html#a31ef33571a4f6ba8847eeee1f4907141">mxnet::gpu</a></div><div class="ttdeci">mshadow::gpu gpu</div><div class="ttdoc">mxnet gpu </div><div class="ttdef"><b>Definition:</b> base.h:105</div></div>
+<div class="ttc" id="structmxnet_1_1RunContext_html_a5da6c1a11d0e6ea09dbe718cf6a4ef8d"><div class="ttname"><a href="structmxnet_1_1RunContext.html#a5da6c1a11d0e6ea09dbe718cf6a4ef8d">mxnet::RunContext::get_ctx</a></div><div class="ttdeci">const Context & get_ctx() const </div><div class="ttdoc">get the base Context from RunContext </div><div class="ttdef"><b>Definition:</b> base.h:230</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a611c227b5e04a2777c3e4507bb76f60eab21bed4d5cee411db15283d7529f28cf"><div class="ttname"><a href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eab21bed4d5cee411db15283d7529f28cf">mxnet::Context::kGPU</a></div><div class="ttdef"><b>Definition:</b> base.h:120</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a611c227b5e04a2777c3e4507bb76f60e"><div class="ttname"><a href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">mxnet::Context::DeviceType</a></div><div class="ttdeci">DeviceType</div><div class="ttdoc">Type of device. </div><div class="ttdef"><b>Definition:</b> base.h:118</div></div>
<div class="ttc" id="namespacemxnet_html_a850af9d76d9d4beaeb5234c1434973e3"><div class="ttname"><a href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">mxnet::cpu</a></div><div class="ttdeci">mshadow::cpu cpu</div><div class="ttdoc">mxnet cpu </div><div class="ttdef"><b>Definition:</b> base.h:103</div></div>
<div class="ttc" id="namespacemxnet_html_ac2fb286fd7ddfbc7af1456f6c7a7ee52"><div class="ttname"><a href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52">mxnet::Op</a></div><div class="ttdeci">nnvm::Op Op</div><div class="ttdoc">operator structure from NNVM </div><div class="ttdef"><b>Definition:</b> base.h:113</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a290642a21363bef82bcb32cf7f98b884"><div class="ttname"><a href="structmxnet_1_1Context.html#a290642a21363bef82bcb32cf7f98b884">mxnet::Context::Context</a></div><div class="ttdeci">Context()</div><div class="ttdoc">default constructor </div><div class="ttdef"><b>Definition:</b> base.h:128</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a7cea13406ae21ae557d3455f54670a4a"><div class="ttname"><a href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">mxnet::Context::Create</a></div><div class="ttdeci">static Context Create(DeviceType dev_type, int32_t dev_id=-1)</div><div class="ttdoc">Create a new context. </div></div>
+<div class="ttc" id="structmxnet_1_1Context_html_a10fded12664afacb95f75c07bb77bfe6"><div class="ttname"><a href="structmxnet_1_1Context.html#a10fded12664afacb95f75c07bb77bfe6">mxnet::Context::operator!=</a></div><div class="ttdeci">bool operator!=(const Context &b) const </div><div class="ttdoc">check if current context not equals another one </div><div class="ttdef"><b>Definition:</b> base.h:156</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a4bb231d70623fde216de51589347e513"><div class="ttname"><a href="structmxnet_1_1Context.html#a4bb231d70623fde216de51589347e513">mxnet::Context::CPU</a></div><div class="ttdeci">static Context CPU(int32_t dev_id=0)</div></div>
-<div class="ttc" id="structmxnet_1_1Context_html_a9ea910ddb7e15d51320806b1666bf6c5"><div class="ttname"><a href="structmxnet_1_1Context.html#a9ea910ddb7e15d51320806b1666bf6c5">mxnet::Context::Save</a></div><div class="ttdeci">void Save(dmlc::Stream *strm) const</div><div class="ttdoc">save the content into binary stream </div><div class="ttdef"><b>Definition:</b> base.h:163</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a420d4e662a5129b202507f42c991391c"><div class="ttname"><a href="structmxnet_1_1Context.html#a420d4e662a5129b202507f42c991391c">mxnet::Context::CPUPinned</a></div><div class="ttdeci">static Context CPUPinned(int32_t dev_id=-1)</div></div>
-<div class="ttc" id="structmxnet_1_1RunContext_html_a323fa88ecd7bfb7d5f7d9ca727101d3e"><div class="ttname"><a href="structmxnet_1_1RunContext.html#a323fa88ecd7bfb7d5f7d9ca727101d3e">mxnet::RunContext::get_ctx</a></div><div class="ttdeci">const Context & get_ctx() const</div><div class="ttdoc">get the base Context from RunContext </div><div class="ttdef"><b>Definition:</b> base.h:230</div></div>
<div class="ttc" id="namespacemxnet_html_abadedc1b42ee3e5b8852e63783b982ef"><div class="ttname"><a href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">mxnet::index_t</a></div><div class="ttdeci">mshadow::index_t index_t</div><div class="ttdoc">index type usually use unsigned </div><div class="ttdef"><b>Definition:</b> base.h:107</div></div>
<div class="ttc" id="tensor__blob_8h_html"><div class="ttname"><a href="tensor__blob_8h.html">tensor_blob.h</a></div><div class="ttdoc">TBlob class that holds common representation of arbirary dimension tensor, can be used to transformed...</div></div>
<div class="ttc" id="structmxnet_1_1Context_html"><div class="ttname"><a href="structmxnet_1_1Context.html">mxnet::Context</a></div><div class="ttdoc">Context information about the execution environment. </div><div class="ttdef"><b>Definition:</b> base.h:116</div></div>
-<div class="ttc" id="structmxnet_1_1Context_html_a7e84dc72147472e72b50e8545780fd1e"><div class="ttname"><a href="structmxnet_1_1Context.html#a7e84dc72147472e72b50e8545780fd1e">mxnet::Context::operator!=</a></div><div class="ttdeci">bool operator!=(const Context &b) const</div><div class="ttdoc">check if current context not equals another one </div><div class="ttdef"><b>Definition:</b> base.h:156</div></div>
+<div class="ttc" id="structmxnet_1_1Context_html_ad59267de7b56e3aebe86a6d0e9fca2f0"><div class="ttname"><a href="structmxnet_1_1Context.html#ad59267de7b56e3aebe86a6d0e9fca2f0">mxnet::Context::operator==</a></div><div class="ttdeci">bool operator==(const Context &b) const </div><div class="ttdoc">check if current context equals another one </div><div class="ttdef"><b>Definition:</b> base.h:148</div></div>
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+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/c__api_8h.html b/versions/master/doxygen/c__api_8h.html
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@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/c_api.h File Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
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+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
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+ </div>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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@@ -75,21 +97,22 @@ $(function() {
<p>C API of mxnet.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <stdint.h></code><br />
-<code>#include <stddef.h></code><br />
-<code>#include <stdbool.h></code><br />
+<div class="textblock"><code>#include <stdint.h></code><br/>
+<code>#include <stddef.h></code><br/>
+<code>#include <stdbool.h></code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for c_api.h:</div>
<div class="dyncontent">
<div class="center"><img src="c__api_8h__incl.png" border="0" usemap="#include_2mxnet_2c__api_8h" alt=""/></div>
+<map name="include_2mxnet_2c__api_8h" id="include_2mxnet_2c__api_8h">
+</map>
</div>
</div><div class="textblock"><div class="dynheader">
This graph shows which files directly or indirectly include this file:</div>
<div class="dyncontent">
<div class="center"><img src="c__api_8h__dep__incl.png" border="0" usemap="#include_2mxnet_2c__api_8hdep" alt=""/></div>
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</div>
</div>
<p><a href="c__api_8h_source.html">Go to the source code of this file.</a></p>
@@ -106,82 +129,82 @@ Classes</h2></td></tr>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="define-members"></a>
Macros</h2></td></tr>
<tr class="memitem:a2380be5ab258c3657553d0cef62936fe"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a2380be5ab258c3657553d0cef62936fe">DEFAULT</a>(x)</td></tr>
-<tr class="memdesc:a2380be5ab258c3657553d0cef62936fe"><td class="mdescLeft"> </td><td class="mdescRight">Inhibit C++ name-mangling for MXNet functions. <a href="#a2380be5ab258c3657553d0cef62936fe">More...</a><br /></td></tr>
+<tr class="memdesc:a2380be5ab258c3657553d0cef62936fe"><td class="mdescLeft"> </td><td class="mdescRight">Inhibit C++ name-mangling for MXNet functions. <a href="#a2380be5ab258c3657553d0cef62936fe">More...</a><br/></td></tr>
<tr class="separator:a2380be5ab258c3657553d0cef62936fe"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5b0e47dde55f9b2f58b99fab92d1e0ac"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a></td></tr>
-<tr class="memdesc:a5b0e47dde55f9b2f58b99fab92d1e0ac"><td class="mdescLeft"> </td><td class="mdescRight">MXNET_DLL prefix for windows. <a href="#a5b0e47dde55f9b2f58b99fab92d1e0ac">More...</a><br /></td></tr>
+<tr class="memdesc:a5b0e47dde55f9b2f58b99fab92d1e0ac"><td class="mdescLeft"> </td><td class="mdescRight">MXNET_DLL prefix for windows. <a href="#a5b0e47dde55f9b2f58b99fab92d1e0ac">More...</a><br/></td></tr>
<tr class="separator:a5b0e47dde55f9b2f58b99fab92d1e0ac"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
<tr class="memitem:a796e7ef8260ef40b24af7d1082aa2d84"><td class="memItemLeft" align="right" valign="top">typedef unsigned int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a></td></tr>
-<tr class="memdesc:a796e7ef8260ef40b24af7d1082aa2d84"><td class="mdescLeft"> </td><td class="mdescRight">manually define unsigned int <a href="#a796e7ef8260ef40b24af7d1082aa2d84">More...</a><br /></td></tr>
+<tr class="memdesc:a796e7ef8260ef40b24af7d1082aa2d84"><td class="mdescLeft"> </td><td class="mdescRight">manually define unsigned int <a href="#a796e7ef8260ef40b24af7d1082aa2d84">More...</a><br/></td></tr>
<tr class="separator:a796e7ef8260ef40b24af7d1082aa2d84"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4e709067707d9f90c534bd65494ae668"><td class="memItemLeft" align="right" valign="top">typedef float </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a4e709067707d9f90c534bd65494ae668">mx_float</a></td></tr>
-<tr class="memdesc:a4e709067707d9f90c534bd65494ae668"><td class="mdescLeft"> </td><td class="mdescRight">manually define float <a href="#a4e709067707d9f90c534bd65494ae668">More...</a><br /></td></tr>
+<tr class="memdesc:a4e709067707d9f90c534bd65494ae668"><td class="mdescLeft"> </td><td class="mdescRight">manually define float <a href="#a4e709067707d9f90c534bd65494ae668">More...</a><br/></td></tr>
<tr class="separator:a4e709067707d9f90c534bd65494ae668"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a99c2939783499d2a5ce0d76881dce9df"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a></td></tr>
-<tr class="memdesc:a99c2939783499d2a5ce0d76881dce9df"><td class="mdescLeft"> </td><td class="mdescRight">handle to NDArray <a href="#a99c2939783499d2a5ce0d76881dce9df">More...</a><br /></td></tr>
+<tr class="memdesc:a99c2939783499d2a5ce0d76881dce9df"><td class="mdescLeft"> </td><td class="mdescRight">handle to NDArray <a href="#a99c2939783499d2a5ce0d76881dce9df">More...</a><br/></td></tr>
<tr class="separator:a99c2939783499d2a5ce0d76881dce9df"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a197f06e1d873d34572b679532c268d35"><td class="memItemLeft" align="right" valign="top">typedef const void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a197f06e1d873d34572b679532c268d35">FunctionHandle</a></td></tr>
-<tr class="memdesc:a197f06e1d873d34572b679532c268d35"><td class="mdescLeft"> </td><td class="mdescRight">handle to a mxnet narray function that changes NDArray <a href="#a197f06e1d873d34572b679532c268d35">More...</a><br /></td></tr>
+<tr class="memdesc:a197f06e1d873d34572b679532c268d35"><td class="mdescLeft"> </td><td class="mdescRight">handle to a mxnet narray function that changes NDArray <a href="#a197f06e1d873d34572b679532c268d35">More...</a><br/></td></tr>
<tr class="separator:a197f06e1d873d34572b679532c268d35"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa2e96133f49cfdc1095da6cd08afbce0"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aa2e96133f49cfdc1095da6cd08afbce0">AtomicSymbolCreator</a></td></tr>
-<tr class="memdesc:aa2e96133f49cfdc1095da6cd08afbce0"><td class="mdescLeft"> </td><td class="mdescRight">handle to a function that takes param and creates symbol <a href="#aa2e96133f49cfdc1095da6cd08afbce0">More...</a><br /></td></tr>
+<tr class="memdesc:aa2e96133f49cfdc1095da6cd08afbce0"><td class="mdescLeft"> </td><td class="mdescRight">handle to a function that takes param and creates symbol <a href="#aa2e96133f49cfdc1095da6cd08afbce0">More...</a><br/></td></tr>
<tr class="separator:aa2e96133f49cfdc1095da6cd08afbce0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aac4a2be6e40b0869bcc390a573a7e489"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aac4a2be6e40b0869bcc390a573a7e489">CachedOpHandle</a></td></tr>
-<tr class="memdesc:aac4a2be6e40b0869bcc390a573a7e489"><td class="mdescLeft"> </td><td class="mdescRight">handle to cached operator <a href="#aac4a2be6e40b0869bcc390a573a7e489">More...</a><br /></td></tr>
+<tr class="memdesc:aac4a2be6e40b0869bcc390a573a7e489"><td class="mdescLeft"> </td><td class="mdescRight">handle to cached operator <a href="#aac4a2be6e40b0869bcc390a573a7e489">More...</a><br/></td></tr>
<tr class="separator:aac4a2be6e40b0869bcc390a573a7e489"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a301e9a7722b326126defdd39ccd99b37"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a></td></tr>
-<tr class="memdesc:a301e9a7722b326126defdd39ccd99b37"><td class="mdescLeft"> </td><td class="mdescRight">handle to a symbol that can be bind as operator <a href="#a301e9a7722b326126defdd39ccd99b37">More...</a><br /></td></tr>
+<tr class="memdesc:a301e9a7722b326126defdd39ccd99b37"><td class="mdescLeft"> </td><td class="mdescRight">handle to a symbol that can be bind as operator <a href="#a301e9a7722b326126defdd39ccd99b37">More...</a><br/></td></tr>
<tr class="separator:a301e9a7722b326126defdd39ccd99b37"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aaccda72ea7a5439c715ff5007b9595b5"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aaccda72ea7a5439c715ff5007b9595b5">AtomicSymbolHandle</a></td></tr>
-<tr class="memdesc:aaccda72ea7a5439c715ff5007b9595b5"><td class="mdescLeft"> </td><td class="mdescRight">handle to a AtomicSymbol <a href="#aaccda72ea7a5439c715ff5007b9595b5">More...</a><br /></td></tr>
+<tr class="memdesc:aaccda72ea7a5439c715ff5007b9595b5"><td class="mdescLeft"> </td><td class="mdescRight">handle to a AtomicSymbol <a href="#aaccda72ea7a5439c715ff5007b9595b5">More...</a><br/></td></tr>
<tr class="separator:aaccda72ea7a5439c715ff5007b9595b5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6f224ee35892507405ced8429d874726"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a></td></tr>
-<tr class="memdesc:a6f224ee35892507405ced8429d874726"><td class="mdescLeft"> </td><td class="mdescRight">handle to an Executor <a href="#a6f224ee35892507405ced8429d874726">More...</a><br /></td></tr>
+<tr class="memdesc:a6f224ee35892507405ced8429d874726"><td class="mdescLeft"> </td><td class="mdescRight">handle to an Executor <a href="#a6f224ee35892507405ced8429d874726">More...</a><br/></td></tr>
<tr class="separator:a6f224ee35892507405ced8429d874726"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0b9b13ccb06a6a4f05d211261fa883dd"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a0b9b13ccb06a6a4f05d211261fa883dd">DataIterCreator</a></td></tr>
-<tr class="memdesc:a0b9b13ccb06a6a4f05d211261fa883dd"><td class="mdescLeft"> </td><td class="mdescRight">handle a dataiter creator <a href="#a0b9b13ccb06a6a4f05d211261fa883dd">More...</a><br /></td></tr>
+<tr class="memdesc:a0b9b13ccb06a6a4f05d211261fa883dd"><td class="mdescLeft"> </td><td class="mdescRight">handle a dataiter creator <a href="#a0b9b13ccb06a6a4f05d211261fa883dd">More...</a><br/></td></tr>
<tr class="separator:a0b9b13ccb06a6a4f05d211261fa883dd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:abce84fe05f55709d643bcfd0a4e4620b"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#abce84fe05f55709d643bcfd0a4e4620b">DataIterHandle</a></td></tr>
-<tr class="memdesc:abce84fe05f55709d643bcfd0a4e4620b"><td class="mdescLeft"> </td><td class="mdescRight">handle to a DataIterator <a href="#abce84fe05f55709d643bcfd0a4e4620b">More...</a><br /></td></tr>
+<tr class="memdesc:abce84fe05f55709d643bcfd0a4e4620b"><td class="mdescLeft"> </td><td class="mdescRight">handle to a DataIterator <a href="#abce84fe05f55709d643bcfd0a4e4620b">More...</a><br/></td></tr>
<tr class="separator:abce84fe05f55709d643bcfd0a4e4620b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0543f53696ffcf5a7a2f0f4403f87623"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a></td></tr>
-<tr class="memdesc:a0543f53696ffcf5a7a2f0f4403f87623"><td class="mdescLeft"> </td><td class="mdescRight">handle to KVStore <a href="#a0543f53696ffcf5a7a2f0f4403f87623">More...</a><br /></td></tr>
+<tr class="memdesc:a0543f53696ffcf5a7a2f0f4403f87623"><td class="mdescLeft"> </td><td class="mdescRight">handle to KVStore <a href="#a0543f53696ffcf5a7a2f0f4403f87623">More...</a><br/></td></tr>
<tr class="separator:a0543f53696ffcf5a7a2f0f4403f87623"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4cc6d9b5a408760fc893c0b568d6aeae"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">RecordIOHandle</a></td></tr>
-<tr class="memdesc:a4cc6d9b5a408760fc893c0b568d6aeae"><td class="mdescLeft"> </td><td class="mdescRight">handle to RecordIO <a href="#a4cc6d9b5a408760fc893c0b568d6aeae">More...</a><br /></td></tr>
+<tr class="memdesc:a4cc6d9b5a408760fc893c0b568d6aeae"><td class="mdescLeft"> </td><td class="mdescRight">handle to RecordIO <a href="#a4cc6d9b5a408760fc893c0b568d6aeae">More...</a><br/></td></tr>
<tr class="separator:a4cc6d9b5a408760fc893c0b568d6aeae"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a58a159a7417ad988206970d46c4c7ee8"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a58a159a7417ad988206970d46c4c7ee8">RtcHandle</a></td></tr>
-<tr class="memdesc:a58a159a7417ad988206970d46c4c7ee8"><td class="mdescLeft"> </td><td class="mdescRight">handle to MXRtc <a href="#a58a159a7417ad988206970d46c4c7ee8">More...</a><br /></td></tr>
+<tr class="memdesc:a58a159a7417ad988206970d46c4c7ee8"><td class="mdescLeft"> </td><td class="mdescRight">handle to MXRtc <a href="#a58a159a7417ad988206970d46c4c7ee8">More...</a><br/></td></tr>
<tr class="separator:a58a159a7417ad988206970d46c4c7ee8"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2731805437dc3d76201fc21154b1337c"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a2731805437dc3d76201fc21154b1337c">ExecutorMonitorCallback</a>) (const char *, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a>, void *)</td></tr>
-<tr class="separator:a2731805437dc3d76201fc21154b1337c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a0ee6dd8389c0dacae0f7c48156ec2f34"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a0ee6dd8389c0dacae0f7c48156ec2f34">MXGenericCallback</a>) (void)</td></tr>
-<tr class="separator:a0ee6dd8389c0dacae0f7c48156ec2f34"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a260ea269f1da99011750fc3e18661f06"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a260ea269f1da99011750fc3e18661f06">CustomOpFBFunc</a>) (int, void **, int *, const int *, const int, void *)</td></tr>
-<tr class="separator:a260ea269f1da99011750fc3e18661f06"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a0056a2f79906cce0f8aa78966c2d4a6c"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a0056a2f79906cce0f8aa78966c2d4a6c">CustomOpDelFunc</a>) (void *)</td></tr>
-<tr class="separator:a0056a2f79906cce0f8aa78966c2d4a6c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a5bcf4b1e26b22c9363a016bd9d27085c"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a5bcf4b1e26b22c9363a016bd9d27085c">CustomOpListFunc</a>) (char ***, void *)</td></tr>
-<tr class="separator:a5bcf4b1e26b22c9363a016bd9d27085c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a004103d4a52000a4cdb377c577aa7a4d"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a004103d4a52000a4cdb377c577aa7a4d">CustomOpInferShapeFunc</a>) (int, int *, unsigned **, void *)</td></tr>
-<tr class="separator:a004103d4a52000a4cdb377c577aa7a4d"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a59855e0408519e73b05419cd0d9d976e"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a59855e0408519e73b05419cd0d9d976e">CustomOpInferTypeFunc</a>) (int, int *, void *)</td></tr>
-<tr class="separator:a59855e0408519e73b05419cd0d9d976e"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a97d01e308e267e939b4d1eebaf2f11d9"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a97d01e308e267e939b4d1eebaf2f11d9">CustomOpBwdDepFunc</a>) (const int *, const int *, const int *, int *, int **, void *)</td></tr>
-<tr class="separator:a97d01e308e267e939b4d1eebaf2f11d9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6c79906616c8c56a409a97c4111a6b88"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6c79906616c8c56a409a97c4111a6b88">CustomOpCreateFunc</a>) (const char *, int, unsigned **, const int *, const int *, struct <a class="el" href="structMXCallbackList.html">MXCallbackList</a> *, void *)</td></tr>
-<tr class="separator:a6c79906616c8c56a409a97c4111a6b88"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a470eba0beb11ed2fd858c50c9b2be33a"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a470eba0beb11ed2fd858c50c9b2be33a">CustomOpPropCreator</a>) (const char *, const int, const char **, const char **, struct <a class="el" href="structMXCallbackList.html">MXCallbackList</a> *)</td></tr>
-<tr class="separator:a470eba0beb11ed2fd858c50c9b2be33a"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a80aad02a0ebd29de5ad0b65b2c8ccdb3"><td class="memItemLeft" align="right" valign="top">typedef void() </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a80aad02a0ebd29de5ad0b65b2c8ccdb3">MXKVStoreUpdater</a>(int key, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> recv, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> local, void *handle)</td></tr>
-<tr class="memdesc:a80aad02a0ebd29de5ad0b65b2c8ccdb3"><td class="mdescLeft"> </td><td class="mdescRight">user-defined updater for the kvstore It's this updater's responsibility to delete <em>recv</em> and <em>local</em> <a href="#a80aad02a0ebd29de5ad0b65b2c8ccdb3">More...</a><br /></td></tr>
-<tr class="separator:a80aad02a0ebd29de5ad0b65b2c8ccdb3"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6968ac8b790fb40a674a364a2a74cba6"><td class="memItemLeft" align="right" valign="top">typedef void() </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6968ac8b790fb40a674a364a2a74cba6">MXKVStoreServerController</a>(int head, const char *body, void *controller_handle)</td></tr>
-<tr class="memdesc:a6968ac8b790fb40a674a364a2a74cba6"><td class="mdescLeft"> </td><td class="mdescRight">the prototype of a server controller <a href="#a6968ac8b790fb40a674a364a2a74cba6">More...</a><br /></td></tr>
-<tr class="separator:a6968ac8b790fb40a674a364a2a74cba6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa7071c027141653df82d2b72db566acd"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aa7071c027141653df82d2b72db566acd">ExecutorMonitorCallback</a> )(const char *, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a>, void *)</td></tr>
+<tr class="separator:aa7071c027141653df82d2b72db566acd"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a581410d3cb679f2d0fc2f65fca91ec17"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a581410d3cb679f2d0fc2f65fca91ec17">MXGenericCallback</a> )(void)</td></tr>
+<tr class="separator:a581410d3cb679f2d0fc2f65fca91ec17"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac153c43c858c3070c63d0eec44f36514"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ac153c43c858c3070c63d0eec44f36514">CustomOpFBFunc</a> )(int, void **, int *, const int *, const int, void *)</td></tr>
+<tr class="separator:ac153c43c858c3070c63d0eec44f36514"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a557fa6ce86e94109ce7797b2cce33e23"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a557fa6ce86e94109ce7797b2cce33e23">CustomOpDelFunc</a> )(void *)</td></tr>
+<tr class="separator:a557fa6ce86e94109ce7797b2cce33e23"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:add8141d7f51e3a77544cc7b79433d16e"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#add8141d7f51e3a77544cc7b79433d16e">CustomOpListFunc</a> )(char ***, void *)</td></tr>
+<tr class="separator:add8141d7f51e3a77544cc7b79433d16e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9a559987f509f9939030addd565d34a3"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a9a559987f509f9939030addd565d34a3">CustomOpInferShapeFunc</a> )(int, int *, unsigned **, void *)</td></tr>
+<tr class="separator:a9a559987f509f9939030addd565d34a3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a09da22ebe9a09b084afc386fa7b0ef53"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a09da22ebe9a09b084afc386fa7b0ef53">CustomOpInferTypeFunc</a> )(int, int *, void *)</td></tr>
+<tr class="separator:a09da22ebe9a09b084afc386fa7b0ef53"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae8feb033b715209653ea91e089d060b0"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ae8feb033b715209653ea91e089d060b0">CustomOpBwdDepFunc</a> )(const int *, const int *, const int *, int *, int **, void *)</td></tr>
+<tr class="separator:ae8feb033b715209653ea91e089d060b0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a201e319981a60877ab9d6f7cf3b3f62d"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a201e319981a60877ab9d6f7cf3b3f62d">CustomOpCreateFunc</a> )(const char *, int, unsigned **, const int *, const int *, struct <a class="el" href="structMXCallbackList.html">MXCallbackList</a> *, void *)</td></tr>
+<tr class="separator:a201e319981a60877ab9d6f7cf3b3f62d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3a7fd875ee6aad3f20981764626a6c7b"><td class="memItemLeft" align="right" valign="top">typedef int(* </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a3a7fd875ee6aad3f20981764626a6c7b">CustomOpPropCreator</a> )(const char *, const int, const char **, const char **, struct <a class="el" href="structMXCallbackList.html">MXCallbackList</a> *)</td></tr>
+<tr class="separator:a3a7fd875ee6aad3f20981764626a6c7b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af59143cc01b560bb13f25a289b83f37b"><td class="memItemLeft" align="right" valign="top">typedef void( </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#af59143cc01b560bb13f25a289b83f37b">MXKVStoreUpdater</a> )(int key, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> recv, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> local, void *handle)</td></tr>
+<tr class="memdesc:af59143cc01b560bb13f25a289b83f37b"><td class="mdescLeft"> </td><td class="mdescRight">user-defined updater for the kvstore It's this updater's responsibility to delete <em>recv</em> and <em>local</em> <a href="#af59143cc01b560bb13f25a289b83f37b">More...</a><br/></td></tr>
+<tr class="separator:af59143cc01b560bb13f25a289b83f37b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a31f639b22167ac51b67381b16dfd5634"><td class="memItemLeft" align="right" valign="top">typedef void( </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a31f639b22167ac51b67381b16dfd5634">MXKVStoreServerController</a> )(int head, const char *body, void *controller_handle)</td></tr>
+<tr class="memdesc:a31f639b22167ac51b67381b16dfd5634"><td class="mdescLeft"> </td><td class="mdescRight">the prototype of a server controller <a href="#a31f639b22167ac51b67381b16dfd5634">More...</a><br/></td></tr>
+<tr class="separator:a31f639b22167ac51b67381b16dfd5634"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="enum-members"></a>
Enumerations</h2></td></tr>
@@ -190,410 +213,417 @@ Enumerations</h2></td></tr>
<a class="el" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792afe6891ef7e38df52ac25db00a2ef73c6">kCustomOpBackward</a>
}</td></tr>
<tr class="separator:aa2395b69772d66cb5c6c4cbe47749792"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a62d6b72cc6b3d920797c32ea8732cbba"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbba">CustomOpPropCallbacks</a> { <br />
+<tr class="memitem:a62d6b72cc6b3d920797c32ea8732cbba"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbba">CustomOpPropCallbacks</a> { <br/>
  <a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa16e932f1ac19d0846d5ec6d2077be6aa">kCustomOpPropDelete</a>,
<a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaab3c71c386f99a193c2b5d1ad3b306712">kCustomOpPropListArguments</a>,
<a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa0a32388e902d5d81a88f62f3eabf56c3">kCustomOpPropListOutputs</a>,
<a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaaabeb765165b4748e68c7f196bf8a1686">kCustomOpPropListAuxiliaryStates</a>,
-<br />
+<br/>
  <a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaaa23caa8f573176ff6985b550df9b2be2">kCustomOpPropInferShape</a>,
<a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa3f2e3562346ac66f3af912db28cafd06">kCustomOpPropDeclareBackwardDependency</a>,
<a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaac97963c158fddf25e10fddd4d9ae7619">kCustomOpPropCreateOperator</a>,
<a class="el" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa86fbe407e39ef10e916cd8872e28334d">kCustomOpPropInferType</a>
-<br />
+<br/>
}</td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:acab1874f3e3077710b77d1a005abf36c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#acab1874f3e3077710b77d1a005abf36c">MXGetLastError</a> ()</td></tr>
-<tr class="memdesc:acab1874f3e3077710b77d1a005abf36c"><td class="mdescLeft"> </td><td class="mdescRight">return str message of the last error all function in this file will return 0 when success and -1 when an error occured, MXGetLastError can be called to retrieve the error <a href="#acab1874f3e3077710b77d1a005abf36c">More...</a><br /></td></tr>
+<tr class="memdesc:acab1874f3e3077710b77d1a005abf36c"><td class="mdescLeft"> </td><td class="mdescRight">return str message of the last error all function in this file will return 0 when success and -1 when an error occured, MXGetLastError can be called to retrieve the error <a href="#acab1874f3e3077710b77d1a005abf36c">More...</a><br/></td></tr>
<tr class="separator:acab1874f3e3077710b77d1a005abf36c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a05e3ead36464b62cd1a9264187640f5a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a05e3ead36464b62cd1a9264187640f5a">MXRandomSeed</a> (int seed)</td></tr>
-<tr class="memdesc:a05e3ead36464b62cd1a9264187640f5a"><td class="mdescLeft"> </td><td class="mdescRight">Seed the global random number generators in mxnet. <a href="#a05e3ead36464b62cd1a9264187640f5a">More...</a><br /></td></tr>
+<tr class="memdesc:a05e3ead36464b62cd1a9264187640f5a"><td class="mdescLeft"> </td><td class="mdescRight">Seed the global random number generators in mxnet. <a href="#a05e3ead36464b62cd1a9264187640f5a">More...</a><br/></td></tr>
<tr class="separator:a05e3ead36464b62cd1a9264187640f5a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa837df9930bbb46d0ca1674523f63e1c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aa837df9930bbb46d0ca1674523f63e1c">MXNotifyShutdown</a> ()</td></tr>
-<tr class="memdesc:aa837df9930bbb46d0ca1674523f63e1c"><td class="mdescLeft"> </td><td class="mdescRight">Notify the engine about a shutdown, This can help engine to print less messages into display. <a href="#aa837df9930bbb46d0ca1674523f63e1c">More...</a><br /></td></tr>
+<tr class="memdesc:aa837df9930bbb46d0ca1674523f63e1c"><td class="mdescLeft"> </td><td class="mdescRight">Notify the engine about a shutdown, This can help engine to print less messages into display. <a href="#aa837df9930bbb46d0ca1674523f63e1c">More...</a><br/></td></tr>
<tr class="separator:aa837df9930bbb46d0ca1674523f63e1c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab3a837622ed93ba390b4e8cbc0286b87"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ab3a837622ed93ba390b4e8cbc0286b87">MXSetProfilerConfig</a> (int mode, const char *filename)</td></tr>
-<tr class="memdesc:ab3a837622ed93ba390b4e8cbc0286b87"><td class="mdescLeft"> </td><td class="mdescRight">Set up configuration of profiler. <a href="#ab3a837622ed93ba390b4e8cbc0286b87">More...</a><br /></td></tr>
+<tr class="memdesc:ab3a837622ed93ba390b4e8cbc0286b87"><td class="mdescLeft"> </td><td class="mdescRight">Set up configuration of profiler. <a href="#ab3a837622ed93ba390b4e8cbc0286b87">More...</a><br/></td></tr>
<tr class="separator:ab3a837622ed93ba390b4e8cbc0286b87"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac373802e0d3a38f297345f87d307a798"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ac373802e0d3a38f297345f87d307a798">MXSetProfilerState</a> (int state)</td></tr>
-<tr class="memdesc:ac373802e0d3a38f297345f87d307a798"><td class="mdescLeft"> </td><td class="mdescRight">Set up state of profiler. <a href="#ac373802e0d3a38f297345f87d307a798">More...</a><br /></td></tr>
+<tr class="memdesc:ac373802e0d3a38f297345f87d307a798"><td class="mdescLeft"> </td><td class="mdescRight">Set up state of profiler. <a href="#ac373802e0d3a38f297345f87d307a798">More...</a><br/></td></tr>
<tr class="separator:ac373802e0d3a38f297345f87d307a798"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a51716c7a38d1b02d26da4d84d6307025"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a51716c7a38d1b02d26da4d84d6307025">MXDumpProfile</a> ()</td></tr>
-<tr class="memdesc:a51716c7a38d1b02d26da4d84d6307025"><td class="mdescLeft"> </td><td class="mdescRight">Save profile and stop profiler. <a href="#a51716c7a38d1b02d26da4d84d6307025">More...</a><br /></td></tr>
+<tr class="memdesc:a51716c7a38d1b02d26da4d84d6307025"><td class="mdescLeft"> </td><td class="mdescRight">Save profile and stop profiler. <a href="#a51716c7a38d1b02d26da4d84d6307025">More...</a><br/></td></tr>
<tr class="separator:a51716c7a38d1b02d26da4d84d6307025"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a13ebc161bc64049a26404ee6eca4ac26"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a13ebc161bc64049a26404ee6eca4ac26">MXSetNumOMPThreads</a> (int thread_num)</td></tr>
-<tr class="memdesc:a13ebc161bc64049a26404ee6eca4ac26"><td class="mdescLeft"> </td><td class="mdescRight">Set the number of OMP threads to use. <a href="#a13ebc161bc64049a26404ee6eca4ac26">More...</a><br /></td></tr>
+<tr class="memdesc:a13ebc161bc64049a26404ee6eca4ac26"><td class="mdescLeft"> </td><td class="mdescRight">Set the number of OMP threads to use. <a href="#a13ebc161bc64049a26404ee6eca4ac26">More...</a><br/></td></tr>
<tr class="separator:a13ebc161bc64049a26404ee6eca4ac26"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3a32f78b54c81564da9834c6c6353b90"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a3a32f78b54c81564da9834c6c6353b90">MXNDArrayCreateNone</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> *out)</td></tr>
-<tr class="memdesc:a3a32f78b54c81564da9834c6c6353b90"><td class="mdescLeft"> </td><td class="mdescRight">create a NDArray handle that is not initialized can be used to pass in as mutate variables to hold the result of NDArray <a href="#a3a32f78b54c81564da9834c6c6353b90">More...</a><br /></td></tr>
+<tr class="memdesc:a3a32f78b54c81564da9834c6c6353b90"><td class="mdescLeft"> </td><td class="mdescRight">create a NDArray handle that is not initialized can be used to pass in as mutate variables to hold the result of NDArray <a href="#a3a32f78b54c81564da9834c6c6353b90">More...</a><br/></td></tr>
<tr class="separator:a3a32f78b54c81564da9834c6c6353b90"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a097e93ed9fd8c8cfb8ae76aa32441b72"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a097e93ed9fd8c8cfb8ae76aa32441b72">MXNDArrayCreate</a> (const <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> *shape, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b [...]
-<tr class="memdesc:a097e93ed9fd8c8cfb8ae76aa32441b72"><td class="mdescLeft"> </td><td class="mdescRight">create a NDArray with specified shape <a href="#a097e93ed9fd8c8cfb8ae76aa32441b72">More...</a><br /></td></tr>
+<tr class="memdesc:a097e93ed9fd8c8cfb8ae76aa32441b72"><td class="mdescLeft"> </td><td class="mdescRight">create a NDArray with specified shape <a href="#a097e93ed9fd8c8cfb8ae76aa32441b72">More...</a><br/></td></tr>
<tr class="separator:a097e93ed9fd8c8cfb8ae76aa32441b72"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:abe1ed22a74c15b24111ce2af39bcca38"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#abe1ed22a74c15b24111ce2af39bcca38">MXNDArrayCreateEx</a> (const <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> *shape, <a class="el" href="c__api_8h.html#a796e7ef8260ef4 [...]
-<tr class="memdesc:abe1ed22a74c15b24111ce2af39bcca38"><td class="mdescLeft"> </td><td class="mdescRight">create a NDArray with specified shape and data type <a href="#abe1ed22a74c15b24111ce2af39bcca38">More...</a><br /></td></tr>
+<tr class="memdesc:abe1ed22a74c15b24111ce2af39bcca38"><td class="mdescLeft"> </td><td class="mdescRight">create a NDArray with specified shape and data type <a href="#abe1ed22a74c15b24111ce2af39bcca38">More...</a><br/></td></tr>
<tr class="separator:abe1ed22a74c15b24111ce2af39bcca38"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a27c9353672df26b68c169fbb857f3a3f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a27c9353672df26b68c169fbb857f3a3f">MXNDArrayLoadFromRawBytes</a> (const void *buf, size_t size, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> *out)</td></tr>
-<tr class="memdesc:a27c9353672df26b68c169fbb857f3a3f"><td class="mdescLeft"> </td><td class="mdescRight">create a NDArray handle that is loaded from raw bytes. <a href="#a27c9353672df26b68c169fbb857f3a3f">More...</a><br /></td></tr>
+<tr class="memdesc:a27c9353672df26b68c169fbb857f3a3f"><td class="mdescLeft"> </td><td class="mdescRight">create a NDArray handle that is loaded from raw bytes. <a href="#a27c9353672df26b68c169fbb857f3a3f">More...</a><br/></td></tr>
<tr class="separator:a27c9353672df26b68c169fbb857f3a3f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab180057ee7a43918d021adfc468150a0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ab180057ee7a43918d021adfc468150a0">MXNDArraySaveRawBytes</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, size_t *out_size, const char **out_buf)</td></tr>
-<tr class="memdesc:ab180057ee7a43918d021adfc468150a0"><td class="mdescLeft"> </td><td class="mdescRight">save the NDArray into raw bytes. <a href="#ab180057ee7a43918d021adfc468150a0">More...</a><br /></td></tr>
+<tr class="memdesc:ab180057ee7a43918d021adfc468150a0"><td class="mdescLeft"> </td><td class="mdescRight">save the NDArray into raw bytes. <a href="#ab180057ee7a43918d021adfc468150a0">More...</a><br/></td></tr>
<tr class="separator:ab180057ee7a43918d021adfc468150a0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5dc95ef008b73103b86f4576fcf9117d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a5dc95ef008b73103b86f4576fcf9117d">MXNDArraySave</a> (const char *fname, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> num_args, <a class="el" href="c__api_8h.html#a99c [...]
-<tr class="memdesc:a5dc95ef008b73103b86f4576fcf9117d"><td class="mdescLeft"> </td><td class="mdescRight">Save list of narray into the file. <a href="#a5dc95ef008b73103b86f4576fcf9117d">More...</a><br /></td></tr>
+<tr class="memdesc:a5dc95ef008b73103b86f4576fcf9117d"><td class="mdescLeft"> </td><td class="mdescRight">Save list of narray into the file. <a href="#a5dc95ef008b73103b86f4576fcf9117d">More...</a><br/></td></tr>
<tr class="separator:a5dc95ef008b73103b86f4576fcf9117d"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2c1d0459d3fe124b5d3aed49386999f1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a2c1d0459d3fe124b5d3aed49386999f1">MXNDArrayLoad</a> (const char *fname, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> *out_size, <a class="el" href="c__api_8h.html#a99 [...]
-<tr class="memdesc:a2c1d0459d3fe124b5d3aed49386999f1"><td class="mdescLeft"> </td><td class="mdescRight">Load list of narray from the file. <a href="#a2c1d0459d3fe124b5d3aed49386999f1">More...</a><br /></td></tr>
+<tr class="memdesc:a2c1d0459d3fe124b5d3aed49386999f1"><td class="mdescLeft"> </td><td class="mdescRight">Load list of narray from the file. <a href="#a2c1d0459d3fe124b5d3aed49386999f1">More...</a><br/></td></tr>
<tr class="separator:a2c1d0459d3fe124b5d3aed49386999f1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ada464e85b86305d167511bb977095a52"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ada464e85b86305d167511bb977095a52">MXNDArraySyncCopyFromCPU</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, const void *data, size_t size)</td></tr>
-<tr class="memdesc:ada464e85b86305d167511bb977095a52"><td class="mdescLeft"> </td><td class="mdescRight">Perform a synchronize copy from a continugous CPU memory region. <a href="#ada464e85b86305d167511bb977095a52">More...</a><br /></td></tr>
+<tr class="memdesc:ada464e85b86305d167511bb977095a52"><td class="mdescLeft"> </td><td class="mdescRight">Perform a synchronize copy from a continugous CPU memory region. <a href="#ada464e85b86305d167511bb977095a52">More...</a><br/></td></tr>
<tr class="separator:ada464e85b86305d167511bb977095a52"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a98df4e0ceae7e198a5c18904cb135ab1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a98df4e0ceae7e198a5c18904cb135ab1">MXNDArraySyncCopyToCPU</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, void *data, size_t size)</td></tr>
-<tr class="memdesc:a98df4e0ceae7e198a5c18904cb135ab1"><td class="mdescLeft"> </td><td class="mdescRight">Perform a synchronize copyto a continugous CPU memory region. <a href="#a98df4e0ceae7e198a5c18904cb135ab1">More...</a><br /></td></tr>
+<tr class="memdesc:a98df4e0ceae7e198a5c18904cb135ab1"><td class="mdescLeft"> </td><td class="mdescRight">Perform a synchronize copyto a continugous CPU memory region. <a href="#a98df4e0ceae7e198a5c18904cb135ab1">More...</a><br/></td></tr>
<tr class="separator:a98df4e0ceae7e198a5c18904cb135ab1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad008e978336534821634e460c3eb9bff"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ad008e978336534821634e460c3eb9bff">MXNDArrayWaitToRead</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle)</td></tr>
-<tr class="memdesc:ad008e978336534821634e460c3eb9bff"><td class="mdescLeft"> </td><td class="mdescRight">Wait until all the pending writes with respect NDArray are finished. Always call this before read data out synchronizely. <a href="#ad008e978336534821634e460c3eb9bff">More...</a><br /></td></tr>
+<tr class="memdesc:ad008e978336534821634e460c3eb9bff"><td class="mdescLeft"> </td><td class="mdescRight">Wait until all the pending writes with respect NDArray are finished. Always call this before read data out synchronizely. <a href="#ad008e978336534821634e460c3eb9bff">More...</a><br/></td></tr>
<tr class="separator:ad008e978336534821634e460c3eb9bff"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6f4d7cb465c2b3723c51449b1aba8af9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6f4d7cb465c2b3723c51449b1aba8af9">MXNDArrayWaitToWrite</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle)</td></tr>
-<tr class="memdesc:a6f4d7cb465c2b3723c51449b1aba8af9"><td class="mdescLeft"> </td><td class="mdescRight">Wait until all the pending read/write with respect NDArray are finished. Always call this before write data into NDArray synchronizely. <a href="#a6f4d7cb465c2b3723c51449b1aba8af9">More...</a><br /></td></tr>
+<tr class="memdesc:a6f4d7cb465c2b3723c51449b1aba8af9"><td class="mdescLeft"> </td><td class="mdescRight">Wait until all the pending read/write with respect NDArray are finished. Always call this before write data into NDArray synchronizely. <a href="#a6f4d7cb465c2b3723c51449b1aba8af9">More...</a><br/></td></tr>
<tr class="separator:a6f4d7cb465c2b3723c51449b1aba8af9"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:adf10f6461b93f46d67289359909d56c3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#adf10f6461b93f46d67289359909d56c3">MXNDArrayWaitAll</a> ()</td></tr>
-<tr class="memdesc:adf10f6461b93f46d67289359909d56c3"><td class="mdescLeft"> </td><td class="mdescRight">wait until all delayed operations in the system is completed <a href="#adf10f6461b93f46d67289359909d56c3">More...</a><br /></td></tr>
+<tr class="memdesc:adf10f6461b93f46d67289359909d56c3"><td class="mdescLeft"> </td><td class="mdescRight">wait until all delayed operations in the system is completed <a href="#adf10f6461b93f46d67289359909d56c3">More...</a><br/></td></tr>
<tr class="separator:adf10f6461b93f46d67289359909d56c3"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a857e66f665360d5fab3ad52528df38dd"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a857e66f665360d5fab3ad52528df38dd">MXNDArrayFree</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle)</td></tr>
-<tr class="memdesc:a857e66f665360d5fab3ad52528df38dd"><td class="mdescLeft"> </td><td class="mdescRight">free the narray handle <a href="#a857e66f665360d5fab3ad52528df38dd">More...</a><br /></td></tr>
+<tr class="memdesc:a857e66f665360d5fab3ad52528df38dd"><td class="mdescLeft"> </td><td class="mdescRight">free the narray handle <a href="#a857e66f665360d5fab3ad52528df38dd">More...</a><br/></td></tr>
<tr class="separator:a857e66f665360d5fab3ad52528df38dd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a67c83f78e6bd15c6ad6eb09a34133086"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a67c83f78e6bd15c6ad6eb09a34133086">MXNDArraySlice</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b2 [...]
-<tr class="memdesc:a67c83f78e6bd15c6ad6eb09a34133086"><td class="mdescLeft"> </td><td class="mdescRight">Slice the NDArray along axis 0. <a href="#a67c83f78e6bd15c6ad6eb09a34133086">More...</a><br /></td></tr>
+<tr class="memdesc:a67c83f78e6bd15c6ad6eb09a34133086"><td class="mdescLeft"> </td><td class="mdescRight">Slice the NDArray along axis 0. <a href="#a67c83f78e6bd15c6ad6eb09a34133086">More...</a><br/></td></tr>
<tr class="separator:a67c83f78e6bd15c6ad6eb09a34133086"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a90fbe78325ba9d67d3629be442623cee"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a90fbe78325ba9d67d3629be442623cee">MXNDArrayAt</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af [...]
-<tr class="memdesc:a90fbe78325ba9d67d3629be442623cee"><td class="mdescLeft"> </td><td class="mdescRight">Index the NDArray along axis 0. <a href="#a90fbe78325ba9d67d3629be442623cee">More...</a><br /></td></tr>
+<tr class="memdesc:a90fbe78325ba9d67d3629be442623cee"><td class="mdescLeft"> </td><td class="mdescRight">Index the NDArray along axis 0. <a href="#a90fbe78325ba9d67d3629be442623cee">More...</a><br/></td></tr>
<tr class="separator:a90fbe78325ba9d67d3629be442623cee"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3743a54910b1862400c3ec4cbf7e9d80"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a3743a54910b1862400c3ec4cbf7e9d80">MXNDArrayReshape</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, int ndim, int *dims, <a class="el" href="c__api_8h. [...]
-<tr class="memdesc:a3743a54910b1862400c3ec4cbf7e9d80"><td class="mdescLeft"> </td><td class="mdescRight">Reshape the NDArray. <a href="#a3743a54910b1862400c3ec4cbf7e9d80">More...</a><br /></td></tr>
+<tr class="memdesc:a3743a54910b1862400c3ec4cbf7e9d80"><td class="mdescLeft"> </td><td class="mdescRight">Reshape the NDArray. <a href="#a3743a54910b1862400c3ec4cbf7e9d80">More...</a><br/></td></tr>
<tr class="separator:a3743a54910b1862400c3ec4cbf7e9d80"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2035651f4392d249d1b904d5eb0c3406"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a2035651f4392d249d1b904d5eb0c3406">MXNDArrayGetShape</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef4 [...]
-<tr class="memdesc:a2035651f4392d249d1b904d5eb0c3406"><td class="mdescLeft"> </td><td class="mdescRight">get the shape of the array <a href="#a2035651f4392d249d1b904d5eb0c3406">More...</a><br /></td></tr>
+<tr class="memdesc:a2035651f4392d249d1b904d5eb0c3406"><td class="mdescLeft"> </td><td class="mdescRight">get the shape of the array <a href="#a2035651f4392d249d1b904d5eb0c3406">More...</a><br/></td></tr>
<tr class="separator:a2035651f4392d249d1b904d5eb0c3406"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6c8d8ceb3c523e8507e813f7afb97f83"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6c8d8ceb3c523e8507e813f7afb97f83">MXNDArrayGetData</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, void **out_pdata)</td></tr>
-<tr class="memdesc:a6c8d8ceb3c523e8507e813f7afb97f83"><td class="mdescLeft"> </td><td class="mdescRight">get the content of the data in NDArray <a href="#a6c8d8ceb3c523e8507e813f7afb97f83">More...</a><br /></td></tr>
+<tr class="memdesc:a6c8d8ceb3c523e8507e813f7afb97f83"><td class="mdescLeft"> </td><td class="mdescRight">get the content of the data in NDArray <a href="#a6c8d8ceb3c523e8507e813f7afb97f83">More...</a><br/></td></tr>
<tr class="separator:a6c8d8ceb3c523e8507e813f7afb97f83"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a69cfaa5de4b9d87070d4823700e64a1d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a69cfaa5de4b9d87070d4823700e64a1d">MXNDArrayGetDType</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, int *out_dtype)</td></tr>
-<tr class="memdesc:a69cfaa5de4b9d87070d4823700e64a1d"><td class="mdescLeft"> </td><td class="mdescRight">get the type of the data in NDArray <a href="#a69cfaa5de4b9d87070d4823700e64a1d">More...</a><br /></td></tr>
+<tr class="memdesc:a69cfaa5de4b9d87070d4823700e64a1d"><td class="mdescLeft"> </td><td class="mdescRight">get the type of the data in NDArray <a href="#a69cfaa5de4b9d87070d4823700e64a1d">More...</a><br/></td></tr>
<tr class="separator:a69cfaa5de4b9d87070d4823700e64a1d"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3ae6292f1e640d54c92a8cc56f513b76"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a3ae6292f1e640d54c92a8cc56f513b76">MXNDArrayGetContext</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, int *out_dev_type, int *out_dev_id)</td></tr>
-<tr class="memdesc:a3ae6292f1e640d54c92a8cc56f513b76"><td class="mdescLeft"> </td><td class="mdescRight">get the context of the NDArray <a href="#a3ae6292f1e640d54c92a8cc56f513b76">More...</a><br /></td></tr>
+<tr class="memdesc:a3ae6292f1e640d54c92a8cc56f513b76"><td class="mdescLeft"> </td><td class="mdescRight">get the context of the NDArray <a href="#a3ae6292f1e640d54c92a8cc56f513b76">More...</a><br/></td></tr>
<tr class="separator:a3ae6292f1e640d54c92a8cc56f513b76"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aedb8d676502a66eef4a6c5988f73dccb"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aedb8d676502a66eef4a6c5988f73dccb">MXNDArrayGetGrad</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, <a class="el" href="c__api_8h.html#a99c2939783499d2 [...]
-<tr class="memdesc:aedb8d676502a66eef4a6c5988f73dccb"><td class="mdescLeft"> </td><td class="mdescRight">return gradient buffer attached to this NDArray <a href="#aedb8d676502a66eef4a6c5988f73dccb">More...</a><br /></td></tr>
+<tr class="memdesc:aedb8d676502a66eef4a6c5988f73dccb"><td class="mdescLeft"> </td><td class="mdescRight">return gradient buffer attached to this NDArray <a href="#aedb8d676502a66eef4a6c5988f73dccb">More...</a><br/></td></tr>
<tr class="separator:aedb8d676502a66eef4a6c5988f73dccb"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8e2c90cfa6c52a012fd3f23ffd8541f2"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a8e2c90cfa6c52a012fd3f23ffd8541f2">MXNDArrayDetach</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, <a class="el" href="c__api_8h.html#a99c2939783499d2a [...]
-<tr class="memdesc:a8e2c90cfa6c52a012fd3f23ffd8541f2"><td class="mdescLeft"> </td><td class="mdescRight">detach and ndarray from computation graph by clearing entry_ <a href="#a8e2c90cfa6c52a012fd3f23ffd8541f2">More...</a><br /></td></tr>
+<tr class="memdesc:a8e2c90cfa6c52a012fd3f23ffd8541f2"><td class="mdescLeft"> </td><td class="mdescRight">detach and ndarray from computation graph by clearing entry_ <a href="#a8e2c90cfa6c52a012fd3f23ffd8541f2">More...</a><br/></td></tr>
<tr class="separator:a8e2c90cfa6c52a012fd3f23ffd8541f2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2ff7ded9017ac7617be4eeebffae06a1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a2ff7ded9017ac7617be4eeebffae06a1">MXNDArraySetGradState</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, int state)</td></tr>
-<tr class="memdesc:a2ff7ded9017ac7617be4eeebffae06a1"><td class="mdescLeft"> </td><td class="mdescRight">set the flag for gradient array state. <a href="#a2ff7ded9017ac7617be4eeebffae06a1">More...</a><br /></td></tr>
+<tr class="memdesc:a2ff7ded9017ac7617be4eeebffae06a1"><td class="mdescLeft"> </td><td class="mdescRight">set the flag for gradient array state. <a href="#a2ff7ded9017ac7617be4eeebffae06a1">More...</a><br/></td></tr>
<tr class="separator:a2ff7ded9017ac7617be4eeebffae06a1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2ab9f498d34d84238e41ecb5e584e435"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a2ab9f498d34d84238e41ecb5e584e435">MXNDArrayGetGradState</a> (<a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> handle, int *out)</td></tr>
-<tr class="memdesc:a2ab9f498d34d84238e41ecb5e584e435"><td class="mdescLeft"> </td><td class="mdescRight">set the flag for gradient array state. <a href="#a2ab9f498d34d84238e41ecb5e584e435">More...</a><br /></td></tr>
+<tr class="memdesc:a2ab9f498d34d84238e41ecb5e584e435"><td class="mdescLeft"> </td><td class="mdescRight">set the flag for gradient array state. <a href="#a2ab9f498d34d84238e41ecb5e584e435">More...</a><br/></td></tr>
<tr class="separator:a2ab9f498d34d84238e41ecb5e584e435"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a553e6791f689a41e63e4cdfbdbbd5c58"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a553e6791f689a41e63e4cdfbdbbd5c58">MXListFunctions</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> *out_size, <a class="el" href="c__api_8h.html#a197f06e1d873d34572b [...]
-<tr class="memdesc:a553e6791f689a41e63e4cdfbdbbd5c58"><td class="mdescLeft"> </td><td class="mdescRight">list all the available functions handles most user can use it to list all the needed functions <a href="#a553e6791f689a41e63e4cdfbdbbd5c58">More...</a><br /></td></tr>
+<tr class="memdesc:a553e6791f689a41e63e4cdfbdbbd5c58"><td class="mdescLeft"> </td><td class="mdescRight">list all the available functions handles most user can use it to list all the needed functions <a href="#a553e6791f689a41e63e4cdfbdbbd5c58">More...</a><br/></td></tr>
<tr class="separator:a553e6791f689a41e63e4cdfbdbbd5c58"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9e2a1cb559d69daf541dac88c07409a8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a9e2a1cb559d69daf541dac88c07409a8">MXGetFunction</a> (const char *name, <a class="el" href="c__api_8h.html#a197f06e1d873d34572b679532c268d35">FunctionHandle</a> *out)</td></tr>
-<tr class="memdesc:a9e2a1cb559d69daf541dac88c07409a8"><td class="mdescLeft"> </td><td class="mdescRight">get the function handle by name <a href="#a9e2a1cb559d69daf541dac88c07409a8">More...</a><br /></td></tr>
+<tr class="memdesc:a9e2a1cb559d69daf541dac88c07409a8"><td class="mdescLeft"> </td><td class="mdescRight">get the function handle by name <a href="#a9e2a1cb559d69daf541dac88c07409a8">More...</a><br/></td></tr>
<tr class="separator:a9e2a1cb559d69daf541dac88c07409a8"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad369531149cf26e8db49958e17bdf237"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ad369531149cf26e8db49958e17bdf237">MXFuncGetInfo</a> (<a class="el" href="c__api_8h.html#a197f06e1d873d34572b679532c268d35">FunctionHandle</a> fun, const char **name, const char **description, <a class="e [...]
-<tr class="memdesc:ad369531149cf26e8db49958e17bdf237"><td class="mdescLeft"> </td><td class="mdescRight">Get the information of the function handle. <a href="#ad369531149cf26e8db49958e17bdf237">More...</a><br /></td></tr>
+<tr class="memdesc:ad369531149cf26e8db49958e17bdf237"><td class="mdescLeft"> </td><td class="mdescRight">Get the information of the function handle. <a href="#ad369531149cf26e8db49958e17bdf237">More...</a><br/></td></tr>
<tr class="separator:ad369531149cf26e8db49958e17bdf237"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af0a6166fe48ce0a175990914b1cb02e0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#af0a6166fe48ce0a175990914b1cb02e0">MXFuncDescribe</a> (<a class="el" href="c__api_8h.html#a197f06e1d873d34572b679532c268d35">FunctionHandle</a> fun, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24a [...]
-<tr class="memdesc:af0a6166fe48ce0a175990914b1cb02e0"><td class="mdescLeft"> </td><td class="mdescRight">get the argument requirements of the function <a href="#af0a6166fe48ce0a175990914b1cb02e0">More...</a><br /></td></tr>
+<tr class="memdesc:af0a6166fe48ce0a175990914b1cb02e0"><td class="mdescLeft"> </td><td class="mdescRight">get the argument requirements of the function <a href="#af0a6166fe48ce0a175990914b1cb02e0">More...</a><br/></td></tr>
<tr class="separator:af0a6166fe48ce0a175990914b1cb02e0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a527f90a410f66db7136e91d27e7ea45c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a527f90a410f66db7136e91d27e7ea45c">MXFuncInvoke</a> (<a class="el" href="c__api_8h.html#a197f06e1d873d34572b679532c268d35">FunctionHandle</a> fun, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d [...]
-<tr class="memdesc:a527f90a410f66db7136e91d27e7ea45c"><td class="mdescLeft"> </td><td class="mdescRight">invoke a function, the array size of passed in arguments must match the values in the <a href="#a527f90a410f66db7136e91d27e7ea45c">More...</a><br /></td></tr>
+<tr class="memdesc:a527f90a410f66db7136e91d27e7ea45c"><td class="mdescLeft"> </td><td class="mdescRight">invoke a function, the array size of passed in arguments must match the values in the <a href="#a527f90a410f66db7136e91d27e7ea45c">More...</a><br/></td></tr>
<tr class="separator:a527f90a410f66db7136e91d27e7ea45c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9afe2f46b1fcd8016c8bfb11a84c6240"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a9afe2f46b1fcd8016c8bfb11a84c6240">MXFuncInvokeEx</a> (<a class="el" href="c__api_8h.html#a197f06e1d873d34572b679532c268d35">FunctionHandle</a> fun, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce [...]
-<tr class="memdesc:a9afe2f46b1fcd8016c8bfb11a84c6240"><td class="mdescLeft"> </td><td class="mdescRight">invoke a function, the array size of passed in arguments must match the values in the <a href="#a9afe2f46b1fcd8016c8bfb11a84c6240">More...</a><br /></td></tr>
+<tr class="memdesc:a9afe2f46b1fcd8016c8bfb11a84c6240"><td class="mdescLeft"> </td><td class="mdescRight">invoke a function, the array size of passed in arguments must match the values in the <a href="#a9afe2f46b1fcd8016c8bfb11a84c6240">More...</a><br/></td></tr>
<tr class="separator:a9afe2f46b1fcd8016c8bfb11a84c6240"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6a9a4608136959389db02ad938201a67"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6a9a4608136959389db02ad938201a67">MXImperativeInvoke</a> (<a class="el" href="c__api_8h.html#aa2e96133f49cfdc1095da6cd08afbce0">AtomicSymbolCreator</a> creator, int num_inputs, <a class="el" href="c__api [...]
-<tr class="memdesc:a6a9a4608136959389db02ad938201a67"><td class="mdescLeft"> </td><td class="mdescRight">invoke a nnvm op and imperative function <a href="#a6a9a4608136959389db02ad938201a67">More...</a><br /></td></tr>
+<tr class="memdesc:a6a9a4608136959389db02ad938201a67"><td class="mdescLeft"> </td><td class="mdescRight">invoke a nnvm op and imperative function <a href="#a6a9a4608136959389db02ad938201a67">More...</a><br/></td></tr>
<tr class="separator:a6a9a4608136959389db02ad938201a67"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a75939e5be09a150d4d8905c9fd6e986e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a75939e5be09a150d4d8905c9fd6e986e">MXAutogradSetIsRecording</a> (int is_recording, int *prev)</td></tr>
+<tr class="memdesc:a75939e5be09a150d4d8905c9fd6e986e"><td class="mdescLeft"> </td><td class="mdescRight">set whether to record operator for autograd <a href="#a75939e5be09a150d4d8905c9fd6e986e">More...</a><br/></td></tr>
+<tr class="separator:a75939e5be09a150d4d8905c9fd6e986e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9cf2b0aa3ff66119fe793f6ed82b61b8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a9cf2b0aa3ff66119fe793f6ed82b61b8">MXAutogradSetIsTraining</a> (int is_training, int *prev)</td></tr>
-<tr class="memdesc:a9cf2b0aa3ff66119fe793f6ed82b61b8"><td class="mdescLeft"> </td><td class="mdescRight">set whether to record operator for autograd <a href="#a9cf2b0aa3ff66119fe793f6ed82b61b8">More...</a><br /></td></tr>
+<tr class="memdesc:a9cf2b0aa3ff66119fe793f6ed82b61b8"><td class="mdescLeft"> </td><td class="mdescRight">set whether to record operator for autograd <a href="#a9cf2b0aa3ff66119fe793f6ed82b61b8">More...</a><br/></td></tr>
<tr class="separator:a9cf2b0aa3ff66119fe793f6ed82b61b8"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a53f3bbc41e275485d1d6cd21f40abb7f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a53f3bbc41e275485d1d6cd21f40abb7f">MXAutogradMarkVariables</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> num_var, <a class="el" href="c__api_8h.html#a99c2939783499 [...]
-<tr class="memdesc:a53f3bbc41e275485d1d6cd21f40abb7f"><td class="mdescLeft"> </td><td class="mdescRight">mark NDArrays as variables to compute gradient for autograd <a href="#a53f3bbc41e275485d1d6cd21f40abb7f">More...</a><br /></td></tr>
+<tr class="memdesc:a53f3bbc41e275485d1d6cd21f40abb7f"><td class="mdescLeft"> </td><td class="mdescRight">mark NDArrays as variables to compute gradient for autograd <a href="#a53f3bbc41e275485d1d6cd21f40abb7f">More...</a><br/></td></tr>
<tr class="separator:a53f3bbc41e275485d1d6cd21f40abb7f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4ea5441aa16bff2121855a5afb7be84e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a4ea5441aa16bff2121855a5afb7be84e">MXAutogradComputeGradient</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> num_output, <a class="el" href="c__api_8h.html#a99c29397 [...]
-<tr class="memdesc:a4ea5441aa16bff2121855a5afb7be84e"><td class="mdescLeft"> </td><td class="mdescRight">compute the gradient of outputs w.r.t variabels <a href="#a4ea5441aa16bff2121855a5afb7be84e">More...</a><br /></td></tr>
+<tr class="memdesc:a4ea5441aa16bff2121855a5afb7be84e"><td class="mdescLeft"> </td><td class="mdescRight">compute the gradient of outputs w.r.t variabels <a href="#a4ea5441aa16bff2121855a5afb7be84e">More...</a><br/></td></tr>
<tr class="separator:a4ea5441aa16bff2121855a5afb7be84e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a133d10a566764530bf3172e52afbe561"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a133d10a566764530bf3172e52afbe561">MXAutogradBackward</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> num_output, <a class="el" href="c__api_8h.html#a99c2939783499d2 [...]
-<tr class="memdesc:a133d10a566764530bf3172e52afbe561"><td class="mdescLeft"> </td><td class="mdescRight">compute the gradient of outputs w.r.t variabels <a href="#a133d10a566764530bf3172e52afbe561">More...</a><br /></td></tr>
+<tr class="memdesc:a133d10a566764530bf3172e52afbe561"><td class="mdescLeft"> </td><td class="mdescRight">compute the gradient of outputs w.r.t variabels <a href="#a133d10a566764530bf3172e52afbe561">More...</a><br/></td></tr>
<tr class="separator:a133d10a566764530bf3172e52afbe561"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a57a5d1ed585f9f431a31f921b4ca00ab"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a57a5d1ed585f9f431a31f921b4ca00ab">MXAutogradBackwardEx</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> num_output, <a class="el" href="c__api_8h.html#a99c2939783499 [...]
+<tr class="memdesc:a57a5d1ed585f9f431a31f921b4ca00ab"><td class="mdescLeft"> </td><td class="mdescRight">compute the gradient of outputs w.r.t variabels <a href="#a57a5d1ed585f9f431a31f921b4ca00ab">More...</a><br/></td></tr>
+<tr class="separator:a57a5d1ed585f9f431a31f921b4ca00ab"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a46d056bad70ea014110923da41524cfc"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a46d056bad70ea014110923da41524cfc">MXCreateCachedOp</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> handle, <a class="el" href="c__api_8h.html#aac4a2be6e40b0869 [...]
-<tr class="memdesc:a46d056bad70ea014110923da41524cfc"><td class="mdescLeft"> </td><td class="mdescRight">create cached operator <a href="#a46d056bad70ea014110923da41524cfc">More...</a><br /></td></tr>
+<tr class="memdesc:a46d056bad70ea014110923da41524cfc"><td class="mdescLeft"> </td><td class="mdescRight">create cached operator <a href="#a46d056bad70ea014110923da41524cfc">More...</a><br/></td></tr>
<tr class="separator:a46d056bad70ea014110923da41524cfc"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a83a34d6df4290e5efd737293cf875a9c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a83a34d6df4290e5efd737293cf875a9c">MXFreeCachedOp</a> (<a class="el" href="c__api_8h.html#aac4a2be6e40b0869bcc390a573a7e489">CachedOpHandle</a> handle)</td></tr>
-<tr class="memdesc:a83a34d6df4290e5efd737293cf875a9c"><td class="mdescLeft"> </td><td class="mdescRight">free cached operator <a href="#a83a34d6df4290e5efd737293cf875a9c">More...</a><br /></td></tr>
+<tr class="memdesc:a83a34d6df4290e5efd737293cf875a9c"><td class="mdescLeft"> </td><td class="mdescRight">free cached operator <a href="#a83a34d6df4290e5efd737293cf875a9c">More...</a><br/></td></tr>
<tr class="separator:a83a34d6df4290e5efd737293cf875a9c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad4c2297db07b9610d39f1d0e12fdc2b9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ad4c2297db07b9610d39f1d0e12fdc2b9">MXInvokeCachedOp</a> (<a class="el" href="c__api_8h.html#aac4a2be6e40b0869bcc390a573a7e489">CachedOpHandle</a> handle, int num_inputs, <a class="el" href="c__api_8h.html [...]
-<tr class="memdesc:ad4c2297db07b9610d39f1d0e12fdc2b9"><td class="mdescLeft"> </td><td class="mdescRight">invoke cached operator <a href="#ad4c2297db07b9610d39f1d0e12fdc2b9">More...</a><br /></td></tr>
+<tr class="memdesc:ad4c2297db07b9610d39f1d0e12fdc2b9"><td class="mdescLeft"> </td><td class="mdescRight">invoke cached operator <a href="#ad4c2297db07b9610d39f1d0e12fdc2b9">More...</a><br/></td></tr>
<tr class="separator:ad4c2297db07b9610d39f1d0e12fdc2b9"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac5a3fafe6616c4d246b24e4d11c65238"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ac5a3fafe6616c4d246b24e4d11c65238">MXListAllOpNames</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> *out_size, const char ***out_array)</td></tr>
-<tr class="memdesc:ac5a3fafe6616c4d246b24e4d11c65238"><td class="mdescLeft"> </td><td class="mdescRight">list all the available operator names, include entries. <a href="#ac5a3fafe6616c4d246b24e4d11c65238">More...</a><br /></td></tr>
+<tr class="memdesc:ac5a3fafe6616c4d246b24e4d11c65238"><td class="mdescLeft"> </td><td class="mdescRight">list all the available operator names, include entries. <a href="#ac5a3fafe6616c4d246b24e4d11c65238">More...</a><br/></td></tr>
<tr class="separator:ac5a3fafe6616c4d246b24e4d11c65238"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6eb35d17154ef093856e60e416d354cc"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6eb35d17154ef093856e60e416d354cc">MXSymbolListAtomicSymbolCreators</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> *out_size, <a class="el" href="c__api_8h.html#aa2 [...]
-<tr class="memdesc:a6eb35d17154ef093856e60e416d354cc"><td class="mdescLeft"> </td><td class="mdescRight">list all the available AtomicSymbolEntry <a href="#a6eb35d17154ef093856e60e416d354cc">More...</a><br /></td></tr>
+<tr class="memdesc:a6eb35d17154ef093856e60e416d354cc"><td class="mdescLeft"> </td><td class="mdescRight">list all the available AtomicSymbolEntry <a href="#a6eb35d17154ef093856e60e416d354cc">More...</a><br/></td></tr>
<tr class="separator:a6eb35d17154ef093856e60e416d354cc"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a272c88973b602d37a87df4a42cf7fb7f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a272c88973b602d37a87df4a42cf7fb7f">MXSymbolGetAtomicSymbolName</a> (<a class="el" href="c__api_8h.html#aa2e96133f49cfdc1095da6cd08afbce0">AtomicSymbolCreator</a> creator, const char **name)</td></tr>
-<tr class="memdesc:a272c88973b602d37a87df4a42cf7fb7f"><td class="mdescLeft"> </td><td class="mdescRight">Get the name of an atomic symbol. <a href="#a272c88973b602d37a87df4a42cf7fb7f">More...</a><br /></td></tr>
+<tr class="memdesc:a272c88973b602d37a87df4a42cf7fb7f"><td class="mdescLeft"> </td><td class="mdescRight">Get the name of an atomic symbol. <a href="#a272c88973b602d37a87df4a42cf7fb7f">More...</a><br/></td></tr>
<tr class="separator:a272c88973b602d37a87df4a42cf7fb7f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa46875e507a638bd6c8a6c891a8f5934"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aa46875e507a638bd6c8a6c891a8f5934">MXSymbolGetAtomicSymbolInfo</a> (<a class="el" href="c__api_8h.html#aa2e96133f49cfdc1095da6cd08afbce0">AtomicSymbolCreator</a> creator, const char **name, const char **d [...]
-<tr class="memdesc:aa46875e507a638bd6c8a6c891a8f5934"><td class="mdescLeft"> </td><td class="mdescRight">Get the detailed information about atomic symbol. <a href="#aa46875e507a638bd6c8a6c891a8f5934">More...</a><br /></td></tr>
+<tr class="memdesc:aa46875e507a638bd6c8a6c891a8f5934"><td class="mdescLeft"> </td><td class="mdescRight">Get the detailed information about atomic symbol. <a href="#aa46875e507a638bd6c8a6c891a8f5934">More...</a><br/></td></tr>
<tr class="separator:aa46875e507a638bd6c8a6c891a8f5934"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9cd6ad8c274665172513707dd73262ce"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a9cd6ad8c274665172513707dd73262ce">MXSymbolCreateAtomicSymbol</a> (<a class="el" href="c__api_8h.html#aa2e96133f49cfdc1095da6cd08afbce0">AtomicSymbolCreator</a> creator, <a class="el" href="c__api_8h.html [...]
-<tr class="memdesc:a9cd6ad8c274665172513707dd73262ce"><td class="mdescLeft"> </td><td class="mdescRight">Create an AtomicSymbol. <a href="#a9cd6ad8c274665172513707dd73262ce">More...</a><br /></td></tr>
+<tr class="memdesc:a9cd6ad8c274665172513707dd73262ce"><td class="mdescLeft"> </td><td class="mdescRight">Create an AtomicSymbol. <a href="#a9cd6ad8c274665172513707dd73262ce">More...</a><br/></td></tr>
<tr class="separator:a9cd6ad8c274665172513707dd73262ce"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a738463b3d661aa4821caa4626092010f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a738463b3d661aa4821caa4626092010f">MXSymbolCreateVariable</a> (const char *name, <a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> *out)</td></tr>
-<tr class="memdesc:a738463b3d661aa4821caa4626092010f"><td class="mdescLeft"> </td><td class="mdescRight">Create a Variable Symbol. <a href="#a738463b3d661aa4821caa4626092010f">More...</a><br /></td></tr>
+<tr class="memdesc:a738463b3d661aa4821caa4626092010f"><td class="mdescLeft"> </td><td class="mdescRight">Create a Variable Symbol. <a href="#a738463b3d661aa4821caa4626092010f">More...</a><br/></td></tr>
<tr class="separator:a738463b3d661aa4821caa4626092010f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a72e684e635bb138c6382bd9c4e5957db"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a72e684e635bb138c6382bd9c4e5957db">MXSymbolCreateGroup</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> num_symbols, <a class="el" href="c__api_8h.html#a301e9a7722b32 [...]
-<tr class="memdesc:a72e684e635bb138c6382bd9c4e5957db"><td class="mdescLeft"> </td><td class="mdescRight">Create a Symbol by grouping list of symbols together. <a href="#a72e684e635bb138c6382bd9c4e5957db">More...</a><br /></td></tr>
+<tr class="memdesc:a72e684e635bb138c6382bd9c4e5957db"><td class="mdescLeft"> </td><td class="mdescRight">Create a Symbol by grouping list of symbols together. <a href="#a72e684e635bb138c6382bd9c4e5957db">More...</a><br/></td></tr>
<tr class="separator:a72e684e635bb138c6382bd9c4e5957db"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a314035f352106ee44039bcbe166353b3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a314035f352106ee44039bcbe166353b3">MXSymbolCreateFromFile</a> (const char *fname, <a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> *out)</td></tr>
-<tr class="memdesc:a314035f352106ee44039bcbe166353b3"><td class="mdescLeft"> </td><td class="mdescRight">Load a symbol from a json file. <a href="#a314035f352106ee44039bcbe166353b3">More...</a><br /></td></tr>
+<tr class="memdesc:a314035f352106ee44039bcbe166353b3"><td class="mdescLeft"> </td><td class="mdescRight">Load a symbol from a json file. <a href="#a314035f352106ee44039bcbe166353b3">More...</a><br/></td></tr>
<tr class="separator:a314035f352106ee44039bcbe166353b3"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac6a2362d9cb7ba763810b971c9524521"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ac6a2362d9cb7ba763810b971c9524521">MXSymbolCreateFromJSON</a> (const char *json, <a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> *out)</td></tr>
-<tr class="memdesc:ac6a2362d9cb7ba763810b971c9524521"><td class="mdescLeft"> </td><td class="mdescRight">Load a symbol from a json string. <a href="#ac6a2362d9cb7ba763810b971c9524521">More...</a><br /></td></tr>
+<tr class="memdesc:ac6a2362d9cb7ba763810b971c9524521"><td class="mdescLeft"> </td><td class="mdescRight">Load a symbol from a json string. <a href="#ac6a2362d9cb7ba763810b971c9524521">More...</a><br/></td></tr>
<tr class="separator:ac6a2362d9cb7ba763810b971c9524521"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8b3a7394a8a641f2490f4c5246be1083"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a8b3a7394a8a641f2490f4c5246be1083">MXSymbolSaveToFile</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, const char *fname)</td></tr>
-<tr class="memdesc:a8b3a7394a8a641f2490f4c5246be1083"><td class="mdescLeft"> </td><td class="mdescRight">Save a symbol into a json file. <a href="#a8b3a7394a8a641f2490f4c5246be1083">More...</a><br /></td></tr>
+<tr class="memdesc:a8b3a7394a8a641f2490f4c5246be1083"><td class="mdescLeft"> </td><td class="mdescRight">Save a symbol into a json file. <a href="#a8b3a7394a8a641f2490f4c5246be1083">More...</a><br/></td></tr>
<tr class="separator:a8b3a7394a8a641f2490f4c5246be1083"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5c99eac0fe3cfa0912259d0fa12b6651"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a5c99eac0fe3cfa0912259d0fa12b6651">MXSymbolSaveToJSON</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, const char **out_json)</td></tr>
-<tr class="memdesc:a5c99eac0fe3cfa0912259d0fa12b6651"><td class="mdescLeft"> </td><td class="mdescRight">Save a symbol into a json string. <a href="#a5c99eac0fe3cfa0912259d0fa12b6651">More...</a><br /></td></tr>
+<tr class="memdesc:a5c99eac0fe3cfa0912259d0fa12b6651"><td class="mdescLeft"> </td><td class="mdescRight">Save a symbol into a json string. <a href="#a5c99eac0fe3cfa0912259d0fa12b6651">More...</a><br/></td></tr>
<tr class="separator:a5c99eac0fe3cfa0912259d0fa12b6651"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1070254ccff925195bf263b43762a06f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a1070254ccff925195bf263b43762a06f">MXSymbolFree</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol)</td></tr>
-<tr class="memdesc:a1070254ccff925195bf263b43762a06f"><td class="mdescLeft"> </td><td class="mdescRight">Free the symbol handle. <a href="#a1070254ccff925195bf263b43762a06f">More...</a><br /></td></tr>
+<tr class="memdesc:a1070254ccff925195bf263b43762a06f"><td class="mdescLeft"> </td><td class="mdescRight">Free the symbol handle. <a href="#a1070254ccff925195bf263b43762a06f">More...</a><br/></td></tr>
<tr class="separator:a1070254ccff925195bf263b43762a06f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6f8fc1fb5c90c92498241b75f74cea86"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6f8fc1fb5c90c92498241b75f74cea86">MXSymbolCopy</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, <a class="el" href="c__api_8h.html#a301e9a7722b326126def [...]
-<tr class="memdesc:a6f8fc1fb5c90c92498241b75f74cea86"><td class="mdescLeft"> </td><td class="mdescRight">Copy the symbol to another handle. <a href="#a6f8fc1fb5c90c92498241b75f74cea86">More...</a><br /></td></tr>
+<tr class="memdesc:a6f8fc1fb5c90c92498241b75f74cea86"><td class="mdescLeft"> </td><td class="mdescRight">Copy the symbol to another handle. <a href="#a6f8fc1fb5c90c92498241b75f74cea86">More...</a><br/></td></tr>
<tr class="separator:a6f8fc1fb5c90c92498241b75f74cea86"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5f4c41b8ca7c92524def534956b7e24a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a5f4c41b8ca7c92524def534956b7e24a">MXSymbolPrint</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, const char **out_str)</td></tr>
-<tr class="memdesc:a5f4c41b8ca7c92524def534956b7e24a"><td class="mdescLeft"> </td><td class="mdescRight">Print the content of symbol, used for debug. <a href="#a5f4c41b8ca7c92524def534956b7e24a">More...</a><br /></td></tr>
+<tr class="memdesc:a5f4c41b8ca7c92524def534956b7e24a"><td class="mdescLeft"> </td><td class="mdescRight">Print the content of symbol, used for debug. <a href="#a5f4c41b8ca7c92524def534956b7e24a">More...</a><br/></td></tr>
<tr class="separator:a5f4c41b8ca7c92524def534956b7e24a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a33606f8bf8413da87c9d499e2b2f1e88"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a33606f8bf8413da87c9d499e2b2f1e88">MXSymbolGetName</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, const char **out, int *success)</td></tr>
-<tr class="memdesc:a33606f8bf8413da87c9d499e2b2f1e88"><td class="mdescLeft"> </td><td class="mdescRight">Get string name from symbol. <a href="#a33606f8bf8413da87c9d499e2b2f1e88">More...</a><br /></td></tr>
+<tr class="memdesc:a33606f8bf8413da87c9d499e2b2f1e88"><td class="mdescLeft"> </td><td class="mdescRight">Get string name from symbol. <a href="#a33606f8bf8413da87c9d499e2b2f1e88">More...</a><br/></td></tr>
<tr class="separator:a33606f8bf8413da87c9d499e2b2f1e88"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:abca605833c1fb045e2e78c8f3be492ac"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#abca605833c1fb045e2e78c8f3be492ac">MXSymbolGetAttr</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, const char *key, const char **out, int *success)</td></tr>
-<tr class="memdesc:abca605833c1fb045e2e78c8f3be492ac"><td class="mdescLeft"> </td><td class="mdescRight">Get string attribute from symbol. <a href="#abca605833c1fb045e2e78c8f3be492ac">More...</a><br /></td></tr>
+<tr class="memdesc:abca605833c1fb045e2e78c8f3be492ac"><td class="mdescLeft"> </td><td class="mdescRight">Get string attribute from symbol. <a href="#abca605833c1fb045e2e78c8f3be492ac">More...</a><br/></td></tr>
<tr class="separator:abca605833c1fb045e2e78c8f3be492ac"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aebeac35c3110eb391035fbbeb29e9436"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aebeac35c3110eb391035fbbeb29e9436">MXSymbolSetAttr</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, const char *key, const char *value)</td></tr>
-<tr class="memdesc:aebeac35c3110eb391035fbbeb29e9436"><td class="mdescLeft"> </td><td class="mdescRight">Set string attribute from symbol. NOTE: Setting attribute to a symbol can affect the semantics(mutable/immutable) of symbolic graph. <a href="#aebeac35c3110eb391035fbbeb29e9436">More...</a><br /></td></tr>
+<tr class="memdesc:aebeac35c3110eb391035fbbeb29e9436"><td class="mdescLeft"> </td><td class="mdescRight">Set string attribute from symbol. NOTE: Setting attribute to a symbol can affect the semantics(mutable/immutable) of symbolic graph. <a href="#aebeac35c3110eb391035fbbeb29e9436">More...</a><br/></td></tr>
<tr class="separator:aebeac35c3110eb391035fbbeb29e9436"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9e4dc341e666c772e1b530920ba4a0e9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a9e4dc341e666c772e1b530920ba4a0e9">MXSymbolListAttr</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b [...]
-<tr class="memdesc:a9e4dc341e666c772e1b530920ba4a0e9"><td class="mdescLeft"> </td><td class="mdescRight">Get all attributes from symbol, including all descendents. <a href="#a9e4dc341e666c772e1b530920ba4a0e9">More...</a><br /></td></tr>
+<tr class="memdesc:a9e4dc341e666c772e1b530920ba4a0e9"><td class="mdescLeft"> </td><td class="mdescRight">Get all attributes from symbol, including all descendents. <a href="#a9e4dc341e666c772e1b530920ba4a0e9">More...</a><br/></td></tr>
<tr class="separator:a9e4dc341e666c772e1b530920ba4a0e9"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac3bb9177224ea3b08a294dfb744e05fd"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ac3bb9177224ea3b08a294dfb744e05fd">MXSymbolListAttrShallow</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, <a class="el" href="c__api_8h.html#a796e7ef82 [...]
-<tr class="memdesc:ac3bb9177224ea3b08a294dfb744e05fd"><td class="mdescLeft"> </td><td class="mdescRight">Get all attributes from symbol, excluding descendents. <a href="#ac3bb9177224ea3b08a294dfb744e05fd">More...</a><br /></td></tr>
+<tr class="memdesc:ac3bb9177224ea3b08a294dfb744e05fd"><td class="mdescLeft"> </td><td class="mdescRight">Get all attributes from symbol, excluding descendents. <a href="#ac3bb9177224ea3b08a294dfb744e05fd">More...</a><br/></td></tr>
<tr class="separator:ac3bb9177224ea3b08a294dfb744e05fd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ade7acc2c570cc5b6eea2f1f2efc67a74"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ade7acc2c570cc5b6eea2f1f2efc67a74">MXSymbolListArguments</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, <a class="el" href="c__api_8h.html#a796e7ef8260 [...]
-<tr class="memdesc:ade7acc2c570cc5b6eea2f1f2efc67a74"><td class="mdescLeft"> </td><td class="mdescRight">List arguments in the symbol. <a href="#ade7acc2c570cc5b6eea2f1f2efc67a74">More...</a><br /></td></tr>
+<tr class="memdesc:ade7acc2c570cc5b6eea2f1f2efc67a74"><td class="mdescLeft"> </td><td class="mdescRight">List arguments in the symbol. <a href="#ade7acc2c570cc5b6eea2f1f2efc67a74">More...</a><br/></td></tr>
<tr class="separator:ade7acc2c570cc5b6eea2f1f2efc67a74"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aedf1da726aaacb87a5d959ed83f03f41"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aedf1da726aaacb87a5d959ed83f03f41">MXSymbolListOutputs</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, <a class="el" href="c__api_8h.html#a796e7ef8260ef [...]
-<tr class="memdesc:aedf1da726aaacb87a5d959ed83f03f41"><td class="mdescLeft"> </td><td class="mdescRight">List returns in the symbol. <a href="#aedf1da726aaacb87a5d959ed83f03f41">More...</a><br /></td></tr>
+<tr class="memdesc:aedf1da726aaacb87a5d959ed83f03f41"><td class="mdescLeft"> </td><td class="mdescRight">List returns in the symbol. <a href="#aedf1da726aaacb87a5d959ed83f03f41">More...</a><br/></td></tr>
<tr class="separator:aedf1da726aaacb87a5d959ed83f03f41"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a13816872b73d6c474686d985747e8c65"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a13816872b73d6c474686d985747e8c65">MXSymbolGetInternals</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, <a class="el" href="c__api_8h.html#a301e9a7722b3 [...]
-<tr class="memdesc:a13816872b73d6c474686d985747e8c65"><td class="mdescLeft"> </td><td class="mdescRight">Get a symbol that contains all the internals. <a href="#a13816872b73d6c474686d985747e8c65">More...</a><br /></td></tr>
+<tr class="memdesc:a13816872b73d6c474686d985747e8c65"><td class="mdescLeft"> </td><td class="mdescRight">Get a symbol that contains all the internals. <a href="#a13816872b73d6c474686d985747e8c65">More...</a><br/></td></tr>
<tr class="separator:a13816872b73d6c474686d985747e8c65"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6050b4cfcb75ed3d0a44afb1b8df45cd"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6050b4cfcb75ed3d0a44afb1b8df45cd">MXSymbolGetChildren</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, <a class="el" href="c__api_8h.html#a301e9a7722b32 [...]
-<tr class="memdesc:a6050b4cfcb75ed3d0a44afb1b8df45cd"><td class="mdescLeft"> </td><td class="mdescRight">Get a symbol that contains only direct children. <a href="#a6050b4cfcb75ed3d0a44afb1b8df45cd">More...</a><br /></td></tr>
+<tr class="memdesc:a6050b4cfcb75ed3d0a44afb1b8df45cd"><td class="mdescLeft"> </td><td class="mdescRight">Get a symbol that contains only direct children. <a href="#a6050b4cfcb75ed3d0a44afb1b8df45cd">More...</a><br/></td></tr>
<tr class="separator:a6050b4cfcb75ed3d0a44afb1b8df45cd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8e601320c59679d9423179f23f281bf2"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a8e601320c59679d9423179f23f281bf2">MXSymbolGetOutput</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, <a class="el" href="c__api_8h.html#a796e7ef8260ef40 [...]
-<tr class="memdesc:a8e601320c59679d9423179f23f281bf2"><td class="mdescLeft"> </td><td class="mdescRight">Get index-th outputs of the symbol. <a href="#a8e601320c59679d9423179f23f281bf2">More...</a><br /></td></tr>
+<tr class="memdesc:a8e601320c59679d9423179f23f281bf2"><td class="mdescLeft"> </td><td class="mdescRight">Get index-th outputs of the symbol. <a href="#a8e601320c59679d9423179f23f281bf2">More...</a><br/></td></tr>
<tr class="separator:a8e601320c59679d9423179f23f281bf2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a38e75c5a41473912e2d6cdfececba84d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a38e75c5a41473912e2d6cdfececba84d">MXSymbolListAuxiliaryStates</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol, <a class="el" href="c__api_8h.html#a796e7 [...]
-<tr class="memdesc:a38e75c5a41473912e2d6cdfececba84d"><td class="mdescLeft"> </td><td class="mdescRight">List auxiliary states in the symbol. <a href="#a38e75c5a41473912e2d6cdfececba84d">More...</a><br /></td></tr>
+<tr class="memdesc:a38e75c5a41473912e2d6cdfececba84d"><td class="mdescLeft"> </td><td class="mdescRight">List auxiliary states in the symbol. <a href="#a38e75c5a41473912e2d6cdfececba84d">More...</a><br/></td></tr>
<tr class="separator:a38e75c5a41473912e2d6cdfececba84d"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a879a5ee991393d364b787e948b4c9961"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a879a5ee991393d364b787e948b4c9961">MXSymbolCompose</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> sym, const char *name, <a class="el" href="c__api_8h.html#a79 [...]
-<tr class="memdesc:a879a5ee991393d364b787e948b4c9961"><td class="mdescLeft"> </td><td class="mdescRight">Compose the symbol on other symbols. <a href="#a879a5ee991393d364b787e948b4c9961">More...</a><br /></td></tr>
+<tr class="memdesc:a879a5ee991393d364b787e948b4c9961"><td class="mdescLeft"> </td><td class="mdescRight">Compose the symbol on other symbols. <a href="#a879a5ee991393d364b787e948b4c9961">More...</a><br/></td></tr>
<tr class="separator:a879a5ee991393d364b787e948b4c9961"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac2268c0bc7ad2f96d36bbbdab9a51c2e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ac2268c0bc7ad2f96d36bbbdab9a51c2e">MXSymbolGrad</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> sym, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1 [...]
-<tr class="memdesc:ac2268c0bc7ad2f96d36bbbdab9a51c2e"><td class="mdescLeft"> </td><td class="mdescRight">Get the gradient graph of the symbol. <a href="#ac2268c0bc7ad2f96d36bbbdab9a51c2e">More...</a><br /></td></tr>
+<tr class="memdesc:ac2268c0bc7ad2f96d36bbbdab9a51c2e"><td class="mdescLeft"> </td><td class="mdescRight">Get the gradient graph of the symbol. <a href="#ac2268c0bc7ad2f96d36bbbdab9a51c2e">More...</a><br/></td></tr>
<tr class="separator:ac2268c0bc7ad2f96d36bbbdab9a51c2e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:afaf03324563098f1ac4c23ecfc88b784"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#afaf03324563098f1ac4c23ecfc88b784">MXSymbolInferShape</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> sym, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b2 [...]
-<tr class="memdesc:afaf03324563098f1ac4c23ecfc88b784"><td class="mdescLeft"> </td><td class="mdescRight">infer shape of unknown input shapes given the known one. The shapes are packed into a CSR matrix represented by arg_ind_ptr and arg_shape_data The call will be treated as a kwargs call if key != nullptr or num_args==0, otherwise it is positional. <a href="#afaf03324563098f1ac4c23ecfc88b784">More...</a><br /></td></tr>
+<tr class="memdesc:afaf03324563098f1ac4c23ecfc88b784"><td class="mdescLeft"> </td><td class="mdescRight">infer shape of unknown input shapes given the known one. The shapes are packed into a CSR matrix represented by arg_ind_ptr and arg_shape_data The call will be treated as a kwargs call if key != nullptr or num_args==0, otherwise it is positional. <a href="#afaf03324563098f1ac4c23ecfc88b784">More...</a><br/></td></tr>
<tr class="separator:afaf03324563098f1ac4c23ecfc88b784"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a664c85b47c69b81be3b9ea1dfcb72a94"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a664c85b47c69b81be3b9ea1dfcb72a94">MXSymbolInferShapePartial</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> sym, <a class="el" href="c__api_8h.html#a796e7ef826 [...]
-<tr class="memdesc:a664c85b47c69b81be3b9ea1dfcb72a94"><td class="mdescLeft"> </td><td class="mdescRight">partially infer shape of unknown input shapes given the known one. <a href="#a664c85b47c69b81be3b9ea1dfcb72a94">More...</a><br /></td></tr>
+<tr class="memdesc:a664c85b47c69b81be3b9ea1dfcb72a94"><td class="mdescLeft"> </td><td class="mdescRight">partially infer shape of unknown input shapes given the known one. <a href="#a664c85b47c69b81be3b9ea1dfcb72a94">More...</a><br/></td></tr>
<tr class="separator:a664c85b47c69b81be3b9ea1dfcb72a94"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1eff7ea3fd2fe56509c75f5a610890e6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a1eff7ea3fd2fe56509c75f5a610890e6">MXSymbolInferType</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> sym, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24 [...]
-<tr class="memdesc:a1eff7ea3fd2fe56509c75f5a610890e6"><td class="mdescLeft"> </td><td class="mdescRight">infer type of unknown input types given the known one. The types are packed into a CSR matrix represented by arg_ind_ptr and arg_type_data The call will be treated as a kwargs call if key != nullptr or num_args==0, otherwise it is positional. <a href="#a1eff7ea3fd2fe56509c75f5a610890e6">More...</a><br /></td></tr>
+<tr class="memdesc:a1eff7ea3fd2fe56509c75f5a610890e6"><td class="mdescLeft"> </td><td class="mdescRight">infer type of unknown input types given the known one. The types are packed into a CSR matrix represented by arg_ind_ptr and arg_type_data The call will be treated as a kwargs call if key != nullptr or num_args==0, otherwise it is positional. <a href="#a1eff7ea3fd2fe56509c75f5a610890e6">More...</a><br/></td></tr>
<tr class="separator:a1eff7ea3fd2fe56509c75f5a610890e6"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ade1487656dfba3cae12c5bf676e08dcf"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ade1487656dfba3cae12c5bf676e08dcf">MXExecutorFree</a> (<a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a> handle)</td></tr>
-<tr class="memdesc:ade1487656dfba3cae12c5bf676e08dcf"><td class="mdescLeft"> </td><td class="mdescRight">Delete the executor. <a href="#ade1487656dfba3cae12c5bf676e08dcf">More...</a><br /></td></tr>
+<tr class="memdesc:ade1487656dfba3cae12c5bf676e08dcf"><td class="mdescLeft"> </td><td class="mdescRight">Delete the executor. <a href="#ade1487656dfba3cae12c5bf676e08dcf">More...</a><br/></td></tr>
<tr class="separator:ade1487656dfba3cae12c5bf676e08dcf"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac9f776a6e7be0bacda4dcd3efa5f7dc5"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ac9f776a6e7be0bacda4dcd3efa5f7dc5">MXExecutorPrint</a> (<a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a> handle, const char **out_str)</td></tr>
-<tr class="memdesc:ac9f776a6e7be0bacda4dcd3efa5f7dc5"><td class="mdescLeft"> </td><td class="mdescRight">Print the content of execution plan, used for debug. <a href="#ac9f776a6e7be0bacda4dcd3efa5f7dc5">More...</a><br /></td></tr>
+<tr class="memdesc:ac9f776a6e7be0bacda4dcd3efa5f7dc5"><td class="mdescLeft"> </td><td class="mdescRight">Print the content of execution plan, used for debug. <a href="#ac9f776a6e7be0bacda4dcd3efa5f7dc5">More...</a><br/></td></tr>
<tr class="separator:ac9f776a6e7be0bacda4dcd3efa5f7dc5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1f9987e701b810e0e79b5207fa215415"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a1f9987e701b810e0e79b5207fa215415">MXExecutorForward</a> (<a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a> handle, int is_train)</td></tr>
-<tr class="memdesc:a1f9987e701b810e0e79b5207fa215415"><td class="mdescLeft"> </td><td class="mdescRight">Executor forward method. <a href="#a1f9987e701b810e0e79b5207fa215415">More...</a><br /></td></tr>
+<tr class="memdesc:a1f9987e701b810e0e79b5207fa215415"><td class="mdescLeft"> </td><td class="mdescRight">Executor forward method. <a href="#a1f9987e701b810e0e79b5207fa215415">More...</a><br/></td></tr>
<tr class="separator:a1f9987e701b810e0e79b5207fa215415"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad8f92bb15e11058abe12959cfc4dcbb3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ad8f92bb15e11058abe12959cfc4dcbb3">MXExecutorBackward</a> (<a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260e [...]
-<tr class="memdesc:ad8f92bb15e11058abe12959cfc4dcbb3"><td class="mdescLeft"> </td><td class="mdescRight">Excecutor run backward. <a href="#ad8f92bb15e11058abe12959cfc4dcbb3">More...</a><br /></td></tr>
+<tr class="memdesc:ad8f92bb15e11058abe12959cfc4dcbb3"><td class="mdescLeft"> </td><td class="mdescRight">Excecutor run backward. <a href="#ad8f92bb15e11058abe12959cfc4dcbb3">More...</a><br/></td></tr>
<tr class="separator:ad8f92bb15e11058abe12959cfc4dcbb3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3a211f3e0c6270271ce4fe79228dd0c2"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a3a211f3e0c6270271ce4fe79228dd0c2">MXExecutorBackwardEx</a> (<a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef826 [...]
+<tr class="memdesc:a3a211f3e0c6270271ce4fe79228dd0c2"><td class="mdescLeft"> </td><td class="mdescRight">Excecutor run backward. <a href="#a3a211f3e0c6270271ce4fe79228dd0c2">More...</a><br/></td></tr>
+<tr class="separator:a3a211f3e0c6270271ce4fe79228dd0c2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a442845c8ef89d4c849f468ea2b7717f7"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a442845c8ef89d4c849f468ea2b7717f7">MXExecutorOutputs</a> (<a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef [...]
-<tr class="memdesc:a442845c8ef89d4c849f468ea2b7717f7"><td class="mdescLeft"> </td><td class="mdescRight">Get executor's head NDArray. <a href="#a442845c8ef89d4c849f468ea2b7717f7">More...</a><br /></td></tr>
+<tr class="memdesc:a442845c8ef89d4c849f468ea2b7717f7"><td class="mdescLeft"> </td><td class="mdescRight">Get executor's head NDArray. <a href="#a442845c8ef89d4c849f468ea2b7717f7">More...</a><br/></td></tr>
<tr class="separator:a442845c8ef89d4c849f468ea2b7717f7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a10a6deacde18b7d52b44eee0ddddf0e1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a10a6deacde18b7d52b44eee0ddddf0e1">MXExecutorBind</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol_handle, int dev_type, int dev_id, <a class="el" href="c [...]
-<tr class="memdesc:a10a6deacde18b7d52b44eee0ddddf0e1"><td class="mdescLeft"> </td><td class="mdescRight">Generate Executor from symbol. <a href="#a10a6deacde18b7d52b44eee0ddddf0e1">More...</a><br /></td></tr>
+<tr class="memdesc:a10a6deacde18b7d52b44eee0ddddf0e1"><td class="mdescLeft"> </td><td class="mdescRight">Generate Executor from symbol. <a href="#a10a6deacde18b7d52b44eee0ddddf0e1">More...</a><br/></td></tr>
<tr class="separator:a10a6deacde18b7d52b44eee0ddddf0e1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad1646370458f8a3ddb9e4f3365c5e510"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ad1646370458f8a3ddb9e4f3365c5e510">MXExecutorBindX</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol_handle, int dev_type, int dev_id, <a class="el" href=" [...]
-<tr class="memdesc:ad1646370458f8a3ddb9e4f3365c5e510"><td class="mdescLeft"> </td><td class="mdescRight">Generate Executor from symbol, This is advanced function, allow specify group2ctx map. The user can annotate "ctx_group" attribute to name each group. <a href="#ad1646370458f8a3ddb9e4f3365c5e510">More...</a><br /></td></tr>
+<tr class="memdesc:ad1646370458f8a3ddb9e4f3365c5e510"><td class="mdescLeft"> </td><td class="mdescRight">Generate Executor from symbol, This is advanced function, allow specify group2ctx map. The user can annotate "ctx_group" attribute to name each group. <a href="#ad1646370458f8a3ddb9e4f3365c5e510">More...</a><br/></td></tr>
<tr class="separator:ad1646370458f8a3ddb9e4f3365c5e510"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8139e691c88ebcf082f9e954598e0e86"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a8139e691c88ebcf082f9e954598e0e86">MXExecutorBindEX</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol_handle, int dev_type, int dev_id, <a class="el" href= [...]
-<tr class="memdesc:a8139e691c88ebcf082f9e954598e0e86"><td class="mdescLeft"> </td><td class="mdescRight">Generate Executor from symbol, This is advanced function, allow specify group2ctx map. The user can annotate "ctx_group" attribute to name each group. <a href="#a8139e691c88ebcf082f9e954598e0e86">More...</a><br /></td></tr>
+<tr class="memdesc:a8139e691c88ebcf082f9e954598e0e86"><td class="mdescLeft"> </td><td class="mdescRight">Generate Executor from symbol, This is advanced function, allow specify group2ctx map. The user can annotate "ctx_group" attribute to name each group. <a href="#a8139e691c88ebcf082f9e954598e0e86">More...</a><br/></td></tr>
<tr class="separator:a8139e691c88ebcf082f9e954598e0e86"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab85ff72ac8364631116ced48939eaa60"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ab85ff72ac8364631116ced48939eaa60">MXExecutorSimpleBind</a> (<a class="el" href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a> symbol_handle, int dev_type, int dev_id, const <a class= [...]
<tr class="separator:ab85ff72ac8364631116ced48939eaa60"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:abae91a5d7847020e14395e078144489a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#abae91a5d7847020e14395e078144489a">MXExecutorSetMonitorCallback</a> (<a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a> handle, <a class="el" href="c__api_8h.html#a27 [...]
-<tr class="memdesc:abae91a5d7847020e14395e078144489a"><td class="mdescLeft"> </td><td class="mdescRight">set a call back to notify the completion of operation <a href="#abae91a5d7847020e14395e078144489a">More...</a><br /></td></tr>
+<tr class="memitem:abae91a5d7847020e14395e078144489a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#abae91a5d7847020e14395e078144489a">MXExecutorSetMonitorCallback</a> (<a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a> handle, <a class="el" href="c__api_8h.html#aa7 [...]
+<tr class="memdesc:abae91a5d7847020e14395e078144489a"><td class="mdescLeft"> </td><td class="mdescRight">set a call back to notify the completion of operation <a href="#abae91a5d7847020e14395e078144489a">More...</a><br/></td></tr>
<tr class="separator:abae91a5d7847020e14395e078144489a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa13fb35019b0c42dcfee185b2f9e09d0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aa13fb35019b0c42dcfee185b2f9e09d0">MXListDataIters</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> *out_size, <a class="el" href="c__api_8h.html#a0b9b13ccb06a6a4f05d [...]
-<tr class="memdesc:aa13fb35019b0c42dcfee185b2f9e09d0"><td class="mdescLeft"> </td><td class="mdescRight">List all the available iterator entries. <a href="#aa13fb35019b0c42dcfee185b2f9e09d0">More...</a><br /></td></tr>
+<tr class="memdesc:aa13fb35019b0c42dcfee185b2f9e09d0"><td class="mdescLeft"> </td><td class="mdescRight">List all the available iterator entries. <a href="#aa13fb35019b0c42dcfee185b2f9e09d0">More...</a><br/></td></tr>
<tr class="separator:aa13fb35019b0c42dcfee185b2f9e09d0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a361c4a5360734e430c54a754cc581354"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a361c4a5360734e430c54a754cc581354">MXDataIterCreateIter</a> (<a class="el" href="c__api_8h.html#a0b9b13ccb06a6a4f05d211261fa883dd">DataIterCreator</a> handle, <a class="el" href="c__api_8h.html#a796e7ef82 [...]
-<tr class="memdesc:a361c4a5360734e430c54a754cc581354"><td class="mdescLeft"> </td><td class="mdescRight">Init an iterator, init with parameters the array size of passed in arguments. <a href="#a361c4a5360734e430c54a754cc581354">More...</a><br /></td></tr>
+<tr class="memdesc:a361c4a5360734e430c54a754cc581354"><td class="mdescLeft"> </td><td class="mdescRight">Init an iterator, init with parameters the array size of passed in arguments. <a href="#a361c4a5360734e430c54a754cc581354">More...</a><br/></td></tr>
<tr class="separator:a361c4a5360734e430c54a754cc581354"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a795614eb68a4fead826202a5b5ba9517"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a795614eb68a4fead826202a5b5ba9517">MXDataIterGetIterInfo</a> (<a class="el" href="c__api_8h.html#a0b9b13ccb06a6a4f05d211261fa883dd">DataIterCreator</a> creator, const char **name, const char **description [...]
-<tr class="memdesc:a795614eb68a4fead826202a5b5ba9517"><td class="mdescLeft"> </td><td class="mdescRight">Get the detailed information about data iterator. <a href="#a795614eb68a4fead826202a5b5ba9517">More...</a><br /></td></tr>
+<tr class="memdesc:a795614eb68a4fead826202a5b5ba9517"><td class="mdescLeft"> </td><td class="mdescRight">Get the detailed information about data iterator. <a href="#a795614eb68a4fead826202a5b5ba9517">More...</a><br/></td></tr>
<tr class="separator:a795614eb68a4fead826202a5b5ba9517"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a29e117922acfbc95828433e736bcc3af"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a29e117922acfbc95828433e736bcc3af">MXDataIterFree</a> (<a class="el" href="c__api_8h.html#abce84fe05f55709d643bcfd0a4e4620b">DataIterHandle</a> handle)</td></tr>
-<tr class="memdesc:a29e117922acfbc95828433e736bcc3af"><td class="mdescLeft"> </td><td class="mdescRight">Free the handle to the IO module. <a href="#a29e117922acfbc95828433e736bcc3af">More...</a><br /></td></tr>
+<tr class="memdesc:a29e117922acfbc95828433e736bcc3af"><td class="mdescLeft"> </td><td class="mdescRight">Free the handle to the IO module. <a href="#a29e117922acfbc95828433e736bcc3af">More...</a><br/></td></tr>
<tr class="separator:a29e117922acfbc95828433e736bcc3af"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8be0adeef4b87243a5eda12f17e64e67"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a8be0adeef4b87243a5eda12f17e64e67">MXDataIterNext</a> (<a class="el" href="c__api_8h.html#abce84fe05f55709d643bcfd0a4e4620b">DataIterHandle</a> handle, int *out)</td></tr>
-<tr class="memdesc:a8be0adeef4b87243a5eda12f17e64e67"><td class="mdescLeft"> </td><td class="mdescRight">Move iterator to next position. <a href="#a8be0adeef4b87243a5eda12f17e64e67">More...</a><br /></td></tr>
+<tr class="memdesc:a8be0adeef4b87243a5eda12f17e64e67"><td class="mdescLeft"> </td><td class="mdescRight">Move iterator to next position. <a href="#a8be0adeef4b87243a5eda12f17e64e67">More...</a><br/></td></tr>
<tr class="separator:a8be0adeef4b87243a5eda12f17e64e67"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a21df19f3242a68d1c536783fcf35968a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a21df19f3242a68d1c536783fcf35968a">MXDataIterBeforeFirst</a> (<a class="el" href="c__api_8h.html#abce84fe05f55709d643bcfd0a4e4620b">DataIterHandle</a> handle)</td></tr>
-<tr class="memdesc:a21df19f3242a68d1c536783fcf35968a"><td class="mdescLeft"> </td><td class="mdescRight">Call iterator.Reset. <a href="#a21df19f3242a68d1c536783fcf35968a">More...</a><br /></td></tr>
+<tr class="memdesc:a21df19f3242a68d1c536783fcf35968a"><td class="mdescLeft"> </td><td class="mdescRight">Call iterator.Reset. <a href="#a21df19f3242a68d1c536783fcf35968a">More...</a><br/></td></tr>
<tr class="separator:a21df19f3242a68d1c536783fcf35968a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0920d095f423b8bb34095406ea93a697"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a0920d095f423b8bb34095406ea93a697">MXDataIterGetData</a> (<a class="el" href="c__api_8h.html#abce84fe05f55709d643bcfd0a4e4620b">DataIterHandle</a> handle, <a class="el" href="c__api_8h.html#a99c2939783499 [...]
-<tr class="memdesc:a0920d095f423b8bb34095406ea93a697"><td class="mdescLeft"> </td><td class="mdescRight">Get the handle to the NDArray of underlying data. <a href="#a0920d095f423b8bb34095406ea93a697">More...</a><br /></td></tr>
+<tr class="memdesc:a0920d095f423b8bb34095406ea93a697"><td class="mdescLeft"> </td><td class="mdescRight">Get the handle to the NDArray of underlying data. <a href="#a0920d095f423b8bb34095406ea93a697">More...</a><br/></td></tr>
<tr class="separator:a0920d095f423b8bb34095406ea93a697"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af1b36a78dd74707c10d31c39c4d429b5"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#af1b36a78dd74707c10d31c39c4d429b5">MXDataIterGetIndex</a> (<a class="el" href="c__api_8h.html#abce84fe05f55709d643bcfd0a4e4620b">DataIterHandle</a> handle, uint64_t **out_index, uint64_t *out_size)</td></tr>
-<tr class="memdesc:af1b36a78dd74707c10d31c39c4d429b5"><td class="mdescLeft"> </td><td class="mdescRight">Get the image index by array. <a href="#af1b36a78dd74707c10d31c39c4d429b5">More...</a><br /></td></tr>
+<tr class="memdesc:af1b36a78dd74707c10d31c39c4d429b5"><td class="mdescLeft"> </td><td class="mdescRight">Get the image index by array. <a href="#af1b36a78dd74707c10d31c39c4d429b5">More...</a><br/></td></tr>
<tr class="separator:af1b36a78dd74707c10d31c39c4d429b5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aec6d0d5055719576337687fc3702a899"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aec6d0d5055719576337687fc3702a899">MXDataIterGetPadNum</a> (<a class="el" href="c__api_8h.html#abce84fe05f55709d643bcfd0a4e4620b">DataIterHandle</a> handle, int *pad)</td></tr>
-<tr class="memdesc:aec6d0d5055719576337687fc3702a899"><td class="mdescLeft"> </td><td class="mdescRight">Get the padding number in current data batch. <a href="#aec6d0d5055719576337687fc3702a899">More...</a><br /></td></tr>
+<tr class="memdesc:aec6d0d5055719576337687fc3702a899"><td class="mdescLeft"> </td><td class="mdescRight">Get the padding number in current data batch. <a href="#aec6d0d5055719576337687fc3702a899">More...</a><br/></td></tr>
<tr class="separator:aec6d0d5055719576337687fc3702a899"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0c36af502d2a5746d841f50e1b0e71c0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a0c36af502d2a5746d841f50e1b0e71c0">MXDataIterGetLabel</a> (<a class="el" href="c__api_8h.html#abce84fe05f55709d643bcfd0a4e4620b">DataIterHandle</a> handle, <a class="el" href="c__api_8h.html#a99c293978349 [...]
-<tr class="memdesc:a0c36af502d2a5746d841f50e1b0e71c0"><td class="mdescLeft"> </td><td class="mdescRight">Get the handle to the NDArray of underlying label. <a href="#a0c36af502d2a5746d841f50e1b0e71c0">More...</a><br /></td></tr>
+<tr class="memdesc:a0c36af502d2a5746d841f50e1b0e71c0"><td class="mdescLeft"> </td><td class="mdescRight">Get the handle to the NDArray of underlying label. <a href="#a0c36af502d2a5746d841f50e1b0e71c0">More...</a><br/></td></tr>
<tr class="separator:a0c36af502d2a5746d841f50e1b0e71c0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5c4a11b4fccf320d1af0f63b40fff58a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a5c4a11b4fccf320d1af0f63b40fff58a">MXInitPSEnv</a> (<a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> num_vars, const char **keys, const char **vals)</td></tr>
-<tr class="memdesc:a5c4a11b4fccf320d1af0f63b40fff58a"><td class="mdescLeft"> </td><td class="mdescRight">Initialized ps-lite environment variables. <a href="#a5c4a11b4fccf320d1af0f63b40fff58a">More...</a><br /></td></tr>
+<tr class="memdesc:a5c4a11b4fccf320d1af0f63b40fff58a"><td class="mdescLeft"> </td><td class="mdescRight">Initialized ps-lite environment variables. <a href="#a5c4a11b4fccf320d1af0f63b40fff58a">More...</a><br/></td></tr>
<tr class="separator:a5c4a11b4fccf320d1af0f63b40fff58a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2ef1a35e886c0addf294429e27ea9637"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a2ef1a35e886c0addf294429e27ea9637">MXKVStoreCreate</a> (const char *type, <a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> *out)</td></tr>
-<tr class="memdesc:a2ef1a35e886c0addf294429e27ea9637"><td class="mdescLeft"> </td><td class="mdescRight">Create a kvstore. <a href="#a2ef1a35e886c0addf294429e27ea9637">More...</a><br /></td></tr>
+<tr class="memdesc:a2ef1a35e886c0addf294429e27ea9637"><td class="mdescLeft"> </td><td class="mdescRight">Create a kvstore. <a href="#a2ef1a35e886c0addf294429e27ea9637">More...</a><br/></td></tr>
<tr class="separator:a2ef1a35e886c0addf294429e27ea9637"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a915215184517bf663b0214fed32af6e7"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a915215184517bf663b0214fed32af6e7">MXKVStoreFree</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle)</td></tr>
-<tr class="memdesc:a915215184517bf663b0214fed32af6e7"><td class="mdescLeft"> </td><td class="mdescRight">Delete a KVStore handle. <a href="#a915215184517bf663b0214fed32af6e7">More...</a><br /></td></tr>
+<tr class="memdesc:a915215184517bf663b0214fed32af6e7"><td class="mdescLeft"> </td><td class="mdescRight">Delete a KVStore handle. <a href="#a915215184517bf663b0214fed32af6e7">More...</a><br/></td></tr>
<tr class="separator:a915215184517bf663b0214fed32af6e7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a475087b5464f9240d940ed4204a219b9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a475087b5464f9240d940ed4204a219b9">MXKVStoreInit</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24 [...]
-<tr class="memdesc:a475087b5464f9240d940ed4204a219b9"><td class="mdescLeft"> </td><td class="mdescRight">Init a list of (key,value) pairs in kvstore. <a href="#a475087b5464f9240d940ed4204a219b9">More...</a><br /></td></tr>
+<tr class="memdesc:a475087b5464f9240d940ed4204a219b9"><td class="mdescLeft"> </td><td class="mdescRight">Init a list of (key,value) pairs in kvstore. <a href="#a475087b5464f9240d940ed4204a219b9">More...</a><br/></td></tr>
<tr class="separator:a475087b5464f9240d940ed4204a219b9"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4dc86980d7922f52970a8e3978bc87cb"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a4dc86980d7922f52970a8e3978bc87cb">MXKVStoreInitEx</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b [...]
-<tr class="memdesc:a4dc86980d7922f52970a8e3978bc87cb"><td class="mdescLeft"> </td><td class="mdescRight">Init a list of (key,value) pairs in kvstore, where each key is a string. <a href="#a4dc86980d7922f52970a8e3978bc87cb">More...</a><br /></td></tr>
+<tr class="memdesc:a4dc86980d7922f52970a8e3978bc87cb"><td class="mdescLeft"> </td><td class="mdescRight">Init a list of (key,value) pairs in kvstore, where each key is a string. <a href="#a4dc86980d7922f52970a8e3978bc87cb">More...</a><br/></td></tr>
<tr class="separator:a4dc86980d7922f52970a8e3978bc87cb"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a66c990fbb8e4bd320e5ad8d369155ad6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a66c990fbb8e4bd320e5ad8d369155ad6">MXKVStorePush</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24 [...]
-<tr class="memdesc:a66c990fbb8e4bd320e5ad8d369155ad6"><td class="mdescLeft"> </td><td class="mdescRight">Push a list of (key,value) pairs to kvstore. <a href="#a66c990fbb8e4bd320e5ad8d369155ad6">More...</a><br /></td></tr>
+<tr class="memdesc:a66c990fbb8e4bd320e5ad8d369155ad6"><td class="mdescLeft"> </td><td class="mdescRight">Push a list of (key,value) pairs to kvstore. <a href="#a66c990fbb8e4bd320e5ad8d369155ad6">More...</a><br/></td></tr>
<tr class="separator:a66c990fbb8e4bd320e5ad8d369155ad6"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa59b7bf29d1a74fb1d1be6689c31913f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aa59b7bf29d1a74fb1d1be6689c31913f">MXKVStorePushEx</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b [...]
-<tr class="memdesc:aa59b7bf29d1a74fb1d1be6689c31913f"><td class="mdescLeft"> </td><td class="mdescRight">Push a list of (key,value) pairs to kvstore, where each key is a string. <a href="#aa59b7bf29d1a74fb1d1be6689c31913f">More...</a><br /></td></tr>
+<tr class="memdesc:aa59b7bf29d1a74fb1d1be6689c31913f"><td class="mdescLeft"> </td><td class="mdescRight">Push a list of (key,value) pairs to kvstore, where each key is a string. <a href="#aa59b7bf29d1a74fb1d1be6689c31913f">More...</a><br/></td></tr>
<tr class="separator:aa59b7bf29d1a74fb1d1be6689c31913f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1230e78ca998f363192dc9b345a2442e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a1230e78ca998f363192dc9b345a2442e">MXKVStorePull</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24 [...]
-<tr class="memdesc:a1230e78ca998f363192dc9b345a2442e"><td class="mdescLeft"> </td><td class="mdescRight">pull a list of (key, value) pairs from the kvstore <a href="#a1230e78ca998f363192dc9b345a2442e">More...</a><br /></td></tr>
+<tr class="memdesc:a1230e78ca998f363192dc9b345a2442e"><td class="mdescLeft"> </td><td class="mdescRight">pull a list of (key, value) pairs from the kvstore <a href="#a1230e78ca998f363192dc9b345a2442e">More...</a><br/></td></tr>
<tr class="separator:a1230e78ca998f363192dc9b345a2442e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a25588eca6c42583151e77bcf71ffc3ac"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a25588eca6c42583151e77bcf71ffc3ac">MXKVStorePullEx</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b [...]
-<tr class="memdesc:a25588eca6c42583151e77bcf71ffc3ac"><td class="mdescLeft"> </td><td class="mdescRight">pull a list of (key, value) pairs from the kvstore, where each key is a string <a href="#a25588eca6c42583151e77bcf71ffc3ac">More...</a><br /></td></tr>
+<tr class="memdesc:a25588eca6c42583151e77bcf71ffc3ac"><td class="mdescLeft"> </td><td class="mdescRight">pull a list of (key, value) pairs from the kvstore, where each key is a string <a href="#a25588eca6c42583151e77bcf71ffc3ac">More...</a><br/></td></tr>
<tr class="separator:a25588eca6c42583151e77bcf71ffc3ac"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:adbdd2035afce961837866c711af4f0ab"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#adbdd2035afce961837866c711af4f0ab">MXKVStoreSetUpdater</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#a80aad02a0ebd [...]
-<tr class="memdesc:adbdd2035afce961837866c711af4f0ab"><td class="mdescLeft"> </td><td class="mdescRight">register an push updater <a href="#adbdd2035afce961837866c711af4f0ab">More...</a><br /></td></tr>
+<tr class="memitem:adbdd2035afce961837866c711af4f0ab"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#adbdd2035afce961837866c711af4f0ab">MXKVStoreSetUpdater</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#af59143cc01b5 [...]
+<tr class="memdesc:adbdd2035afce961837866c711af4f0ab"><td class="mdescLeft"> </td><td class="mdescRight">register an push updater <a href="#adbdd2035afce961837866c711af4f0ab">More...</a><br/></td></tr>
<tr class="separator:adbdd2035afce961837866c711af4f0ab"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9dba9ada3ed98c76fe78221013e37f07"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a9dba9ada3ed98c76fe78221013e37f07">MXKVStoreGetType</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, const char **type)</td></tr>
-<tr class="memdesc:a9dba9ada3ed98c76fe78221013e37f07"><td class="mdescLeft"> </td><td class="mdescRight">get the type of the kvstore <a href="#a9dba9ada3ed98c76fe78221013e37f07">More...</a><br /></td></tr>
+<tr class="memdesc:a9dba9ada3ed98c76fe78221013e37f07"><td class="mdescLeft"> </td><td class="mdescRight">get the type of the kvstore <a href="#a9dba9ada3ed98c76fe78221013e37f07">More...</a><br/></td></tr>
<tr class="separator:a9dba9ada3ed98c76fe78221013e37f07"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1edf063b82bdd94d1f5214056dd55144"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a1edf063b82bdd94d1f5214056dd55144">MXKVStoreGetRank</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, int *ret)</td></tr>
-<tr class="memdesc:a1edf063b82bdd94d1f5214056dd55144"><td class="mdescLeft"> </td><td class="mdescRight">return The rank of this node in its group, which is in [0, GroupSize). <a href="#a1edf063b82bdd94d1f5214056dd55144">More...</a><br /></td></tr>
+<tr class="memdesc:a1edf063b82bdd94d1f5214056dd55144"><td class="mdescLeft"> </td><td class="mdescRight">return The rank of this node in its group, which is in [0, GroupSize). <a href="#a1edf063b82bdd94d1f5214056dd55144">More...</a><br/></td></tr>
<tr class="separator:a1edf063b82bdd94d1f5214056dd55144"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aac41a2bd1dfcdebb920c97eab40ea07b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aac41a2bd1dfcdebb920c97eab40ea07b">MXKVStoreGetGroupSize</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, int *ret)</td></tr>
-<tr class="memdesc:aac41a2bd1dfcdebb920c97eab40ea07b"><td class="mdescLeft"> </td><td class="mdescRight">return The number of nodes in this group, which is <a href="#aac41a2bd1dfcdebb920c97eab40ea07b">More...</a><br /></td></tr>
+<tr class="memdesc:aac41a2bd1dfcdebb920c97eab40ea07b"><td class="mdescLeft"> </td><td class="mdescRight">return The number of nodes in this group, which is <a href="#aac41a2bd1dfcdebb920c97eab40ea07b">More...</a><br/></td></tr>
<tr class="separator:aac41a2bd1dfcdebb920c97eab40ea07b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a67d719a0a5470f6292acb5896fbca4aa"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a67d719a0a5470f6292acb5896fbca4aa">MXKVStoreIsWorkerNode</a> (int *ret)</td></tr>
-<tr class="memdesc:a67d719a0a5470f6292acb5896fbca4aa"><td class="mdescLeft"> </td><td class="mdescRight">return whether or not this process is a worker node. <a href="#a67d719a0a5470f6292acb5896fbca4aa">More...</a><br /></td></tr>
+<tr class="memdesc:a67d719a0a5470f6292acb5896fbca4aa"><td class="mdescLeft"> </td><td class="mdescRight">return whether or not this process is a worker node. <a href="#a67d719a0a5470f6292acb5896fbca4aa">More...</a><br/></td></tr>
<tr class="separator:a67d719a0a5470f6292acb5896fbca4aa"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a06bf33dff7bc03cdd7474460e515882d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a06bf33dff7bc03cdd7474460e515882d">MXKVStoreIsServerNode</a> (int *ret)</td></tr>
-<tr class="memdesc:a06bf33dff7bc03cdd7474460e515882d"><td class="mdescLeft"> </td><td class="mdescRight">return whether or not this process is a server node. <a href="#a06bf33dff7bc03cdd7474460e515882d">More...</a><br /></td></tr>
+<tr class="memdesc:a06bf33dff7bc03cdd7474460e515882d"><td class="mdescLeft"> </td><td class="mdescRight">return whether or not this process is a server node. <a href="#a06bf33dff7bc03cdd7474460e515882d">More...</a><br/></td></tr>
<tr class="separator:a06bf33dff7bc03cdd7474460e515882d"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aad401ff990cb4142070e024af7ac0781"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aad401ff990cb4142070e024af7ac0781">MXKVStoreIsSchedulerNode</a> (int *ret)</td></tr>
-<tr class="memdesc:aad401ff990cb4142070e024af7ac0781"><td class="mdescLeft"> </td><td class="mdescRight">return whether or not this process is a scheduler node. <a href="#aad401ff990cb4142070e024af7ac0781">More...</a><br /></td></tr>
+<tr class="memdesc:aad401ff990cb4142070e024af7ac0781"><td class="mdescLeft"> </td><td class="mdescRight">return whether or not this process is a scheduler node. <a href="#aad401ff990cb4142070e024af7ac0781">More...</a><br/></td></tr>
<tr class="separator:aad401ff990cb4142070e024af7ac0781"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a96e8df6a3170c52b369954215dabe71c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a96e8df6a3170c52b369954215dabe71c">MXKVStoreBarrier</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle)</td></tr>
-<tr class="memdesc:a96e8df6a3170c52b369954215dabe71c"><td class="mdescLeft"> </td><td class="mdescRight">global barrier among all worker machines <a href="#a96e8df6a3170c52b369954215dabe71c">More...</a><br /></td></tr>
+<tr class="memdesc:a96e8df6a3170c52b369954215dabe71c"><td class="mdescLeft"> </td><td class="mdescRight">global barrier among all worker machines <a href="#a96e8df6a3170c52b369954215dabe71c">More...</a><br/></td></tr>
<tr class="separator:a96e8df6a3170c52b369954215dabe71c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:add5b8e9bbd098e8fef5deab4c7ab2737"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#add5b8e9bbd098e8fef5deab4c7ab2737">MXKVStoreSetBarrierBeforeExit</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, const int barrier_before_exit)</td></tr>
-<tr class="memdesc:add5b8e9bbd098e8fef5deab4c7ab2737"><td class="mdescLeft"> </td><td class="mdescRight">whether to do barrier when finalize <a href="#add5b8e9bbd098e8fef5deab4c7ab2737">More...</a><br /></td></tr>
+<tr class="memdesc:add5b8e9bbd098e8fef5deab4c7ab2737"><td class="mdescLeft"> </td><td class="mdescRight">whether to do barrier when finalize <a href="#add5b8e9bbd098e8fef5deab4c7ab2737">More...</a><br/></td></tr>
<tr class="separator:add5b8e9bbd098e8fef5deab4c7ab2737"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a732c3edf14dc24784abb27fe2613a10a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a732c3edf14dc24784abb27fe2613a10a">MXKVStoreRunServer</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#a6968ac8b790fb [...]
+<tr class="memitem:a732c3edf14dc24784abb27fe2613a10a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a732c3edf14dc24784abb27fe2613a10a">MXKVStoreRunServer</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, <a class="el" href="c__api_8h.html#a31f639b22167a [...]
<tr class="separator:a732c3edf14dc24784abb27fe2613a10a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5de45491037df7bf6b757b627100acd7"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a5de45491037df7bf6b757b627100acd7">MXKVStoreSendCommmandToServers</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, int cmd_id, const char *cmd_body)</td></tr>
<tr class="separator:a5de45491037df7bf6b757b627100acd7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac8b5138bcb7959cc831a97a24755e969"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ac8b5138bcb7959cc831a97a24755e969">MXKVStoreGetNumDeadNode</a> (<a class="el" href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a> handle, const int node_id, int *number, const int ti [...]
-<tr class="memdesc:ac8b5138bcb7959cc831a97a24755e969"><td class="mdescLeft"> </td><td class="mdescRight">Get the number of ps dead node(s) specified by {node_id}. <a href="#ac8b5138bcb7959cc831a97a24755e969">More...</a><br /></td></tr>
+<tr class="memdesc:ac8b5138bcb7959cc831a97a24755e969"><td class="mdescLeft"> </td><td class="mdescRight">Get the number of ps dead node(s) specified by {node_id}. <a href="#ac8b5138bcb7959cc831a97a24755e969">More...</a><br/></td></tr>
<tr class="separator:ac8b5138bcb7959cc831a97a24755e969"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab2b93d730f48591b4f77f3968f6a1b98"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ab2b93d730f48591b4f77f3968f6a1b98">MXRecordIOWriterCreate</a> (const char *uri, <a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">RecordIOHandle</a> *out)</td></tr>
-<tr class="memdesc:ab2b93d730f48591b4f77f3968f6a1b98"><td class="mdescLeft"> </td><td class="mdescRight">Create a RecordIO writer object. <a href="#ab2b93d730f48591b4f77f3968f6a1b98">More...</a><br /></td></tr>
+<tr class="memdesc:ab2b93d730f48591b4f77f3968f6a1b98"><td class="mdescLeft"> </td><td class="mdescRight">Create a RecordIO writer object. <a href="#ab2b93d730f48591b4f77f3968f6a1b98">More...</a><br/></td></tr>
<tr class="separator:ab2b93d730f48591b4f77f3968f6a1b98"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab2a55917ffd918e334464f1b8a7a1ab9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#ab2a55917ffd918e334464f1b8a7a1ab9">MXRecordIOWriterFree</a> (<a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">RecordIOHandle</a> handle)</td></tr>
-<tr class="memdesc:ab2a55917ffd918e334464f1b8a7a1ab9"><td class="mdescLeft"> </td><td class="mdescRight">Delete a RecordIO writer object. <a href="#ab2a55917ffd918e334464f1b8a7a1ab9">More...</a><br /></td></tr>
+<tr class="memdesc:ab2a55917ffd918e334464f1b8a7a1ab9"><td class="mdescLeft"> </td><td class="mdescRight">Delete a RecordIO writer object. <a href="#ab2a55917ffd918e334464f1b8a7a1ab9">More...</a><br/></td></tr>
<tr class="separator:ab2a55917ffd918e334464f1b8a7a1ab9"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0c380b6aa00446e7cb6021c8e2df75f3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a0c380b6aa00446e7cb6021c8e2df75f3">MXRecordIOWriterWriteRecord</a> (<a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">RecordIOHandle</a> handle, const char *buf, size_t size)</td></tr>
-<tr class="memdesc:a0c380b6aa00446e7cb6021c8e2df75f3"><td class="mdescLeft"> </td><td class="mdescRight">Write a record to a RecordIO object. <a href="#a0c380b6aa00446e7cb6021c8e2df75f3">More...</a><br /></td></tr>
+<tr class="memdesc:a0c380b6aa00446e7cb6021c8e2df75f3"><td class="mdescLeft"> </td><td class="mdescRight">Write a record to a RecordIO object. <a href="#a0c380b6aa00446e7cb6021c8e2df75f3">More...</a><br/></td></tr>
<tr class="separator:a0c380b6aa00446e7cb6021c8e2df75f3"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6cc8e9f94ec431edf8d9502f20d7b833"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a6cc8e9f94ec431edf8d9502f20d7b833">MXRecordIOWriterTell</a> (<a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">RecordIOHandle</a> handle, size_t *pos)</td></tr>
-<tr class="memdesc:a6cc8e9f94ec431edf8d9502f20d7b833"><td class="mdescLeft"> </td><td class="mdescRight">Get the current writer pointer position. <a href="#a6cc8e9f94ec431edf8d9502f20d7b833">More...</a><br /></td></tr>
+<tr class="memdesc:a6cc8e9f94ec431edf8d9502f20d7b833"><td class="mdescLeft"> </td><td class="mdescRight">Get the current writer pointer position. <a href="#a6cc8e9f94ec431edf8d9502f20d7b833">More...</a><br/></td></tr>
<tr class="separator:a6cc8e9f94ec431edf8d9502f20d7b833"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4464ddbe13b8a4542d3f91bf055d50af"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a4464ddbe13b8a4542d3f91bf055d50af">MXRecordIOReaderCreate</a> (const char *uri, <a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">RecordIOHandle</a> *out)</td></tr>
-<tr class="memdesc:a4464ddbe13b8a4542d3f91bf055d50af"><td class="mdescLeft"> </td><td class="mdescRight">Create a RecordIO reader object. <a href="#a4464ddbe13b8a4542d3f91bf055d50af">More...</a><br /></td></tr>
+<tr class="memdesc:a4464ddbe13b8a4542d3f91bf055d50af"><td class="mdescLeft"> </td><td class="mdescRight">Create a RecordIO reader object. <a href="#a4464ddbe13b8a4542d3f91bf055d50af">More...</a><br/></td></tr>
<tr class="separator:a4464ddbe13b8a4542d3f91bf055d50af"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a92736f17a5d9fd58e53e621358e4ef31"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a92736f17a5d9fd58e53e621358e4ef31">MXRecordIOReaderFree</a> (<a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">RecordIOHandle</a> handle)</td></tr>
-<tr class="memdesc:a92736f17a5d9fd58e53e621358e4ef31"><td class="mdescLeft"> </td><td class="mdescRight">Delete a RecordIO reader object. <a href="#a92736f17a5d9fd58e53e621358e4ef31">More...</a><br /></td></tr>
+<tr class="memdesc:a92736f17a5d9fd58e53e621358e4ef31"><td class="mdescLeft"> </td><td class="mdescRight">Delete a RecordIO reader object. <a href="#a92736f17a5d9fd58e53e621358e4ef31">More...</a><br/></td></tr>
<tr class="separator:a92736f17a5d9fd58e53e621358e4ef31"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a38e350f748967e0caa2c781a507667e4"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a38e350f748967e0caa2c781a507667e4">MXRecordIOReaderReadRecord</a> (<a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">RecordIOHandle</a> handle, char const **buf, size_t *size)</td></tr>
-<tr class="memdesc:a38e350f748967e0caa2c781a507667e4"><td class="mdescLeft"> </td><td class="mdescRight">Write a record to a RecordIO object. <a href="#a38e350f748967e0caa2c781a507667e4">More...</a><br /></td></tr>
+<tr class="memdesc:a38e350f748967e0caa2c781a507667e4"><td class="mdescLeft"> </td><td class="mdescRight">Write a record to a RecordIO object. <a href="#a38e350f748967e0caa2c781a507667e4">More...</a><br/></td></tr>
<tr class="separator:a38e350f748967e0caa2c781a507667e4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa992161083826ee6a434ade2790fbb31"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#aa992161083826ee6a434ade2790fbb31">MXRecordIOReaderSeek</a> (<a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">RecordIOHandle</a> handle, size_t pos)</td></tr>
-<tr class="memdesc:aa992161083826ee6a434ade2790fbb31"><td class="mdescLeft"> </td><td class="mdescRight">Set the current reader pointer position. <a href="#aa992161083826ee6a434ade2790fbb31">More...</a><br /></td></tr>
+<tr class="memdesc:aa992161083826ee6a434ade2790fbb31"><td class="mdescLeft"> </td><td class="mdescRight">Set the current reader pointer position. <a href="#aa992161083826ee6a434ade2790fbb31">More...</a><br/></td></tr>
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<tr class="memitem:a87e98002ae1a925b65a8cf0773f802d3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a87e98002ae1a925b65a8cf0773f802d3">MXRtcCreate</a> (char *name, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> num_input, <a class="el" href="c__api_8h.html#a796e7ef8260 [...]
-<tr class="memdesc:a87e98002ae1a925b65a8cf0773f802d3"><td class="mdescLeft"> </td><td class="mdescRight">Create a MXRtc object. <a href="#a87e98002ae1a925b65a8cf0773f802d3">More...</a><br /></td></tr>
+<tr class="memdesc:a87e98002ae1a925b65a8cf0773f802d3"><td class="mdescLeft"> </td><td class="mdescRight">Create a MXRtc object. <a href="#a87e98002ae1a925b65a8cf0773f802d3">More...</a><br/></td></tr>
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<tr class="memitem:a20f72cb4e18d5081eaf40875adcb6e98"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a20f72cb4e18d5081eaf40875adcb6e98">MXRtcPush</a> (<a class="el" href="c__api_8h.html#a58a159a7417ad988206970d46c4c7ee8">RtcHandle</a> handle, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082 [...]
-<tr class="memdesc:a20f72cb4e18d5081eaf40875adcb6e98"><td class="mdescLeft"> </td><td class="mdescRight">Run cuda kernel. <a href="#a20f72cb4e18d5081eaf40875adcb6e98">More...</a><br /></td></tr>
+<tr class="memdesc:a20f72cb4e18d5081eaf40875adcb6e98"><td class="mdescLeft"> </td><td class="mdescRight">Run cuda kernel. <a href="#a20f72cb4e18d5081eaf40875adcb6e98">More...</a><br/></td></tr>
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<tr class="memitem:a13819b19ab3cdd91566b1d5cf8bc6b0a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a13819b19ab3cdd91566b1d5cf8bc6b0a">MXRtcFree</a> (<a class="el" href="c__api_8h.html#a58a159a7417ad988206970d46c4c7ee8">RtcHandle</a> handle)</td></tr>
-<tr class="memdesc:a13819b19ab3cdd91566b1d5cf8bc6b0a"><td class="mdescLeft"> </td><td class="mdescRight">Delete a MXRtc object. <a href="#a13819b19ab3cdd91566b1d5cf8bc6b0a">More...</a><br /></td></tr>
+<tr class="memdesc:a13819b19ab3cdd91566b1d5cf8bc6b0a"><td class="mdescLeft"> </td><td class="mdescRight">Delete a MXRtc object. <a href="#a13819b19ab3cdd91566b1d5cf8bc6b0a">More...</a><br/></td></tr>
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-<tr class="memitem:a92233cd9477ce12458bbd2b3e628dec9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a92233cd9477ce12458bbd2b3e628dec9">MXCustomOpRegister</a> (const char *op_type, <a class="el" href="c__api_8h.html#a470eba0beb11ed2fd858c50c9b2be33a">CustomOpPropCreator</a> creator)</td></tr>
+<tr class="memitem:a92233cd9477ce12458bbd2b3e628dec9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html#a92233cd9477ce12458bbd2b3e628dec9">MXCustomOpRegister</a> (const char *op_type, <a class="el" href="c__api_8h.html#a3a7fd875ee6aad3f20981764626a6c7b">CustomOpPropCreator</a> creator)</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>C API of mxnet. </p>
<p>Copyright (c) 2015 by Contributors </p>
</div><h2 class="groupheader">Macro Definition Documentation</h2>
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<h2 class="groupheader">Typedef Documentation</h2>
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- <td class="memname">typedef int(* CustomOpCreateFunc) (const char *, int, unsigned **, const int *, const int *, struct <a class="el" href="structMXCallbackList.html">MXCallbackList</a> *, void *)</td>
+ <td class="memname">typedef int(* CustomOpCreateFunc)(const char *, int, unsigned **, const int *, const int *, struct <a class="el" href="structMXCallbackList.html">MXCallbackList</a> *, void *)</td>
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-<h2 class="memtitle"><span class="permalink"><a href="#a2731805437dc3d76201fc21154b1337c">◆ </a></span>ExecutorMonitorCallback</h2>
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- <td class="memname">typedef void(* ExecutorMonitorCallback) (const char *, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a>, void *)</td>
+ <td class="memname">typedef void(* ExecutorMonitorCallback)(const char *, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a>, void *)</td>
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-<h2 class="memtitle"><span class="permalink"><a href="#a0543f53696ffcf5a7a2f0f4403f87623">◆ </a></span>KVStoreHandle</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a796e7ef8260ef40b24af7d1082aa2d84">◆ </a></span>mx_uint</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a0ee6dd8389c0dacae0f7c48156ec2f34">◆ </a></span>MXGenericCallback</h2>
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- <td class="memname">typedef int(* MXGenericCallback) (void)</td>
+ <td class="memname">typedef int(* MXGenericCallback)(void)</td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
-<a id="a6968ac8b790fb40a674a364a2a74cba6"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a6968ac8b790fb40a674a364a2a74cba6">◆ </a></span>MXKVStoreServerController</h2>
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- <td class="memname">typedef void() MXKVStoreServerController(int head, const char *body, void *controller_handle)</td>
+ <td class="memname">typedef void( MXKVStoreServerController)(int head, const char *body, void *controller_handle)</td>
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-<h2 class="memtitle"><span class="permalink"><a href="#a80aad02a0ebd29de5ad0b65b2c8ccdb3">◆ </a></span>MXKVStoreUpdater</h2>
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- <td class="memname">typedef void() MXKVStoreUpdater(int key, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> recv, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> local, void *handle)</td>
+ <td class="memname">typedef void( MXKVStoreUpdater)(int key, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> recv, <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> local, void *handle)</td>
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-<h2 class="memtitle"><span class="permalink"><a href="#a99c2939783499d2a5ce0d76881dce9df">◆ </a></span>NDArrayHandle</h2>
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<h2 class="groupheader">Enumeration Type Documentation</h2>
-<a id="aa2395b69772d66cb5c6c4cbe47749792"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aa2395b69772d66cb5c6c4cbe47749792">◆ </a></span>CustomOpCallbacks</h2>
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</table>
</div><div class="memdoc">
<table class="fieldtable">
-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="aa2395b69772d66cb5c6c4cbe47749792a2d5d86c1a9bac7de4ceb1c641a4dce4a"></a>kCustomOpDelete </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="aa2395b69772d66cb5c6c4cbe47749792a3189c8e5988867480e3ee81e262bba4e"></a>kCustomOpForward </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="aa2395b69772d66cb5c6c4cbe47749792afe6891ef7e38df52ac25db00a2ef73c6"></a>kCustomOpBackward </td><td class="fielddoc"></td></tr>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="aa2395b69772d66cb5c6c4cbe47749792a2d5d86c1a9bac7de4ceb1c641a4dce4a"></a>kCustomOpDelete</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="aa2395b69772d66cb5c6c4cbe47749792a3189c8e5988867480e3ee81e262bba4e"></a>kCustomOpForward</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="aa2395b69772d66cb5c6c4cbe47749792afe6891ef7e38df52ac25db00a2ef73c6"></a>kCustomOpBackward</em> </td><td class="fielddoc">
+</td></tr>
</table>
</div>
</div>
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-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a62d6b72cc6b3d920797c32ea8732cbbaa16e932f1ac19d0846d5ec6d2077be6aa"></a>kCustomOpPropDelete </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a62d6b72cc6b3d920797c32ea8732cbbaab3c71c386f99a193c2b5d1ad3b306712"></a>kCustomOpPropListArguments </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a62d6b72cc6b3d920797c32ea8732cbbaa0a32388e902d5d81a88f62f3eabf56c3"></a>kCustomOpPropListOutputs </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a62d6b72cc6b3d920797c32ea8732cbbaaabeb765165b4748e68c7f196bf8a1686"></a>kCustomOpPropListAuxiliaryStates </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a62d6b72cc6b3d920797c32ea8732cbbaaa23caa8f573176ff6985b550df9b2be2"></a>kCustomOpPropInferShape </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a62d6b72cc6b3d920797c32ea8732cbbaa3f2e3562346ac66f3af912db28cafd06"></a>kCustomOpPropDeclareBackwardDependency </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a62d6b72cc6b3d920797c32ea8732cbbaac97963c158fddf25e10fddd4d9ae7619"></a>kCustomOpPropCreateOperator </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a62d6b72cc6b3d920797c32ea8732cbbaa86fbe407e39ef10e916cd8872e28334d"></a>kCustomOpPropInferType </td><td class="fielddoc"></td></tr>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="a62d6b72cc6b3d920797c32ea8732cbbaa16e932f1ac19d0846d5ec6d2077be6aa"></a>kCustomOpPropDelete</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a62d6b72cc6b3d920797c32ea8732cbbaab3c71c386f99a193c2b5d1ad3b306712"></a>kCustomOpPropListArguments</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a62d6b72cc6b3d920797c32ea8732cbbaa0a32388e902d5d81a88f62f3eabf56c3"></a>kCustomOpPropListOutputs</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a62d6b72cc6b3d920797c32ea8732cbbaaabeb765165b4748e68c7f196bf8a1686"></a>kCustomOpPropListAuxiliaryStates</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a62d6b72cc6b3d920797c32ea8732cbbaaa23caa8f573176ff6985b550df9b2be2"></a>kCustomOpPropInferShape</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a62d6b72cc6b3d920797c32ea8732cbbaa3f2e3562346ac66f3af912db28cafd06"></a>kCustomOpPropDeclareBackwardDependency</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a62d6b72cc6b3d920797c32ea8732cbbaac97963c158fddf25e10fddd4d9ae7619"></a>kCustomOpPropCreateOperator</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a62d6b72cc6b3d920797c32ea8732cbbaa86fbe407e39ef10e916cd8872e28334d"></a>kCustomOpPropInferType</em> </td><td class="fielddoc">
+</td></tr>
</table>
</div>
</div>
<h2 class="groupheader">Function Documentation</h2>
-<a id="a133d10a566764530bf3172e52afbe561"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a133d10a566764530bf3172e52afbe561">◆ </a></span>MXAutogradBackward()</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a4ea5441aa16bff2121855a5afb7be84e">◆ </a></span>MXAutogradComputeGradient()</h2>
+<a class="anchor" id="a57a5d1ed585f9f431a31f921b4ca00ab"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int MXAutogradBackwardEx </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> </td>
+ <td class="paramname"><em>num_output</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype"><a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> * </td>
+ <td class="paramname"><em>output_handles</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype"><a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> * </td>
+ <td class="paramname"><em>ograd_handles</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>retain_graph</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>is_train</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<p>compute the gradient of outputs w.r.t variabels </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">num_output</td><td>number of output NDArray </td></tr>
+ <tr><td class="paramname">output_handles</td><td>output NDArrays </td></tr>
+ <tr><td class="paramname">ograd_handles</td><td>head gradient for NDArrays </td></tr>
+ <tr><td class="paramname">retain_graph</td><td>whether to keep the graph after backward </td></tr>
+ <tr><td class="paramname">is_train</td><td>whether to do backward for training or inference </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>0 when success, -1 when failure happens </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a4ea5441aa16bff2121855a5afb7be84e"></a>
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-<h2 class="memtitle"><span class="permalink"><a href="#a9cf2b0aa3ff66119fe793f6ed82b61b8">◆ </a></span>MXAutogradSetIsTraining()</h2>
+<a class="anchor" id="a75939e5be09a150d4d8905c9fd6e986e"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int MXAutogradSetIsRecording </td>
+ <td>(</td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>is_recording</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int * </td>
+ <td class="paramname"><em>prev</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>set whether to record operator for autograd </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">is_recording</td><td>1 when recording, 0 when not recording. </td></tr>
+ <tr><td class="paramname">prev</td><td>returns the previous status before this set. </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>0 when success, -1 when failure happens </dd></dl>
+</div>
+</div>
+<a class="anchor" id="a9cf2b0aa3ff66119fe793f6ed82b61b8"></a>
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-<h2 class="memtitle"><span class="permalink"><a href="#a46d056bad70ea014110923da41524cfc">◆ </a></span>MXCreateCachedOp()</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a92233cd9477ce12458bbd2b3e628dec9">◆ </a></span>MXCustomOpRegister()</h2>
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+ <td class="paramtype"><a class="el" href="c__api_8h.html#a3a7fd875ee6aad3f20981764626a6c7b">CustomOpPropCreator</a> </td>
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+ <td class="memname"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int MXExecutorBackwardEx </td>
+ <td>(</td>
+ <td class="paramtype"><a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a> </td>
+ <td class="paramname"><em>handle</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype"><a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a> </td>
+ <td class="paramname"><em>len</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype"><a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a> * </td>
+ <td class="paramname"><em>head_grads</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int </td>
+ <td class="paramname"><em>is_train</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
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+<p>Excecutor run backward. </p>
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">handle</td><td>execute handle </td></tr>
+ <tr><td class="paramname">len</td><td>lenth </td></tr>
+ <tr><td class="paramname">head_grads</td><td>NDArray handle for heads' gradient </td></tr>
+ <tr><td class="paramname">is_train</td><td>int value to indicate whether the backward pass is for evaluation</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>0 when success, -1 when failure happens </dd></dl>
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-<h2 class="memtitle"><span class="permalink"><a href="#a8b3a7394a8a641f2490f4c5246be1083">◆ </a></span>MXSymbolSaveToFile()</h2>
-
+<a class="anchor" id="a8b3a7394a8a641f2490f4c5246be1083"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -7049,9 +6925,7 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a5c99eac0fe3cfa0912259d0fa12b6651"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a5c99eac0fe3cfa0912259d0fa12b6651">◆ </a></span>MXSymbolSaveToJSON()</h2>
-
+<a class="anchor" id="a5c99eac0fe3cfa0912259d0fa12b6651"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -7087,9 +6961,7 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="aebeac35c3110eb391035fbbeb29e9436"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aebeac35c3110eb391035fbbeb29e9436">◆ </a></span>MXSymbolSetAttr()</h2>
-
+<a class="anchor" id="aebeac35c3110eb391035fbbeb29e9436"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -7120,10 +6992,12 @@ Functions</h2></td></tr>
</div><div class="memdoc">
<p>Set string attribute from symbol. NOTE: Setting attribute to a symbol can affect the semantics(mutable/immutable) of symbolic graph. </p>
-<p>Safe recommendaton: use immutable graph</p><ul>
+<p>Safe recommendaton: use immutable graph</p>
+<ul>
<li>Only allow set attributes during creation of new symbol as optional parameter</li>
</ul>
-<p>Mutable graph (be careful about the semantics):</p><ul>
+<p>Mutable graph (be careful about the semantics):</p>
+<ul>
<li>Allow set attr at any point.</li>
<li>Mutating an attribute of some common node of two graphs can cause confusion from user.</li>
</ul>
@@ -7142,9 +7016,9 @@ Functions</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/c__api_8h__dep__incl.map b/versions/master/doxygen/c__api_8h__dep__incl.map
index d1ad958..c0b58ea 100644
--- a/versions/master/doxygen/c__api_8h__dep__incl.map
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@@ -1,3 +1,3 @@
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-<area shape="rect" id="node2" href="$executor_8h.html" title="Symbolic executor interface of mxnet. " alt="" coords="5,79,174,104"/>
+<area shape="rect" id="node2" href="$executor_8h.html" title="Symbolic executor interface of mxnet. " alt="" coords="5,80,187,107"/>
</map>
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index 31b05b0..d10e29d 100644
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diff --git a/versions/master/doxygen/c__api_8h_source.html b/versions/master/doxygen/c__api_8h_source.html
index aaebadf..e1e33b5 100644
--- a/versions/master/doxygen/c__api_8h_source.html
+++ b/versions/master/doxygen/c__api_8h_source.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/c_api.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
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-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -66,9 +88,657 @@ $(function() {
<div class="title">c_api.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="c__api_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_C_API_H_</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#define MXNET_C_API_H_</span></div><div class="line"><a name="l00008"></a>< [...]
+<a href="c__api_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_C_API_H_</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_C_API_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#ifdef __cplusplus</span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor"></span><span class="keyword">extern</span> <span class="stringliteral">"C"</span> {</div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#endif // __cplusplus</span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#ifdef __cplusplus</span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor"></span><span class="preprocessor">#define DEFAULT(x) = x</span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor"></span><span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"><a class="line" href="c__api_8h.html#a2380be5ab258c3657553d0cef62936fe"> 18</a></span> <span class="preprocessor"></span><span class="preprocessor">#define DEFAULT(x)</span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor"></span><span class="preprocessor">#endif // __cplusplus</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <stdint.h></span></div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include <stdint.h></span></div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include <stddef.h></span></div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include <stdbool.h></span></div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div>
+<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#ifdef _WIN32</span></div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor"></span><span class="preprocessor">#ifdef MXNET_EXPORTS</span></div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_DLL __declspec(dllexport)</span></div>
+<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor"></span><span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_DLL __declspec(dllimport)</span></div>
+<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor"></span><span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00035"></a><span class="lineno"><a class="line" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac"> 35</a></span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_DLL</span></div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84"> 39</a></span> <span class="keyword">typedef</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a>;</div>
+<div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="c__api_8h.html#a4e709067707d9f90c534bd65494ae668"> 41</a></span> <span class="keyword">typedef</span> <span class="keywordtype">float</span> <a class="code" href="c__api_8h.html#a4e709067707d9f90c534bd65494ae668">mx_float</a>;</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="comment">// all the handles are simply void *</span></div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment">// will be casted internally to specific pointers types</span></div>
+<div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment">// these typedefs are mainly used for readablity reasons</span></div>
+<div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df"> 46</a></span> <span class="comment"></span><span class="keyword">typedef</span> <span class="keywordtype">void</span> *<a class="code" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a>;</div>
+<div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="c__api_8h.html#a197f06e1d873d34572b679532c268d35"> 48</a></span> <span class="keyword">typedef</span> <span class="keyword">const</span> <span class="keywordtype">void</span> *<a class="code" href="c__api_8h.html#a197f06e1d873d34572b679532c268d35">FunctionHandle</a>;</div>
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+<div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="c__api_8h.html#aaccda72ea7a5439c715ff5007b9595b5"> 56</a></span> <span class="keyword">typedef</span> <span class="keywordtype">void</span> *<a class="code" href="c__api_8h.html#aaccda72ea7a5439c715ff5007b9595b5">AtomicSymbolHandle</a>;</div>
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+<div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="c__api_8h.html#a0b9b13ccb06a6a4f05d211261fa883dd"> 60</a></span> <span class="keyword">typedef</span> <span class="keywordtype">void</span> *<a class="code" href="c__api_8h.html#a0b9b13ccb06a6a4f05d211261fa883dd">DataIterCreator</a>;</div>
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+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div>
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+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a>,</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordtype">void</span> *);</div>
+<div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div>
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+<div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#a65f675dec9ef798a96122e91d4cf3fa8"> 75</a></span>  void (*<a class="code" href="structNativeOpInfo.html#a65f675dec9ef798a96122e91d4cf3fa8">forward</a>)(int, <span class="keywordtype">float</span>**, <span class="keywordtype">int</span>*, <span class="keywordtype">unsigned</span>**, <span class="keywordtype">int</span>*, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00076"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#a2d571a373ff0ca0da5d96295cc03f465"> 76</a></span>  void (*<a class="code" href="structNativeOpInfo.html#a2d571a373ff0ca0da5d96295cc03f465">backward</a>)(int, <span class="keywordtype">float</span>**, <span class="keywordtype">int</span>*, <span class="keywordtype">unsigned</span>**, <span class="keywordtype">int</span>*, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00077"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#af4c12b25f9d708835619b768db5805ff"> 77</a></span>  void (*<a class="code" href="structNativeOpInfo.html#af4c12b25f9d708835619b768db5805ff">infer_shape</a>)(int, <span class="keywordtype">int</span>*, <span class="keywordtype">unsigned</span>**, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00078"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#a1dfb6e3206e76dcf755ae0bca655081b"> 78</a></span>  void (*<a class="code" href="structNativeOpInfo.html#a1dfb6e3206e76dcf755ae0bca655081b">list_outputs</a>)(<span class="keywordtype">char</span>***, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00079"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#ab1f5d01bbb1d6507c23e640ac3eea834"> 79</a></span>  void (*<a class="code" href="structNativeOpInfo.html#ab1f5d01bbb1d6507c23e640ac3eea834">list_arguments</a>)(<span class="keywordtype">char</span>***, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="comment">// all functions also pass a payload void* pointer</span></div>
+<div class="line"><a name="l00081"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#acf0017f1f716c2e88ce9feedc9b4759f"> 81</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNativeOpInfo.html#acf0017f1f716c2e88ce9feedc9b4759f">p_forward</a>;</div>
+<div class="line"><a name="l00082"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#a60beae73be72aeb308b55aeed8cbd3c1"> 82</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNativeOpInfo.html#a60beae73be72aeb308b55aeed8cbd3c1">p_backward</a>;</div>
+<div class="line"><a name="l00083"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#a1f9c9294685a6e3e953acd26728e0692"> 83</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNativeOpInfo.html#a1f9c9294685a6e3e953acd26728e0692">p_infer_shape</a>;</div>
+<div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#a32ec41926eee694c1bfb8e8b5697a146"> 84</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNativeOpInfo.html#a32ec41926eee694c1bfb8e8b5697a146">p_list_outputs</a>;</div>
+<div class="line"><a name="l00085"></a><span class="lineno"><a class="line" href="structNativeOpInfo.html#a0e5c803876bbcc5d8162b6ae30bce231"> 85</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNativeOpInfo.html#a0e5c803876bbcc5d8162b6ae30bce231">p_list_arguments</a>;</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span> };</div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div>
+<div class="line"><a name="l00088"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html"> 88</a></span> <span class="keyword">struct </span><a class="code" href="structNDArrayOpInfo.html">NDArrayOpInfo</a> {</div>
+<div class="line"><a name="l00089"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#ac11f40fb25fa4abd275b6f1c7b3d3213"> 89</a></span>  bool (*<a class="code" href="structNDArrayOpInfo.html#ac11f40fb25fa4abd275b6f1c7b3d3213">forward</a>)(int, <span class="keywordtype">void</span>**, <span class="keywordtype">int</span>*, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#a6cafb44b639ed5256fe80a976e351cc1"> 90</a></span>  bool (*<a class="code" href="structNDArrayOpInfo.html#a6cafb44b639ed5256fe80a976e351cc1">backward</a>)(int, <span class="keywordtype">void</span>**, <span class="keywordtype">int</span>*, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#a139351ad186cde67a376d2f9d6682baf"> 91</a></span>  bool (*<a class="code" href="structNDArrayOpInfo.html#a139351ad186cde67a376d2f9d6682baf">infer_shape</a>)(int, <span class="keywordtype">int</span>*, <span class="keywordtype">unsigned</span>**, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#acd6f9dbac22ba35f0f9ac5062dc5c8ec"> 92</a></span>  bool (*<a class="code" href="structNDArrayOpInfo.html#acd6f9dbac22ba35f0f9ac5062dc5c8ec">list_outputs</a>)(<span class="keywordtype">char</span>***, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00093"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#ad6bb579606e6bd83569d035fa92a857c"> 93</a></span>  bool (*<a class="code" href="structNDArrayOpInfo.html#ad6bb579606e6bd83569d035fa92a857c">list_arguments</a>)(<span class="keywordtype">char</span>***, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00094"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#a4327dd20a65eb3a7241f67ede18f9950"> 94</a></span>  bool (*<a class="code" href="structNDArrayOpInfo.html#a4327dd20a65eb3a7241f67ede18f9950">declare_backward_dependency</a>)(<span class="keyword">const</span> <span class="keywordtype">int</span>*, <span class="keyword">const</span> <span class="keywordtype">int</span>*, <span class="keyword">const</span> <span class="keywordt [...]
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordtype">int</span>*, <span class="keywordtype">int</span>**, <span class="keywordtype">void</span>*);</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// all functions also pass a payload void* pointer</span></div>
+<div class="line"><a name="l00097"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#ae40b148a45687b8e4e9d475fef1ed13a"> 97</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNDArrayOpInfo.html#ae40b148a45687b8e4e9d475fef1ed13a">p_forward</a>;</div>
+<div class="line"><a name="l00098"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#afb5ee1e890c2499a5a845b21f4df518f"> 98</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNDArrayOpInfo.html#afb5ee1e890c2499a5a845b21f4df518f">p_backward</a>;</div>
+<div class="line"><a name="l00099"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#afe27107d0470918edda2d2c5c62fd8d7"> 99</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNDArrayOpInfo.html#afe27107d0470918edda2d2c5c62fd8d7">p_infer_shape</a>;</div>
+<div class="line"><a name="l00100"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#aa7ba9b33d0e48357207d6ce6c62707ba"> 100</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNDArrayOpInfo.html#aa7ba9b33d0e48357207d6ce6c62707ba">p_list_outputs</a>;</div>
+<div class="line"><a name="l00101"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#aad953647557d6f2cb13ced8c00e4da0b"> 101</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNDArrayOpInfo.html#aad953647557d6f2cb13ced8c00e4da0b">p_list_arguments</a>;</div>
+<div class="line"><a name="l00102"></a><span class="lineno"><a class="line" href="structNDArrayOpInfo.html#af17150d574bc8ca7e7a82b9cb4965f05"> 102</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structNDArrayOpInfo.html#af17150d574bc8ca7e7a82b9cb4965f05">p_declare_backward_dependency</a>;</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span> };</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div>
+<div class="line"><a name="l00105"></a><span class="lineno"><a class="line" href="c__api_8h.html#a581410d3cb679f2d0fc2f65fca91ec17"> 105</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__api_8h.html#a581410d3cb679f2d0fc2f65fca91ec17">MXGenericCallback</a>)(void);</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div>
+<div class="line"><a name="l00107"></a><span class="lineno"><a class="line" href="structMXCallbackList.html"> 107</a></span> <span class="keyword">struct </span><a class="code" href="structMXCallbackList.html">MXCallbackList</a> {</div>
+<div class="line"><a name="l00108"></a><span class="lineno"><a class="line" href="structMXCallbackList.html#aa593dcc4096dcc38a2af477e057d8861"> 108</a></span>  <span class="keywordtype">int</span> <a class="code" href="structMXCallbackList.html#aa593dcc4096dcc38a2af477e057d8861">num_callbacks</a>;</div>
+<div class="line"><a name="l00109"></a><span class="lineno"><a class="line" href="structMXCallbackList.html#ad56325e57f5fffc2b920eb3f8f378199"> 109</a></span>  int (**<a class="code" href="structMXCallbackList.html#ad56325e57f5fffc2b920eb3f8f378199">callbacks</a>)(void);</div>
+<div class="line"><a name="l00110"></a><span class="lineno"><a class="line" href="structMXCallbackList.html#a332323a5f414bd3f5e423788804a0dcd"> 110</a></span>  <span class="keywordtype">void</span> **<a class="code" href="structMXCallbackList.html#a332323a5f414bd3f5e423788804a0dcd">contexts</a>;</div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span> };</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div>
+<div class="line"><a name="l00113"></a><span class="lineno"><a class="line" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792"> 113</a></span> <span class="keyword">enum</span> <a class="code" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792">CustomOpCallbacks</a> {</div>
+<div class="line"><a name="l00114"></a><span class="lineno"><a class="line" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792a2d5d86c1a9bac7de4ceb1c641a4dce4a"> 114</a></span>  <a class="code" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792a2d5d86c1a9bac7de4ceb1c641a4dce4a">kCustomOpDelete</a>,</div>
+<div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792a3189c8e5988867480e3ee81e262bba4e"> 115</a></span>  <a class="code" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792a3189c8e5988867480e3ee81e262bba4e">kCustomOpForward</a>,</div>
+<div class="line"><a name="l00116"></a><span class="lineno"><a class="line" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792afe6891ef7e38df52ac25db00a2ef73c6"> 116</a></span>  <a class="code" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792afe6891ef7e38df52ac25db00a2ef73c6">kCustomOpBackward</a></div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span> };</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div>
+<div class="line"><a name="l00119"></a><span class="lineno"><a class="line" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbba"> 119</a></span> <span class="keyword">enum</span> <a class="code" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbba">CustomOpPropCallbacks</a> {</div>
+<div class="line"><a name="l00120"></a><span class="lineno"><a class="line" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa16e932f1ac19d0846d5ec6d2077be6aa"> 120</a></span>  <a class="code" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa16e932f1ac19d0846d5ec6d2077be6aa">kCustomOpPropDelete</a>,</div>
+<div class="line"><a name="l00121"></a><span class="lineno"><a class="line" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaab3c71c386f99a193c2b5d1ad3b306712"> 121</a></span>  <a class="code" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaab3c71c386f99a193c2b5d1ad3b306712">kCustomOpPropListArguments</a>,</div>
+<div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa0a32388e902d5d81a88f62f3eabf56c3"> 122</a></span>  <a class="code" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa0a32388e902d5d81a88f62f3eabf56c3">kCustomOpPropListOutputs</a>,</div>
+<div class="line"><a name="l00123"></a><span class="lineno"><a class="line" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaaabeb765165b4748e68c7f196bf8a1686"> 123</a></span>  <a class="code" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaaabeb765165b4748e68c7f196bf8a1686">kCustomOpPropListAuxiliaryStates</a>,</div>
+<div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaaa23caa8f573176ff6985b550df9b2be2"> 124</a></span>  <a class="code" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaaa23caa8f573176ff6985b550df9b2be2">kCustomOpPropInferShape</a>,</div>
+<div class="line"><a name="l00125"></a><span class="lineno"><a class="line" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa3f2e3562346ac66f3af912db28cafd06"> 125</a></span>  <a class="code" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa3f2e3562346ac66f3af912db28cafd06">kCustomOpPropDeclareBackwardDependency</a>,</div>
+<div class="line"><a name="l00126"></a><span class="lineno"><a class="line" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaac97963c158fddf25e10fddd4d9ae7619"> 126</a></span>  <a class="code" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaac97963c158fddf25e10fddd4d9ae7619">kCustomOpPropCreateOperator</a>,</div>
+<div class="line"><a name="l00127"></a><span class="lineno"><a class="line" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa86fbe407e39ef10e916cd8872e28334d"> 127</a></span>  <a class="code" href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa86fbe407e39ef10e916cd8872e28334d">kCustomOpPropInferType</a></div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span> };</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div>
+<div class="line"><a name="l00130"></a><span class="lineno"><a class="line" href="c__api_8h.html#ac153c43c858c3070c63d0eec44f36514"> 130</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__api_8h.html#ac153c43c858c3070c63d0eec44f36514">CustomOpFBFunc</a>)(<span class="keywordtype">int</span> <span class="comment">/*size*/</span>, <span class="keywordtype">void</span>** <span class="comment">/*ptrs*/</span>, <span class="keywordtype">int</span>* <span clas [...]
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* <span class="comment">/*reqs*/</span>, <span class="keyword">const</span> <span class="keywordtype">int</span> <span class="comment">/*is_train*/</span>,</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordtype">void</span>* <span class="comment">/*state*/</span>);</div>
+<div class="line"><a name="l00133"></a><span class="lineno"><a class="line" href="c__api_8h.html#a557fa6ce86e94109ce7797b2cce33e23"> 133</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__api_8h.html#a557fa6ce86e94109ce7797b2cce33e23">CustomOpDelFunc</a>)(<span class="keywordtype">void</span>* <span class="comment">/*state*/</span>);</div>
+<div class="line"><a name="l00134"></a><span class="lineno"><a class="line" href="c__api_8h.html#add8141d7f51e3a77544cc7b79433d16e"> 134</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__api_8h.html#add8141d7f51e3a77544cc7b79433d16e">CustomOpListFunc</a>)(<span class="keywordtype">char</span>*** <span class="comment">/*args*/</span>, <span class="keywordtype">void</span>* <span class="comment">/*state*/</span>);</div>
+<div class="line"><a name="l00135"></a><span class="lineno"><a class="line" href="c__api_8h.html#a9a559987f509f9939030addd565d34a3"> 135</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__api_8h.html#a9a559987f509f9939030addd565d34a3">CustomOpInferShapeFunc</a>)(<span class="keywordtype">int</span> <span class="comment">/*num_input*/</span>, <span class="keywordtype">int</span>* <span class="comment">/*ndims*/</span>,</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordtype">unsigned</span>** <span class="comment">/*shapes*/</span>, <span class="keywordtype">void</span>* <span class="comment">/*state*/</span>);</div>
+<div class="line"><a name="l00137"></a><span class="lineno"><a class="line" href="c__api_8h.html#a09da22ebe9a09b084afc386fa7b0ef53"> 137</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__api_8h.html#a09da22ebe9a09b084afc386fa7b0ef53">CustomOpInferTypeFunc</a>)(<span class="keywordtype">int</span> <span class="comment">/*num_input*/</span>, <span class="keywordtype">int</span>* <span class="comment">/*types*/</span>, <span class="keywordtype">void</span> [...]
+<div class="line"><a name="l00138"></a><span class="lineno"><a class="line" href="c__api_8h.html#ae8feb033b715209653ea91e089d060b0"> 138</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__api_8h.html#ae8feb033b715209653ea91e089d060b0">CustomOpBwdDepFunc</a>)(<span class="keyword">const</span> <span class="keywordtype">int</span>* <span class="comment">/*out_grad*/</span>, <span class="keyword">const</span> <span class="keywordtype">int</span>* <span clas [...]
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* <span class="comment">/*out_data*/</span>, <span class="keywordtype">int</span>* <span class="comment">/*num_deps*/</span>,</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordtype">int</span>** <span class="comment">/*rdeps*/</span>, <span class="keywordtype">void</span>* <span class="comment">/*state*/</span>);</div>
+<div class="line"><a name="l00141"></a><span class="lineno"><a class="line" href="c__api_8h.html#a201e319981a60877ab9d6f7cf3b3f62d"> 141</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__api_8h.html#a201e319981a60877ab9d6f7cf3b3f62d">CustomOpCreateFunc</a>)(<span class="keyword">const</span> <span class="keywordtype">char</span>* <span class="comment">/*ctx*/</span>, <span class="keywordtype">int</span> <span class="comment">/*num_inputs*/</span>,</div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordtype">unsigned</span>** <span class="comment">/*shapes*/</span>, <span class="keyword">const</span> <span class="keywordtype">int</span>* <span class="comment">/*ndims*/</span>,</div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* <span class="comment">/*dtypes*/</span>, <span class="keyword">struct </span><a class="code" href="structMXCallbackList.html">MXCallbackList</a>* <span class="comment">/*ret*/</span>,</div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordtype">void</span>* <span class="comment">/*state*/</span>);</div>
+<div class="line"><a name="l00145"></a><span class="lineno"><a class="line" href="c__api_8h.html#a3a7fd875ee6aad3f20981764626a6c7b"> 145</a></span> <span class="keyword">typedef</span> int (*<a class="code" href="c__api_8h.html#a3a7fd875ee6aad3f20981764626a6c7b">CustomOpPropCreator</a>)(<span class="keyword">const</span> <span class="keywordtype">char</span>* <span class="comment">/*op_type*/</span>, <span class="keyword">const</span> <span class="keywordtype">int</span> <span clas [...]
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** <span class="comment">/*keys*/</span>, <span class="keyword">const</span> <span class="keywordtype">char</span>** <span class="comment">/*values*/</span>,</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">struct </span><a class="code" href="structMXCallbackList.html">MXCallbackList</a>* <span class="comment">/*ret*/</span>);</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keyword">const</span> <span class="keywordtype">char</span> *<a class="code" href="c__api_8h.html#acab1874f3e3077710b77d1a005abf36c">MXGetLastError</a>();</div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <span class="comment">//-------------------------------------</span></div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="comment">// Part 0: Global State setups</span></div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="comment">//-------------------------------------</span></div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="comment"></span><a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a05e3ead36464b62cd1a9264187640f5a">MXRandomSeed</a>(<span class="keywordtype">int</span> seed);</div>
+<div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aa837df9930bbb46d0ca1674523f63e1c">MXNotifyShutdown</a>();</div>
+<div class="line"><a name="l00185"></a><span class="lineno"> 185</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ab3a837622ed93ba390b4e8cbc0286b87">MXSetProfilerConfig</a>(<span class="keywordtype">int</span> mode, <span class="keyword">const</span> <span class="keywordtype">char</span>* filename);</div>
+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ac373802e0d3a38f297345f87d307a798">MXSetProfilerState</a>(<span class="keywordtype">int</span> state);</div>
+<div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div>
+<div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a51716c7a38d1b02d26da4d84d6307025">MXDumpProfile</a>();</div>
+<div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div>
+<div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a13ebc161bc64049a26404ee6eca4ac26">MXSetNumOMPThreads</a>(<span class="keywordtype">int</span> thread_num);</div>
+<div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div>
+<div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="comment">//-------------------------------------</span></div>
+<div class="line"><a name="l00202"></a><span class="lineno"> 202</span> <span class="comment">// Part 1: NDArray creation and deletion</span></div>
+<div class="line"><a name="l00203"></a><span class="lineno"> 203</span> <span class="comment">//-------------------------------------</span></div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span> <span class="comment"></span><a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a3a32f78b54c81564da9834c6c6353b90">MXNDArrayCreateNone</a>(NDArrayHandle *out);</div>
+<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a097e93ed9fd8c8cfb8ae76aa32441b72">MXNDArrayCreate</a>(<span class="keyword">const</span> mx_uint *shape,</div>
+<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  mx_uint ndim,</div>
+<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordtype">int</span> dev_type,</div>
+<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordtype">int</span> dev_id,</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordtype">int</span> delay_alloc,</div>
+<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  NDArrayHandle *out);</div>
+<div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div>
+<div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#abe1ed22a74c15b24111ce2af39bcca38">MXNDArrayCreateEx</a>(<span class="keyword">const</span> mx_uint *shape,</div>
+<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  mx_uint ndim,</div>
+<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordtype">int</span> dev_type,</div>
+<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordtype">int</span> dev_id,</div>
+<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordtype">int</span> delay_alloc,</div>
+<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keywordtype">int</span> dtype,</div>
+<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  NDArrayHandle *out);</div>
+<div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a27c9353672df26b68c169fbb857f3a3f">MXNDArrayLoadFromRawBytes</a>(<span class="keyword">const</span> <span class="keywordtype">void</span> *buf,</div>
+<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordtype">size_t</span> size,</div>
+<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  NDArrayHandle *out);</div>
+<div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ab180057ee7a43918d021adfc468150a0">MXNDArraySaveRawBytes</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordtype">size_t</span> *out_size,</div>
+<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **out_buf);</div>
+<div class="line"><a name="l00277"></a><span class="lineno"> 277</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a5dc95ef008b73103b86f4576fcf9117d">MXNDArraySave</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* fname,</div>
+<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  mx_uint num_args,</div>
+<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  NDArrayHandle* args,</div>
+<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** keys);</div>
+<div class="line"><a name="l00290"></a><span class="lineno"> 290</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a2c1d0459d3fe124b5d3aed49386999f1">MXNDArrayLoad</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* fname,</div>
+<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  mx_uint *out_size,</div>
+<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  NDArrayHandle** out_arr,</div>
+<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  mx_uint *out_name_size,</div>
+<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>*** out_names);</div>
+<div class="line"><a name="l00306"></a><span class="lineno"> 306</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ada464e85b86305d167511bb977095a52">MXNDArraySyncCopyFromCPU</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keyword">const</span> <span class="keywordtype">void</span> *data,</div>
+<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordtype">size_t</span> size);</div>
+<div class="line"><a name="l00320"></a><span class="lineno"> 320</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a98df4e0ceae7e198a5c18904cb135ab1">MXNDArraySyncCopyToCPU</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keywordtype">void</span> *data,</div>
+<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keywordtype">size_t</span> size);</div>
+<div class="line"><a name="l00329"></a><span class="lineno"> 329</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ad008e978336534821634e460c3eb9bff">MXNDArrayWaitToRead</a>(NDArrayHandle handle);</div>
+<div class="line"><a name="l00336"></a><span class="lineno"> 336</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a6f4d7cb465c2b3723c51449b1aba8af9">MXNDArrayWaitToWrite</a>(NDArrayHandle handle);</div>
+<div class="line"><a name="l00342"></a><span class="lineno"> 342</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#adf10f6461b93f46d67289359909d56c3">MXNDArrayWaitAll</a>();</div>
+<div class="line"><a name="l00348"></a><span class="lineno"> 348</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a857e66f665360d5fab3ad52528df38dd">MXNDArrayFree</a>(NDArrayHandle handle);</div>
+<div class="line"><a name="l00357"></a><span class="lineno"> 357</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a67c83f78e6bd15c6ad6eb09a34133086">MXNDArraySlice</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  mx_uint slice_begin,</div>
+<div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  mx_uint slice_end,</div>
+<div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  NDArrayHandle *out);</div>
+<div class="line"><a name="l00368"></a><span class="lineno"> 368</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a90fbe78325ba9d67d3629be442623cee">MXNDArrayAt</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  mx_uint idx,</div>
+<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  NDArrayHandle *out);</div>
+<div class="line"><a name="l00379"></a><span class="lineno"> 379</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a3743a54910b1862400c3ec4cbf7e9d80">MXNDArrayReshape</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keywordtype">int</span> ndim,</div>
+<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordtype">int</span> *dims,</div>
+<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  NDArrayHandle *out);</div>
+<div class="line"><a name="l00390"></a><span class="lineno"> 390</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a2035651f4392d249d1b904d5eb0c3406">MXNDArrayGetShape</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  mx_uint *out_dim,</div>
+<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">const</span> mx_uint **out_pdata);</div>
+<div class="line"><a name="l00399"></a><span class="lineno"> 399</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a6c8d8ceb3c523e8507e813f7afb97f83">MXNDArrayGetData</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordtype">void</span> **out_pdata);</div>
+<div class="line"><a name="l00407"></a><span class="lineno"> 407</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a69cfaa5de4b9d87070d4823700e64a1d">MXNDArrayGetDType</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordtype">int</span> *out_dtype);</div>
+<div class="line"><a name="l00416"></a><span class="lineno"> 416</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a3ae6292f1e640d54c92a8cc56f513b76">MXNDArrayGetContext</a>(NDArrayHandle handle,</div>
+<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keywordtype">int</span> *out_dev_type,</div>
+<div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keywordtype">int</span> *out_dev_id);</div>
+<div class="line"><a name="l00424"></a><span class="lineno"> 424</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aedb8d676502a66eef4a6c5988f73dccb">MXNDArrayGetGrad</a>(NDArrayHandle handle, NDArrayHandle *out);</div>
+<div class="line"><a name="l00430"></a><span class="lineno"> 430</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a8e2c90cfa6c52a012fd3f23ffd8541f2">MXNDArrayDetach</a>(NDArrayHandle handle, NDArrayHandle *out);</div>
+<div class="line"><a name="l00437"></a><span class="lineno"> 437</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a2ff7ded9017ac7617be4eeebffae06a1">MXNDArraySetGradState</a>(NDArrayHandle handle, <span class="keywordtype">int</span> state);</div>
+<div class="line"><a name="l00444"></a><span class="lineno"> 444</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a2ab9f498d34d84238e41ecb5e584e435">MXNDArrayGetGradState</a>(NDArrayHandle handle, <span class="keywordtype">int</span> *out);</div>
+<div class="line"><a name="l00445"></a><span class="lineno"> 445</span> <span class="comment">//--------------------------------</span></div>
+<div class="line"><a name="l00446"></a><span class="lineno"> 446</span> <span class="comment">// Part 2: functions on NDArray</span></div>
+<div class="line"><a name="l00447"></a><span class="lineno"> 447</span> <span class="comment">//--------------------------------</span></div>
+<div class="line"><a name="l00455"></a><span class="lineno"> 455</span> <span class="comment"></span><a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a553e6791f689a41e63e4cdfbdbbd5c58">MXListFunctions</a>(mx_uint *out_size,</div>
+<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  FunctionHandle **out_array);</div>
+<div class="line"><a name="l00463"></a><span class="lineno"> 463</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a9e2a1cb559d69daf541dac88c07409a8">MXGetFunction</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> *name,</div>
+<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  FunctionHandle *out);</div>
+<div class="line"><a name="l00477"></a><span class="lineno"> 477</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ad369531149cf26e8db49958e17bdf237">MXFuncGetInfo</a>(FunctionHandle fun,</div>
+<div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **name,</div>
+<div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **description,</div>
+<div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  mx_uint *num_args,</div>
+<div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***arg_names,</div>
+<div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***arg_type_infos,</div>
+<div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***arg_descriptions,</div>
+<div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **return_type <a class="code" href="c__api_8h.html#a2380be5ab258c3657553d0cef62936fe">DEFAULT</a>(NULL));</div>
+<div class="line"><a name="l00495"></a><span class="lineno"> 495</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#af0a6166fe48ce0a175990914b1cb02e0">MXFuncDescribe</a>(FunctionHandle fun,</div>
+<div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  mx_uint *num_use_vars,</div>
+<div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  mx_uint *num_scalars,</div>
+<div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  mx_uint *num_mutate_vars,</div>
+<div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="keywordtype">int</span> *type_mask);</div>
+<div class="line"><a name="l00510"></a><span class="lineno"> 510</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a527f90a410f66db7136e91d27e7ea45c">MXFuncInvoke</a>(FunctionHandle fun,</div>
+<div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  NDArrayHandle *use_vars,</div>
+<div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  mx_float *scalar_args,</div>
+<div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  NDArrayHandle *mutate_vars);</div>
+<div class="line"><a name="l00527"></a><span class="lineno"> 527</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a9afe2f46b1fcd8016c8bfb11a84c6240">MXFuncInvokeEx</a>(FunctionHandle fun,</div>
+<div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  NDArrayHandle *use_vars,</div>
+<div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  mx_float *scalar_args,</div>
+<div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  NDArrayHandle *mutate_vars,</div>
+<div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordtype">int</span> num_params,</div>
+<div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keywordtype">char</span> **param_keys,</div>
+<div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keywordtype">char</span> **param_vals);</div>
+<div class="line"><a name="l00546"></a><span class="lineno"> 546</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a6a9a4608136959389db02ad938201a67">MXImperativeInvoke</a>(AtomicSymbolCreator creator,</div>
+<div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keywordtype">int</span> num_inputs,</div>
+<div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  NDArrayHandle *inputs,</div>
+<div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keywordtype">int</span> *num_outputs,</div>
+<div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  NDArrayHandle **outputs,</div>
+<div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keywordtype">int</span> num_params,</div>
+<div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **param_keys,</div>
+<div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **param_vals);</div>
+<div class="line"><a name="l00560"></a><span class="lineno"> 560</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a75939e5be09a150d4d8905c9fd6e986e">MXAutogradSetIsRecording</a>(<span class="keywordtype">int</span> is_recording, <span class="keywordtype">int</span>* prev);</div>
+<div class="line"><a name="l00567"></a><span class="lineno"> 567</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a9cf2b0aa3ff66119fe793f6ed82b61b8">MXAutogradSetIsTraining</a>(<span class="keywordtype">int</span> is_training, <span class="keywordtype">int</span>* prev);</div>
+<div class="line"><a name="l00574"></a><span class="lineno"> 574</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a53f3bbc41e275485d1d6cd21f40abb7f">MXAutogradMarkVariables</a>(mx_uint num_var,</div>
+<div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  NDArrayHandle *var_handles,</div>
+<div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  mx_uint *reqs_array,</div>
+<div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  NDArrayHandle *grad_handles);</div>
+<div class="line"><a name="l00584"></a><span class="lineno"> 584</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a4ea5441aa16bff2121855a5afb7be84e">MXAutogradComputeGradient</a>(mx_uint num_output,</div>
+<div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  NDArrayHandle* output_handles);</div>
+<div class="line"><a name="l00594"></a><span class="lineno"> 594</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a133d10a566764530bf3172e52afbe561">MXAutogradBackward</a>(mx_uint num_output,</div>
+<div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  NDArrayHandle* output_handles,</div>
+<div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  NDArrayHandle* ograd_handles,</div>
+<div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <span class="keywordtype">int</span> retain_graph);</div>
+<div class="line"><a name="l00607"></a><span class="lineno"> 607</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a57a5d1ed585f9f431a31f921b4ca00ab">MXAutogradBackwardEx</a>(mx_uint num_output,</div>
+<div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  NDArrayHandle* output_handles,</div>
+<div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  NDArrayHandle* ograd_handles,</div>
+<div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keywordtype">int</span> retain_graph,</div>
+<div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="keywordtype">int</span> is_train);</div>
+<div class="line"><a name="l00615"></a><span class="lineno"> 615</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a46d056bad70ea014110923da41524cfc">MXCreateCachedOp</a>(SymbolHandle handle,</div>
+<div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  CachedOpHandle *out);</div>
+<div class="line"><a name="l00620"></a><span class="lineno"> 620</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a83a34d6df4290e5efd737293cf875a9c">MXFreeCachedOp</a>(CachedOpHandle handle);</div>
+<div class="line"><a name="l00624"></a><span class="lineno"> 624</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ad4c2297db07b9610d39f1d0e12fdc2b9">MXInvokeCachedOp</a>(CachedOpHandle handle,</div>
+<div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keywordtype">int</span> num_inputs,</div>
+<div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  NDArrayHandle *inputs,</div>
+<div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="keywordtype">int</span> *num_outputs,</div>
+<div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  NDArrayHandle **outputs);</div>
+<div class="line"><a name="l00629"></a><span class="lineno"> 629</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l00630"></a><span class="lineno"> 630</span> <span class="comment">// Part 3: symbolic configuration generation</span></div>
+<div class="line"><a name="l00631"></a><span class="lineno"> 631</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l00638"></a><span class="lineno"> 638</span> <span class="comment"></span><a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ac5a3fafe6616c4d246b24e4d11c65238">MXListAllOpNames</a>(mx_uint *out_size,</div>
+<div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***out_array);</div>
+<div class="line"><a name="l00646"></a><span class="lineno"> 646</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a6eb35d17154ef093856e60e416d354cc">MXSymbolListAtomicSymbolCreators</a>(mx_uint *out_size,</div>
+<div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  AtomicSymbolCreator **out_array);</div>
+<div class="line"><a name="l00648"></a><span class="lineno"> 648</span> </div>
+<div class="line"><a name="l00654"></a><span class="lineno"> 654</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a272c88973b602d37a87df4a42cf7fb7f">MXSymbolGetAtomicSymbolName</a>(AtomicSymbolCreator creator,</div>
+<div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **name);</div>
+<div class="line"><a name="l00673"></a><span class="lineno"> 673</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aa46875e507a638bd6c8a6c891a8f5934">MXSymbolGetAtomicSymbolInfo</a>(AtomicSymbolCreator creator,</div>
+<div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **name,</div>
+<div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **description,</div>
+<div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  mx_uint *num_args,</div>
+<div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***arg_names,</div>
+<div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***arg_type_infos,</div>
+<div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***arg_descriptions,</div>
+<div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **key_var_num_args,</div>
+<div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **return_type <a class="code" href="c__api_8h.html#a2380be5ab258c3657553d0cef62936fe">DEFAULT</a>(NULL));</div>
+<div class="line"><a name="l00691"></a><span class="lineno"> 691</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a9cd6ad8c274665172513707dd73262ce">MXSymbolCreateAtomicSymbol</a>(AtomicSymbolCreator creator,</div>
+<div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  mx_uint num_param,</div>
+<div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **keys,</div>
+<div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **vals,</div>
+<div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  SymbolHandle *out);</div>
+<div class="line"><a name="l00702"></a><span class="lineno"> 702</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a738463b3d661aa4821caa4626092010f">MXSymbolCreateVariable</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> *name, SymbolHandle *out);</div>
+<div class="line"><a name="l00710"></a><span class="lineno"> 710</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a72e684e635bb138c6382bd9c4e5957db">MXSymbolCreateGroup</a>(mx_uint num_symbols,</div>
+<div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  SymbolHandle *symbols,</div>
+<div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  SymbolHandle *out);</div>
+<div class="line"><a name="l00719"></a><span class="lineno"> 719</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a314035f352106ee44039bcbe166353b3">MXSymbolCreateFromFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> *fname, SymbolHandle *out);</div>
+<div class="line"><a name="l00726"></a><span class="lineno"> 726</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ac6a2362d9cb7ba763810b971c9524521">MXSymbolCreateFromJSON</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> *json, SymbolHandle *out);</div>
+<div class="line"><a name="l00733"></a><span class="lineno"> 733</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a8b3a7394a8a641f2490f4c5246be1083">MXSymbolSaveToFile</a>(SymbolHandle symbol, <span class="keyword">const</span> <span class="keywordtype">char</span> *fname);</div>
+<div class="line"><a name="l00740"></a><span class="lineno"> 740</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a5c99eac0fe3cfa0912259d0fa12b6651">MXSymbolSaveToJSON</a>(SymbolHandle symbol, <span class="keyword">const</span> <span class="keywordtype">char</span> **out_json);</div>
+<div class="line"><a name="l00746"></a><span class="lineno"> 746</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a1070254ccff925195bf263b43762a06f">MXSymbolFree</a>(SymbolHandle symbol);</div>
+<div class="line"><a name="l00753"></a><span class="lineno"> 753</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a6f8fc1fb5c90c92498241b75f74cea86">MXSymbolCopy</a>(SymbolHandle symbol, SymbolHandle *out);</div>
+<div class="line"><a name="l00760"></a><span class="lineno"> 760</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a5f4c41b8ca7c92524def534956b7e24a">MXSymbolPrint</a>(SymbolHandle symbol, <span class="keyword">const</span> <span class="keywordtype">char</span> **out_str);</div>
+<div class="line"><a name="l00768"></a><span class="lineno"> 768</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a33606f8bf8413da87c9d499e2b2f1e88">MXSymbolGetName</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** out,</div>
+<div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  <span class="keywordtype">int</span> *success);</div>
+<div class="line"><a name="l00779"></a><span class="lineno"> 779</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#abca605833c1fb045e2e78c8f3be492ac">MXSymbolGetAttr</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* key,</div>
+<div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** out,</div>
+<div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <span class="keywordtype">int</span> *success);</div>
+<div class="line"><a name="l00799"></a><span class="lineno"> 799</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aebeac35c3110eb391035fbbeb29e9436">MXSymbolSetAttr</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* key,</div>
+<div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* value);</div>
+<div class="line"><a name="l00809"></a><span class="lineno"> 809</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a9e4dc341e666c772e1b530920ba4a0e9">MXSymbolListAttr</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  mx_uint *out_size,</div>
+<div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>*** out);</div>
+<div class="line"><a name="l00819"></a><span class="lineno"> 819</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ac3bb9177224ea3b08a294dfb744e05fd">MXSymbolListAttrShallow</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  mx_uint *out_size,</div>
+<div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>*** out);</div>
+<div class="line"><a name="l00829"></a><span class="lineno"> 829</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ade7acc2c570cc5b6eea2f1f2efc67a74">MXSymbolListArguments</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  mx_uint *out_size,</div>
+<div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***out_str_array);</div>
+<div class="line"><a name="l00839"></a><span class="lineno"> 839</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aedf1da726aaacb87a5d959ed83f03f41">MXSymbolListOutputs</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  mx_uint *out_size,</div>
+<div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***out_str_array);</div>
+<div class="line"><a name="l00848"></a><span class="lineno"> 848</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a13816872b73d6c474686d985747e8c65">MXSymbolGetInternals</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  SymbolHandle *out);</div>
+<div class="line"><a name="l00856"></a><span class="lineno"> 856</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a6050b4cfcb75ed3d0a44afb1b8df45cd">MXSymbolGetChildren</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  SymbolHandle *out);</div>
+<div class="line"><a name="l00865"></a><span class="lineno"> 865</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a8e601320c59679d9423179f23f281bf2">MXSymbolGetOutput</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  mx_uint index,</div>
+<div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  SymbolHandle *out);</div>
+<div class="line"><a name="l00875"></a><span class="lineno"> 875</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a38e75c5a41473912e2d6cdfececba84d">MXSymbolListAuxiliaryStates</a>(SymbolHandle symbol,</div>
+<div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  mx_uint *out_size,</div>
+<div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***out_str_array);</div>
+<div class="line"><a name="l00892"></a><span class="lineno"> 892</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a879a5ee991393d364b787e948b4c9961">MXSymbolCompose</a>(SymbolHandle sym,</div>
+<div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> *name,</div>
+<div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  mx_uint num_args,</div>
+<div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** keys,</div>
+<div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  SymbolHandle* args);</div>
+<div class="line"><a name="l00906"></a><span class="lineno"> 906</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ac2268c0bc7ad2f96d36bbbdab9a51c2e">MXSymbolGrad</a>(SymbolHandle sym,</div>
+<div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  mx_uint num_wrt,</div>
+<div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** wrt,</div>
+<div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  SymbolHandle* out);</div>
+<div class="line"><a name="l00932"></a><span class="lineno"> 932</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#afaf03324563098f1ac4c23ecfc88b784">MXSymbolInferShape</a>(SymbolHandle sym,</div>
+<div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  mx_uint num_args,</div>
+<div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** keys,</div>
+<div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="keyword">const</span> mx_uint *arg_ind_ptr,</div>
+<div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="keyword">const</span> mx_uint *arg_shape_data,</div>
+<div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  mx_uint *in_shape_size,</div>
+<div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="keyword">const</span> mx_uint **in_shape_ndim,</div>
+<div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <span class="keyword">const</span> mx_uint ***in_shape_data,</div>
+<div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  mx_uint *out_shape_size,</div>
+<div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <span class="keyword">const</span> mx_uint **out_shape_ndim,</div>
+<div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <span class="keyword">const</span> mx_uint ***out_shape_data,</div>
+<div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  mx_uint *aux_shape_size,</div>
+<div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  <span class="keyword">const</span> mx_uint **aux_shape_ndim,</div>
+<div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <span class="keyword">const</span> mx_uint ***aux_shape_data,</div>
+<div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  <span class="keywordtype">int</span> *complete);</div>
+<div class="line"><a name="l00971"></a><span class="lineno"> 971</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a664c85b47c69b81be3b9ea1dfcb72a94">MXSymbolInferShapePartial</a>(SymbolHandle sym,</div>
+<div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  mx_uint num_args,</div>
+<div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** keys,</div>
+<div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <span class="keyword">const</span> mx_uint *arg_ind_ptr,</div>
+<div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <span class="keyword">const</span> mx_uint *arg_shape_data,</div>
+<div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  mx_uint *in_shape_size,</div>
+<div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="keyword">const</span> mx_uint **in_shape_ndim,</div>
+<div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <span class="keyword">const</span> mx_uint ***in_shape_data,</div>
+<div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  mx_uint *out_shape_size,</div>
+<div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <span class="keyword">const</span> mx_uint **out_shape_ndim,</div>
+<div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <span class="keyword">const</span> mx_uint ***out_shape_data,</div>
+<div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  mx_uint *aux_shape_size,</div>
+<div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <span class="keyword">const</span> mx_uint **aux_shape_ndim,</div>
+<div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <span class="keyword">const</span> mx_uint ***aux_shape_data,</div>
+<div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <span class="keywordtype">int</span> *complete);</div>
+<div class="line"><a name="l00986"></a><span class="lineno"> 986</span> </div>
+<div class="line"><a name="l01005"></a><span class="lineno"> 1005</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a1eff7ea3fd2fe56509c75f5a610890e6">MXSymbolInferType</a>(SymbolHandle sym,</div>
+<div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  mx_uint num_args,</div>
+<div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** keys,</div>
+<div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> *arg_type_data,</div>
+<div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  mx_uint *in_type_size,</div>
+<div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> **in_type_data,</div>
+<div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  mx_uint *out_type_size,</div>
+<div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> **out_type_data,</div>
+<div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  mx_uint *aux_type_size,</div>
+<div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> **aux_type_data,</div>
+<div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  <span class="keywordtype">int</span> *complete);</div>
+<div class="line"><a name="l01016"></a><span class="lineno"> 1016</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l01017"></a><span class="lineno"> 1017</span> <span class="comment">// Part 4: Executor interface</span></div>
+<div class="line"><a name="l01018"></a><span class="lineno"> 1018</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l01024"></a><span class="lineno"> 1024</span> <span class="comment"></span><a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ade1487656dfba3cae12c5bf676e08dcf">MXExecutorFree</a>(ExecutorHandle handle);</div>
+<div class="line"><a name="l01031"></a><span class="lineno"> 1031</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ac9f776a6e7be0bacda4dcd3efa5f7dc5">MXExecutorPrint</a>(ExecutorHandle handle, <span class="keyword">const</span> <span class="keywordtype">char</span> **out_str);</div>
+<div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a1f9987e701b810e0e79b5207fa215415">MXExecutorForward</a>(ExecutorHandle handle, <span class="keywordtype">int</span> is_train);</div>
+<div class="line"><a name="l01049"></a><span class="lineno"> 1049</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ad8f92bb15e11058abe12959cfc4dcbb3">MXExecutorBackward</a>(ExecutorHandle handle,</div>
+<div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  mx_uint len,</div>
+<div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  NDArrayHandle *head_grads);</div>
+<div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a3a211f3e0c6270271ce4fe79228dd0c2">MXExecutorBackwardEx</a>(ExecutorHandle handle,</div>
+<div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  mx_uint len,</div>
+<div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  NDArrayHandle *head_grads,</div>
+<div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <span class="keywordtype">int</span> is_train);</div>
+<div class="line"><a name="l01074"></a><span class="lineno"> 1074</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a442845c8ef89d4c849f468ea2b7717f7">MXExecutorOutputs</a>(ExecutorHandle handle,</div>
+<div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  mx_uint *out_size,</div>
+<div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  NDArrayHandle **out);</div>
+<div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> </div>
+<div class="line"><a name="l01093"></a><span class="lineno"> 1093</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a10a6deacde18b7d52b44eee0ddddf0e1">MXExecutorBind</a>(SymbolHandle symbol_handle,</div>
+<div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  <span class="keywordtype">int</span> dev_type,</div>
+<div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <span class="keywordtype">int</span> dev_id,</div>
+<div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  mx_uint len,</div>
+<div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  NDArrayHandle *in_args,</div>
+<div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  NDArrayHandle *arg_grad_store,</div>
+<div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  mx_uint *grad_req_type,</div>
+<div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  mx_uint aux_states_len,</div>
+<div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  NDArrayHandle *aux_states,</div>
+<div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  ExecutorHandle *out);</div>
+<div class="line"><a name="l01124"></a><span class="lineno"> 1124</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ad1646370458f8a3ddb9e4f3365c5e510">MXExecutorBindX</a>(SymbolHandle symbol_handle,</div>
+<div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  <span class="keywordtype">int</span> dev_type,</div>
+<div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  <span class="keywordtype">int</span> dev_id,</div>
+<div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  mx_uint num_map_keys,</div>
+<div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** map_keys,</div>
+<div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* map_dev_types,</div>
+<div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* map_dev_ids,</div>
+<div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  mx_uint len,</div>
+<div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  NDArrayHandle *in_args,</div>
+<div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  NDArrayHandle *arg_grad_store,</div>
+<div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  mx_uint *grad_req_type,</div>
+<div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  mx_uint aux_states_len,</div>
+<div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  NDArrayHandle *aux_states,</div>
+<div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  ExecutorHandle *out);</div>
+<div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a8139e691c88ebcf082f9e954598e0e86">MXExecutorBindEX</a>(SymbolHandle symbol_handle,</div>
+<div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  <span class="keywordtype">int</span> dev_type,</div>
+<div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  <span class="keywordtype">int</span> dev_id,</div>
+<div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  mx_uint num_map_keys,</div>
+<div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** map_keys,</div>
+<div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* map_dev_types,</div>
+<div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* map_dev_ids,</div>
+<div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  mx_uint len,</div>
+<div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  NDArrayHandle *in_args,</div>
+<div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  NDArrayHandle *arg_grad_store,</div>
+<div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  mx_uint *grad_req_type,</div>
+<div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  mx_uint aux_states_len,</div>
+<div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  NDArrayHandle *aux_states,</div>
+<div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  ExecutorHandle shared_exec,</div>
+<div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  ExecutorHandle *out);</div>
+<div class="line"><a name="l01175"></a><span class="lineno"> 1175</span> </div>
+<div class="line"><a name="l01176"></a><span class="lineno"> 1176</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ab85ff72ac8364631116ced48939eaa60">MXExecutorSimpleBind</a>(SymbolHandle symbol_handle,</div>
+<div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  <span class="keywordtype">int</span> dev_type,</div>
+<div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  <span class="keywordtype">int</span> dev_id,</div>
+<div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  <span class="keyword">const</span> mx_uint num_g2c_keys,</div>
+<div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** g2c_keys,</div>
+<div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* g2c_dev_types,</div>
+<div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* g2c_dev_ids,</div>
+<div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="keyword">const</span> mx_uint provided_grad_req_list_len,</div>
+<div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** provided_grad_req_names,</div>
+<div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** provided_grad_req_types,</div>
+<div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  <span class="keyword">const</span> mx_uint num_provided_arg_shapes,</div>
+<div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** provided_arg_shape_names,</div>
+<div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  <span class="keyword">const</span> mx_uint* provided_arg_shape_data,</div>
+<div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  <span class="keyword">const</span> mx_uint* provided_arg_shape_idx,</div>
+<div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  <span class="keyword">const</span> mx_uint num_provided_arg_dtypes,</div>
+<div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** provided_arg_dtype_names,</div>
+<div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* provided_arg_dtypes,</div>
+<div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  <span class="keyword">const</span> mx_uint num_shared_arg_names,</div>
+<div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** shared_arg_name_list,</div>
+<div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  <span class="keywordtype">int</span>* shared_buffer_len,</div>
+<div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** shared_buffer_name_list,</div>
+<div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  NDArrayHandle* shared_buffer_handle_list,</div>
+<div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>*** updated_shared_buffer_name_list,</div>
+<div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  NDArrayHandle** updated_shared_buffer_handle_list,</div>
+<div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  mx_uint* num_in_args,</div>
+<div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  NDArrayHandle** in_args,</div>
+<div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  NDArrayHandle** arg_grads,</div>
+<div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  mx_uint* num_aux_states,</div>
+<div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  NDArrayHandle** aux_states,</div>
+<div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  ExecutorHandle shared_exec_handle,</div>
+<div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  ExecutorHandle* out);</div>
+<div class="line"><a name="l01210"></a><span class="lineno"> 1210</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#abae91a5d7847020e14395e078144489a">MXExecutorSetMonitorCallback</a>(ExecutorHandle handle,</div>
+<div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  <a class="code" href="c__api_8h.html#aa7071c027141653df82d2b72db566acd">ExecutorMonitorCallback</a> callback,</div>
+<div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <span class="keywordtype">void</span>* callback_handle);</div>
+<div class="line"><a name="l01213"></a><span class="lineno"> 1213</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l01214"></a><span class="lineno"> 1214</span> <span class="comment">// Part 5: IO Interface</span></div>
+<div class="line"><a name="l01215"></a><span class="lineno"> 1215</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l01222"></a><span class="lineno"> 1222</span> <span class="comment"></span><a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aa13fb35019b0c42dcfee185b2f9e09d0">MXListDataIters</a>(mx_uint *out_size,</div>
+<div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  DataIterCreator **out_array);</div>
+<div class="line"><a name="l01234"></a><span class="lineno"> 1234</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a361c4a5360734e430c54a754cc581354">MXDataIterCreateIter</a>(DataIterCreator handle,</div>
+<div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  mx_uint num_param,</div>
+<div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **keys,</div>
+<div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **vals,</div>
+<div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  DataIterHandle *out);</div>
+<div class="line"><a name="l01250"></a><span class="lineno"> 1250</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a795614eb68a4fead826202a5b5ba9517">MXDataIterGetIterInfo</a>(DataIterCreator creator,</div>
+<div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **name,</div>
+<div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **description,</div>
+<div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  mx_uint *num_args,</div>
+<div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***arg_names,</div>
+<div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***arg_type_infos,</div>
+<div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> ***arg_descriptions);</div>
+<div class="line"><a name="l01262"></a><span class="lineno"> 1262</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a29e117922acfbc95828433e736bcc3af">MXDataIterFree</a>(DataIterHandle handle);</div>
+<div class="line"><a name="l01269"></a><span class="lineno"> 1269</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a8be0adeef4b87243a5eda12f17e64e67">MXDataIterNext</a>(DataIterHandle handle,</div>
+<div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <span class="keywordtype">int</span> *out);</div>
+<div class="line"><a name="l01276"></a><span class="lineno"> 1276</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a21df19f3242a68d1c536783fcf35968a">MXDataIterBeforeFirst</a>(DataIterHandle handle);</div>
+<div class="line"><a name="l01277"></a><span class="lineno"> 1277</span> </div>
+<div class="line"><a name="l01284"></a><span class="lineno"> 1284</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a0920d095f423b8bb34095406ea93a697">MXDataIterGetData</a>(DataIterHandle handle,</div>
+<div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  NDArrayHandle *out);</div>
+<div class="line"><a name="l01293"></a><span class="lineno"> 1293</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#af1b36a78dd74707c10d31c39c4d429b5">MXDataIterGetIndex</a>(DataIterHandle handle,</div>
+<div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  uint64_t **out_index,</div>
+<div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  uint64_t *out_size);</div>
+<div class="line"><a name="l01302"></a><span class="lineno"> 1302</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aec6d0d5055719576337687fc3702a899">MXDataIterGetPadNum</a>(DataIterHandle handle,</div>
+<div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  <span class="keywordtype">int</span> *pad);</div>
+<div class="line"><a name="l01304"></a><span class="lineno"> 1304</span> </div>
+<div class="line"><a name="l01311"></a><span class="lineno"> 1311</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a0c36af502d2a5746d841f50e1b0e71c0">MXDataIterGetLabel</a>(DataIterHandle handle,</div>
+<div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  NDArrayHandle *out);</div>
+<div class="line"><a name="l01313"></a><span class="lineno"> 1313</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l01314"></a><span class="lineno"> 1314</span> <span class="comment">// Part 6: basic KVStore interface</span></div>
+<div class="line"><a name="l01315"></a><span class="lineno"> 1315</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l01322"></a><span class="lineno"> 1322</span> <span class="comment"></span><a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a5c4a11b4fccf320d1af0f63b40fff58a">MXInitPSEnv</a>(mx_uint num_vars,</div>
+<div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **keys,</div>
+<div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> **vals);</div>
+<div class="line"><a name="l01325"></a><span class="lineno"> 1325</span> </div>
+<div class="line"><a name="l01326"></a><span class="lineno"> 1326</span> </div>
+<div class="line"><a name="l01333"></a><span class="lineno"> 1333</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a2ef1a35e886c0addf294429e27ea9637">MXKVStoreCreate</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> *type,</div>
+<div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  KVStoreHandle *out);</div>
+<div class="line"><a name="l01340"></a><span class="lineno"> 1340</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a915215184517bf663b0214fed32af6e7">MXKVStoreFree</a>(KVStoreHandle handle);</div>
+<div class="line"><a name="l01349"></a><span class="lineno"> 1349</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a475087b5464f9240d940ed4204a219b9">MXKVStoreInit</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  mx_uint num,</div>
+<div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* keys,</div>
+<div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  NDArrayHandle* vals);</div>
+<div class="line"><a name="l01353"></a><span class="lineno"> 1353</span> </div>
+<div class="line"><a name="l01362"></a><span class="lineno"> 1362</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a4dc86980d7922f52970a8e3978bc87cb">MXKVStoreInitEx</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  mx_uint num,</div>
+<div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** keys,</div>
+<div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  NDArrayHandle* vals);</div>
+<div class="line"><a name="l01366"></a><span class="lineno"> 1366</span> </div>
+<div class="line"><a name="l01376"></a><span class="lineno"> 1376</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a66c990fbb8e4bd320e5ad8d369155ad6">MXKVStorePush</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  mx_uint num,</div>
+<div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* keys,</div>
+<div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  NDArrayHandle* vals,</div>
+<div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  <span class="keywordtype">int</span> priority);</div>
+<div class="line"><a name="l01390"></a><span class="lineno"> 1390</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aa59b7bf29d1a74fb1d1be6689c31913f">MXKVStorePushEx</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  mx_uint num,</div>
+<div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** keys,</div>
+<div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  NDArrayHandle* vals,</div>
+<div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  <span class="keywordtype">int</span> priority);</div>
+<div class="line"><a name="l01404"></a><span class="lineno"> 1404</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a1230e78ca998f363192dc9b345a2442e">MXKVStorePull</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  mx_uint num,</div>
+<div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* keys,</div>
+<div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  NDArrayHandle* vals,</div>
+<div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  <span class="keywordtype">int</span> priority);</div>
+<div class="line"><a name="l01418"></a><span class="lineno"> 1418</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a25588eca6c42583151e77bcf71ffc3ac">MXKVStorePullEx</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  mx_uint num,</div>
+<div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** keys,</div>
+<div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  NDArrayHandle* vals,</div>
+<div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  <span class="keywordtype">int</span> priority);</div>
+<div class="line"><a name="l01431"></a><span class="lineno"><a class="line" href="c__api_8h.html#af59143cc01b560bb13f25a289b83f37b"> 1431</a></span> <span class="keyword">typedef</span> void (<a class="code" href="c__api_8h.html#af59143cc01b560bb13f25a289b83f37b">MXKVStoreUpdater</a>)(<span class="keywordtype">int</span> key,</div>
+<div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  NDArrayHandle recv,</div>
+<div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  NDArrayHandle local,</div>
+<div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  <span class="keywordtype">void</span> *handle);</div>
+<div class="line"><a name="l01442"></a><span class="lineno"> 1442</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#adbdd2035afce961837866c711af4f0ab">MXKVStoreSetUpdater</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  <a class="code" href="c__api_8h.html#af59143cc01b560bb13f25a289b83f37b">MXKVStoreUpdater</a> updater,</div>
+<div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  <span class="keywordtype">void</span> *updater_handle);</div>
+<div class="line"><a name="l01451"></a><span class="lineno"> 1451</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a9dba9ada3ed98c76fe78221013e37f07">MXKVStoreGetType</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** type);</div>
+<div class="line"><a name="l01453"></a><span class="lineno"> 1453</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l01454"></a><span class="lineno"> 1454</span> <span class="comment">// Part 6: advanced KVStore for multi-machines</span></div>
+<div class="line"><a name="l01455"></a><span class="lineno"> 1455</span> <span class="comment">//--------------------------------------------</span></div>
+<div class="line"><a name="l01456"></a><span class="lineno"> 1456</span> </div>
+<div class="line"><a name="l01464"></a><span class="lineno"> 1464</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a1edf063b82bdd94d1f5214056dd55144">MXKVStoreGetRank</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  <span class="keywordtype">int</span> *ret);</div>
+<div class="line"><a name="l01466"></a><span class="lineno"> 1466</span> </div>
+<div class="line"><a name="l01476"></a><span class="lineno"> 1476</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aac41a2bd1dfcdebb920c97eab40ea07b">MXKVStoreGetGroupSize</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  <span class="keywordtype">int</span> *ret);</div>
+<div class="line"><a name="l01478"></a><span class="lineno"> 1478</span> </div>
+<div class="line"><a name="l01484"></a><span class="lineno"> 1484</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a67d719a0a5470f6292acb5896fbca4aa">MXKVStoreIsWorkerNode</a>(<span class="keywordtype">int</span> *ret);</div>
+<div class="line"><a name="l01485"></a><span class="lineno"> 1485</span> </div>
+<div class="line"><a name="l01486"></a><span class="lineno"> 1486</span> </div>
+<div class="line"><a name="l01492"></a><span class="lineno"> 1492</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a06bf33dff7bc03cdd7474460e515882d">MXKVStoreIsServerNode</a>(<span class="keywordtype">int</span> *ret);</div>
+<div class="line"><a name="l01493"></a><span class="lineno"> 1493</span> </div>
+<div class="line"><a name="l01494"></a><span class="lineno"> 1494</span> </div>
+<div class="line"><a name="l01500"></a><span class="lineno"> 1500</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aad401ff990cb4142070e024af7ac0781">MXKVStoreIsSchedulerNode</a>(<span class="keywordtype">int</span> *ret);</div>
+<div class="line"><a name="l01501"></a><span class="lineno"> 1501</span> </div>
+<div class="line"><a name="l01508"></a><span class="lineno"> 1508</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a96e8df6a3170c52b369954215dabe71c">MXKVStoreBarrier</a>(KVStoreHandle handle);</div>
+<div class="line"><a name="l01509"></a><span class="lineno"> 1509</span> </div>
+<div class="line"><a name="l01517"></a><span class="lineno"> 1517</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#add5b8e9bbd098e8fef5deab4c7ab2737">MXKVStoreSetBarrierBeforeExit</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> barrier_before_exit);</div>
+<div class="line"><a name="l01519"></a><span class="lineno"> 1519</span> </div>
+<div class="line"><a name="l01526"></a><span class="lineno"><a class="line" href="c__api_8h.html#a31f639b22167ac51b67381b16dfd5634"> 1526</a></span> <span class="keyword">typedef</span> void (<a class="code" href="c__api_8h.html#a31f639b22167ac51b67381b16dfd5634">MXKVStoreServerController</a>)(<span class="keywordtype">int</span> head,</div>
+<div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> *body,</div>
+<div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <span class="keywordtype">void</span> *controller_handle);</div>
+<div class="line"><a name="l01529"></a><span class="lineno"> 1529</span> </div>
+<div class="line"><a name="l01538"></a><span class="lineno"> 1538</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a732c3edf14dc24784abb27fe2613a10a">MXKVStoreRunServer</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  <a class="code" href="c__api_8h.html#a31f639b22167ac51b67381b16dfd5634">MXKVStoreServerController</a> controller,</div>
+<div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  <span class="keywordtype">void</span> *controller_handle);</div>
+<div class="line"><a name="l01541"></a><span class="lineno"> 1541</span> </div>
+<div class="line"><a name="l01550"></a><span class="lineno"> 1550</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a5de45491037df7bf6b757b627100acd7">MXKVStoreSendCommmandToServers</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  <span class="keywordtype">int</span> cmd_id,</div>
+<div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* cmd_body);</div>
+<div class="line"><a name="l01553"></a><span class="lineno"> 1553</span> </div>
+<div class="line"><a name="l01564"></a><span class="lineno"> 1564</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ac8b5138bcb7959cc831a97a24755e969">MXKVStoreGetNumDeadNode</a>(KVStoreHandle handle,</div>
+<div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> node_id,</div>
+<div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  <span class="keywordtype">int</span> *number,</div>
+<div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> timeout_sec <a class="code" href="c__api_8h.html#a2380be5ab258c3657553d0cef62936fe">DEFAULT</a>(60));</div>
+<div class="line"><a name="l01568"></a><span class="lineno"> 1568</span> </div>
+<div class="line"><a name="l01575"></a><span class="lineno"> 1575</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ab2b93d730f48591b4f77f3968f6a1b98">MXRecordIOWriterCreate</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> *uri, RecordIOHandle *out);</div>
+<div class="line"><a name="l01576"></a><span class="lineno"> 1576</span> </div>
+<div class="line"><a name="l01582"></a><span class="lineno"> 1582</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#ab2a55917ffd918e334464f1b8a7a1ab9">MXRecordIOWriterFree</a>(RecordIOHandle handle);</div>
+<div class="line"><a name="l01583"></a><span class="lineno"> 1583</span> </div>
+<div class="line"><a name="l01591"></a><span class="lineno"> 1591</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a0c380b6aa00446e7cb6021c8e2df75f3">MXRecordIOWriterWriteRecord</a>(RecordIOHandle handle,</div>
+<div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> *buf, <span class="keywordtype">size_t</span> size);</div>
+<div class="line"><a name="l01593"></a><span class="lineno"> 1593</span> </div>
+<div class="line"><a name="l01600"></a><span class="lineno"> 1600</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a6cc8e9f94ec431edf8d9502f20d7b833">MXRecordIOWriterTell</a>(RecordIOHandle handle, <span class="keywordtype">size_t</span> *pos);</div>
+<div class="line"><a name="l01601"></a><span class="lineno"> 1601</span> </div>
+<div class="line"><a name="l01608"></a><span class="lineno"> 1608</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a4464ddbe13b8a4542d3f91bf055d50af">MXRecordIOReaderCreate</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> *uri, RecordIOHandle *out);</div>
+<div class="line"><a name="l01609"></a><span class="lineno"> 1609</span> </div>
+<div class="line"><a name="l01615"></a><span class="lineno"> 1615</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a92736f17a5d9fd58e53e621358e4ef31">MXRecordIOReaderFree</a>(RecordIOHandle handle);</div>
+<div class="line"><a name="l01616"></a><span class="lineno"> 1616</span> </div>
+<div class="line"><a name="l01624"></a><span class="lineno"> 1624</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a38e350f748967e0caa2c781a507667e4">MXRecordIOReaderReadRecord</a>(RecordIOHandle handle,</div>
+<div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  <span class="keywordtype">char</span> <span class="keyword">const</span> **buf, <span class="keywordtype">size_t</span> *size);</div>
+<div class="line"><a name="l01626"></a><span class="lineno"> 1626</span> </div>
+<div class="line"><a name="l01633"></a><span class="lineno"> 1633</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#aa992161083826ee6a434ade2790fbb31">MXRecordIOReaderSeek</a>(RecordIOHandle handle, <span class="keywordtype">size_t</span> pos);</div>
+<div class="line"><a name="l01634"></a><span class="lineno"> 1634</span> </div>
+<div class="line"><a name="l01638"></a><span class="lineno"> 1638</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a87e98002ae1a925b65a8cf0773f802d3">MXRtcCreate</a>(<span class="keywordtype">char</span>* name, mx_uint num_input, mx_uint num_output,</div>
+<div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  <span class="keywordtype">char</span>** input_names, <span class="keywordtype">char</span>** output_names,</div>
+<div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  NDArrayHandle* inputs, NDArrayHandle* outputs,</div>
+<div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>  <span class="keywordtype">char</span>* kernel, RtcHandle *out);</div>
+<div class="line"><a name="l01642"></a><span class="lineno"> 1642</span> </div>
+<div class="line"><a name="l01646"></a><span class="lineno"> 1646</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a20f72cb4e18d5081eaf40875adcb6e98">MXRtcPush</a>(RtcHandle handle, mx_uint num_input, mx_uint num_output,</div>
+<div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  NDArrayHandle* inputs, NDArrayHandle* outputs,</div>
+<div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  mx_uint gridDimX,</div>
+<div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  mx_uint gridDimY,</div>
+<div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  mx_uint gridDimZ,</div>
+<div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  mx_uint blockDimX,</div>
+<div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  mx_uint blockDimY,</div>
+<div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  mx_uint blockDimZ);</div>
+<div class="line"><a name="l01654"></a><span class="lineno"> 1654</span> </div>
+<div class="line"><a name="l01658"></a><span class="lineno"> 1658</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a13819b19ab3cdd91566b1d5cf8bc6b0a">MXRtcFree</a>(RtcHandle handle);</div>
+<div class="line"><a name="l01659"></a><span class="lineno"> 1659</span> </div>
+<div class="line"><a name="l01660"></a><span class="lineno"> 1660</span> <a class="code" href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__api_8h.html#a92233cd9477ce12458bbd2b3e628dec9">MXCustomOpRegister</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* op_type, <a class="code" href="c__api_8h.html#a3a7fd875ee6aad3f20981764626a6c7b">CustomOpPropCreator</a> creator);</div>
+<div class="line"><a name="l01661"></a><span class="lineno"> 1661</span> </div>
+<div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> <span class="preprocessor">#ifdef __cplusplus</span></div>
+<div class="line"><a name="l01663"></a><span class="lineno"> 1663</span> <span class="preprocessor"></span>}</div>
+<div class="line"><a name="l01664"></a><span class="lineno"> 1664</span> <span class="preprocessor">#endif // __cplusplus</span></div>
+<div class="line"><a name="l01665"></a><span class="lineno"> 1665</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l01666"></a><span class="lineno"> 1666</span> <span class="preprocessor">#endif // MXNET_C_API_H_</span></div>
+<div class="ttc" id="c__api_8h_html_a5de45491037df7bf6b757b627100acd7"><div class="ttname"><a href="c__api_8h.html#a5de45491037df7bf6b757b627100acd7">MXKVStoreSendCommmandToServers</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreSendCommmandToServers(KVStoreHandle handle, int cmd_id, const char *cmd_body)</div></div>
+<div class="ttc" id="structNDArrayOpInfo_html_ad6bb579606e6bd83569d035fa92a857c"><div class="ttname"><a href="structNDArrayOpInfo.html#ad6bb579606e6bd83569d035fa92a857c">NDArrayOpInfo::list_arguments</a></div><div class="ttdeci">bool(* list_arguments)(char ***, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:93</div></div>
<div class="ttc" id="c__api_8h_html_a8e2c90cfa6c52a012fd3f23ffd8541f2"><div class="ttname"><a href="c__api_8h.html#a8e2c90cfa6c52a012fd3f23ffd8541f2">MXNDArrayDetach</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayDetach(NDArrayHandle handle, NDArrayHandle *out)</div><div class="ttdoc">detach and ndarray from computation graph by clearing entry_ </div></div>
<div class="ttc" id="c__api_8h_html_ac5a3fafe6616c4d246b24e4d11c65238"><div class="ttname"><a href="c__api_8h.html#ac5a3fafe6616c4d246b24e4d11c65238">MXListAllOpNames</a></div><div class="ttdeci">MXNET_DLL int MXListAllOpNames(mx_uint *out_size, const char ***out_array)</div><div class="ttdoc">list all the available operator names, include entries. </div></div>
+<div class="ttc" id="c__api_8h_html_ac153c43c858c3070c63d0eec44f36514"><div class="ttname"><a href="c__api_8h.html#ac153c43c858c3070c63d0eec44f36514">CustomOpFBFunc</a></div><div class="ttdeci">int(* CustomOpFBFunc)(int, void **, int *, const int *, const int, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:130</div></div>
+<div class="ttc" id="structMXCallbackList_html_ad56325e57f5fffc2b920eb3f8f378199"><div class="ttname"><a href="structMXCallbackList.html#ad56325e57f5fffc2b920eb3f8f378199">MXCallbackList::callbacks</a></div><div class="ttdeci">int(** callbacks)(void)</div><div class="ttdef"><b>Definition:</b> c_api.h:109</div></div>
<div class="ttc" id="c__api_8h_html_adbdd2035afce961837866c711af4f0ab"><div class="ttname"><a href="c__api_8h.html#adbdd2035afce961837866c711af4f0ab">MXKVStoreSetUpdater</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreSetUpdater(KVStoreHandle handle, MXKVStoreUpdater updater, void *updater_handle)</div><div class="ttdoc">register an push updater </div></div>
<div class="ttc" id="c__api_8h_html_a96e8df6a3170c52b369954215dabe71c"><div class="ttname"><a href="c__api_8h.html#a96e8df6a3170c52b369954215dabe71c">MXKVStoreBarrier</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreBarrier(KVStoreHandle handle)</div><div class="ttdoc">global barrier among all worker machines </div></div>
<div class="ttc" id="c__api_8h_html_abce84fe05f55709d643bcfd0a4e4620b"><div class="ttname"><a href="c__api_8h.html#abce84fe05f55709d643bcfd0a4e4620b">DataIterHandle</a></div><div class="ttdeci">void * DataIterHandle</div><div class="ttdoc">handle to a DataIterator </div><div class="ttdef"><b>Definition:</b> c_api.h:62</div></div>
@@ -76,13 +746,13 @@ $(function() {
<div class="ttc" id="c__api_8h_html_aa2395b69772d66cb5c6c4cbe47749792a2d5d86c1a9bac7de4ceb1c641a4dce4a"><div class="ttname"><a href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792a2d5d86c1a9bac7de4ceb1c641a4dce4a">kCustomOpDelete</a></div><div class="ttdef"><b>Definition:</b> c_api.h:114</div></div>
<div class="ttc" id="c__api_8h_html_a361c4a5360734e430c54a754cc581354"><div class="ttname"><a href="c__api_8h.html#a361c4a5360734e430c54a754cc581354">MXDataIterCreateIter</a></div><div class="ttdeci">MXNET_DLL int MXDataIterCreateIter(DataIterCreator handle, mx_uint num_param, const char **keys, const char **vals, DataIterHandle *out)</div><div class="ttdoc">Init an iterator, init with parameters the array size of passed in arguments. </div></div>
<div class="ttc" id="c__api_8h_html_a8be0adeef4b87243a5eda12f17e64e67"><div class="ttname"><a href="c__api_8h.html#a8be0adeef4b87243a5eda12f17e64e67">MXDataIterNext</a></div><div class="ttdeci">MXNET_DLL int MXDataIterNext(DataIterHandle handle, int *out)</div><div class="ttdoc">Move iterator to next position. </div></div>
+<div class="ttc" id="c__api_8h_html_a09da22ebe9a09b084afc386fa7b0ef53"><div class="ttname"><a href="c__api_8h.html#a09da22ebe9a09b084afc386fa7b0ef53">CustomOpInferTypeFunc</a></div><div class="ttdeci">int(* CustomOpInferTypeFunc)(int, int *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:137</div></div>
<div class="ttc" id="c__api_8h_html_a3743a54910b1862400c3ec4cbf7e9d80"><div class="ttname"><a href="c__api_8h.html#a3743a54910b1862400c3ec4cbf7e9d80">MXNDArrayReshape</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayReshape(NDArrayHandle handle, int ndim, int *dims, NDArrayHandle *out)</div><div class="ttdoc">Reshape the NDArray. </div></div>
<div class="ttc" id="structNDArrayOpInfo_html_afe27107d0470918edda2d2c5c62fd8d7"><div class="ttname"><a href="structNDArrayOpInfo.html#afe27107d0470918edda2d2c5c62fd8d7">NDArrayOpInfo::p_infer_shape</a></div><div class="ttdeci">void * p_infer_shape</div><div class="ttdef"><b>Definition:</b> c_api.h:99</div></div>
<div class="ttc" id="c__api_8h_html_a53f3bbc41e275485d1d6cd21f40abb7f"><div class="ttname"><a href="c__api_8h.html#a53f3bbc41e275485d1d6cd21f40abb7f">MXAutogradMarkVariables</a></div><div class="ttdeci">MXNET_DLL int MXAutogradMarkVariables(mx_uint num_var, NDArrayHandle *var_handles, mx_uint *reqs_array, NDArrayHandle *grad_handles)</div><div class="ttdoc">mark NDArrays as variables to compute gradient for autograd </div></div>
<div class="ttc" id="c__api_8h_html_a62d6b72cc6b3d920797c32ea8732cbbaaa23caa8f573176ff6985b550df9b2be2"><div class="ttname"><a href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaaa23caa8f573176ff6985b550df9b2be2">kCustomOpPropInferShape</a></div><div class="ttdef"><b>Definition:</b> c_api.h:124</div></div>
<div class="ttc" id="c__api_8h_html_ac9f776a6e7be0bacda4dcd3efa5f7dc5"><div class="ttname"><a href="c__api_8h.html#ac9f776a6e7be0bacda4dcd3efa5f7dc5">MXExecutorPrint</a></div><div class="ttdeci">MXNET_DLL int MXExecutorPrint(ExecutorHandle handle, const char **out_str)</div><div class="ttdoc">Print the content of execution plan, used for debug. </div></div>
<div class="ttc" id="c__api_8h_html_a732c3edf14dc24784abb27fe2613a10a"><div class="ttname"><a href="c__api_8h.html#a732c3edf14dc24784abb27fe2613a10a">MXKVStoreRunServer</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreRunServer(KVStoreHandle handle, MXKVStoreServerController controller, void *controller_handle)</div></div>
-<div class="ttc" id="structNativeOpInfo_html_af25c58d65a2350cab76403f1ce21922e"><div class="ttname"><a href="structNativeOpInfo.html#af25c58d65a2350cab76403f1ce21922e">NativeOpInfo::list_arguments</a></div><div class="ttdeci">void(* list_arguments)(char ***, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:79</div></div>
<div class="ttc" id="c__api_8h_html_a5c99eac0fe3cfa0912259d0fa12b6651"><div class="ttname"><a href="c__api_8h.html#a5c99eac0fe3cfa0912259d0fa12b6651">MXSymbolSaveToJSON</a></div><div class="ttdeci">MXNET_DLL int MXSymbolSaveToJSON(SymbolHandle symbol, const char **out_json)</div><div class="ttdoc">Save a symbol into a json string. </div></div>
<div class="ttc" id="c__api_8h_html_afaf03324563098f1ac4c23ecfc88b784"><div class="ttname"><a href="c__api_8h.html#afaf03324563098f1ac4c23ecfc88b784">MXSymbolInferShape</a></div><div class="ttdeci">MXNET_DLL int MXSymbolInferShape(SymbolHandle sym, mx_uint num_args, const char **keys, const mx_uint *arg_ind_ptr, const mx_uint *arg_shape_data, mx_uint *in_shape_size, const mx_uint **in_shape_ndim, const mx_uint ***in_shape_data, mx_uint *out_shape_size, const mx_uint **out_shape_ndim, con [...]
<div class="ttc" id="c__api_8h_html_abca605833c1fb045e2e78c8f3be492ac"><div class="ttname"><a href="c__api_8h.html#abca605833c1fb045e2e78c8f3be492ac">MXSymbolGetAttr</a></div><div class="ttdeci">MXNET_DLL int MXSymbolGetAttr(SymbolHandle symbol, const char *key, const char **out, int *success)</div><div class="ttdoc">Get string attribute from symbol. </div></div>
@@ -99,53 +769,61 @@ $(function() {
<div class="ttc" id="c__api_8h_html_aa2395b69772d66cb5c6c4cbe47749792"><div class="ttname"><a href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792">CustomOpCallbacks</a></div><div class="ttdeci">CustomOpCallbacks</div><div class="ttdef"><b>Definition:</b> c_api.h:113</div></div>
<div class="ttc" id="c__api_8h_html_a6050b4cfcb75ed3d0a44afb1b8df45cd"><div class="ttname"><a href="c__api_8h.html#a6050b4cfcb75ed3d0a44afb1b8df45cd">MXSymbolGetChildren</a></div><div class="ttdeci">MXNET_DLL int MXSymbolGetChildren(SymbolHandle symbol, SymbolHandle *out)</div><div class="ttdoc">Get a symbol that contains only direct children. </div></div>
<div class="ttc" id="c__api_8h_html_a92736f17a5d9fd58e53e621358e4ef31"><div class="ttname"><a href="c__api_8h.html#a92736f17a5d9fd58e53e621358e4ef31">MXRecordIOReaderFree</a></div><div class="ttdeci">MXNET_DLL int MXRecordIOReaderFree(RecordIOHandle handle)</div><div class="ttdoc">Delete a RecordIO reader object. </div></div>
-<div class="ttc" id="c__api_8h_html_a442845c8ef89d4c849f468ea2b7717f7"><div class="ttname"><a href="c__api_8h.html#a442845c8ef89d4c849f468ea2b7717f7">MXExecutorOutputs</a></div><div class="ttdeci">MXNET_DLL int MXExecutorOutputs(ExecutorHandle handle, mx_uint *out_size, NDArrayHandle **out)</div><div class="ttdoc">Get executor&#39;s head NDArray. </div></div>
+<div class="ttc" id="c__api_8h_html_a442845c8ef89d4c849f468ea2b7717f7"><div class="ttname"><a href="c__api_8h.html#a442845c8ef89d4c849f468ea2b7717f7">MXExecutorOutputs</a></div><div class="ttdeci">MXNET_DLL int MXExecutorOutputs(ExecutorHandle handle, mx_uint *out_size, NDArrayHandle **out)</div><div class="ttdoc">Get executor's head NDArray. </div></div>
<div class="ttc" id="c__api_8h_html_a3ae6292f1e640d54c92a8cc56f513b76"><div class="ttname"><a href="c__api_8h.html#a3ae6292f1e640d54c92a8cc56f513b76">MXNDArrayGetContext</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayGetContext(NDArrayHandle handle, int *out_dev_type, int *out_dev_id)</div><div class="ttdoc">get the context of the NDArray </div></div>
<div class="ttc" id="c__api_8h_html_af0a6166fe48ce0a175990914b1cb02e0"><div class="ttname"><a href="c__api_8h.html#af0a6166fe48ce0a175990914b1cb02e0">MXFuncDescribe</a></div><div class="ttdeci">MXNET_DLL int MXFuncDescribe(FunctionHandle fun, mx_uint *num_use_vars, mx_uint *num_scalars, mx_uint *num_mutate_vars, int *type_mask)</div><div class="ttdoc">get the argument requirements of the function </div></div>
+<div class="ttc" id="structNativeOpInfo_html_af4c12b25f9d708835619b768db5805ff"><div class="ttname"><a href="structNativeOpInfo.html#af4c12b25f9d708835619b768db5805ff">NativeOpInfo::infer_shape</a></div><div class="ttdeci">void(* infer_shape)(int, int *, unsigned **, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:77</div></div>
<div class="ttc" id="c__api_8h_html_a06bf33dff7bc03cdd7474460e515882d"><div class="ttname"><a href="c__api_8h.html#a06bf33dff7bc03cdd7474460e515882d">MXKVStoreIsServerNode</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreIsServerNode(int *ret)</div><div class="ttdoc">return whether or not this process is a server node. </div></div>
<div class="ttc" id="c__api_8h_html_a67c83f78e6bd15c6ad6eb09a34133086"><div class="ttname"><a href="c__api_8h.html#a67c83f78e6bd15c6ad6eb09a34133086">MXNDArraySlice</a></div><div class="ttdeci">MXNET_DLL int MXNDArraySlice(NDArrayHandle handle, mx_uint slice_begin, mx_uint slice_end, NDArrayHandle *out)</div><div class="ttdoc">Slice the NDArray along axis 0. </div></div>
+<div class="ttc" id="structNDArrayOpInfo_html_ac11f40fb25fa4abd275b6f1c7b3d3213"><div class="ttname"><a href="structNDArrayOpInfo.html#ac11f40fb25fa4abd275b6f1c7b3d3213">NDArrayOpInfo::forward</a></div><div class="ttdeci">bool(* forward)(int, void **, int *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:89</div></div>
<div class="ttc" id="c__api_8h_html_a6f224ee35892507405ced8429d874726"><div class="ttname"><a href="c__api_8h.html#a6f224ee35892507405ced8429d874726">ExecutorHandle</a></div><div class="ttdeci">void * ExecutorHandle</div><div class="ttdoc">handle to an Executor </div><div class="ttdef"><b>Definition:</b> c_api.h:58</div></div>
-<div class="ttc" id="c__api_8h_html_a6968ac8b790fb40a674a364a2a74cba6"><div class="ttname"><a href="c__api_8h.html#a6968ac8b790fb40a674a364a2a74cba6">MXKVStoreServerController</a></div><div class="ttdeci">void() MXKVStoreServerController(int head, const char *body, void *controller_handle)</div><div class="ttdoc">the prototype of a server controller </div><div class="ttdef"><b>Definition:</b> c_api.h:1492</div></div>
<div class="ttc" id="c__api_8h_html_a8b3a7394a8a641f2490f4c5246be1083"><div class="ttname"><a href="c__api_8h.html#a8b3a7394a8a641f2490f4c5246be1083">MXSymbolSaveToFile</a></div><div class="ttdeci">MXNET_DLL int MXSymbolSaveToFile(SymbolHandle symbol, const char *fname)</div><div class="ttdoc">Save a symbol into a json file. </div></div>
<div class="ttc" id="c__api_8h_html_aaccda72ea7a5439c715ff5007b9595b5"><div class="ttname"><a href="c__api_8h.html#aaccda72ea7a5439c715ff5007b9595b5">AtomicSymbolHandle</a></div><div class="ttdeci">void * AtomicSymbolHandle</div><div class="ttdoc">handle to a AtomicSymbol </div><div class="ttdef"><b>Definition:</b> c_api.h:56</div></div>
<div class="ttc" id="structNativeOpInfo_html_a60beae73be72aeb308b55aeed8cbd3c1"><div class="ttname"><a href="structNativeOpInfo.html#a60beae73be72aeb308b55aeed8cbd3c1">NativeOpInfo::p_backward</a></div><div class="ttdeci">void * p_backward</div><div class="ttdef"><b>Definition:</b> c_api.h:82</div></div>
<div class="ttc" id="c__api_8h_html_ad369531149cf26e8db49958e17bdf237"><div class="ttname"><a href="c__api_8h.html#ad369531149cf26e8db49958e17bdf237">MXFuncGetInfo</a></div><div class="ttdeci">MXNET_DLL int MXFuncGetInfo(FunctionHandle fun, const char **name, const char **description, mx_uint *num_args, const char ***arg_names, const char ***arg_type_infos, const char ***arg_descriptions, const char **return_type DEFAULT(NULL))</div><div class="ttdoc">Get the information of the function [...]
<div class="ttc" id="c__api_8h_html_a9cf2b0aa3ff66119fe793f6ed82b61b8"><div class="ttname"><a href="c__api_8h.html#a9cf2b0aa3ff66119fe793f6ed82b61b8">MXAutogradSetIsTraining</a></div><div class="ttdeci">MXNET_DLL int MXAutogradSetIsTraining(int is_training, int *prev)</div><div class="ttdoc">set whether to record operator for autograd </div></div>
<div class="ttc" id="c__api_8h_html_a857e66f665360d5fab3ad52528df38dd"><div class="ttname"><a href="c__api_8h.html#a857e66f665360d5fab3ad52528df38dd">MXNDArrayFree</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayFree(NDArrayHandle handle)</div><div class="ttdoc">free the narray handle </div></div>
+<div class="ttc" id="structNativeOpInfo_html_ab1f5d01bbb1d6507c23e640ac3eea834"><div class="ttname"><a href="structNativeOpInfo.html#ab1f5d01bbb1d6507c23e640ac3eea834">NativeOpInfo::list_arguments</a></div><div class="ttdeci">void(* list_arguments)(char ***, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:79</div></div>
<div class="ttc" id="c__api_8h_html_ac6a2362d9cb7ba763810b971c9524521"><div class="ttname"><a href="c__api_8h.html#ac6a2362d9cb7ba763810b971c9524521">MXSymbolCreateFromJSON</a></div><div class="ttdeci">MXNET_DLL int MXSymbolCreateFromJSON(const char *json, SymbolHandle *out)</div><div class="ttdoc">Load a symbol from a json string. </div></div>
<div class="ttc" id="c__api_8h_html_a62d6b72cc6b3d920797c32ea8732cbba"><div class="ttname"><a href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbba">CustomOpPropCallbacks</a></div><div class="ttdeci">CustomOpPropCallbacks</div><div class="ttdef"><b>Definition:</b> c_api.h:119</div></div>
-<div class="ttc" id="c__api_8h_html_a59855e0408519e73b05419cd0d9d976e"><div class="ttname"><a href="c__api_8h.html#a59855e0408519e73b05419cd0d9d976e">CustomOpInferTypeFunc</a></div><div class="ttdeci">int(* CustomOpInferTypeFunc)(int, int *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:137</div></div>
+<div class="ttc" id="structNDArrayOpInfo_html_a139351ad186cde67a376d2f9d6682baf"><div class="ttname"><a href="structNDArrayOpInfo.html#a139351ad186cde67a376d2f9d6682baf">NDArrayOpInfo::infer_shape</a></div><div class="ttdeci">bool(* infer_shape)(int, int *, unsigned **, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:91</div></div>
<div class="ttc" id="c__api_8h_html_aad401ff990cb4142070e024af7ac0781"><div class="ttname"><a href="c__api_8h.html#aad401ff990cb4142070e024af7ac0781">MXKVStoreIsSchedulerNode</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreIsSchedulerNode(int *ret)</div><div class="ttdoc">return whether or not this process is a scheduler node. </div></div>
+<div class="ttc" id="c__api_8h_html_a557fa6ce86e94109ce7797b2cce33e23"><div class="ttname"><a href="c__api_8h.html#a557fa6ce86e94109ce7797b2cce33e23">CustomOpDelFunc</a></div><div class="ttdeci">int(* CustomOpDelFunc)(void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:133</div></div>
<div class="ttc" id="c__api_8h_html_aa46875e507a638bd6c8a6c891a8f5934"><div class="ttname"><a href="c__api_8h.html#aa46875e507a638bd6c8a6c891a8f5934">MXSymbolGetAtomicSymbolInfo</a></div><div class="ttdeci">MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator, const char **name, const char **description, mx_uint *num_args, const char ***arg_names, const char ***arg_type_infos, const char ***arg_descriptions, const char **key_var_num_args, const char **return_type DEFAULT [...]
+<div class="ttc" id="structNativeOpInfo_html_a2d571a373ff0ca0da5d96295cc03f465"><div class="ttname"><a href="structNativeOpInfo.html#a2d571a373ff0ca0da5d96295cc03f465">NativeOpInfo::backward</a></div><div class="ttdeci">void(* backward)(int, float **, int *, unsigned **, int *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:76</div></div>
<div class="ttc" id="c__api_8h_html_a2380be5ab258c3657553d0cef62936fe"><div class="ttname"><a href="c__api_8h.html#a2380be5ab258c3657553d0cef62936fe">DEFAULT</a></div><div class="ttdeci">#define DEFAULT(x)</div><div class="ttdoc">Inhibit C++ name-mangling for MXNet functions. </div><div class="ttdef"><b>Definition:</b> c_api.h:18</div></div>
+<div class="ttc" id="c__api_8h_html_a57a5d1ed585f9f431a31f921b4ca00ab"><div class="ttname"><a href="c__api_8h.html#a57a5d1ed585f9f431a31f921b4ca00ab">MXAutogradBackwardEx</a></div><div class="ttdeci">MXNET_DLL int MXAutogradBackwardEx(mx_uint num_output, NDArrayHandle *output_handles, NDArrayHandle *ograd_handles, int retain_graph, int is_train)</div><div class="ttdoc">compute the gradient of outputs w.r.t variabels </div></div>
<div class="ttc" id="c__api_8h_html_ac373802e0d3a38f297345f87d307a798"><div class="ttname"><a href="c__api_8h.html#ac373802e0d3a38f297345f87d307a798">MXSetProfilerState</a></div><div class="ttdeci">MXNET_DLL int MXSetProfilerState(int state)</div><div class="ttdoc">Set up state of profiler. </div></div>
<div class="ttc" id="c__api_8h_html_a6a9a4608136959389db02ad938201a67"><div class="ttname"><a href="c__api_8h.html#a6a9a4608136959389db02ad938201a67">MXImperativeInvoke</a></div><div class="ttdeci">MXNET_DLL int MXImperativeInvoke(AtomicSymbolCreator creator, int num_inputs, NDArrayHandle *inputs, int *num_outputs, NDArrayHandle **outputs, int num_params, const char **param_keys, const char **param_vals)</div><div class="ttdoc">invoke a nnvm op and imperative function </div></div>
<div class="ttc" id="c__api_8h_html_a9e4dc341e666c772e1b530920ba4a0e9"><div class="ttname"><a href="c__api_8h.html#a9e4dc341e666c772e1b530920ba4a0e9">MXSymbolListAttr</a></div><div class="ttdeci">MXNET_DLL int MXSymbolListAttr(SymbolHandle symbol, mx_uint *out_size, const char ***out)</div><div class="ttdoc">Get all attributes from symbol, including all descendents. </div></div>
<div class="ttc" id="c__api_8h_html_a90fbe78325ba9d67d3629be442623cee"><div class="ttname"><a href="c__api_8h.html#a90fbe78325ba9d67d3629be442623cee">MXNDArrayAt</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayAt(NDArrayHandle handle, mx_uint idx, NDArrayHandle *out)</div><div class="ttdoc">Index the NDArray along axis 0. </div></div>
+<div class="ttc" id="c__api_8h_html_a9a559987f509f9939030addd565d34a3"><div class="ttname"><a href="c__api_8h.html#a9a559987f509f9939030addd565d34a3">CustomOpInferShapeFunc</a></div><div class="ttdeci">int(* CustomOpInferShapeFunc)(int, int *, unsigned **, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:135</div></div>
<div class="ttc" id="c__api_8h_html_a2c1d0459d3fe124b5d3aed49386999f1"><div class="ttname"><a href="c__api_8h.html#a2c1d0459d3fe124b5d3aed49386999f1">MXNDArrayLoad</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayLoad(const char *fname, mx_uint *out_size, NDArrayHandle **out_arr, mx_uint *out_name_size, const char ***out_names)</div><div class="ttdoc">Load list of narray from the file. </div></div>
<div class="ttc" id="c__api_8h_html_ada464e85b86305d167511bb977095a52"><div class="ttname"><a href="c__api_8h.html#ada464e85b86305d167511bb977095a52">MXNDArraySyncCopyFromCPU</a></div><div class="ttdeci">MXNET_DLL int MXNDArraySyncCopyFromCPU(NDArrayHandle handle, const void *data, size_t size)</div><div class="ttdoc">Perform a synchronize copy from a continugous CPU memory region. </div></div>
<div class="ttc" id="c__api_8h_html_a314035f352106ee44039bcbe166353b3"><div class="ttname"><a href="c__api_8h.html#a314035f352106ee44039bcbe166353b3">MXSymbolCreateFromFile</a></div><div class="ttdeci">MXNET_DLL int MXSymbolCreateFromFile(const char *fname, SymbolHandle *out)</div><div class="ttdoc">Load a symbol from a json file. </div></div>
<div class="ttc" id="c__api_8h_html_aa59b7bf29d1a74fb1d1be6689c31913f"><div class="ttname"><a href="c__api_8h.html#aa59b7bf29d1a74fb1d1be6689c31913f">MXKVStorePushEx</a></div><div class="ttdeci">MXNET_DLL int MXKVStorePushEx(KVStoreHandle handle, mx_uint num, const char **keys, NDArrayHandle *vals, int priority)</div><div class="ttdoc">Push a list of (key,value) pairs to kvstore, where each key is a string. </div></div>
<div class="ttc" id="structNDArrayOpInfo_html"><div class="ttname"><a href="structNDArrayOpInfo.html">NDArrayOpInfo</a></div><div class="ttdef"><b>Definition:</b> c_api.h:88</div></div>
-<div class="ttc" id="c__api_8h_html_a2731805437dc3d76201fc21154b1337c"><div class="ttname"><a href="c__api_8h.html#a2731805437dc3d76201fc21154b1337c">ExecutorMonitorCallback</a></div><div class="ttdeci">void(* ExecutorMonitorCallback)(const char *, NDArrayHandle, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:70</div></div>
<div class="ttc" id="structNativeOpInfo_html_acf0017f1f716c2e88ce9feedc9b4759f"><div class="ttname"><a href="structNativeOpInfo.html#acf0017f1f716c2e88ce9feedc9b4759f">NativeOpInfo::p_forward</a></div><div class="ttdeci">void * p_forward</div><div class="ttdef"><b>Definition:</b> c_api.h:81</div></div>
<div class="ttc" id="c__api_8h_html_abe1ed22a74c15b24111ce2af39bcca38"><div class="ttname"><a href="c__api_8h.html#abe1ed22a74c15b24111ce2af39bcca38">MXNDArrayCreateEx</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayCreateEx(const mx_uint *shape, mx_uint ndim, int dev_type, int dev_id, int delay_alloc, int dtype, NDArrayHandle *out)</div><div class="ttdoc">create a NDArray with specified shape and data type </div></div>
<div class="ttc" id="structNativeOpInfo_html_a0e5c803876bbcc5d8162b6ae30bce231"><div class="ttname"><a href="structNativeOpInfo.html#a0e5c803876bbcc5d8162b6ae30bce231">NativeOpInfo::p_list_arguments</a></div><div class="ttdeci">void * p_list_arguments</div><div class="ttdef"><b>Definition:</b> c_api.h:85</div></div>
<div class="ttc" id="c__api_8h_html_a2ef1a35e886c0addf294429e27ea9637"><div class="ttname"><a href="c__api_8h.html#a2ef1a35e886c0addf294429e27ea9637">MXKVStoreCreate</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreCreate(const char *type, KVStoreHandle *out)</div><div class="ttdoc">Create a kvstore. </div></div>
-<div class="ttc" id="c__api_8h_html_a0056a2f79906cce0f8aa78966c2d4a6c"><div class="ttname"><a href="c__api_8h.html#a0056a2f79906cce0f8aa78966c2d4a6c">CustomOpDelFunc</a></div><div class="ttdeci">int(* CustomOpDelFunc)(void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:133</div></div>
+<div class="ttc" id="c__api_8h_html_ae8feb033b715209653ea91e089d060b0"><div class="ttname"><a href="c__api_8h.html#ae8feb033b715209653ea91e089d060b0">CustomOpBwdDepFunc</a></div><div class="ttdeci">int(* CustomOpBwdDepFunc)(const int *, const int *, const int *, int *, int **, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:138</div></div>
<div class="ttc" id="structNDArrayOpInfo_html_af17150d574bc8ca7e7a82b9cb4965f05"><div class="ttname"><a href="structNDArrayOpInfo.html#af17150d574bc8ca7e7a82b9cb4965f05">NDArrayOpInfo::p_declare_backward_dependency</a></div><div class="ttdeci">void * p_declare_backward_dependency</div><div class="ttdef"><b>Definition:</b> c_api.h:102</div></div>
<div class="ttc" id="c__api_8h_html_a3a32f78b54c81564da9834c6c6353b90"><div class="ttname"><a href="c__api_8h.html#a3a32f78b54c81564da9834c6c6353b90">MXNDArrayCreateNone</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayCreateNone(NDArrayHandle *out)</div><div class="ttdoc">create a NDArray handle that is not initialized can be used to pass in as mutate variables to hold th...</div></div>
<div class="ttc" id="c__api_8h_html_a915215184517bf663b0214fed32af6e7"><div class="ttname"><a href="c__api_8h.html#a915215184517bf663b0214fed32af6e7">MXKVStoreFree</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreFree(KVStoreHandle handle)</div><div class="ttdoc">Delete a KVStore handle. </div></div>
<div class="ttc" id="structNDArrayOpInfo_html_aad953647557d6f2cb13ced8c00e4da0b"><div class="ttname"><a href="structNDArrayOpInfo.html#aad953647557d6f2cb13ced8c00e4da0b">NDArrayOpInfo::p_list_arguments</a></div><div class="ttdeci">void * p_list_arguments</div><div class="ttdef"><b>Definition:</b> c_api.h:101</div></div>
<div class="ttc" id="c__api_8h_html_a92233cd9477ce12458bbd2b3e628dec9"><div class="ttname"><a href="c__api_8h.html#a92233cd9477ce12458bbd2b3e628dec9">MXCustomOpRegister</a></div><div class="ttdeci">MXNET_DLL int MXCustomOpRegister(const char *op_type, CustomOpPropCreator creator)</div></div>
+<div class="ttc" id="c__api_8h_html_a3a211f3e0c6270271ce4fe79228dd0c2"><div class="ttname"><a href="c__api_8h.html#a3a211f3e0c6270271ce4fe79228dd0c2">MXExecutorBackwardEx</a></div><div class="ttdeci">MXNET_DLL int MXExecutorBackwardEx(ExecutorHandle handle, mx_uint len, NDArrayHandle *head_grads, int is_train)</div><div class="ttdoc">Excecutor run backward. </div></div>
<div class="ttc" id="c__api_8h_html_a301e9a7722b326126defdd39ccd99b37"><div class="ttname"><a href="c__api_8h.html#a301e9a7722b326126defdd39ccd99b37">SymbolHandle</a></div><div class="ttdeci">void * SymbolHandle</div><div class="ttdoc">handle to a symbol that can be bind as operator </div><div class="ttdef"><b>Definition:</b> c_api.h:54</div></div>
<div class="ttc" id="c__api_8h_html_a6f8fc1fb5c90c92498241b75f74cea86"><div class="ttname"><a href="c__api_8h.html#a6f8fc1fb5c90c92498241b75f74cea86">MXSymbolCopy</a></div><div class="ttdeci">MXNET_DLL int MXSymbolCopy(SymbolHandle symbol, SymbolHandle *out)</div><div class="ttdoc">Copy the symbol to another handle. </div></div>
+<div class="ttc" id="structNativeOpInfo_html_a1dfb6e3206e76dcf755ae0bca655081b"><div class="ttname"><a href="structNativeOpInfo.html#a1dfb6e3206e76dcf755ae0bca655081b">NativeOpInfo::list_outputs</a></div><div class="ttdeci">void(* list_outputs)(char ***, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:78</div></div>
<div class="ttc" id="c__api_8h_html_aac4a2be6e40b0869bcc390a573a7e489"><div class="ttname"><a href="c__api_8h.html#aac4a2be6e40b0869bcc390a573a7e489">CachedOpHandle</a></div><div class="ttdeci">void * CachedOpHandle</div><div class="ttdoc">handle to cached operator </div><div class="ttdef"><b>Definition:</b> c_api.h:52</div></div>
<div class="ttc" id="c__api_8h_html_aebeac35c3110eb391035fbbeb29e9436"><div class="ttname"><a href="c__api_8h.html#aebeac35c3110eb391035fbbeb29e9436">MXSymbolSetAttr</a></div><div class="ttdeci">MXNET_DLL int MXSymbolSetAttr(SymbolHandle symbol, const char *key, const char *value)</div><div class="ttdoc">Set string attribute from symbol. NOTE: Setting attribute to a symbol can affect the semantics(mutabl...</div></div>
<div class="ttc" id="c__api_8h_html_a13ebc161bc64049a26404ee6eca4ac26"><div class="ttname"><a href="c__api_8h.html#a13ebc161bc64049a26404ee6eca4ac26">MXSetNumOMPThreads</a></div><div class="ttdeci">MXNET_DLL int MXSetNumOMPThreads(int thread_num)</div><div class="ttdoc">Set the number of OMP threads to use. </div></div>
<div class="ttc" id="c__api_8h_html_a21df19f3242a68d1c536783fcf35968a"><div class="ttname"><a href="c__api_8h.html#a21df19f3242a68d1c536783fcf35968a">MXDataIterBeforeFirst</a></div><div class="ttdeci">MXNET_DLL int MXDataIterBeforeFirst(DataIterHandle handle)</div><div class="ttdoc">Call iterator.Reset. </div></div>
<div class="ttc" id="c__api_8h_html_a664c85b47c69b81be3b9ea1dfcb72a94"><div class="ttname"><a href="c__api_8h.html#a664c85b47c69b81be3b9ea1dfcb72a94">MXSymbolInferShapePartial</a></div><div class="ttdeci">MXNET_DLL int MXSymbolInferShapePartial(SymbolHandle sym, mx_uint num_args, const char **keys, const mx_uint *arg_ind_ptr, const mx_uint *arg_shape_data, mx_uint *in_shape_size, const mx_uint **in_shape_ndim, const mx_uint ***in_shape_data, mx_uint *out_shape_size, const mx_uint **out_s [...]
<div class="ttc" id="c__api_8h_html_aedf1da726aaacb87a5d959ed83f03f41"><div class="ttname"><a href="c__api_8h.html#aedf1da726aaacb87a5d959ed83f03f41">MXSymbolListOutputs</a></div><div class="ttdeci">MXNET_DLL int MXSymbolListOutputs(SymbolHandle symbol, mx_uint *out_size, const char ***out_str_array)</div><div class="ttdoc">List returns in the symbol. </div></div>
+<div class="ttc" id="c__api_8h_html_af59143cc01b560bb13f25a289b83f37b"><div class="ttname"><a href="c__api_8h.html#af59143cc01b560bb13f25a289b83f37b">MXKVStoreUpdater</a></div><div class="ttdeci">void( MXKVStoreUpdater)(int key, NDArrayHandle recv, NDArrayHandle local, void *handle)</div><div class="ttdoc">user-defined updater for the kvstore It's this updater's responsibility to delete recv and local ...</div><div class="ttdef"><b>Definition:</b> c_api.h:1431</div></div>
<div class="ttc" id="c__api_8h_html_aa13fb35019b0c42dcfee185b2f9e09d0"><div class="ttname"><a href="c__api_8h.html#aa13fb35019b0c42dcfee185b2f9e09d0">MXListDataIters</a></div><div class="ttdeci">MXNET_DLL int MXListDataIters(mx_uint *out_size, DataIterCreator **out_array)</div><div class="ttdoc">List all the available iterator entries. </div></div>
<div class="ttc" id="c__api_8h_html_a2035651f4392d249d1b904d5eb0c3406"><div class="ttname"><a href="c__api_8h.html#a2035651f4392d249d1b904d5eb0c3406">MXNDArrayGetShape</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayGetShape(NDArrayHandle handle, mx_uint *out_dim, const mx_uint **out_pdata)</div><div class="ttdoc">get the shape of the array </div></div>
<div class="ttc" id="c__api_8h_html_aec6d0d5055719576337687fc3702a899"><div class="ttname"><a href="c__api_8h.html#aec6d0d5055719576337687fc3702a899">MXDataIterGetPadNum</a></div><div class="ttdeci">MXNET_DLL int MXDataIterGetPadNum(DataIterHandle handle, int *pad)</div><div class="ttdoc">Get the padding number in current data batch. </div></div>
@@ -153,28 +831,28 @@ $(function() {
<div class="ttc" id="c__api_8h_html_a9afe2f46b1fcd8016c8bfb11a84c6240"><div class="ttname"><a href="c__api_8h.html#a9afe2f46b1fcd8016c8bfb11a84c6240">MXFuncInvokeEx</a></div><div class="ttdeci">MXNET_DLL int MXFuncInvokeEx(FunctionHandle fun, NDArrayHandle *use_vars, mx_float *scalar_args, NDArrayHandle *mutate_vars, int num_params, char **param_keys, char **param_vals)</div><div class="ttdoc">invoke a function, the array size of passed in arguments must match the values in the ...</div></div>
<div class="ttc" id="structMXCallbackList_html_aa593dcc4096dcc38a2af477e057d8861"><div class="ttname"><a href="structMXCallbackList.html#aa593dcc4096dcc38a2af477e057d8861">MXCallbackList::num_callbacks</a></div><div class="ttdeci">int num_callbacks</div><div class="ttdef"><b>Definition:</b> c_api.h:108</div></div>
<div class="ttc" id="c__api_8h_html_a05e3ead36464b62cd1a9264187640f5a"><div class="ttname"><a href="c__api_8h.html#a05e3ead36464b62cd1a9264187640f5a">MXRandomSeed</a></div><div class="ttdeci">MXNET_DLL int MXRandomSeed(int seed)</div><div class="ttdoc">Seed the global random number generators in mxnet. </div></div>
-<div class="ttc" id="c__api_8h_html_a80aad02a0ebd29de5ad0b65b2c8ccdb3"><div class="ttname"><a href="c__api_8h.html#a80aad02a0ebd29de5ad0b65b2c8ccdb3">MXKVStoreUpdater</a></div><div class="ttdeci">void() MXKVStoreUpdater(int key, NDArrayHandle recv, NDArrayHandle local, void *handle)</div><div class="ttdoc">user-defined updater for the kvstore It&#39;s this updater&#39;s responsibility to delete recv and local ...</div><div class="ttdef"><b>Definition:</b> c_api.h:1397</div></div>
<div class="ttc" id="c__api_8h_html_a8e601320c59679d9423179f23f281bf2"><div class="ttname"><a href="c__api_8h.html#a8e601320c59679d9423179f23f281bf2">MXSymbolGetOutput</a></div><div class="ttdeci">MXNET_DLL int MXSymbolGetOutput(SymbolHandle symbol, mx_uint index, SymbolHandle *out)</div><div class="ttdoc">Get index-th outputs of the symbol. </div></div>
<div class="ttc" id="c__api_8h_html_a69cfaa5de4b9d87070d4823700e64a1d"><div class="ttname"><a href="c__api_8h.html#a69cfaa5de4b9d87070d4823700e64a1d">MXNDArrayGetDType</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayGetDType(NDArrayHandle handle, int *out_dtype)</div><div class="ttdoc">get the type of the data in NDArray </div></div>
-<div class="ttc" id="structNativeOpInfo_html_ae4deb88375716334d58f3dc28e693d2d"><div class="ttname"><a href="structNativeOpInfo.html#ae4deb88375716334d58f3dc28e693d2d">NativeOpInfo::forward</a></div><div class="ttdeci">void(* forward)(int, float **, int *, unsigned **, int *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:75</div></div>
-<div class="ttc" id="c__api_8h_html_a0ee6dd8389c0dacae0f7c48156ec2f34"><div class="ttname"><a href="c__api_8h.html#a0ee6dd8389c0dacae0f7c48156ec2f34">MXGenericCallback</a></div><div class="ttdeci">int(* MXGenericCallback)(void)</div><div class="ttdef"><b>Definition:</b> c_api.h:105</div></div>
<div class="ttc" id="c__api_8h_html_a13816872b73d6c474686d985747e8c65"><div class="ttname"><a href="c__api_8h.html#a13816872b73d6c474686d985747e8c65">MXSymbolGetInternals</a></div><div class="ttdeci">MXNET_DLL int MXSymbolGetInternals(SymbolHandle symbol, SymbolHandle *out)</div><div class="ttdoc">Get a symbol that contains all the internals. </div></div>
<div class="ttc" id="c__api_8h_html_ab85ff72ac8364631116ced48939eaa60"><div class="ttname"><a href="c__api_8h.html#ab85ff72ac8364631116ced48939eaa60">MXExecutorSimpleBind</a></div><div class="ttdeci">MXNET_DLL int MXExecutorSimpleBind(SymbolHandle symbol_handle, int dev_type, int dev_id, const mx_uint num_g2c_keys, const char **g2c_keys, const int *g2c_dev_types, const int *g2c_dev_ids, const mx_uint provided_grad_req_list_len, const char **provided_grad_req_names, const char **provided_ [...]
+<div class="ttc" id="c__api_8h_html_a3a7fd875ee6aad3f20981764626a6c7b"><div class="ttname"><a href="c__api_8h.html#a3a7fd875ee6aad3f20981764626a6c7b">CustomOpPropCreator</a></div><div class="ttdeci">int(* CustomOpPropCreator)(const char *, const int, const char **, const char **, struct MXCallbackList *)</div><div class="ttdef"><b>Definition:</b> c_api.h:145</div></div>
<div class="ttc" id="c__api_8h_html_a5c4a11b4fccf320d1af0f63b40fff58a"><div class="ttname"><a href="c__api_8h.html#a5c4a11b4fccf320d1af0f63b40fff58a">MXInitPSEnv</a></div><div class="ttdeci">MXNET_DLL int MXInitPSEnv(mx_uint num_vars, const char **keys, const char **vals)</div><div class="ttdoc">Initialized ps-lite environment variables. </div></div>
<div class="ttc" id="c__api_8h_html_a0920d095f423b8bb34095406ea93a697"><div class="ttname"><a href="c__api_8h.html#a0920d095f423b8bb34095406ea93a697">MXDataIterGetData</a></div><div class="ttdeci">MXNET_DLL int MXDataIterGetData(DataIterHandle handle, NDArrayHandle *out)</div><div class="ttdoc">Get the handle to the NDArray of underlying data. </div></div>
<div class="ttc" id="c__api_8h_html_a13819b19ab3cdd91566b1d5cf8bc6b0a"><div class="ttname"><a href="c__api_8h.html#a13819b19ab3cdd91566b1d5cf8bc6b0a">MXRtcFree</a></div><div class="ttdeci">MXNET_DLL int MXRtcFree(RtcHandle handle)</div><div class="ttdoc">Delete a MXRtc object. </div></div>
<div class="ttc" id="c__api_8h_html_a98df4e0ceae7e198a5c18904cb135ab1"><div class="ttname"><a href="c__api_8h.html#a98df4e0ceae7e198a5c18904cb135ab1">MXNDArraySyncCopyToCPU</a></div><div class="ttdeci">MXNET_DLL int MXNDArraySyncCopyToCPU(NDArrayHandle handle, void *data, size_t size)</div><div class="ttdoc">Perform a synchronize copyto a continugous CPU memory region. </div></div>
+<div class="ttc" id="c__api_8h_html_a75939e5be09a150d4d8905c9fd6e986e"><div class="ttname"><a href="c__api_8h.html#a75939e5be09a150d4d8905c9fd6e986e">MXAutogradSetIsRecording</a></div><div class="ttdeci">MXNET_DLL int MXAutogradSetIsRecording(int is_recording, int *prev)</div><div class="ttdoc">set whether to record operator for autograd </div></div>
<div class="ttc" id="c__api_8h_html_aa2395b69772d66cb5c6c4cbe47749792a3189c8e5988867480e3ee81e262bba4e"><div class="ttname"><a href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792a3189c8e5988867480e3ee81e262bba4e">kCustomOpForward</a></div><div class="ttdef"><b>Definition:</b> c_api.h:115</div></div>
<div class="ttc" id="c__api_8h_html_a0543f53696ffcf5a7a2f0f4403f87623"><div class="ttname"><a href="c__api_8h.html#a0543f53696ffcf5a7a2f0f4403f87623">KVStoreHandle</a></div><div class="ttdeci">void * KVStoreHandle</div><div class="ttdoc">handle to KVStore </div><div class="ttdef"><b>Definition:</b> c_api.h:64</div></div>
<div class="ttc" id="c__api_8h_html_ab3a837622ed93ba390b4e8cbc0286b87"><div class="ttname"><a href="c__api_8h.html#ab3a837622ed93ba390b4e8cbc0286b87">MXSetProfilerConfig</a></div><div class="ttdeci">MXNET_DLL int MXSetProfilerConfig(int mode, const char *filename)</div><div class="ttdoc">Set up configuration of profiler. </div></div>
<div class="ttc" id="c__api_8h_html_a99c2939783499d2a5ce0d76881dce9df"><div class="ttname"><a href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">NDArrayHandle</a></div><div class="ttdeci">void * NDArrayHandle</div><div class="ttdoc">handle to NDArray </div><div class="ttdef"><b>Definition:</b> c_api.h:46</div></div>
<div class="ttc" id="c__api_8h_html_aa837df9930bbb46d0ca1674523f63e1c"><div class="ttname"><a href="c__api_8h.html#aa837df9930bbb46d0ca1674523f63e1c">MXNotifyShutdown</a></div><div class="ttdeci">MXNET_DLL int MXNotifyShutdown()</div><div class="ttdoc">Notify the engine about a shutdown, This can help engine to print less messages into display...</div></div>
+<div class="ttc" id="structNDArrayOpInfo_html_acd6f9dbac22ba35f0f9ac5062dc5c8ec"><div class="ttname"><a href="structNDArrayOpInfo.html#acd6f9dbac22ba35f0f9ac5062dc5c8ec">NDArrayOpInfo::list_outputs</a></div><div class="ttdeci">bool(* list_outputs)(char ***, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:92</div></div>
<div class="ttc" id="structNDArrayOpInfo_html_aa7ba9b33d0e48357207d6ce6c62707ba"><div class="ttname"><a href="structNDArrayOpInfo.html#aa7ba9b33d0e48357207d6ce6c62707ba">NDArrayOpInfo::p_list_outputs</a></div><div class="ttdeci">void * p_list_outputs</div><div class="ttdef"><b>Definition:</b> c_api.h:100</div></div>
+<div class="ttc" id="structNDArrayOpInfo_html_a4327dd20a65eb3a7241f67ede18f9950"><div class="ttname"><a href="structNDArrayOpInfo.html#a4327dd20a65eb3a7241f67ede18f9950">NDArrayOpInfo::declare_backward_dependency</a></div><div class="ttdeci">bool(* declare_backward_dependency)(const int *, const int *, const int *, int *, int **, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:94</div></div>
<div class="ttc" id="c__api_8h_html_af1b36a78dd74707c10d31c39c4d429b5"><div class="ttname"><a href="c__api_8h.html#af1b36a78dd74707c10d31c39c4d429b5">MXDataIterGetIndex</a></div><div class="ttdeci">MXNET_DLL int MXDataIterGetIndex(DataIterHandle handle, uint64_t **out_index, uint64_t *out_size)</div><div class="ttdoc">Get the image index by array. </div></div>
<div class="ttc" id="c__api_8h_html_a0c36af502d2a5746d841f50e1b0e71c0"><div class="ttname"><a href="c__api_8h.html#a0c36af502d2a5746d841f50e1b0e71c0">MXDataIterGetLabel</a></div><div class="ttdeci">MXNET_DLL int MXDataIterGetLabel(DataIterHandle handle, NDArrayHandle *out)</div><div class="ttdoc">Get the handle to the NDArray of underlying label. </div></div>
<div class="ttc" id="c__api_8h_html_a62d6b72cc6b3d920797c32ea8732cbbaab3c71c386f99a193c2b5d1ad3b306712"><div class="ttname"><a href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaab3c71c386f99a193c2b5d1ad3b306712">kCustomOpPropListArguments</a></div><div class="ttdef"><b>Definition:</b> c_api.h:121</div></div>
-<div class="ttc" id="c__api_8h_html_a6c79906616c8c56a409a97c4111a6b88"><div class="ttname"><a href="c__api_8h.html#a6c79906616c8c56a409a97c4111a6b88">CustomOpCreateFunc</a></div><div class="ttdeci">int(* CustomOpCreateFunc)(const char *, int, unsigned **, const int *, const int *, struct MXCallbackList *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:141</div></div>
-<div class="ttc" id="c__api_8h_html_a260ea269f1da99011750fc3e18661f06"><div class="ttname"><a href="c__api_8h.html#a260ea269f1da99011750fc3e18661f06">CustomOpFBFunc</a></div><div class="ttdeci">int(* CustomOpFBFunc)(int, void **, int *, const int *, const int, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:130</div></div>
+<div class="ttc" id="structNativeOpInfo_html_a65f675dec9ef798a96122e91d4cf3fa8"><div class="ttname"><a href="structNativeOpInfo.html#a65f675dec9ef798a96122e91d4cf3fa8">NativeOpInfo::forward</a></div><div class="ttdeci">void(* forward)(int, float **, int *, unsigned **, int *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:75</div></div>
<div class="ttc" id="c__api_8h_html_a72e684e635bb138c6382bd9c4e5957db"><div class="ttname"><a href="c__api_8h.html#a72e684e635bb138c6382bd9c4e5957db">MXSymbolCreateGroup</a></div><div class="ttdeci">MXNET_DLL int MXSymbolCreateGroup(mx_uint num_symbols, SymbolHandle *symbols, SymbolHandle *out)</div><div class="ttdoc">Create a Symbol by grouping list of symbols together. </div></div>
<div class="ttc" id="c__api_8h_html_a097e93ed9fd8c8cfb8ae76aa32441b72"><div class="ttname"><a href="c__api_8h.html#a097e93ed9fd8c8cfb8ae76aa32441b72">MXNDArrayCreate</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayCreate(const mx_uint *shape, mx_uint ndim, int dev_type, int dev_id, int delay_alloc, NDArrayHandle *out)</div><div class="ttdoc">create a NDArray with specified shape </div></div>
<div class="ttc" id="c__api_8h_html_add5b8e9bbd098e8fef5deab4c7ab2737"><div class="ttname"><a href="c__api_8h.html#add5b8e9bbd098e8fef5deab4c7ab2737">MXKVStoreSetBarrierBeforeExit</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreSetBarrierBeforeExit(KVStoreHandle handle, const int barrier_before_exit)</div><div class="ttdoc">whether to do barrier when finalize </div></div>
@@ -186,6 +864,7 @@ $(function() {
<div class="ttc" id="c__api_8h_html_a25588eca6c42583151e77bcf71ffc3ac"><div class="ttname"><a href="c__api_8h.html#a25588eca6c42583151e77bcf71ffc3ac">MXKVStorePullEx</a></div><div class="ttdeci">MXNET_DLL int MXKVStorePullEx(KVStoreHandle handle, mx_uint num, const char **keys, NDArrayHandle *vals, int priority)</div><div class="ttdoc">pull a list of (key, value) pairs from the kvstore, where each key is a string </div></div>
<div class="ttc" id="c__api_8h_html_a4464ddbe13b8a4542d3f91bf055d50af"><div class="ttname"><a href="c__api_8h.html#a4464ddbe13b8a4542d3f91bf055d50af">MXRecordIOReaderCreate</a></div><div class="ttdeci">MXNET_DLL int MXRecordIOReaderCreate(const char *uri, RecordIOHandle *out)</div><div class="ttdoc">Create a RecordIO reader object. </div></div>
<div class="ttc" id="c__api_8h_html_ac8b5138bcb7959cc831a97a24755e969"><div class="ttname"><a href="c__api_8h.html#ac8b5138bcb7959cc831a97a24755e969">MXKVStoreGetNumDeadNode</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreGetNumDeadNode(KVStoreHandle handle, const int node_id, int *number, const int timeout_sec DEFAULT(60))</div><div class="ttdoc">Get the number of ps dead node(s) specified by {node_id}. </div></div>
+<div class="ttc" id="c__api_8h_html_a581410d3cb679f2d0fc2f65fca91ec17"><div class="ttname"><a href="c__api_8h.html#a581410d3cb679f2d0fc2f65fca91ec17">MXGenericCallback</a></div><div class="ttdeci">int(* MXGenericCallback)(void)</div><div class="ttdef"><b>Definition:</b> c_api.h:105</div></div>
<div class="ttc" id="c__api_8h_html_a796e7ef8260ef40b24af7d1082aa2d84"><div class="ttname"><a href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a></div><div class="ttdeci">unsigned int mx_uint</div><div class="ttdoc">manually define unsigned int </div><div class="ttdef"><b>Definition:</b> c_api.h:39</div></div>
<div class="ttc" id="c__api_8h_html_aa2395b69772d66cb5c6c4cbe47749792afe6891ef7e38df52ac25db00a2ef73c6"><div class="ttname"><a href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792afe6891ef7e38df52ac25db00a2ef73c6">kCustomOpBackward</a></div><div class="ttdef"><b>Definition:</b> c_api.h:116</div></div>
<div class="ttc" id="c__api_8h_html_a6cc8e9f94ec431edf8d9502f20d7b833"><div class="ttname"><a href="c__api_8h.html#a6cc8e9f94ec431edf8d9502f20d7b833">MXRecordIOWriterTell</a></div><div class="ttdeci">MXNET_DLL int MXRecordIOWriterTell(RecordIOHandle handle, size_t *pos)</div><div class="ttdoc">Get the current writer pointer position. </div></div>
@@ -194,13 +873,14 @@ $(function() {
<div class="ttc" id="c__api_8h_html_a33606f8bf8413da87c9d499e2b2f1e88"><div class="ttname"><a href="c__api_8h.html#a33606f8bf8413da87c9d499e2b2f1e88">MXSymbolGetName</a></div><div class="ttdeci">MXNET_DLL int MXSymbolGetName(SymbolHandle symbol, const char **out, int *success)</div><div class="ttdoc">Get string name from symbol. </div></div>
<div class="ttc" id="structNDArrayOpInfo_html_afb5ee1e890c2499a5a845b21f4df518f"><div class="ttname"><a href="structNDArrayOpInfo.html#afb5ee1e890c2499a5a845b21f4df518f">NDArrayOpInfo::p_backward</a></div><div class="ttdeci">void * p_backward</div><div class="ttdef"><b>Definition:</b> c_api.h:98</div></div>
<div class="ttc" id="c__api_8h_html_a62d6b72cc6b3d920797c32ea8732cbbaa16e932f1ac19d0846d5ec6d2077be6aa"><div class="ttname"><a href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa16e932f1ac19d0846d5ec6d2077be6aa">kCustomOpPropDelete</a></div><div class="ttdef"><b>Definition:</b> c_api.h:120</div></div>
+<div class="ttc" id="c__api_8h_html_a201e319981a60877ab9d6f7cf3b3f62d"><div class="ttname"><a href="c__api_8h.html#a201e319981a60877ab9d6f7cf3b3f62d">CustomOpCreateFunc</a></div><div class="ttdeci">int(* CustomOpCreateFunc)(const char *, int, unsigned **, const int *, const int *, struct MXCallbackList *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:141</div></div>
<div class="ttc" id="c__api_8h_html_a8139e691c88ebcf082f9e954598e0e86"><div class="ttname"><a href="c__api_8h.html#a8139e691c88ebcf082f9e954598e0e86">MXExecutorBindEX</a></div><div class="ttdeci">MXNET_DLL int MXExecutorBindEX(SymbolHandle symbol_handle, int dev_type, int dev_id, mx_uint num_map_keys, const char **map_keys, const int *map_dev_types, const int *map_dev_ids, mx_uint len, NDArrayHandle *in_args, NDArrayHandle *arg_grad_store, mx_uint *grad_req_type, mx_uint aux_states_len, [...]
+<div class="ttc" id="c__api_8h_html_add8141d7f51e3a77544cc7b79433d16e"><div class="ttname"><a href="c__api_8h.html#add8141d7f51e3a77544cc7b79433d16e">CustomOpListFunc</a></div><div class="ttdeci">int(* CustomOpListFunc)(char ***, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:134</div></div>
<div class="ttc" id="c__api_8h_html_ab2a55917ffd918e334464f1b8a7a1ab9"><div class="ttname"><a href="c__api_8h.html#ab2a55917ffd918e334464f1b8a7a1ab9">MXRecordIOWriterFree</a></div><div class="ttdeci">MXNET_DLL int MXRecordIOWriterFree(RecordIOHandle handle)</div><div class="ttdoc">Delete a RecordIO writer object. </div></div>
<div class="ttc" id="c__api_8h_html_ad8f92bb15e11058abe12959cfc4dcbb3"><div class="ttname"><a href="c__api_8h.html#ad8f92bb15e11058abe12959cfc4dcbb3">MXExecutorBackward</a></div><div class="ttdeci">MXNET_DLL int MXExecutorBackward(ExecutorHandle handle, mx_uint len, NDArrayHandle *head_grads)</div><div class="ttdoc">Excecutor run backward. </div></div>
<div class="ttc" id="c__api_8h_html_a1edf063b82bdd94d1f5214056dd55144"><div class="ttname"><a href="c__api_8h.html#a1edf063b82bdd94d1f5214056dd55144">MXKVStoreGetRank</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreGetRank(KVStoreHandle handle, int *ret)</div><div class="ttdoc">return The rank of this node in its group, which is in [0, GroupSize). </div></div>
<div class="ttc" id="c__api_8h_html_aac41a2bd1dfcdebb920c97eab40ea07b"><div class="ttname"><a href="c__api_8h.html#aac41a2bd1dfcdebb920c97eab40ea07b">MXKVStoreGetGroupSize</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreGetGroupSize(KVStoreHandle handle, int *ret)</div><div class="ttdoc">return The number of nodes in this group, which is </div></div>
<div class="ttc" id="c__api_8h_html_a0c380b6aa00446e7cb6021c8e2df75f3"><div class="ttname"><a href="c__api_8h.html#a0c380b6aa00446e7cb6021c8e2df75f3">MXRecordIOWriterWriteRecord</a></div><div class="ttdeci">MXNET_DLL int MXRecordIOWriterWriteRecord(RecordIOHandle handle, const char *buf, size_t size)</div><div class="ttdoc">Write a record to a RecordIO object. </div></div>
-<div class="ttc" id="c__api_8h_html_a470eba0beb11ed2fd858c50c9b2be33a"><div class="ttname"><a href="c__api_8h.html#a470eba0beb11ed2fd858c50c9b2be33a">CustomOpPropCreator</a></div><div class="ttdeci">int(* CustomOpPropCreator)(const char *, const int, const char **, const char **, struct MXCallbackList *)</div><div class="ttdef"><b>Definition:</b> c_api.h:145</div></div>
<div class="ttc" id="c__api_8h_html_a738463b3d661aa4821caa4626092010f"><div class="ttname"><a href="c__api_8h.html#a738463b3d661aa4821caa4626092010f">MXSymbolCreateVariable</a></div><div class="ttdeci">MXNET_DLL int MXSymbolCreateVariable(const char *name, SymbolHandle *out)</div><div class="ttdoc">Create a Variable Symbol. </div></div>
<div class="ttc" id="c__api_8h_html_abae91a5d7847020e14395e078144489a"><div class="ttname"><a href="c__api_8h.html#abae91a5d7847020e14395e078144489a">MXExecutorSetMonitorCallback</a></div><div class="ttdeci">MXNET_DLL int MXExecutorSetMonitorCallback(ExecutorHandle handle, ExecutorMonitorCallback callback, void *callback_handle)</div><div class="ttdoc">set a call back to notify the completion of operation </div></div>
<div class="ttc" id="c__api_8h_html_a133d10a566764530bf3172e52afbe561"><div class="ttname"><a href="c__api_8h.html#a133d10a566764530bf3172e52afbe561">MXAutogradBackward</a></div><div class="ttdeci">MXNET_DLL int MXAutogradBackward(mx_uint num_output, NDArrayHandle *output_handles, NDArrayHandle *ograd_handles, int retain_graph)</div><div class="ttdoc">compute the gradient of outputs w.r.t variabels </div></div>
@@ -214,14 +894,16 @@ $(function() {
<div class="ttc" id="c__api_8h_html_a5b0e47dde55f9b2f58b99fab92d1e0ac"><div class="ttname"><a href="c__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a></div><div class="ttdeci">#define MXNET_DLL</div><div class="ttdoc">MXNET_DLL prefix for windows. </div><div class="ttdef"><b>Definition:</b> c_api.h:35</div></div>
<div class="ttc" id="c__api_8h_html_a9cd6ad8c274665172513707dd73262ce"><div class="ttname"><a href="c__api_8h.html#a9cd6ad8c274665172513707dd73262ce">MXSymbolCreateAtomicSymbol</a></div><div class="ttdeci">MXNET_DLL int MXSymbolCreateAtomicSymbol(AtomicSymbolCreator creator, mx_uint num_param, const char **keys, const char **vals, SymbolHandle *out)</div><div class="ttdoc">Create an AtomicSymbol. </div></div>
<div class="ttc" id="c__api_8h_html_a2ff7ded9017ac7617be4eeebffae06a1"><div class="ttname"><a href="c__api_8h.html#a2ff7ded9017ac7617be4eeebffae06a1">MXNDArraySetGradState</a></div><div class="ttdeci">MXNET_DLL int MXNDArraySetGradState(NDArrayHandle handle, int state)</div><div class="ttdoc">set the flag for gradient array state. </div></div>
+<div class="ttc" id="c__api_8h_html_aa7071c027141653df82d2b72db566acd"><div class="ttname"><a href="c__api_8h.html#aa7071c027141653df82d2b72db566acd">ExecutorMonitorCallback</a></div><div class="ttdeci">void(* ExecutorMonitorCallback)(const char *, NDArrayHandle, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:70</div></div>
+<div class="ttc" id="structNDArrayOpInfo_html_a6cafb44b639ed5256fe80a976e351cc1"><div class="ttname"><a href="structNDArrayOpInfo.html#a6cafb44b639ed5256fe80a976e351cc1">NDArrayOpInfo::backward</a></div><div class="ttdeci">bool(* backward)(int, void **, int *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:90</div></div>
<div class="ttc" id="c__api_8h_html_acab1874f3e3077710b77d1a005abf36c"><div class="ttname"><a href="c__api_8h.html#acab1874f3e3077710b77d1a005abf36c">MXGetLastError</a></div><div class="ttdeci">MXNET_DLL const char * MXGetLastError()</div><div class="ttdoc">return str message of the last error all function in this file will return 0 when success and -1 when...</div></div>
<div class="ttc" id="c__api_8h_html_a2ab9f498d34d84238e41ecb5e584e435"><div class="ttname"><a href="c__api_8h.html#a2ab9f498d34d84238e41ecb5e584e435">MXNDArrayGetGradState</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayGetGradState(NDArrayHandle handle, int *out)</div><div class="ttdoc">set the flag for gradient array state. </div></div>
<div class="ttc" id="structMXCallbackList_html_a332323a5f414bd3f5e423788804a0dcd"><div class="ttname"><a href="structMXCallbackList.html#a332323a5f414bd3f5e423788804a0dcd">MXCallbackList::contexts</a></div><div class="ttdeci">void ** contexts</div><div class="ttdef"><b>Definition:</b> c_api.h:110</div></div>
<div class="ttc" id="c__api_8h_html_aa2e96133f49cfdc1095da6cd08afbce0"><div class="ttname"><a href="c__api_8h.html#aa2e96133f49cfdc1095da6cd08afbce0">AtomicSymbolCreator</a></div><div class="ttdeci">void * AtomicSymbolCreator</div><div class="ttdoc">handle to a function that takes param and creates symbol </div><div class="ttdef"><b>Definition:</b> c_api.h:50</div></div>
<div class="ttc" id="c__api_8h_html_a4dc86980d7922f52970a8e3978bc87cb"><div class="ttname"><a href="c__api_8h.html#a4dc86980d7922f52970a8e3978bc87cb">MXKVStoreInitEx</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreInitEx(KVStoreHandle handle, mx_uint num, const char **keys, NDArrayHandle *vals)</div><div class="ttdoc">Init a list of (key,value) pairs in kvstore, where each key is a string. </div></div>
<div class="ttc" id="c__api_8h_html_ad1646370458f8a3ddb9e4f3365c5e510"><div class="ttname"><a href="c__api_8h.html#ad1646370458f8a3ddb9e4f3365c5e510">MXExecutorBindX</a></div><div class="ttdeci">MXNET_DLL int MXExecutorBindX(SymbolHandle symbol_handle, int dev_type, int dev_id, mx_uint num_map_keys, const char **map_keys, const int *map_dev_types, const int *map_dev_ids, mx_uint len, NDArrayHandle *in_args, NDArrayHandle *arg_grad_store, mx_uint *grad_req_type, mx_uint aux_states_len, ND [...]
-<div class="ttc" id="structNativeOpInfo_html_a2d8f03e8586a1a1cb6b46ab8872a214a"><div class="ttname"><a href="structNativeOpInfo.html#a2d8f03e8586a1a1cb6b46ab8872a214a">NativeOpInfo::infer_shape</a></div><div class="ttdeci">void(* infer_shape)(int, int *, unsigned **, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:77</div></div>
<div class="ttc" id="structNativeOpInfo_html_a32ec41926eee694c1bfb8e8b5697a146"><div class="ttname"><a href="structNativeOpInfo.html#a32ec41926eee694c1bfb8e8b5697a146">NativeOpInfo::p_list_outputs</a></div><div class="ttdeci">void * p_list_outputs</div><div class="ttdef"><b>Definition:</b> c_api.h:84</div></div>
+<div class="ttc" id="c__api_8h_html_a31f639b22167ac51b67381b16dfd5634"><div class="ttname"><a href="c__api_8h.html#a31f639b22167ac51b67381b16dfd5634">MXKVStoreServerController</a></div><div class="ttdeci">void( MXKVStoreServerController)(int head, const char *body, void *controller_handle)</div><div class="ttdoc">the prototype of a server controller </div><div class="ttdef"><b>Definition:</b> c_api.h:1526</div></div>
<div class="ttc" id="c__api_8h_html_a5dc95ef008b73103b86f4576fcf9117d"><div class="ttname"><a href="c__api_8h.html#a5dc95ef008b73103b86f4576fcf9117d">MXNDArraySave</a></div><div class="ttdeci">MXNET_DLL int MXNDArraySave(const char *fname, mx_uint num_args, NDArrayHandle *args, const char **keys)</div><div class="ttdoc">Save list of narray into the file. </div></div>
<div class="ttc" id="c__api_8h_html_a27c9353672df26b68c169fbb857f3a3f"><div class="ttname"><a href="c__api_8h.html#a27c9353672df26b68c169fbb857f3a3f">MXNDArrayLoadFromRawBytes</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayLoadFromRawBytes(const void *buf, size_t size, NDArrayHandle *out)</div><div class="ttdoc">create a NDArray handle that is loaded from raw bytes. </div></div>
<div class="ttc" id="c__api_8h_html_a20f72cb4e18d5081eaf40875adcb6e98"><div class="ttname"><a href="c__api_8h.html#a20f72cb4e18d5081eaf40875adcb6e98">MXRtcPush</a></div><div class="ttdeci">MXNET_DLL int MXRtcPush(RtcHandle handle, mx_uint num_input, mx_uint num_output, NDArrayHandle *inputs, NDArrayHandle *outputs, mx_uint gridDimX, mx_uint gridDimY, mx_uint gridDimZ, mx_uint blockDimX, mx_uint blockDimY, mx_uint blockDimZ)</div><div class="ttdoc">Run cuda kernel. </div></div>
@@ -234,11 +916,9 @@ $(function() {
<div class="ttc" id="c__api_8h_html_a9dba9ada3ed98c76fe78221013e37f07"><div class="ttname"><a href="c__api_8h.html#a9dba9ada3ed98c76fe78221013e37f07">MXKVStoreGetType</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreGetType(KVStoreHandle handle, const char **type)</div><div class="ttdoc">get the type of the kvstore </div></div>
<div class="ttc" id="c__api_8h_html_a58a159a7417ad988206970d46c4c7ee8"><div class="ttname"><a href="c__api_8h.html#a58a159a7417ad988206970d46c4c7ee8">RtcHandle</a></div><div class="ttdeci">void * RtcHandle</div><div class="ttdoc">handle to MXRtc </div><div class="ttdef"><b>Definition:</b> c_api.h:68</div></div>
<div class="ttc" id="c__api_8h_html_aedb8d676502a66eef4a6c5988f73dccb"><div class="ttname"><a href="c__api_8h.html#aedb8d676502a66eef4a6c5988f73dccb">MXNDArrayGetGrad</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayGetGrad(NDArrayHandle handle, NDArrayHandle *out)</div><div class="ttdoc">return gradient buffer attached to this NDArray </div></div>
-<div class="ttc" id="c__api_8h_html_a97d01e308e267e939b4d1eebaf2f11d9"><div class="ttname"><a href="c__api_8h.html#a97d01e308e267e939b4d1eebaf2f11d9">CustomOpBwdDepFunc</a></div><div class="ttdeci">int(* CustomOpBwdDepFunc)(const int *, const int *, const int *, int *, int **, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:138</div></div>
<div class="ttc" id="c__api_8h_html_a1eff7ea3fd2fe56509c75f5a610890e6"><div class="ttname"><a href="c__api_8h.html#a1eff7ea3fd2fe56509c75f5a610890e6">MXSymbolInferType</a></div><div class="ttdeci">MXNET_DLL int MXSymbolInferType(SymbolHandle sym, mx_uint num_args, const char **keys, const int *arg_type_data, mx_uint *in_type_size, const int **in_type_data, mx_uint *out_type_size, const int **out_type_data, mx_uint *aux_type_size, const int **aux_type_data, int *complete)</div><div class= [...]
<div class="ttc" id="c__api_8h_html_a62d6b72cc6b3d920797c32ea8732cbbaa0a32388e902d5d81a88f62f3eabf56c3"><div class="ttname"><a href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaa0a32388e902d5d81a88f62f3eabf56c3">kCustomOpPropListOutputs</a></div><div class="ttdef"><b>Definition:</b> c_api.h:122</div></div>
<div class="ttc" id="c__api_8h_html_a475087b5464f9240d940ed4204a219b9"><div class="ttname"><a href="c__api_8h.html#a475087b5464f9240d940ed4204a219b9">MXKVStoreInit</a></div><div class="ttdeci">MXNET_DLL int MXKVStoreInit(KVStoreHandle handle, mx_uint num, const int *keys, NDArrayHandle *vals)</div><div class="ttdoc">Init a list of (key,value) pairs in kvstore. </div></div>
-<div class="ttc" id="structNativeOpInfo_html_a2faec6cfc4510b455de950a4b104ea9f"><div class="ttname"><a href="structNativeOpInfo.html#a2faec6cfc4510b455de950a4b104ea9f">NativeOpInfo::backward</a></div><div class="ttdeci">void(* backward)(int, float **, int *, unsigned **, int *, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:76</div></div>
<div class="ttc" id="c__api_8h_html_a51716c7a38d1b02d26da4d84d6307025"><div class="ttname"><a href="c__api_8h.html#a51716c7a38d1b02d26da4d84d6307025">MXDumpProfile</a></div><div class="ttdeci">MXNET_DLL int MXDumpProfile()</div><div class="ttdoc">Save profile and stop profiler. </div></div>
<div class="ttc" id="structMXCallbackList_html"><div class="ttname"><a href="structMXCallbackList.html">MXCallbackList</a></div><div class="ttdef"><b>Definition:</b> c_api.h:107</div></div>
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@@ -246,22 +926,19 @@ $(function() {
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<div class="ttc" id="c__api_8h_html_a62d6b72cc6b3d920797c32ea8732cbbaaabeb765165b4748e68c7f196bf8a1686"><div class="ttname"><a href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaaabeb765165b4748e68c7f196bf8a1686">kCustomOpPropListAuxiliaryStates</a></div><div class="ttdef"><b>Definition:</b> c_api.h:123</div></div>
<div class="ttc" id="c__api_8h_html_a62d6b72cc6b3d920797c32ea8732cbbaac97963c158fddf25e10fddd4d9ae7619"><div class="ttname"><a href="c__api_8h.html#a62d6b72cc6b3d920797c32ea8732cbbaac97963c158fddf25e10fddd4d9ae7619">kCustomOpPropCreateOperator</a></div><div class="ttdef"><b>Definition:</b> c_api.h:126</div></div>
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<div class="ttc" id="c__api_8h_html_a1070254ccff925195bf263b43762a06f"><div class="ttname"><a href="c__api_8h.html#a1070254ccff925195bf263b43762a06f">MXSymbolFree</a></div><div class="ttdeci">MXNET_DLL int MXSymbolFree(SymbolHandle symbol)</div><div class="ttdoc">Free the symbol handle. </div></div>
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<div class="ttc" id="c__api_8h_html_a6f4d7cb465c2b3723c51449b1aba8af9"><div class="ttname"><a href="c__api_8h.html#a6f4d7cb465c2b3723c51449b1aba8af9">MXNDArrayWaitToWrite</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayWaitToWrite(NDArrayHandle handle)</div><div class="ttdoc">Wait until all the pending read/write with respect NDArray are finished. Always call this before writ...</div></div>
-<div class="ttc" id="c__api_8h_html_a5bcf4b1e26b22c9363a016bd9d27085c"><div class="ttname"><a href="c__api_8h.html#a5bcf4b1e26b22c9363a016bd9d27085c">CustomOpListFunc</a></div><div class="ttdeci">int(* CustomOpListFunc)(char ***, void *)</div><div class="ttdef"><b>Definition:</b> c_api.h:134</div></div>
<div class="ttc" id="c__api_8h_html_a1f9987e701b810e0e79b5207fa215415"><div class="ttname"><a href="c__api_8h.html#a1f9987e701b810e0e79b5207fa215415">MXExecutorForward</a></div><div class="ttdeci">MXNET_DLL int MXExecutorForward(ExecutorHandle handle, int is_train)</div><div class="ttdoc">Executor forward method. </div></div>
<div class="ttc" id="c__api_8h_html_a6c8d8ceb3c523e8507e813f7afb97f83"><div class="ttname"><a href="c__api_8h.html#a6c8d8ceb3c523e8507e813f7afb97f83">MXNDArrayGetData</a></div><div class="ttdeci">MXNET_DLL int MXNDArrayGetData(NDArrayHandle handle, void **out_pdata)</div><div class="ttdoc">get the content of the data in NDArray </div></div>
</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
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+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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+ <td style="padding-left: 0.5em;">
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+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
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@@ -72,8 +94,8 @@ $(function() {
<p>Unified interface for LAPACK calls from within mxnet. Purpose is to hide the platform specific differences.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/logging.h></code><br />
-<code>#include "mshadow/tensor.h"</code><br />
+<div class="textblock"><code>#include <dmlc/logging.h></code><br/>
+<code>#include "mshadow/tensor.h"</code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for c_lapack_api.h:</div>
<div class="dyncontent">
@@ -86,17 +108,17 @@ Include dependency graph for c_lapack_api.h:</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="define-members"></a>
Macros</h2></td></tr>
-<tr class="memitem:a56f112686aa64bf8a42204a3ff87ea46"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#a56f112686aa64bf8a42204a3ff87ea46">MXNET_LAPACK_FSIGNATURE1</a>(func, dtype)   void func##_(char* uplo, int* n, dtype* a, int* lda, int *info);</td></tr>
+<tr class="memitem:a56f112686aa64bf8a42204a3ff87ea46"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#a56f112686aa64bf8a42204a3ff87ea46">MXNET_LAPACK_FSIGNATURE1</a>(func, dtype)   void func##_(char* uplo, int* n, dtype* a, int* lda, int *info);</td></tr>
<tr class="separator:a56f112686aa64bf8a42204a3ff87ea46"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a220562c35966392841ae49fefe2dee5e"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#a220562c35966392841ae49fefe2dee5e">MXNET_LAPACK_ROW_MAJOR</a>   101</td></tr>
<tr class="separator:a220562c35966392841ae49fefe2dee5e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a49f324c038a0a1447ed094c679739ee7"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#a49f324c038a0a1447ed094c679739ee7">MXNET_LAPACK_COL_MAJOR</a>   102</td></tr>
<tr class="separator:a49f324c038a0a1447ed094c679739ee7"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2fa12bf140fecda6aac9336a61923713"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#a2fa12bf140fecda6aac9336a61923713">CHECK_LAPACK_CONTIGUOUS</a>(a, b)   CHECK_EQ(a, b) << "non contiguous memory for array in lapack call";</td></tr>
+<tr class="memitem:a2fa12bf140fecda6aac9336a61923713"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#a2fa12bf140fecda6aac9336a61923713">CHECK_LAPACK_CONTIGUOUS</a>(a, b)   CHECK_EQ(a, b) << "non contiguous memory for array in lapack call";</td></tr>
<tr class="separator:a2fa12bf140fecda6aac9336a61923713"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0c95bf8b79e51085d846c7a09ec22720"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#a0c95bf8b79e51085d846c7a09ec22720">CHECK_LAPACK_UPLO</a>(a)   CHECK(a == 'U' || a == 'L') << "neither L nor U specified as triangle in lapack call";</td></tr>
<tr class="separator:a0c95bf8b79e51085d846c7a09ec22720"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aa5297300313445fa46d2d1d2fb38970c"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(func, dtype)</td></tr>
+<tr class="memitem:aa5297300313445fa46d2d1d2fb38970c"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(func, dtype)</td></tr>
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<tr class="memitem:a0c98356495b08e7d69a4cafd8b034ab4"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#a0c98356495b08e7d69a4cafd8b034ab4">MXNET_LAPACK_UNAVAILABLE</a>(func)</td></tr>
<tr class="separator:a0c98356495b08e7d69a4cafd8b034ab4"><td class="memSeparator" colspan="2"> </td></tr>
@@ -111,7 +133,7 @@ Functions</h2></td></tr>
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<tr class="memitem:ae6920bb50ceca504aefa6082a6158b98"><td class="memTemplParams" colspan="2">template<typename xpu , typename DType > </td></tr>
<tr class="memitem:ae6920bb50ceca504aefa6082a6158b98"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#ae6920bb50ceca504aefa6082a6158b98">flip</a> (int m, int n, DType *b, int ldb, DType *a, int lda)</td></tr>
-<tr class="memdesc:ae6920bb50ceca504aefa6082a6158b98"><td class="mdescLeft"> </td><td class="mdescRight">Transpose matrix data in memory. <a href="#ae6920bb50ceca504aefa6082a6158b98">More...</a><br /></td></tr>
+<tr class="memdesc:ae6920bb50ceca504aefa6082a6158b98"><td class="mdescLeft"> </td><td class="mdescRight">Transpose matrix data in memory. <a href="#ae6920bb50ceca504aefa6082a6158b98">More...</a><br/></td></tr>
<tr class="separator:ae6920bb50ceca504aefa6082a6158b98"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad308cb2cd9a08c654b38c62f0ea0c902"><td class="memTemplParams" colspan="2">template<> </td></tr>
<tr class="memitem:ad308cb2cd9a08c654b38c62f0ea0c902"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="c__lapack__api_8h.html#ad308cb2cd9a08c654b38c62f0ea0c902">flip< cpu, float ></a> (int m, int n, float *b, int ldb, float *a, int lda)</td></tr>
@@ -133,9 +155,7 @@ Functions</h2></td></tr>
<div class="textblock"><p>Unified interface for LAPACK calls from within mxnet. Purpose is to hide the platform specific differences. </p>
<p>Copyright (c) 2017 by Contributors </p>
</div><h2 class="groupheader">Macro Definition Documentation</h2>
-<a id="a2fa12bf140fecda6aac9336a61923713"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a2fa12bf140fecda6aac9336a61923713">◆ </a></span>CHECK_LAPACK_CONTIGUOUS</h2>
-
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<div class="memproto">
<table class="memname">
@@ -161,9 +181,7 @@ Functions</h2></td></tr>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a0c95bf8b79e51085d846c7a09ec22720">◆ </a></span>CHECK_LAPACK_UPLO</h2>
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@@ -179,9 +197,7 @@ Functions</h2></td></tr>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a49f324c038a0a1447ed094c679739ee7">◆ </a></span>MXNET_LAPACK_COL_MAJOR</h2>
-
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@@ -193,9 +209,7 @@ Functions</h2></td></tr>
</div>
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-<a id="aa5297300313445fa46d2d1d2fb38970c"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aa5297300313445fa46d2d1d2fb38970c">◆ </a></span>MXNET_LAPACK_CWRAPPER1</h2>
-
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@@ -218,12 +232,14 @@ Functions</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<b>Value:</b><div class="fragment"><div class="line"><span class="keyword">inline</span> <span class="keywordtype">int</span> MXNET_LAPACK_##func(<span class="keywordtype">int</span> matrix_layout, <span class="keywordtype">char</span> uplo, <span class="keywordtype">int</span> n, dtype* a, <span class="keywordtype">int</span> lda ) { \</div><div class="line"> LOG(FATAL) << <span class="stringliteral">"MXNet build without lapack. Function "</span> << #func <&l [...]
+<b>Value:</b><div class="fragment"><div class="line"><span class="keyword">inline</span> <span class="keywordtype">int</span> MXNET_LAPACK_##func(<span class="keywordtype">int</span> matrix_layout, <span class="keywordtype">char</span> uplo, <span class="keywordtype">int</span> n, dtype* a, <span class="keywordtype">int</span> lda ) { \</div>
+<div class="line"> LOG(FATAL) << <span class="stringliteral">"MXNet build without lapack. Function "</span> << #func << <span class="stringliteral">" is not available."</span>; \</div>
+<div class="line"> return 1; \</div>
+<div class="line"> }</div>
+</div><!-- fragment -->
</div>
</div>
-<a id="a56f112686aa64bf8a42204a3ff87ea46"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a56f112686aa64bf8a42204a3ff87ea46">◆ </a></span>MXNET_LAPACK_FSIGNATURE1</h2>
-
+<a class="anchor" id="a56f112686aa64bf8a42204a3ff87ea46"></a>
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@@ -249,9 +265,7 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a220562c35966392841ae49fefe2dee5e"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a220562c35966392841ae49fefe2dee5e">◆ </a></span>MXNET_LAPACK_ROW_MAJOR</h2>
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+<a class="anchor" id="a220562c35966392841ae49fefe2dee5e"></a>
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@@ -263,9 +277,7 @@ Functions</h2></td></tr>
</div>
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-<a id="a0c98356495b08e7d69a4cafd8b034ab4"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a0c98356495b08e7d69a4cafd8b034ab4">◆ </a></span>MXNET_LAPACK_UNAVAILABLE</h2>
-
+<a class="anchor" id="a0c98356495b08e7d69a4cafd8b034ab4"></a>
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@@ -278,13 +290,15 @@ Functions</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<b>Value:</b><div class="fragment"><div class="line"><span class="keyword">inline</span> <span class="keywordtype">int</span> mxnet_lapack_##func(...) { \</div><div class="line"> LOG(FATAL) << <span class="stringliteral">"MXNet build without lapack. Function "</span> << #func << <span class="stringliteral">" is not available."</span>; \</div><div class="line"> return 1; \</div><div class="line"> }</div></div><!-- fragment -->
+<b>Value:</b><div class="fragment"><div class="line"><span class="keyword">inline</span> <span class="keywordtype">int</span> mxnet_lapack_##func(...) { \</div>
+<div class="line"> LOG(FATAL) << <span class="stringliteral">"MXNet build without lapack. Function "</span> << #func << <span class="stringliteral">" is not available."</span>; \</div>
+<div class="line"> return 1; \</div>
+<div class="line"> }</div>
+</div><!-- fragment -->
</div>
</div>
<h2 class="groupheader">Function Documentation</h2>
-<a id="ac9fe022dd825c3bd86d3b175d0a1c02b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ac9fe022dd825c3bd86d3b175d0a1c02b">◆ </a></span>dposv_()</h2>
-
+<a class="anchor" id="ac9fe022dd825c3bd86d3b175d0a1c02b"></a>
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<div class="memproto">
<table class="memname">
@@ -346,9 +360,7 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="ae6920bb50ceca504aefa6082a6158b98"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ae6920bb50ceca504aefa6082a6158b98">◆ </a></span>flip()</h2>
-
+<a class="anchor" id="ae6920bb50ceca504aefa6082a6158b98"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -422,9 +434,7 @@ template<typename xpu , typename DType > </div>
</div>
</div>
-<a id="ad4dda6fa4cdd0998eea0028721b91206"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad4dda6fa4cdd0998eea0028721b91206">◆ </a></span>flip< cpu, double >()</h2>
-
+<a class="anchor" id="ad4dda6fa4cdd0998eea0028721b91206"></a>
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<div class="memtemplate">
@@ -484,9 +494,7 @@ template<> </div>
</div>
</div>
-<a id="ad308cb2cd9a08c654b38c62f0ea0c902"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad308cb2cd9a08c654b38c62f0ea0c902">◆ </a></span>flip< cpu, float >()</h2>
-
+<a class="anchor" id="ad308cb2cd9a08c654b38c62f0ea0c902"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -546,9 +554,7 @@ template<> </div>
</div>
</div>
-<a id="af111a2f312a185185d0937bd091637e4"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#af111a2f312a185185d0937bd091637e4">◆ </a></span>loup()</h2>
-
+<a class="anchor" id="af111a2f312a185185d0937bd091637e4"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -582,9 +588,7 @@ template<> </div>
</div>
</div>
-<a id="a78608e9c2fdb06c8456ac2bf14d8cb21"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a78608e9c2fdb06c8456ac2bf14d8cb21">◆ </a></span>MXNET_LAPACK_posv()</h2>
-
+<a class="anchor" id="a78608e9c2fdb06c8456ac2bf14d8cb21"></a>
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@@ -656,9 +660,7 @@ template<typename DType > </div>
</div>
</div>
-<a id="a3d0a26827fd974f10b7696f4be07a6e3"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a3d0a26827fd974f10b7696f4be07a6e3">◆ </a></span>MXNET_LAPACK_posv< double >()</h2>
-
+<a class="anchor" id="a3d0a26827fd974f10b7696f4be07a6e3"></a>
<div class="memitem">
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@@ -730,9 +732,7 @@ template<> </div>
</div>
</div>
-<a id="ad475995bc48dc06a96aaaf3542b1f0f0"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad475995bc48dc06a96aaaf3542b1f0f0">◆ </a></span>MXNET_LAPACK_posv< float >()</h2>
-
+<a class="anchor" id="ad475995bc48dc06a96aaaf3542b1f0f0"></a>
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@@ -804,9 +804,7 @@ template<> </div>
</div>
</div>
-<a id="a960d4d5f204ab766924726b650c7105c"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a960d4d5f204ab766924726b650c7105c">◆ </a></span>sposv_()</h2>
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+<a class="anchor" id="a960d4d5f204ab766924726b650c7105c"></a>
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<table class="memname">
@@ -871,9 +869,9 @@ template<> </div>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/c__lapack__api_8h__incl.md5 b/versions/master/doxygen/c__lapack__api_8h__incl.md5
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@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/c_lapack_api.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
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<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
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+ </li>
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -66,7 +88,183 @@ $(function() {
<div class="title">c_lapack_api.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="c__lapack__api_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_C_LAPACK_API_H_</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#define MXNET_C_LAPACK_API_H_</span></div><div class="line"> [...]
+<a href="c__lapack__api_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_C_LAPACK_API_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_C_LAPACK_API_H_</span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment">// Manually maintained list of LAPACK interfaces that can be used</span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment">// within MXNET. Conventions:</span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment">// - Interfaces must be compliant with lapacke.h in terms of signature and</span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment">// naming conventions so wrapping a function "foo" which has the</span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment">// signature</span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment">// lapack_int LAPACKE_foo(int, char, lapack_int, float* , lapack_int)</span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment">// within lapacke.h should result in a wrapper with the following signature</span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment">// int MXNET_LAPACK_foo(int, char, int, float* , int)</span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment">// Note that function signatures in lapacke.h will always have as first</span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment">// argument the storage order (row/col-major). All wrappers have to support</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment">// that argument. The underlying fortran functions will always assume a</span></div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment">// column-major layout. It is the responsibility of the wrapper function</span></div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment">// to handle the (usual) case that it is called with data in row-major</span></div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment">// format, either by doing appropriate transpositions explicitly or using</span></div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment">// transposition options of the underlying fortran function.</span></div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="comment">// - It is ok to assume that matrices are stored in contiguous memory</span></div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="comment">// (which removes the need to do special handling for lda/ldb parameters</span></div>
+<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="comment">// and enables us to save additional matrix transpositions around</span></div>
+<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="comment">// the fortran calls).</span></div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="comment">// - It is desired to add some basic checking in the C++-wrappers in order</span></div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment">// to catch simple mistakes when calling these wrappers.</span></div>
+<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="comment">// - Must support compilation without lapack-package but issue runtime error in this case.</span></div>
+<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div>
+<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <dmlc/logging.h></span></div>
+<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "mshadow/tensor.h"</span></div>
+<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">using namespace </span>mshadow;</div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">extern</span> <span class="stringliteral">"C"</span> {</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="comment">// Fortran signatures</span></div>
+<div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#a56f112686aa64bf8a42204a3ff87ea46"> 40</a></span> <span class="preprocessor"> #define MXNET_LAPACK_FSIGNATURE1(func, dtype) \</span></div>
+<div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor"> void func##_(char* uplo, int* n, dtype* a, int* lda, int *info);</span></div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <a class="code" href="c__lapack__api_8h.html#a56f112686aa64bf8a42204a3ff87ea46">MXNET_LAPACK_FSIGNATURE1</a>(spotrf, <span class="keywordtype">float</span>)</div>
+<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <a class="code" href="c__lapack__api_8h.html#a56f112686aa64bf8a42204a3ff87ea46">MXNET_LAPACK_FSIGNATURE1</a>(dpotrf, <span class="keywordtype">double</span>)</div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="c__lapack__api_8h.html#a56f112686aa64bf8a42204a3ff87ea46">MXNET_LAPACK_FSIGNATURE1</a>(spotri, <span class="keywordtype">float</span>)</div>
+<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="c__lapack__api_8h.html#a56f112686aa64bf8a42204a3ff87ea46">MXNET_LAPACK_FSIGNATURE1</a>(dpotri, <span class="keywordtype">double</span>)</div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordtype">void</span> <a class="code" href="c__lapack__api_8h.html#ac9fe022dd825c3bd86d3b175d0a1c02b">dposv_</a>(<span class="keywordtype">char</span> *uplo, <span class="keywordtype">int</span> *n, <span class="keywordtype">int</span> *nrhs,</div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordtype">double</span> *a, <span class="keywordtype">int</span> *lda, <span class="keywordtype">double</span> *b, <span class="keywordtype">int</span> *ldb, <span class="keywordtype">int</span> *info);</div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordtype">void</span> <a class="code" href="c__lapack__api_8h.html#a960d4d5f204ab766924726b650c7105c">sposv_</a>(<span class="keywordtype">char</span> *uplo, <span class="keywordtype">int</span> *n, <span class="keywordtype">int</span> *nrhs,</div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordtype">float</span> *a, <span class="keywordtype">int</span> *lda, <span class="keywordtype">float</span> *b, <span class="keywordtype">int</span> *ldb, <span class="keywordtype">int</span> *info);</div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span> }</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div>
+<div class="line"><a name="l00055"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#a220562c35966392841ae49fefe2dee5e"> 55</a></span> <span class="preprocessor">#define MXNET_LAPACK_ROW_MAJOR 101</span></div>
+<div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#a49f324c038a0a1447ed094c679739ee7"> 56</a></span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_LAPACK_COL_MAJOR 102</span></div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00058"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#a2fa12bf140fecda6aac9336a61923713"> 58</a></span> <span class="preprocessor">#define CHECK_LAPACK_CONTIGUOUS(a, b) \</span></div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="preprocessor"> CHECK_EQ(a, b) << "non contiguous memory for array in lapack call";</span></div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00061"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#a0c95bf8b79e51085d846c7a09ec22720"> 61</a></span> <span class="preprocessor">#define CHECK_LAPACK_UPLO(a) \</span></div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="preprocessor"> CHECK(a == 'U' || a == 'L') << "neither L nor U specified as triangle in lapack call";</span></div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00064"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#af111a2f312a185185d0937bd091637e4"> 64</a></span> <span class="keyword">inline</span> <span class="keywordtype">char</span> <a class="code" href="c__lapack__api_8h.html#af111a2f312a185185d0937bd091637e4">loup</a>(<span class="keywordtype">char</span> uplo, <span class="keywordtype">bool</span> invert) { <span class="keywordflow">return</span> invert ? (uplo == <span class="char [...]
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div>
+<div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="keyword">template</span> <<span class="keyword">typename</span> xpu, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="c__lapack__api_8h.html#ae6920bb50ceca504aefa6082a6158b98">flip</a>(<span class="keywordtype">int</span> m, <span class="keywordtype">int</span> n, DType *b, <span class="keywordtype">int</span> ldb, DType *a, <span class="keywordtype">int</span> lda);</div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="keyword">template</span> <></div>
+<div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#ad308cb2cd9a08c654b38c62f0ea0c902"> 84</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="c__lapack__api_8h.html#ad308cb2cd9a08c654b38c62f0ea0c902">flip<cpu, float></a>(<span class="keywordtype">int</span> m, <span class="keywordtype">int</span> n,</div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordtype">float</span> *b, <span class="keywordtype">int</span> ldb, <span class="keywordtype">float</span> *a, <span class="keywordtype">int</span> lda) {</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < m; ++i)</div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < n; ++j)</div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  b[j * ldb + i] = a[i * lda + j];</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span> }</div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="keyword">template</span> <></div>
+<div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#ad4dda6fa4cdd0998eea0028721b91206"> 92</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="c__lapack__api_8h.html#ad4dda6fa4cdd0998eea0028721b91206">flip<cpu, double></a>(<span class="keywordtype">int</span> m, <span class="keywordtype">int</span> n,</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordtype">double</span> *b, <span class="keywordtype">int</span> ldb, <span class="keywordtype">double</span> *a, <span class="keywordtype">int</span> lda) {</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < m; ++i)</div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < n; ++j)</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  b[j * ldb + i] = a[i * lda + j];</div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span> }</div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="preprocessor">#if MXNET_USE_LAPACK</span></div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="preprocessor"> #define MXNET_LAPACK_CWRAPPER1(func, dtype) \</span></div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span> <span class="preprocessor"> inline int MXNET_LAPACK_##func(int matrix_layout, char uplo, int n, dtype* a, int lda ) { \</span></div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="preprocessor"> CHECK_LAPACK_CONTIGUOUS(n, lda); \</span></div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="preprocessor"> CHECK_LAPACK_UPLO(uplo); \</span></div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="preprocessor"> char o(loup(uplo, (matrix_layout == MXNET_LAPACK_ROW_MAJOR))); \</span></div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="preprocessor"> int ret(0); \</span></div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span> <span class="preprocessor"> func##_(&o, &n, a, &lda, &ret); \</span></div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <span class="preprocessor"> return ret; \</span></div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="preprocessor"> }</span></div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="preprocessor"></span> <a class="code" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(spotrf, <span class="keywordtype">float</span>)</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <a class="code" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(dpotrf, <span class="keywordtype">double</span>)</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(spotri, <span class="keywordtype">float</span>)</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(dpotri, <span class="keywordtype">double</span>)</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  inline <span class="keywordtype">int</span> mxnet_lapack_sposv(<span class="keywordtype">int</span> matrix_layout, <span class="keywordtype">char</span> uplo, <span class="keywordtype">int</span> n, <span class="keywordtype">int</span> nrhs,</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordtype">float</span> *a, <span class="keywordtype">int</span> lda, <span class="keywordtype">float</span> *b, <span class="keywordtype">int</span> ldb) {</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordtype">int</span> info;</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keywordflow">if</span> (matrix_layout == <a class="code" href="c__lapack__api_8h.html#a220562c35966392841ae49fefe2dee5e">MXNET_LAPACK_ROW_MAJOR</a>) {</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// Transpose b to b_t of shape (nrhs, n)</span></div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordtype">float</span> *b_t = <span class="keyword">new</span> <span class="keywordtype">float</span>[nrhs * n];</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="c__lapack__api_8h.html#ad308cb2cd9a08c654b38c62f0ea0c902">flip<cpu, float></a>(n, nrhs, b_t, n, b, ldb);</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="c__lapack__api_8h.html#a960d4d5f204ab766924726b650c7105c">sposv_</a>(&uplo, &n, &nrhs, a, &lda, b_t, &n, &info);</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="c__lapack__api_8h.html#ad308cb2cd9a08c654b38c62f0ea0c902">flip<cpu, float></a>(nrhs, n, b, ldb, b_t, n);</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">delete</span> [] b_t;</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">return</span> info;</div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  }</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="c__lapack__api_8h.html#a960d4d5f204ab766924726b650c7105c">sposv_</a>(&uplo, &n, &nrhs, a, &lda, b, &ldb, &info);</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordflow">return</span> info;</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  }</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keyword">inline</span> <span class="keywordtype">int</span> mxnet_lapack_dposv(<span class="keywordtype">int</span> matrix_layout, <span class="keywordtype">char</span> uplo, <span class="keywordtype">int</span> n, <span class="keywordtype">int</span> nrhs,</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordtype">double</span> *a, <span class="keywordtype">int</span> lda, <span class="keywordtype">double</span> *b, <span class="keywordtype">int</span> ldb) {</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordtype">int</span> info;</div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">if</span> (matrix_layout == <a class="code" href="c__lapack__api_8h.html#a220562c35966392841ae49fefe2dee5e">MXNET_LAPACK_ROW_MAJOR</a>) {</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// Transpose b to b_t of shape (nrhs, n)</span></div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordtype">double</span> *b_t = <span class="keyword">new</span> <span class="keywordtype">double</span>[nrhs * n];</div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="c__lapack__api_8h.html#ad4dda6fa4cdd0998eea0028721b91206">flip<cpu, double></a>(n, nrhs, b_t, n, b, ldb);</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <a class="code" href="c__lapack__api_8h.html#ac9fe022dd825c3bd86d3b175d0a1c02b">dposv_</a>(&uplo, &n, &nrhs, a, &lda, b_t, &n, &info);</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <a class="code" href="c__lapack__api_8h.html#ad4dda6fa4cdd0998eea0028721b91206">flip<cpu, double></a>(nrhs, n, b, ldb, b_t, n);</div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keyword">delete</span> [] b_t;</div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordflow">return</span> info;</div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  }</div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="c__lapack__api_8h.html#ac9fe022dd825c3bd86d3b175d0a1c02b">dposv_</a>(&uplo, &n, &nrhs, a, &lda, b, &ldb, &info);</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">return</span> info;</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="comment">// use pragma message instead of warning</span></div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="preprocessor"> #pragma message("Warning: lapack usage not enabled, linalg-operators will not be available." \</span></div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <span class="preprocessor"> " Ensure that lapack library is installed and build with USE_LAPACK=1 to get lapack" \</span></div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="preprocessor"> " functionalities.")</span></div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="comment">// Define compilable stubs.</span></div>
+<div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c"> 156</a></span> <span class="preprocessor"> #define MXNET_LAPACK_CWRAPPER1(func, dtype) \</span></div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span> <span class="preprocessor"> inline int MXNET_LAPACK_##func(int matrix_layout, char uplo, int n, dtype* a, int lda ) { \</span></div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <span class="preprocessor"> LOG(FATAL) << "MXNet build without lapack. Function " << #func << " is not available."; \</span></div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span> <span class="preprocessor"> return 1; \</span></div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <span class="preprocessor"> }</span></div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00162"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#a0c98356495b08e7d69a4cafd8b034ab4"> 162</a></span> <span class="preprocessor"> #define MXNET_LAPACK_UNAVAILABLE(func) \</span></div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span> <span class="preprocessor"> inline int mxnet_lapack_##func(...) { \</span></div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span> <span class="preprocessor"> LOG(FATAL) << "MXNet build without lapack. Function " << #func << " is not available."; \</span></div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span> <span class="preprocessor"> return 1; \</span></div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="preprocessor"> }</span></div>
+<div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(spotrf, <span class="keywordtype">float</span>)</div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(dpotrf, <span class="keywordtype">double</span>)</div>
+<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(spotri, <span class="keywordtype">float</span>)</div>
+<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a>(dpotri, <span class="keywordtype">double</span>)</div>
+<div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div>
+<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="c__lapack__api_8h.html#a0c98356495b08e7d69a4cafd8b034ab4">MXNET_LAPACK_UNAVAILABLE</a>(sposv)</div>
+<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="c__lapack__api_8h.html#a0c98356495b08e7d69a4cafd8b034ab4">MXNET_LAPACK_UNAVAILABLE</a>(dposv)</div>
+<div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div>
+<div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="c__lapack__api_8h.html#a78608e9c2fdb06c8456ac2bf14d8cb21">MXNET_LAPACK_posv</a>(<span class="keywordtype">int</span> matrix_layout, <span class="keywordtype">char</span> uplo, <span class="keywordtype">int</span> n, <span class="keywordtype">int</span> nrhs,</div>
+<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  DType *a, <span class="keywordtype">int</span> lda, DType *b, <span class="keywordtype">int</span> ldb);</div>
+<div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span> <span class="keyword">template</span> <></div>
+<div class="line"><a name="l00183"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#ad475995bc48dc06a96aaaf3542b1f0f0"> 183</a></span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="c__lapack__api_8h.html#ad475995bc48dc06a96aaaf3542b1f0f0">MXNET_LAPACK_posv<float></a>(<span class="keywordtype">int</span> matrix_layout, <span class="keywordtype">char</span> uplo, <span class="keywordtype">int</span> n,</div>
+<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordtype">int</span> nrhs, <span class="keywordtype">float</span> *a, <span class="keywordtype">int</span> lda, <span class="keywordtype">float</span> *b, <span class="keywordtype">int</span> ldb) {</div>
+<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordflow">return</span> mxnet_lapack_sposv(matrix_layout, uplo, n, nrhs, a, lda, b, ldb);</div>
+<div class="line"><a name="l00186"></a><span class="lineno"> 186</span> }</div>
+<div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div>
+<div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="keyword">template</span> <></div>
+<div class="line"><a name="l00189"></a><span class="lineno"><a class="line" href="c__lapack__api_8h.html#a3d0a26827fd974f10b7696f4be07a6e3"> 189</a></span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="c__lapack__api_8h.html#a3d0a26827fd974f10b7696f4be07a6e3">MXNET_LAPACK_posv<double></a>(<span class="keywordtype">int</span> matrix_layout, <span class="keywordtype">char</span> uplo, <span class="keywordtype">int</span> n,</div>
+<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordtype">int</span> nrhs, <span class="keywordtype">double</span> *a, <span class="keywordtype">int</span> lda, <span class="keywordtype">double</span> *b, <span class="keywordtype">int</span> ldb) {</div>
+<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordflow">return</span> mxnet_lapack_dposv(matrix_layout, uplo, n, nrhs, a, lda, b, ldb);</div>
+<div class="line"><a name="l00192"></a><span class="lineno"> 192</span> }</div>
+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div>
+<div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="preprocessor">#endif // MXNET_C_LAPACK_API_H_</span></div>
+<div class="ttc" id="c__lapack__api_8h_html_a3d0a26827fd974f10b7696f4be07a6e3"><div class="ttname"><a href="c__lapack__api_8h.html#a3d0a26827fd974f10b7696f4be07a6e3">MXNET_LAPACK_posv< double ></a></div><div class="ttdeci">int MXNET_LAPACK_posv< double >(int matrix_layout, char uplo, int n, int nrhs, double *a, int lda, double *b, int ldb)</div><div class="ttdef"><b>Definition:</b> c_lapack_api.h:189</div></div>
<div class="ttc" id="c__lapack__api_8h_html_a56f112686aa64bf8a42204a3ff87ea46"><div class="ttname"><a href="c__lapack__api_8h.html#a56f112686aa64bf8a42204a3ff87ea46">MXNET_LAPACK_FSIGNATURE1</a></div><div class="ttdeci">#define MXNET_LAPACK_FSIGNATURE1(func, dtype)</div><div class="ttdef"><b>Definition:</b> c_lapack_api.h:40</div></div>
<div class="ttc" id="c__lapack__api_8h_html_ad4dda6fa4cdd0998eea0028721b91206"><div class="ttname"><a href="c__lapack__api_8h.html#ad4dda6fa4cdd0998eea0028721b91206">flip< cpu, double ></a></div><div class="ttdeci">void flip< cpu, double >(int m, int n, double *b, int ldb, double *a, int lda)</div><div class="ttdef"><b>Definition:</b> c_lapack_api.h:92</div></div>
<div class="ttc" id="c__lapack__api_8h_html_af111a2f312a185185d0937bd091637e4"><div class="ttname"><a href="c__lapack__api_8h.html#af111a2f312a185185d0937bd091637e4">loup</a></div><div class="ttdeci">char loup(char uplo, bool invert)</div><div class="ttdef"><b>Definition:</b> c_lapack_api.h:64</div></div>
@@ -76,16 +274,15 @@ $(function() {
<div class="ttc" id="c__lapack__api_8h_html_ad475995bc48dc06a96aaaf3542b1f0f0"><div class="ttname"><a href="c__lapack__api_8h.html#ad475995bc48dc06a96aaaf3542b1f0f0">MXNET_LAPACK_posv< float ></a></div><div class="ttdeci">int MXNET_LAPACK_posv< float >(int matrix_layout, char uplo, int n, int nrhs, float *a, int lda, float *b, int ldb)</div><div class="ttdef"><b>Definition:</b> c_lapack_api.h:183</div></div>
<div class="ttc" id="c__lapack__api_8h_html_a0c98356495b08e7d69a4cafd8b034ab4"><div class="ttname"><a href="c__lapack__api_8h.html#a0c98356495b08e7d69a4cafd8b034ab4">MXNET_LAPACK_UNAVAILABLE</a></div><div class="ttdeci">#define MXNET_LAPACK_UNAVAILABLE(func)</div><div class="ttdef"><b>Definition:</b> c_lapack_api.h:162</div></div>
<div class="ttc" id="c__lapack__api_8h_html_ad308cb2cd9a08c654b38c62f0ea0c902"><div class="ttname"><a href="c__lapack__api_8h.html#ad308cb2cd9a08c654b38c62f0ea0c902">flip< cpu, float ></a></div><div class="ttdeci">void flip< cpu, float >(int m, int n, float *b, int ldb, float *a, int lda)</div><div class="ttdef"><b>Definition:</b> c_lapack_api.h:84</div></div>
-<div class="ttc" id="namespacemshadow_html"><div class="ttname"><a href="namespacemshadow.html">mshadow</a></div></div>
<div class="ttc" id="c__lapack__api_8h_html_a960d4d5f204ab766924726b650c7105c"><div class="ttname"><a href="c__lapack__api_8h.html#a960d4d5f204ab766924726b650c7105c">sposv_</a></div><div class="ttdeci">void sposv_(char *uplo, int *n, int *nrhs, float *a, int *lda, float *b, int *ldb, int *info)</div></div>
<div class="ttc" id="c__lapack__api_8h_html_aa5297300313445fa46d2d1d2fb38970c"><div class="ttname"><a href="c__lapack__api_8h.html#aa5297300313445fa46d2d1d2fb38970c">MXNET_LAPACK_CWRAPPER1</a></div><div class="ttdeci">#define MXNET_LAPACK_CWRAPPER1(func, dtype)</div><div class="ttdef"><b>Definition:</b> c_lapack_api.h:156</div></div>
<div class="ttc" id="c__lapack__api_8h_html_a78608e9c2fdb06c8456ac2bf14d8cb21"><div class="ttname"><a href="c__lapack__api_8h.html#a78608e9c2fdb06c8456ac2bf14d8cb21">MXNET_LAPACK_posv</a></div><div class="ttdeci">int MXNET_LAPACK_posv(int matrix_layout, char uplo, int n, int nrhs, DType *a, int lda, DType *b, int ldb)</div></div>
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diff --git a/versions/master/doxygen/c__predict__api_8h.html b/versions/master/doxygen/c__predict__api_8h.html
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+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/c_predict_api.h File Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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@@ -20,7 +21,7 @@
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<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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</div>
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</div>
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-<!-- Generated by Doxygen 1.8.13 -->
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
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<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
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@@ -79,70 +101,68 @@ $(function() {
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="define-members"></a>
Macros</h2></td></tr>
<tr class="memitem:a5b0e47dde55f9b2f58b99fab92d1e0ac"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a></td></tr>
-<tr class="memdesc:a5b0e47dde55f9b2f58b99fab92d1e0ac"><td class="mdescLeft"> </td><td class="mdescRight">Inhibit C++ name-mangling for MXNet functions. <a href="#a5b0e47dde55f9b2f58b99fab92d1e0ac">More...</a><br /></td></tr>
+<tr class="memdesc:a5b0e47dde55f9b2f58b99fab92d1e0ac"><td class="mdescLeft"> </td><td class="mdescRight">Inhibit C++ name-mangling for MXNet functions. <a href="#a5b0e47dde55f9b2f58b99fab92d1e0ac">More...</a><br/></td></tr>
<tr class="separator:a5b0e47dde55f9b2f58b99fab92d1e0ac"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
<tr class="memitem:a796e7ef8260ef40b24af7d1082aa2d84"><td class="memItemLeft" align="right" valign="top">typedef unsigned int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a></td></tr>
-<tr class="memdesc:a796e7ef8260ef40b24af7d1082aa2d84"><td class="mdescLeft"> </td><td class="mdescRight">manually define unsigned int <a href="#a796e7ef8260ef40b24af7d1082aa2d84">More...</a><br /></td></tr>
+<tr class="memdesc:a796e7ef8260ef40b24af7d1082aa2d84"><td class="mdescLeft"> </td><td class="mdescRight">manually define unsigned int <a href="#a796e7ef8260ef40b24af7d1082aa2d84">More...</a><br/></td></tr>
<tr class="separator:a796e7ef8260ef40b24af7d1082aa2d84"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4e709067707d9f90c534bd65494ae668"><td class="memItemLeft" align="right" valign="top">typedef float </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a4e709067707d9f90c534bd65494ae668">mx_float</a></td></tr>
-<tr class="memdesc:a4e709067707d9f90c534bd65494ae668"><td class="mdescLeft"> </td><td class="mdescRight">manually define float <a href="#a4e709067707d9f90c534bd65494ae668">More...</a><br /></td></tr>
+<tr class="memdesc:a4e709067707d9f90c534bd65494ae668"><td class="mdescLeft"> </td><td class="mdescRight">manually define float <a href="#a4e709067707d9f90c534bd65494ae668">More...</a><br/></td></tr>
<tr class="separator:a4e709067707d9f90c534bd65494ae668"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0aacfd245edcae2dcd19e01192fcd7a2"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a0aacfd245edcae2dcd19e01192fcd7a2">PredictorHandle</a></td></tr>
-<tr class="memdesc:a0aacfd245edcae2dcd19e01192fcd7a2"><td class="mdescLeft"> </td><td class="mdescRight">handle to Predictor <a href="#a0aacfd245edcae2dcd19e01192fcd7a2">More...</a><br /></td></tr>
+<tr class="memdesc:a0aacfd245edcae2dcd19e01192fcd7a2"><td class="mdescLeft"> </td><td class="mdescRight">handle to Predictor <a href="#a0aacfd245edcae2dcd19e01192fcd7a2">More...</a><br/></td></tr>
<tr class="separator:a0aacfd245edcae2dcd19e01192fcd7a2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3058aee7831edae29e9ac39556e5fe5c"><td class="memItemLeft" align="right" valign="top">typedef void * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a3058aee7831edae29e9ac39556e5fe5c">NDListHandle</a></td></tr>
-<tr class="memdesc:a3058aee7831edae29e9ac39556e5fe5c"><td class="mdescLeft"> </td><td class="mdescRight">handle to NDArray list <a href="#a3058aee7831edae29e9ac39556e5fe5c">More...</a><br /></td></tr>
+<tr class="memdesc:a3058aee7831edae29e9ac39556e5fe5c"><td class="mdescLeft"> </td><td class="mdescRight">handle to NDArray list <a href="#a3058aee7831edae29e9ac39556e5fe5c">More...</a><br/></td></tr>
<tr class="separator:a3058aee7831edae29e9ac39556e5fe5c"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:acab1874f3e3077710b77d1a005abf36c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#acab1874f3e3077710b77d1a005abf36c">MXGetLastError</a> ()</td></tr>
-<tr class="memdesc:acab1874f3e3077710b77d1a005abf36c"><td class="mdescLeft"> </td><td class="mdescRight">Get the last error happeneed. <a href="#acab1874f3e3077710b77d1a005abf36c">More...</a><br /></td></tr>
+<tr class="memdesc:acab1874f3e3077710b77d1a005abf36c"><td class="mdescLeft"> </td><td class="mdescRight">Get the last error happeneed. <a href="#acab1874f3e3077710b77d1a005abf36c">More...</a><br/></td></tr>
<tr class="separator:acab1874f3e3077710b77d1a005abf36c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2ce2bd607a3690b34f45491b040efb02"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a2ce2bd607a3690b34f45491b040efb02">MXPredCreate</a> (const char *symbol_json_str, const void *param_bytes, int param_size, int dev_type, int dev_id, <a class="el" href="c__api_8h.html#a796e7ef826 [...]
-<tr class="memdesc:a2ce2bd607a3690b34f45491b040efb02"><td class="mdescLeft"> </td><td class="mdescRight">create a predictor <a href="#a2ce2bd607a3690b34f45491b040efb02">More...</a><br /></td></tr>
+<tr class="memdesc:a2ce2bd607a3690b34f45491b040efb02"><td class="mdescLeft"> </td><td class="mdescRight">create a predictor <a href="#a2ce2bd607a3690b34f45491b040efb02">More...</a><br/></td></tr>
<tr class="separator:a2ce2bd607a3690b34f45491b040efb02"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac61691812a6e910ab671d83a759b4209"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#ac61691812a6e910ab671d83a759b4209">MXPredCreatePartialOut</a> (const char *symbol_json_str, const void *param_bytes, int param_size, int dev_type, int dev_id, <a class="el" href="c__api_8h.html#a [...]
-<tr class="memdesc:ac61691812a6e910ab671d83a759b4209"><td class="mdescLeft"> </td><td class="mdescRight">create a predictor wich customized outputs <a href="#ac61691812a6e910ab671d83a759b4209">More...</a><br /></td></tr>
+<tr class="memdesc:ac61691812a6e910ab671d83a759b4209"><td class="mdescLeft"> </td><td class="mdescRight">create a predictor wich customized outputs <a href="#ac61691812a6e910ab671d83a759b4209">More...</a><br/></td></tr>
<tr class="separator:ac61691812a6e910ab671d83a759b4209"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a67200d0aeece20f690eb9b0e72cb2c9b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a67200d0aeece20f690eb9b0e72cb2c9b">MXPredGetOutputShape</a> (<a class="el" href="c__predict__api_8h.html#a0aacfd245edcae2dcd19e01192fcd7a2">PredictorHandle</a> handle, <a class="el" href="c__api_ [...]
-<tr class="memdesc:a67200d0aeece20f690eb9b0e72cb2c9b"><td class="mdescLeft"> </td><td class="mdescRight">Get the shape of output node. The returned shape_data and shape_ndim is only valid before next call to MXPred function. <a href="#a67200d0aeece20f690eb9b0e72cb2c9b">More...</a><br /></td></tr>
+<tr class="memdesc:a67200d0aeece20f690eb9b0e72cb2c9b"><td class="mdescLeft"> </td><td class="mdescRight">Get the shape of output node. The returned shape_data and shape_ndim is only valid before next call to MXPred function. <a href="#a67200d0aeece20f690eb9b0e72cb2c9b">More...</a><br/></td></tr>
<tr class="separator:a67200d0aeece20f690eb9b0e72cb2c9b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a68288592fa108c032b7ffee245984f83"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a68288592fa108c032b7ffee245984f83">MXPredSetInput</a> (<a class="el" href="c__predict__api_8h.html#a0aacfd245edcae2dcd19e01192fcd7a2">PredictorHandle</a> handle, const char *key, const <a class=" [...]
-<tr class="memdesc:a68288592fa108c032b7ffee245984f83"><td class="mdescLeft"> </td><td class="mdescRight">Set the input data of predictor. <a href="#a68288592fa108c032b7ffee245984f83">More...</a><br /></td></tr>
+<tr class="memdesc:a68288592fa108c032b7ffee245984f83"><td class="mdescLeft"> </td><td class="mdescRight">Set the input data of predictor. <a href="#a68288592fa108c032b7ffee245984f83">More...</a><br/></td></tr>
<tr class="separator:a68288592fa108c032b7ffee245984f83"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1ce8a6c852f1ae11c2504b9bc186e723"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a1ce8a6c852f1ae11c2504b9bc186e723">MXPredForward</a> (<a class="el" href="c__predict__api_8h.html#a0aacfd245edcae2dcd19e01192fcd7a2">PredictorHandle</a> handle)</td></tr>
-<tr class="memdesc:a1ce8a6c852f1ae11c2504b9bc186e723"><td class="mdescLeft"> </td><td class="mdescRight">Run a forward pass to get the output. <a href="#a1ce8a6c852f1ae11c2504b9bc186e723">More...</a><br /></td></tr>
+<tr class="memdesc:a1ce8a6c852f1ae11c2504b9bc186e723"><td class="mdescLeft"> </td><td class="mdescRight">Run a forward pass to get the output. <a href="#a1ce8a6c852f1ae11c2504b9bc186e723">More...</a><br/></td></tr>
<tr class="separator:a1ce8a6c852f1ae11c2504b9bc186e723"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa4ac0ebf9f6494c383d3fe3b72ae2f52"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#aa4ac0ebf9f6494c383d3fe3b72ae2f52">MXPredPartialForward</a> (<a class="el" href="c__predict__api_8h.html#a0aacfd245edcae2dcd19e01192fcd7a2">PredictorHandle</a> handle, int step, int *step_left)</ [...]
-<tr class="memdesc:aa4ac0ebf9f6494c383d3fe3b72ae2f52"><td class="mdescLeft"> </td><td class="mdescRight">Run a interactive forward pass to get the output. This is helpful for displaying progress of prediction which can be slow. User must call PartialForward from step=0, keep increasing it until step_left=0. <a href="#aa4ac0ebf9f6494c383d3fe3b72ae2f52">More...</a><br /></td></tr>
+<tr class="memdesc:aa4ac0ebf9f6494c383d3fe3b72ae2f52"><td class="mdescLeft"> </td><td class="mdescRight">Run a interactive forward pass to get the output. This is helpful for displaying progress of prediction which can be slow. User must call PartialForward from step=0, keep increasing it until step_left=0. <a href="#aa4ac0ebf9f6494c383d3fe3b72ae2f52">More...</a><br/></td></tr>
<tr class="separator:aa4ac0ebf9f6494c383d3fe3b72ae2f52"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aeaadef67a6a9a2308f2d8160f91724af"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#aeaadef67a6a9a2308f2d8160f91724af">MXPredGetOutput</a> (<a class="el" href="c__predict__api_8h.html#a0aacfd245edcae2dcd19e01192fcd7a2">PredictorHandle</a> handle, <a class="el" href="c__api_8h.ht [...]
-<tr class="memdesc:aeaadef67a6a9a2308f2d8160f91724af"><td class="mdescLeft"> </td><td class="mdescRight">Get the output value of prediction. <a href="#aeaadef67a6a9a2308f2d8160f91724af">More...</a><br /></td></tr>
+<tr class="memdesc:aeaadef67a6a9a2308f2d8160f91724af"><td class="mdescLeft"> </td><td class="mdescRight">Get the output value of prediction. <a href="#aeaadef67a6a9a2308f2d8160f91724af">More...</a><br/></td></tr>
<tr class="separator:aeaadef67a6a9a2308f2d8160f91724af"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af21704e92e5b519bdcfef5fcb504195b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#af21704e92e5b519bdcfef5fcb504195b">MXPredFree</a> (<a class="el" href="c__predict__api_8h.html#a0aacfd245edcae2dcd19e01192fcd7a2">PredictorHandle</a> handle)</td></tr>
-<tr class="memdesc:af21704e92e5b519bdcfef5fcb504195b"><td class="mdescLeft"> </td><td class="mdescRight">Free a predictor handle. <a href="#af21704e92e5b519bdcfef5fcb504195b">More...</a><br /></td></tr>
+<tr class="memdesc:af21704e92e5b519bdcfef5fcb504195b"><td class="mdescLeft"> </td><td class="mdescRight">Free a predictor handle. <a href="#af21704e92e5b519bdcfef5fcb504195b">More...</a><br/></td></tr>
<tr class="separator:af21704e92e5b519bdcfef5fcb504195b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1ffd608b7df02883a54dbbfaa20dcb3e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a1ffd608b7df02883a54dbbfaa20dcb3e">MXNDListCreate</a> (const char *nd_file_bytes, int nd_file_size, <a class="el" href="c__predict__api_8h.html#a3058aee7831edae29e9ac39556e5fe5c">NDListHandle</a> [...]
-<tr class="memdesc:a1ffd608b7df02883a54dbbfaa20dcb3e"><td class="mdescLeft"> </td><td class="mdescRight">Create a NDArray List by loading from ndarray file. This can be used to load mean image file. <a href="#a1ffd608b7df02883a54dbbfaa20dcb3e">More...</a><br /></td></tr>
+<tr class="memdesc:a1ffd608b7df02883a54dbbfaa20dcb3e"><td class="mdescLeft"> </td><td class="mdescRight">Create a NDArray List by loading from ndarray file. This can be used to load mean image file. <a href="#a1ffd608b7df02883a54dbbfaa20dcb3e">More...</a><br/></td></tr>
<tr class="separator:a1ffd608b7df02883a54dbbfaa20dcb3e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0fa7cfab6362f3f66c1ca44a8b809674"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#a0fa7cfab6362f3f66c1ca44a8b809674">MXNDListGet</a> (<a class="el" href="c__predict__api_8h.html#a3058aee7831edae29e9ac39556e5fe5c">NDListHandle</a> handle, <a class="el" href="c__api_8h.html#a796 [...]
-<tr class="memdesc:a0fa7cfab6362f3f66c1ca44a8b809674"><td class="mdescLeft"> </td><td class="mdescRight">Get an element from list. <a href="#a0fa7cfab6362f3f66c1ca44a8b809674">More...</a><br /></td></tr>
+<tr class="memdesc:a0fa7cfab6362f3f66c1ca44a8b809674"><td class="mdescLeft"> </td><td class="mdescRight">Get an element from list. <a href="#a0fa7cfab6362f3f66c1ca44a8b809674">More...</a><br/></td></tr>
<tr class="separator:a0fa7cfab6362f3f66c1ca44a8b809674"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af7db1b8ab8c7f3aab5b06f5c1f75ac62"><td class="memItemLeft" align="right" valign="top"><a class="el" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> int </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html#af7db1b8ab8c7f3aab5b06f5c1f75ac62">MXNDListFree</a> (<a class="el" href="c__predict__api_8h.html#a3058aee7831edae29e9ac39556e5fe5c">NDListHandle</a> handle)</td></tr>
-<tr class="memdesc:af7db1b8ab8c7f3aab5b06f5c1f75ac62"><td class="mdescLeft"> </td><td class="mdescRight">Free a MXAPINDList. <a href="#af7db1b8ab8c7f3aab5b06f5c1f75ac62">More...</a><br /></td></tr>
+<tr class="memdesc:af7db1b8ab8c7f3aab5b06f5c1f75ac62"><td class="mdescLeft"> </td><td class="mdescRight">Free a MXAPINDList. <a href="#af7db1b8ab8c7f3aab5b06f5c1f75ac62">More...</a><br/></td></tr>
<tr class="separator:af7db1b8ab8c7f3aab5b06f5c1f75ac62"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>C predict API of mxnet, contains a minimum API to run prediction. This file is self-contained, and do not dependent on any other files. </p>
<p>Copyright (c) 2015 by Contributors </p>
</div><h2 class="groupheader">Macro Definition Documentation</h2>
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<h2 class="groupheader">Typedef Documentation</h2>
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<h2 class="groupheader">Function Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#acab1874f3e3077710b77d1a005abf36c">◆ </a></span>MXGetLastError()</h2>
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<p>Run a interactive forward pass to get the output. This is helpful for displaying progress of prediction which can be slow. User must call PartialForward from step=0, keep increasing it until step_left=0. </p>
-<div class="fragment"><div class="line"><span class="keywordtype">int</span> step_left = 1;</div><div class="line"><span class="keywordflow">for</span> (<span class="keywordtype">int</span> step = 0; step_left != 0; ++step) {</div><div class="line"> <a class="code" href="c__predict__api_8h.html#aa4ac0ebf9f6494c383d3fe3b72ae2f52">MXPredPartialForward</a>(handle, step, &step_left);</div><div class="line"> printf(<span class="stringliteral">"Current progress [%d/%d]\n"</sp [...]
+<div class="fragment"><div class="line"><span class="keywordtype">int</span> step_left = 1;</div>
+<div class="line"><span class="keywordflow">for</span> (<span class="keywordtype">int</span> step = 0; step_left != 0; ++step) {</div>
+<div class="line"> <a class="code" href="c__predict__api_8h.html#aa4ac0ebf9f6494c383d3fe3b72ae2f52">MXPredPartialForward</a>(handle, step, &step_left);</div>
+<div class="line"> printf(<span class="stringliteral">"Current progress [%d/%d]\n"</span>, step, step + step_left + 1);</div>
+<div class="line">}</div>
+</div><!-- fragment --> <dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">handle</td><td>The handle of the predictor. </td></tr>
<tr><td class="paramname">step</td><td>The current step to run forward on. </td></tr>
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</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<table cellspacing="0" cellpadding="0">
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -66,7 +88,85 @@ $(function() {
<div class="title">c_predict_api.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="c__predict__api_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_C_PREDICT_API_H_</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#define MXNET_C_PREDICT_API_H_</span></div><div class="lin [...]
+<a href="c__predict__api_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_C_PREDICT_API_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_C_PREDICT_API_H_</span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#ifdef __cplusplus</span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor"></span><span class="keyword">extern</span> <span class="stringliteral">"C"</span> {</div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#endif // __cplusplus</span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#ifdef _WIN32</span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor"></span><span class="preprocessor">#ifdef MXNET_EXPORTS</span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_DLL __declspec(dllexport)</span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor"></span><span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_DLL __declspec(dllimport)</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor"></span><span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00022"></a><span class="lineno"><a class="line" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac"> 22</a></span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_DLL</span></div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00026"></a><span class="lineno"><a class="line" href="c__predict__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84"> 26</a></span> <span class="keyword">typedef</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">mx_uint</a>;</div>
+<div class="line"><a name="l00028"></a><span class="lineno"><a class="line" href="c__predict__api_8h.html#a4e709067707d9f90c534bd65494ae668"> 28</a></span> <span class="keyword">typedef</span> <span class="keywordtype">float</span> <a class="code" href="c__predict__api_8h.html#a4e709067707d9f90c534bd65494ae668">mx_float</a>;</div>
+<div class="line"><a name="l00030"></a><span class="lineno"><a class="line" href="c__predict__api_8h.html#a0aacfd245edcae2dcd19e01192fcd7a2"> 30</a></span> <span class="keyword">typedef</span> <span class="keywordtype">void</span> *<a class="code" href="c__predict__api_8h.html#a0aacfd245edcae2dcd19e01192fcd7a2">PredictorHandle</a>;</div>
+<div class="line"><a name="l00032"></a><span class="lineno"><a class="line" href="c__predict__api_8h.html#a3058aee7831edae29e9ac39556e5fe5c"> 32</a></span> <span class="keyword">typedef</span> <span class="keywordtype">void</span> *<a class="code" href="c__predict__api_8h.html#a3058aee7831edae29e9ac39556e5fe5c">NDListHandle</a>;</div>
+<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="c__predict__api_8h.html#acab1874f3e3077710b77d1a005abf36c">MXGetLastError</a>();</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#a2ce2bd607a3690b34f45491b040efb02">MXPredCreate</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* symbol_json_str,</div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">const</span> <span class="keywordtype">void</span>* param_bytes,</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordtype">int</span> param_size,</div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordtype">int</span> dev_type, <span class="keywordtype">int</span> dev_id,</div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  mx_uint num_input_nodes,</div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** input_keys,</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keyword">const</span> mx_uint* input_shape_indptr,</div>
+<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keyword">const</span> mx_uint* input_shape_data,</div>
+<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  PredictorHandle* out);</div>
+<div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#ac61691812a6e910ab671d83a759b4209">MXPredCreatePartialOut</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* symbol_json_str,</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> <span class="keywordtype">void</span>* param_bytes,</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordtype">int</span> param_size,</div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordtype">int</span> dev_type, <span class="keywordtype">int</span> dev_id,</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  mx_uint num_input_nodes,</div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** input_keys,</div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">const</span> mx_uint* input_shape_indptr,</div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">const</span> mx_uint* input_shape_data,</div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  mx_uint num_output_nodes,</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** output_keys,</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  PredictorHandle* out);</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#a67200d0aeece20f690eb9b0e72cb2c9b">MXPredGetOutputShape</a>(PredictorHandle handle,</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  mx_uint index,</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  mx_uint** shape_data,</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  mx_uint* shape_ndim);</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#a68288592fa108c032b7ffee245984f83">MXPredSetInput</a>(PredictorHandle handle,</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* key,</div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keyword">const</span> mx_float* data,</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  mx_uint size);</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#a1ce8a6c852f1ae11c2504b9bc186e723">MXPredForward</a>(PredictorHandle handle);</div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#aa4ac0ebf9f6494c383d3fe3b72ae2f52">MXPredPartialForward</a>(PredictorHandle handle, <span class="keywordtype">int</span> step, <span class="keywordtype">int</span>* step_left);</div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#aeaadef67a6a9a2308f2d8160f91724af">MXPredGetOutput</a>(PredictorHandle handle,</div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  mx_uint index,</div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  mx_float* data,</div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  mx_uint size);</div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#af21704e92e5b519bdcfef5fcb504195b">MXPredFree</a>(PredictorHandle handle);</div>
+<div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#a1ffd608b7df02883a54dbbfaa20dcb3e">MXNDListCreate</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* nd_file_bytes,</div>
+<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordtype">int</span> nd_file_size,</div>
+<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  NDListHandle *out,</div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  mx_uint* out_length);</div>
+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#a0fa7cfab6362f3f66c1ca44a8b809674">MXNDListGet</a>(NDListHandle handle,</div>
+<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  mx_uint index,</div>
+<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>** out_key,</div>
+<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keyword">const</span> mx_float** out_data,</div>
+<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keyword">const</span> mx_uint** out_shape,</div>
+<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  mx_uint* out_ndim);</div>
+<div class="line"><a name="l00204"></a><span class="lineno"> 204</span> <a class="code" href="c__predict__api_8h.html#a5b0e47dde55f9b2f58b99fab92d1e0ac">MXNET_DLL</a> <span class="keywordtype">int</span> <a class="code" href="c__predict__api_8h.html#af7db1b8ab8c7f3aab5b06f5c1f75ac62">MXNDListFree</a>(NDListHandle handle);</div>
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span> <span class="preprocessor">#ifdef __cplusplus</span></div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span> <span class="preprocessor"></span>}</div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span> <span class="preprocessor">#endif // __cplusplus</span></div>
+<div class="line"><a name="l00209"></a><span class="lineno"> 209</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span> <span class="preprocessor">#endif // MXNET_C_PREDICT_API_H_</span></div>
+<div class="ttc" id="c__predict__api_8h_html_a1ce8a6c852f1ae11c2504b9bc186e723"><div class="ttname"><a href="c__predict__api_8h.html#a1ce8a6c852f1ae11c2504b9bc186e723">MXPredForward</a></div><div class="ttdeci">MXNET_DLL int MXPredForward(PredictorHandle handle)</div><div class="ttdoc">Run a forward pass to get the output. </div></div>
<div class="ttc" id="c__predict__api_8h_html_a1ffd608b7df02883a54dbbfaa20dcb3e"><div class="ttname"><a href="c__predict__api_8h.html#a1ffd608b7df02883a54dbbfaa20dcb3e">MXNDListCreate</a></div><div class="ttdeci">MXNET_DLL int MXNDListCreate(const char *nd_file_bytes, int nd_file_size, NDListHandle *out, mx_uint *out_length)</div><div class="ttdoc">Create a NDArray List by loading from ndarray file. This can be used to load mean image file...</div></div>
<div class="ttc" id="c__predict__api_8h_html_a68288592fa108c032b7ffee245984f83"><div class="ttname"><a href="c__predict__api_8h.html#a68288592fa108c032b7ffee245984f83">MXPredSetInput</a></div><div class="ttdeci">MXNET_DLL int MXPredSetInput(PredictorHandle handle, const char *key, const mx_float *data, mx_uint size)</div><div class="ttdoc">Set the input data of predictor. </div></div>
<div class="ttc" id="c__predict__api_8h_html_ac61691812a6e910ab671d83a759b4209"><div class="ttname"><a href="c__predict__api_8h.html#ac61691812a6e910ab671d83a759b4209">MXPredCreatePartialOut</a></div><div class="ttdeci">MXNET_DLL int MXPredCreatePartialOut(const char *symbol_json_str, const void *param_bytes, int param_size, int dev_type, int dev_id, mx_uint num_input_nodes, const char **input_keys, const mx_uint *input_shape_indptr, const mx_uint *input_shape_data, mx_uint num_output_no [...]
@@ -86,9 +186,9 @@ $(function() {
</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4-members.html b/versions/master/doxygen/classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4-members.html
index 3bf85c1..2ef8ccd 100644
--- a/versions/master/doxygen/classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4-members.html
+++ b/versions/master/doxygen/classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4-members.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
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@@ -69,7 +93,7 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html">dmlc::parameter::FieldEntry< mxnet::TShape ></a>, including all inherited members.</p>
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+ <tr class="even"><td class="entry"><a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a63affafc149cac4c39702c55cf94fdcf">Check</a>(void *head) const </td><td class="entry"><a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html">dmlc::parameter::FieldEntry< mxnet::TShape ></a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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diff --git a/versions/master/doxygen/classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html b/versions/master/doxygen/classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html
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<title>mxnet: dmlc::parameter::FieldEntry< mxnet::TShape > Class Template Reference</title>
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@@ -20,7 +21,7 @@
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<div class="header">
@@ -76,33 +100,37 @@ $(function() {
Inheritance diagram for dmlc::parameter::FieldEntry< mxnet::TShape >:</div>
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Collaboration diagram for dmlc::parameter::FieldEntry< mxnet::TShape >:</div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
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Public Member Functions</h2></td></tr>
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<h2 class="groupheader">Member Typedef Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a0755f0abe443e4b57da234ec7b49fde0">◆ </a></span>FieldEntry()</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#afa1da539bf9bcb64d3b5a55aec23e8c2">◆ </a></span>enforce_nonzero()</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a9e17b866c2cbae2c0fb1af2003c0cfde">◆ </a></span>set_expect_ndim()</h2>
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<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Index</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,49 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li class="current"><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -62,52 +86,52 @@ $(function() {
<div class="title">Class Index</div> </div>
</div><!--header-->
<div class="contents">
-<div class="qindex"><a class="qindex" href="#letter_a">a</a> | <a class="qindex" href="#letter_c">c</a> | <a class="qindex" href="#letter_d">d</a> | <a class="qindex" href="#letter_e">e</a> | <a class="qindex" href="#letter_f">f</a> | <a class="qindex" href="#letter_g">g</a> | <a class="qindex" href="#letter_h">h</a> | <a class="qindex" href="#letter_i">i</a> | <a class="qindex" href="#letter_k">k</a> |& [...]
-<table class="classindex">
-<tr><td rowspan="2" valign="bottom"><a name="letter_a"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  a  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classmxnet_1_1Executor.html">Executor</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_l"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  l  </div></td></tr></table>
+<div class="qindex"><a class="qindex" href="#letter_A">A</a> | <a class="qindex" href="#letter_C">C</a> | <a class="qindex" href="#letter_D">D</a> | <a class="qindex" href="#letter_E">E</a> | <a class="qindex" href="#letter_F">F</a> | <a class="qindex" href="#letter_G">G</a> | <a class="qindex" href="#letter_H">H</a> | <a class="qindex" href="#letter_I">I</a> | <a class="qindex" href="#letter_K">K</a> |& [...]
+<table style="margin: 10px; white-space: nowrap;" align="center" width="95%" border="0" cellspacing="0" cellpadding="0">
+<tr><td rowspan="2" valign="bottom"><a name="letter_A"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  A  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classmxnet_1_1Executor.html">Executor</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_L"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  L  </div></td></tr></table>
</td><td valign="top"><a class="el" href="structmxnet_1_1OpContext.html">OpContext</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html">SimpleOpRegistry</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td></tr>
-<tr><td rowspan="2" valign="bottom"><a name="letter_f"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  f  </div></td></tr></table>
+<tr><td rowspan="2" valign="bottom"><a name="letter_F"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  F  </div></td></tr></table>
</td><td valign="top"><a class="el" href="classmxnet_1_1Operator.html">Operator</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1Storage.html">Storage</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">AGNodeEntry</a> (<a class="el" href="namespacemxnet_1_1autograd.html">mxnet::autograd</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html">LazyAllocArray</a> (<a class="el" href="namespacemxnet_1_1common.html">mxnet::common</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a> (<a c [...]
+<tr><td valign="top"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">AGNodeEntry</a> (<a class="el" href="namespacemxnet_1_1autograd.html">mxnet::autograd</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html">LazyAllocArray</a> (<a class="el" href="namespacemxnet_1_1common.html">mxnet::common</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a> (<a c [...]
</td></tr>
-<tr><td rowspan="2" valign="bottom"><a name="letter_c"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  c  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html">FieldEntry< mxnet::TShape ></a> (<a class="el" href="namespacedmlc_1_1parameter.html">dmlc::parameter</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_m"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  m  </div></td></tr></table>
+<tr><td rowspan="2" valign="bottom"><a name="letter_C"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  C  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html">FieldEntry< mxnet::TShape ></a> (<a class="el" href="namespacedmlc_1_1parameter.html">dmlc::parameter</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_M"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  M  </div></td></tr></table>
</td><td valign="top"><a class="el" href="structmxnet_1_1OperatorPropertyReg.html">OperatorPropertyReg</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td></tr>
-<tr><td rowspan="2" valign="bottom"><a name="letter_g"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  g  </div></td></tr></table>
+<tr><td rowspan="2" valign="bottom"><a name="letter_G"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  G  </div></td></tr></table>
</td><td valign="top"><a class="el" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td></tr>
-<tr><td valign="top"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">CallbackOnComplete</a> (<a class="el" href="namespacemxnet_1_1engine.html">mxnet::engine</a>)   </td><td valign="top"><a class="el" href="structMXCallbackList.html">MXCallbackList</a>   </td><td valign="top"><a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td ro [...]
+<tr><td valign="top"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">CallbackOnComplete</a> (<a class="el" href="namespacemxnet_1_1engine.html">mxnet::engine</a>)   </td><td valign="top"><a class="el" href="structMXCallbackList.html">MXCallbackList</a>   </td><td valign="top"><a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td ro [...]
</td></tr>
-<tr><td valign="top"><a class="el" href="structmxnet_1_1Context.html">Context</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html">GradFunctionArgument</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_n"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">   [...]
+<tr><td valign="top"><a class="el" href="structmxnet_1_1Context.html">Context</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html">GradFunctionArgument</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_N"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">   [...]
</td><td valign="top"><a class="el" href="structmxnet_1_1op_1_1OutputValue.html">OutputValue</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td></tr>
-<tr><td rowspan="2" valign="bottom"><a name="letter_d"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  d  </div></td></tr></table>
-</td><td rowspan="2" valign="bottom"><a name="letter_h"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  h  </div></td></tr></table>
-</td><td rowspan="2" valign="bottom"><a name="letter_r"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  r  </div></td></tr></table>
+<tr><td rowspan="2" valign="bottom"><a name="letter_D"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  D  </div></td></tr></table>
+</td><td rowspan="2" valign="bottom"><a name="letter_H"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  H  </div></td></tr></table>
+</td><td rowspan="2" valign="bottom"><a name="letter_R"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  R  </div></td></tr></table>
</td><td valign="top"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html">UniqueIf</a> (<a class="el" href="namespacemxnet_1_1common_1_1helper.html">mxnet::common::helper</a>)   </td></tr>
<tr><td valign="top"><a class="el" href="structNativeOpInfo.html">NativeOpInfo</a>   </td><td valign="top"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html">UniqueIf< T[]></a> (<a class="el" href="namespacemxnet_1_1common_1_1helper.html">mxnet::common::helper</a>)   </td></tr>
<tr><td valign="top"><a class="el" href="structmxnet_1_1DataBatch.html">DataBatch</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="structmxnet_1_1Storage_1_1Handle.html">Storage::Handle</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td vali [...]
-<tr><td valign="top"><a class="el" href="structmxnet_1_1DataInst.html">DataInst</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_i"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  i  </div></td></tr></table>
+<tr><td valign="top"><a class="el" href="structmxnet_1_1DataInst.html">DataInst</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_I"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  I  </div></td></tr></table>
</td><td valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1ResourceManager.html">ResourceManager</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td></td></tr>
<tr><td valign="top"><a class="el" href="structmxnet_1_1DataIteratorReg.html">DataIteratorReg</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="structNDArrayOpInfo.html">NDArrayOpInfo</a>   </td><td valign="top"><a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td></td></tr>
-<tr><td rowspan="2" valign="bottom"><a name="letter_e"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  e  </div></td></tr></table>
-</td><td valign="top"><a class="el" href="classmxnet_1_1IIterator.html">IIterator</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_o"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  o  </div></td></tr></table>
+<tr><td rowspan="2" valign="bottom"><a name="letter_E"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  E  </div></td></tr></table>
+</td><td valign="top"><a class="el" href="classmxnet_1_1IIterator.html">IIterator</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_O"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  O  </div></td></tr></table>
</td><td valign="top"><a class="el" href="structmxnet_1_1RunContext.html">RunContext</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td></td></tr>
-<tr><td valign="top"><a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_s"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  s  </div></td></tr></table>
+<tr><td valign="top"><a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_S"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  S  </div></td></tr></table>
</td><td></td></tr>
<tr><td valign="top"><a class="el" href="classmxnet_1_1Engine.html">Engine</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td valign="top"><a class="el" href="structmxnet_1_1op_1_1Input1.html">Input1</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool</a> (<a class="el" href="namespacemxnet_1_1common.html">mxnet::common</a>) &# [...]
-<tr><td valign="top"><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_k"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  k  </div></td></tr></table>
+<tr><td valign="top"><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td rowspan="2" valign="bottom"><a name="letter_K"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  K  </div></td></tr></table>
</td><td valign="top"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html">ObjectPoolAllocatable</a> (<a class="el" href="namespacemxnet_1_1common.html">mxnet::common</a>)   </td><td valign="top"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">SimpleOpRegEntry</a> (<a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a>)   </td><td></td></tr>
<tr><td></td><td></td><td></td><td></td></tr>
<tr><td></td><td valign="top"><a class="el" href="classmxnet_1_1KVStore.html">KVStore</a> (<a class="el" href="namespacemxnet.html">mxnet</a>)   </td><td></td><td></td><td></td></tr>
<tr><td></td><td></td><td></td><td></td><td></td></tr>
</table>
-<div class="qindex"><a class="qindex" href="#letter_a">a</a> | <a class="qindex" href="#letter_c">c</a> | <a class="qindex" href="#letter_d">d</a> | <a class="qindex" href="#letter_e">e</a> | <a class="qindex" href="#letter_f">f</a> | <a class="qindex" href="#letter_g">g</a> | <a class="qindex" href="#letter_h">h</a> | <a class="qindex" href="#letter_i">i</a> | <a class="qindex" href="#letter_k">k</a> |& [...]
+<div class="qindex"><a class="qindex" href="#letter_A">A</a> | <a class="qindex" href="#letter_C">C</a> | <a class="qindex" href="#letter_D">D</a> | <a class="qindex" href="#letter_E">E</a> | <a class="qindex" href="#letter_F">F</a> | <a class="qindex" href="#letter_G">G</a> | <a class="qindex" href="#letter_H">H</a> | <a class="qindex" href="#letter_I">I</a> | <a class="qindex" href="#letter_K">K</a> |& [...]
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-</a> 1.8.13
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diff --git a/versions/master/doxygen/classmxnet_1_1Engine-members.html b/versions/master/doxygen/classmxnet_1_1Engine-members.html
index b2d29d4..fac268b 100644
--- a/versions/master/doxygen/classmxnet_1_1Engine-members.html
+++ b/versions/master/doxygen/classmxnet_1_1Engine-members.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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-<meta name="generator" content="Doxygen 1.8.13"/>
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+<meta name="generator" content="Doxygen 1.8.6"/>
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+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
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@@ -92,9 +116,9 @@ $(function() {
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/classmxnet_1_1Engine.html b/versions/master/doxygen/classmxnet_1_1Engine.html
index 68df9fc..9abddfa 100644
--- a/versions/master/doxygen/classmxnet_1_1Engine.html
+++ b/versions/master/doxygen/classmxnet_1_1Engine.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::Engine Class Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -80,63 +104,66 @@ $(function() {
Collaboration diagram for mxnet::Engine:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1Engine__coll__graph.png" border="0" usemap="#mxnet_1_1Engine_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1Engine_coll__map" id="mxnet_1_1Engine_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:a16b757432556f835d27f1b5e1dbe1b06"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">engine::CallbackOnComplete</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a16b757432556f835d27f1b5e1dbe1b06">CallbackOnComplete</a></td></tr>
-<tr class="memdesc:a16b757432556f835d27f1b5e1dbe1b06"><td class="mdescLeft"> </td><td class="mdescRight">callback on complete <a href="#a16b757432556f835d27f1b5e1dbe1b06">More...</a><br /></td></tr>
+<tr class="memdesc:a16b757432556f835d27f1b5e1dbe1b06"><td class="mdescLeft"> </td><td class="mdescRight">callback on complete <a href="#a16b757432556f835d27f1b5e1dbe1b06">More...</a><br/></td></tr>
<tr class="separator:a16b757432556f835d27f1b5e1dbe1b06"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a07f30ab85fca436e1bbcc72cd4d8bb35"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(<a class="el" href="structmxnet_1_1RunContext.html">RunContext</a>)> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a07f30ab85fca436e1bbcc72cd4d8bb35">SyncFn</a></td></tr>
-<tr class="memdesc:a07f30ab85fca436e1bbcc72cd4d8bb35"><td class="mdescLeft"> </td><td class="mdescRight">Synchronous operation to pass to engine. <a href="#a07f30ab85fca436e1bbcc72cd4d8bb35">More...</a><br /></td></tr>
+<tr class="memdesc:a07f30ab85fca436e1bbcc72cd4d8bb35"><td class="mdescLeft"> </td><td class="mdescRight">Synchronous operation to pass to engine. <a href="#a07f30ab85fca436e1bbcc72cd4d8bb35">More...</a><br/></td></tr>
<tr class="separator:a07f30ab85fca436e1bbcc72cd4d8bb35"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad41feff70bba0f29fc24f60b5381984c"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(<a class="el" href="structmxnet_1_1RunContext.html">RunContext</a>, <a class="el" href="classmxnet_1_1Engine.html#a16b757432556f835d27f1b5e1dbe1b06">CallbackOnComplete</a>)> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c">AsyncFn</a></td></tr>
-<tr class="memdesc:ad41feff70bba0f29fc24f60b5381984c"><td class="mdescLeft"> </td><td class="mdescRight">Asynchronous operation to pass to engine. <a href="#ad41feff70bba0f29fc24f60b5381984c">More...</a><br /></td></tr>
+<tr class="memitem:ad41feff70bba0f29fc24f60b5381984c"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(<a class="el" href="structmxnet_1_1RunContext.html">RunContext</a>, <br class="typebreak"/>
+<a class="el" href="classmxnet_1_1Engine.html#a16b757432556f835d27f1b5e1dbe1b06">CallbackOnComplete</a>)> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c">AsyncFn</a></td></tr>
+<tr class="memdesc:ad41feff70bba0f29fc24f60b5381984c"><td class="mdescLeft"> </td><td class="mdescRight">Asynchronous operation to pass to engine. <a href="#ad41feff70bba0f29fc24f60b5381984c">More...</a><br/></td></tr>
<tr class="separator:ad41feff70bba0f29fc24f60b5381984c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aac31510c793a12944c33f9cac6150491"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">engine::VarHandle</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">VarHandle</a></td></tr>
-<tr class="memdesc:aac31510c793a12944c33f9cac6150491"><td class="mdescLeft"> </td><td class="mdescRight">Variable pointer. <a href="#aac31510c793a12944c33f9cac6150491">More...</a><br /></td></tr>
+<tr class="memdesc:aac31510c793a12944c33f9cac6150491"><td class="mdescLeft"> </td><td class="mdescRight">Variable pointer. <a href="#aac31510c793a12944c33f9cac6150491">More...</a><br/></td></tr>
<tr class="separator:aac31510c793a12944c33f9cac6150491"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a832436e413a075291aa1a631942c3f01"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="namespacemxnet_1_1engine.html#a2d9b14b658e3f3c4e03ca49cd38ace94">engine::OprHandle</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">OprHandle</a></td></tr>
-<tr class="memdesc:a832436e413a075291aa1a631942c3f01"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> pointer. <a href="#a832436e413a075291aa1a631942c3f01">More...</a><br /></td></tr>
+<tr class="memdesc:a832436e413a075291aa1a631942c3f01"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> pointer. <a href="#a832436e413a075291aa1a631942c3f01">More...</a><br/></td></tr>
<tr class="separator:a832436e413a075291aa1a631942c3f01"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a3c0e2989538b5369c1592eddbcf0181c"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a3c0e2989538b5369c1592eddbcf0181c">NotifyShutdown</a> ()=0</td></tr>
-<tr class="memdesc:a3c0e2989538b5369c1592eddbcf0181c"><td class="mdescLeft"> </td><td class="mdescRight">Notify the engine about a shutdown, This can help engine to print less messages into display. <a href="#a3c0e2989538b5369c1592eddbcf0181c">More...</a><br /></td></tr>
+<tr class="memdesc:a3c0e2989538b5369c1592eddbcf0181c"><td class="mdescLeft"> </td><td class="mdescRight">Notify the engine about a shutdown, This can help engine to print less messages into display. <a href="#a3c0e2989538b5369c1592eddbcf0181c">More...</a><br/></td></tr>
<tr class="separator:a3c0e2989538b5369c1592eddbcf0181c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6e141b188f018d5d933ab99868631d5e"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">VarHandle</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a6e141b188f018d5d933ab99868631d5e">NewVariable</a> ()=0</td></tr>
-<tr class="memdesc:a6e141b188f018d5d933ab99868631d5e"><td class="mdescLeft"> </td><td class="mdescRight">Allocate a new variable, the variable can then be used to schedule the operation concurrently via dependency patterns. <a href="#a6e141b188f018d5d933ab99868631d5e">More...</a><br /></td></tr>
+<tr class="memdesc:a6e141b188f018d5d933ab99868631d5e"><td class="mdescLeft"> </td><td class="mdescRight">Allocate a new variable, the variable can then be used to schedule the operation concurrently via dependency patterns. <a href="#a6e141b188f018d5d933ab99868631d5e">More...</a><br/></td></tr>
<tr class="separator:a6e141b188f018d5d933ab99868631d5e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aff3332258a158ef33a9a4b7bcdc2fe6f"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">OprHandle</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#aff3332258a158ef33a9a4b7bcdc2fe6f">NewOperator</a> (<a class="el" href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c">AsyncFn</a> fn, std::vector< <a class="el" href="cl [...]
-<tr class="memdesc:aff3332258a158ef33a9a4b7bcdc2fe6f"><td class="mdescLeft"> </td><td class="mdescRight">Create a new operator. The returned operator could be saved externally so that it could be resued for scheduling. <a href="#aff3332258a158ef33a9a4b7bcdc2fe6f">More...</a><br /></td></tr>
+<tr class="memdesc:aff3332258a158ef33a9a4b7bcdc2fe6f"><td class="mdescLeft"> </td><td class="mdescRight">Create a new operator. The returned operator could be saved externally so that it could be resued for scheduling. <a href="#aff3332258a158ef33a9a4b7bcdc2fe6f">More...</a><br/></td></tr>
<tr class="separator:aff3332258a158ef33a9a4b7bcdc2fe6f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3fd7c8b35a2f52805506cc242bf82ca7"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a3fd7c8b35a2f52805506cc242bf82ca7">DeleteOperator</a> (<a class="el" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">OprHandle</a> op)=0</td></tr>
-<tr class="memdesc:a3fd7c8b35a2f52805506cc242bf82ca7"><td class="mdescLeft"> </td><td class="mdescRight">Delete the given operator. <a href="#a3fd7c8b35a2f52805506cc242bf82ca7">More...</a><br /></td></tr>
+<tr class="memdesc:a3fd7c8b35a2f52805506cc242bf82ca7"><td class="mdescLeft"> </td><td class="mdescRight">Delete the given operator. <a href="#a3fd7c8b35a2f52805506cc242bf82ca7">More...</a><br/></td></tr>
<tr class="separator:a3fd7c8b35a2f52805506cc242bf82ca7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad213d6b1a7c1e0d4d41275b9efe5f097"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#ad213d6b1a7c1e0d4d41275b9efe5f097">Push</a> (<a class="el" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">OprHandle</a> op, <a class="el" href="structmxnet_1_1Context.html">Context</a> exec_ctx, int priority=0, bool profiling=false)=0</td></tr>
-<tr class="memdesc:ad213d6b1a7c1e0d4d41275b9efe5f097"><td class="mdescLeft"> </td><td class="mdescRight">Push an operator to the engine. <a href="#ad213d6b1a7c1e0d4d41275b9efe5f097">More...</a><br /></td></tr>
+<tr class="memdesc:ad213d6b1a7c1e0d4d41275b9efe5f097"><td class="mdescLeft"> </td><td class="mdescRight">Push an operator to the engine. <a href="#ad213d6b1a7c1e0d4d41275b9efe5f097">More...</a><br/></td></tr>
<tr class="separator:ad213d6b1a7c1e0d4d41275b9efe5f097"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac71feb4f966cd4573452bd148b850c82"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#ac71feb4f966cd4573452bd148b850c82">PushAsync</a> (<a class="el" href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c">AsyncFn</a> exec_fun, <a class="el" href="structmxnet_1_1Context.html">Context</a> exec_ctx, std::vector< <a class="el" href="classmxnet_1_1Eng [...]
-<tr class="memdesc:ac71feb4f966cd4573452bd148b850c82"><td class="mdescLeft"> </td><td class="mdescRight">Push an asynchronous operation to the engine. <a href="#ac71feb4f966cd4573452bd148b850c82">More...</a><br /></td></tr>
+<tr class="memdesc:ac71feb4f966cd4573452bd148b850c82"><td class="mdescLeft"> </td><td class="mdescRight">Push an asynchronous operation to the engine. <a href="#ac71feb4f966cd4573452bd148b850c82">More...</a><br/></td></tr>
<tr class="separator:ac71feb4f966cd4573452bd148b850c82"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a738e5192dab345ab0ec9888b095903cf"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a738e5192dab345ab0ec9888b095903cf">DeleteVariable</a> (<a class="el" href="classmxnet_1_1Engine.html#a07f30ab85fca436e1bbcc72cd4d8bb35">SyncFn</a> delete_fn, <a class="el" href="structmxnet_1_1Context.html">Context</a> exec_ctx, <a class="el" href="classmxnet_1_1Engine.html#aa [...]
-<tr class="memdesc:a738e5192dab345ab0ec9888b095903cf"><td class="mdescLeft"> </td><td class="mdescRight">Schedule the deletion of a variable. <a href="#a738e5192dab345ab0ec9888b095903cf">More...</a><br /></td></tr>
+<tr class="memdesc:a738e5192dab345ab0ec9888b095903cf"><td class="mdescLeft"> </td><td class="mdescRight">Schedule the deletion of a variable. <a href="#a738e5192dab345ab0ec9888b095903cf">More...</a><br/></td></tr>
<tr class="separator:a738e5192dab345ab0ec9888b095903cf"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aed51bd7f294d9f2b569764a0c151d883"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#aed51bd7f294d9f2b569764a0c151d883">WaitForVar</a> (<a class="el" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">VarHandle</a> var)=0</td></tr>
-<tr class="memdesc:aed51bd7f294d9f2b569764a0c151d883"><td class="mdescLeft"> </td><td class="mdescRight">Wait for a variable. <a href="#aed51bd7f294d9f2b569764a0c151d883">More...</a><br /></td></tr>
+<tr class="memdesc:aed51bd7f294d9f2b569764a0c151d883"><td class="mdescLeft"> </td><td class="mdescRight">Wait for a variable. <a href="#aed51bd7f294d9f2b569764a0c151d883">More...</a><br/></td></tr>
<tr class="separator:aed51bd7f294d9f2b569764a0c151d883"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a64483aecce780e96056be89d6289e782"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a64483aecce780e96056be89d6289e782">WaitForAll</a> ()=0</td></tr>
-<tr class="memdesc:a64483aecce780e96056be89d6289e782"><td class="mdescLeft"> </td><td class="mdescRight">Wait until all the activity of engine finishes. <a href="#a64483aecce780e96056be89d6289e782">More...</a><br /></td></tr>
+<tr class="memdesc:a64483aecce780e96056be89d6289e782"><td class="mdescLeft"> </td><td class="mdescRight">Wait until all the activity of engine finishes. <a href="#a64483aecce780e96056be89d6289e782">More...</a><br/></td></tr>
<tr class="separator:a64483aecce780e96056be89d6289e782"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aff025321827e15096c02342225f2395b"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#aff025321827e15096c02342225f2395b">~Engine</a> () noexcept(false)</td></tr>
-<tr class="memdesc:aff025321827e15096c02342225f2395b"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#aff025321827e15096c02342225f2395b">More...</a><br /></td></tr>
+<tr class="memdesc:aff025321827e15096c02342225f2395b"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#aff025321827e15096c02342225f2395b">More...</a><br/></td></tr>
<tr class="separator:aff025321827e15096c02342225f2395b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1c2f38927e4bf7a62e23353b0bd3d619"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a1c2f38927e4bf7a62e23353b0bd3d619">PushSync</a> (<a class="el" href="classmxnet_1_1Engine.html#a07f30ab85fca436e1bbcc72cd4d8bb35">SyncFn</a> exec_fn, <a class="el" href="structmxnet_1_1Context.html">Context</a> exec_ctx, std::vector< <a class="el" href="classmxnet_1_1Engine.html#aa [...]
-<tr class="memdesc:a1c2f38927e4bf7a62e23353b0bd3d619"><td class="mdescLeft"> </td><td class="mdescRight">Push an synchronous operation to the engine. <a href="#a1c2f38927e4bf7a62e23353b0bd3d619">More...</a><br /></td></tr>
+<tr class="memdesc:a1c2f38927e4bf7a62e23353b0bd3d619"><td class="mdescLeft"> </td><td class="mdescRight">Push an synchronous operation to the engine. <a href="#a1c2f38927e4bf7a62e23353b0bd3d619">More...</a><br/></td></tr>
<tr class="separator:a1c2f38927e4bf7a62e23353b0bd3d619"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:acf25be815b4200b48ee6e4e169bf95b8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1Engine.html#a16b757432556f835d27f1b5e1dbe1b06">CallbackOnComplete</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#acf25be815b4200b48ee6e4e169bf95b8">CreateCallback</a> (void(*callback)(<a class="el" href="classmxnet_1_1Engine.html">Engine</a> *, void *), void *param)</td></tr>
-<tr class="memdesc:acf25be815b4200b48ee6e4e169bf95b8"><td class="mdescLeft"> </td><td class="mdescRight">factory function to create OnComplete callback. <a href="#acf25be815b4200b48ee6e4e169bf95b8">More...</a><br /></td></tr>
+<tr class="memdesc:acf25be815b4200b48ee6e4e169bf95b8"><td class="mdescLeft"> </td><td class="mdescRight">factory function to create OnComplete callback. <a href="#acf25be815b4200b48ee6e4e169bf95b8">More...</a><br/></td></tr>
<tr class="separator:acf25be815b4200b48ee6e4e169bf95b8"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9d8ac1987a6844dba9b0463030fb3430"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#a9d8ac1987a6844dba9b0463030fb3430">DeduplicateVarHandle</a> (std::vector< <a class="el" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">engine::VarHandle</a> > *read_vars, std::vector< <a class="el" href="namespacemxnet_1_1engine.html#a9d36c4f33eae85315 [...]
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@@ -146,15 +173,13 @@ Static Public Member Functions</h2></td></tr>
<tr class="memitem:ae0a23da15ef63d9479c7468e1f2f825f"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classmxnet_1_1Engine.html">Engine</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Get</a> ()</td></tr>
<tr class="separator:ae0a23da15ef63d9479c7468e1f2f825f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab6417f2ae519b946c104f975d84d55d5"><td class="memItemLeft" align="right" valign="top">static std::shared_ptr< <a class="el" href="classmxnet_1_1Engine.html">Engine</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html#ab6417f2ae519b946c104f975d84d55d5">_GetSharedRef</a> ()</td></tr>
-<tr class="memdesc:ab6417f2ae519b946c104f975d84d55d5"><td class="mdescLeft"> </td><td class="mdescRight">Get shared pointer reference to engine singleton. Most user should not call this function. This function is called by another singleton X who requires engine to be destructed after X. <a href="#ab6417f2ae519b946c104f975d84d55d5">More...</a><br /></td></tr>
+<tr class="memdesc:ab6417f2ae519b946c104f975d84d55d5"><td class="mdescLeft"> </td><td class="mdescRight">Get shared pointer reference to engine singleton. Most user should not call this function. This function is called by another singleton X who requires engine to be destructed after X. <a href="#ab6417f2ae519b946c104f975d84d55d5">More...</a><br/></td></tr>
<tr class="separator:ab6417f2ae519b946c104f975d84d55d5"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Dependency engine that schedules operations. </p>
</div><h2 class="groupheader">Member Typedef Documentation</h2>
-<a id="ad41feff70bba0f29fc24f60b5381984c"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad41feff70bba0f29fc24f60b5381984c">◆ </a></span>AsyncFn</h2>
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@@ -168,9 +193,7 @@ Static Public Member Functions</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a16b757432556f835d27f1b5e1dbe1b06">◆ </a></span>CallbackOnComplete</h2>
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@@ -184,9 +207,7 @@ Static Public Member Functions</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a832436e413a075291aa1a631942c3f01">◆ </a></span>OprHandle</h2>
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@@ -200,9 +221,7 @@ Static Public Member Functions</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a07f30ab85fca436e1bbcc72cd4d8bb35">◆ </a></span>SyncFn</h2>
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@@ -216,9 +235,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="aac31510c793a12944c33f9cac6150491"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aac31510c793a12944c33f9cac6150491">◆ </a></span>VarHandle</h2>
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@@ -233,9 +250,7 @@ Static Public Member Functions</h2></td></tr>
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</div>
<h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="aff025321827e15096c02342225f2395b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aff025321827e15096c02342225f2395b">◆ </a></span>~Engine()</h2>
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<h2 class="groupheader">Member Function Documentation</h2>
-<a id="ab6417f2ae519b946c104f975d84d55d5"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ab6417f2ae519b946c104f975d84d55d5">◆ </a></span>_GetSharedRef()</h2>
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@@ -289,9 +302,7 @@ Static Public Member Functions</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#acf25be815b4200b48ee6e4e169bf95b8">◆ </a></span>CreateCallback()</h2>
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@@ -334,9 +345,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="a9d8ac1987a6844dba9b0463030fb3430"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a9d8ac1987a6844dba9b0463030fb3430">◆ </a></span>DeduplicateVarHandle()</h2>
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@@ -370,9 +379,7 @@ Static Public Member Functions</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a3fd7c8b35a2f52805506cc242bf82ca7">◆ </a></span>DeleteOperator()</h2>
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@@ -405,9 +412,7 @@ Static Public Member Functions</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a738e5192dab345ab0ec9888b095903cf">◆ </a></span>DeleteVariable()</h2>
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@@ -458,9 +463,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="ae0a23da15ef63d9479c7468e1f2f825f"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ae0a23da15ef63d9479c7468e1f2f825f">◆ </a></span>Get()</h2>
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@@ -484,9 +487,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="aff3332258a158ef33a9a4b7bcdc2fe6f"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aff3332258a158ef33a9a4b7bcdc2fe6f">◆ </a></span>NewOperator()</h2>
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@@ -551,9 +552,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="a6e141b188f018d5d933ab99868631d5e"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a6e141b188f018d5d933ab99868631d5e">◆ </a></span>NewVariable()</h2>
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+<a class="anchor" id="a6e141b188f018d5d933ab99868631d5e"></a>
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@@ -579,9 +578,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="a3c0e2989538b5369c1592eddbcf0181c"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a3c0e2989538b5369c1592eddbcf0181c">◆ </a></span>NotifyShutdown()</h2>
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@@ -603,13 +600,12 @@ Static Public Member Functions</h2></td></tr>
</div><div class="memdoc">
<p>Notify the engine about a shutdown, This can help engine to print less messages into display. </p>
-<p>User do not have to call this function. </p><dl class="section return"><dt>Returns</dt><dd>0 when success, -1 when failure happens. </dd></dl>
+<p>User do not have to call this function. </p>
+<dl class="section return"><dt>Returns</dt><dd>0 when success, -1 when failure happens. </dd></dl>
</div>
</div>
-<a id="ad213d6b1a7c1e0d4d41275b9efe5f097"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad213d6b1a7c1e0d4d41275b9efe5f097">◆ </a></span>Push()</h2>
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@@ -666,9 +662,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="ac71feb4f966cd4573452bd148b850c82"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ac71feb4f966cd4573452bd148b850c82">◆ </a></span>PushAsync()</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a1c2f38927e4bf7a62e23353b0bd3d619">◆ </a></span>PushSync()</h2>
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@@ -832,9 +824,7 @@ Static Public Member Functions</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a64483aecce780e96056be89d6289e782">◆ </a></span>WaitForAll()</h2>
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@@ -859,9 +849,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="aed51bd7f294d9f2b569764a0c151d883"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aed51bd7f294d9f2b569764a0c151d883">◆ </a></span>WaitForVar()</h2>
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@@ -899,9 +887,9 @@ Static Public Member Functions</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/classmxnet_1_1Engine__coll__graph.md5 b/versions/master/doxygen/classmxnet_1_1Engine__coll__graph.md5
index 9765365..c94419b 100644
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diff --git a/versions/master/doxygen/classmxnet_1_1Executor-members.html b/versions/master/doxygen/classmxnet_1_1Executor-members.html
index 7b846fa..5554ba9 100644
--- a/versions/master/doxygen/classmxnet_1_1Executor-members.html
+++ b/versions/master/doxygen/classmxnet_1_1Executor-members.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
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@@ -71,23 +95,23 @@ $(function() {
<table class="directory">
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a84552a918a65633678f30c00a2a0ae7f">arg_grad_map</a>() const =0</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a0a38a98fc4dcddc4205b5980ee7bdc4b">aux_state_map</a>() const =0</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a15157351d5770c4e5e7e652b2a0e2cbd">Backward</a>(const std::vector< NDArray > &head_grads)=0</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#aa2fd32510a718aff38323e63be0d65e3">Backward</a>(const std::vector< NDArray > &head_grads, bool is_train=true)=0</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a8217aa9fbdd99d107bd836872d7d9692">Bind</a>(nnvm::Symbol symbol, const Context &default_ctx, const std::map< std::string, Context > &group2ctx, const std::vector< NDArray > &in_args, const std::vector< NDArray > &arg_grad_store, const std::vector< OpReqType > &grad_req_type, const std::vector< NDArray > &aux_states, Executor *shared_exec=NULL)</td><td class="entry [...]
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#ab3716be8e6c265b091e618f46eeb6f9b">Forward</a>(bool is_train)=0</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a07a3e917596ed08718ee2859283c2476">in_arg_map</a>() const =0</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a5684c74daafc9b80681cde6a9b7b4587">MonitorCallback</a> typedef</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a16d0f1ab276e32242685702aa10b9a3d">outputs</a>() const =0</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a3e261d44065848e434c527000d4390c8">PartialForward</a>(bool is_train, int step, int *step_left)=0</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#ac391ee6cf8cf8afb60acd0480db2ae86">Print</a>(std::ostream &os) const</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a3803afcb87e4c69940c8d3d88a03e6ee">Print</a>(std::ostream &os) const </td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#a3c701dda22a9c1e474d23b11edda9b0d">SetMonitorCallback</a>(const MonitorCallback &callback)</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#ae1a319bca5e26ef58057f8410f46e484">SimpleBind</a>(nnvm::Symbol symbol, const Context &default_ctx, const std::map< std::string, Context > &group2ctx, const std::vector< Context > &in_arg_ctxes, const std::vector< Context > &arg_grad_ctxes, const std::vector< Context > &aux_state_ctxes, const std::unordered_map< std::string, TShape > &arg_shape_map, const std::unor [...]
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1Executor.html#adf445ff665c954061889d6bbee3c1c2a">~Executor</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
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<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/classmxnet_1_1Executor.html b/versions/master/doxygen/classmxnet_1_1Executor.html
index 48fd1d4..bc9228d 100644
--- a/versions/master/doxygen/classmxnet_1_1Executor.html
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@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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<title>mxnet: mxnet::Executor Class Reference</title>
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@@ -20,7 +21,7 @@
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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@@ -29,25 +30,48 @@
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</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -80,51 +104,61 @@ $(function() {
Collaboration diagram for mxnet::Executor:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1Executor__coll__graph.png" border="0" usemap="#mxnet_1_1Executor_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1Executor_coll__map" id="mxnet_1_1Executor_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
-<tr class="memitem:a5684c74daafc9b80681cde6a9b7b4587"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(const char *, void *)> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a5684c74daafc9b80681cde6a9b7b4587">MonitorCallback</a></td></tr>
-<tr class="memdesc:a5684c74daafc9b80681cde6a9b7b4587"><td class="mdescLeft"> </td><td class="mdescRight">the prototype of user-defined monitor callback <a href="#a5684c74daafc9b80681cde6a9b7b4587">More...</a><br /></td></tr>
+<tr class="memitem:a5684c74daafc9b80681cde6a9b7b4587"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(const <br class="typebreak"/>
+char *, void *)> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a5684c74daafc9b80681cde6a9b7b4587">MonitorCallback</a></td></tr>
+<tr class="memdesc:a5684c74daafc9b80681cde6a9b7b4587"><td class="mdescLeft"> </td><td class="mdescRight">the prototype of user-defined monitor callback <a href="#a5684c74daafc9b80681cde6a9b7b4587">More...</a><br/></td></tr>
<tr class="separator:a5684c74daafc9b80681cde6a9b7b4587"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:adf445ff665c954061889d6bbee3c1c2a"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#adf445ff665c954061889d6bbee3c1c2a">~Executor</a> ()</td></tr>
-<tr class="memdesc:adf445ff665c954061889d6bbee3c1c2a"><td class="mdescLeft"> </td><td class="mdescRight">destructor <a href="#adf445ff665c954061889d6bbee3c1c2a">More...</a><br /></td></tr>
+<tr class="memdesc:adf445ff665c954061889d6bbee3c1c2a"><td class="mdescLeft"> </td><td class="mdescRight">destructor <a href="#adf445ff665c954061889d6bbee3c1c2a">More...</a><br/></td></tr>
<tr class="separator:adf445ff665c954061889d6bbee3c1c2a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab3716be8e6c265b091e618f46eeb6f9b"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#ab3716be8e6c265b091e618f46eeb6f9b">Forward</a> (bool is_train)=0</td></tr>
-<tr class="memdesc:ab3716be8e6c265b091e618f46eeb6f9b"><td class="mdescLeft"> </td><td class="mdescRight">Perform a Forward operation of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> After this operation, user can get the result by using function head. <a href="#ab3716be8e6c265b091e618f46eeb6f9b">More...</a><br /></td></tr>
+<tr class="memdesc:ab3716be8e6c265b091e618f46eeb6f9b"><td class="mdescLeft"> </td><td class="mdescRight">Perform a Forward operation of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> After this operation, user can get the result by using function head. <a href="#ab3716be8e6c265b091e618f46eeb6f9b">More...</a><br/></td></tr>
<tr class="separator:ab3716be8e6c265b091e618f46eeb6f9b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3e261d44065848e434c527000d4390c8"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a3e261d44065848e434c527000d4390c8">PartialForward</a> (bool is_train, int step, int *step_left)=0</td></tr>
-<tr class="memdesc:a3e261d44065848e434c527000d4390c8"><td class="mdescLeft"> </td><td class="mdescRight">Perform a Partial Forward operation of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. Only issue operation specified by step. The caller must keep calling PartialForward with increasing steps, until step_left=0. <a href="#a3e261d44065848e434c527000d4390c8" [...]
+<tr class="memdesc:a3e261d44065848e434c527000d4390c8"><td class="mdescLeft"> </td><td class="mdescRight">Perform a Partial Forward operation of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. Only issue operation specified by step. The caller must keep calling PartialForward with increasing steps, until step_left=0. <a href="#a3e261d44065848e434c527000d4390c8" [...]
<tr class="separator:a3e261d44065848e434c527000d4390c8"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a15157351d5770c4e5e7e652b2a0e2cbd"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a15157351d5770c4e5e7e652b2a0e2cbd">Backward</a> (const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &head_grads)=0</td></tr>
-<tr class="memdesc:a15157351d5770c4e5e7e652b2a0e2cbd"><td class="mdescLeft"> </td><td class="mdescRight">Perform a Backward operation of the <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. This must be called after Forward. After this operation, NDArrays specified by grad_in_args_store will be updated accordingly. User is allowed to pass in an empty Array if th [...]
-<tr class="separator:a15157351d5770c4e5e7e652b2a0e2cbd"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ac391ee6cf8cf8afb60acd0480db2ae86"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#ac391ee6cf8cf8afb60acd0480db2ae86">Print</a> (std::ostream &os) const</td></tr>
-<tr class="memdesc:ac391ee6cf8cf8afb60acd0480db2ae86"><td class="mdescLeft"> </td><td class="mdescRight">print the execution plan info to output stream. <a href="#ac391ee6cf8cf8afb60acd0480db2ae86">More...</a><br /></td></tr>
-<tr class="separator:ac391ee6cf8cf8afb60acd0480db2ae86"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a16d0f1ab276e32242685702aa10b9a3d"><td class="memItemLeft" align="right" valign="top">virtual const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a16d0f1ab276e32242685702aa10b9a3d">outputs</a> () const =0</td></tr>
-<tr class="memdesc:a16d0f1ab276e32242685702aa10b9a3d"><td class="mdescLeft"> </td><td class="mdescRight">get array of outputs in the executor. <a href="#a16d0f1ab276e32242685702aa10b9a3d">More...</a><br /></td></tr>
+<tr class="memitem:aa2fd32510a718aff38323e63be0d65e3"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#aa2fd32510a718aff38323e63be0d65e3">Backward</a> (const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &head_grads, bool is_train=true)=0</td></tr>
+<tr class="memdesc:aa2fd32510a718aff38323e63be0d65e3"><td class="mdescLeft"> </td><td class="mdescRight">Perform a Backward operation of the <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. This must be called after Forward. After this operation, NDArrays specified by grad_in_args_store will be updated accordingly. User is allowed to pass in an empty Array if th [...]
+<tr class="separator:aa2fd32510a718aff38323e63be0d65e3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3803afcb87e4c69940c8d3d88a03e6ee"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a3803afcb87e4c69940c8d3d88a03e6ee">Print</a> (std::ostream &os) const </td></tr>
+<tr class="memdesc:a3803afcb87e4c69940c8d3d88a03e6ee"><td class="mdescLeft"> </td><td class="mdescRight">print the execution plan info to output stream. <a href="#a3803afcb87e4c69940c8d3d88a03e6ee">More...</a><br/></td></tr>
+<tr class="separator:a3803afcb87e4c69940c8d3d88a03e6ee"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a16d0f1ab276e32242685702aa10b9a3d"><td class="memItemLeft" align="right" valign="top">virtual const std::vector<br class="typebreak"/>
+< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a16d0f1ab276e32242685702aa10b9a3d">outputs</a> () const =0</td></tr>
+<tr class="memdesc:a16d0f1ab276e32242685702aa10b9a3d"><td class="mdescLeft"> </td><td class="mdescRight">get array of outputs in the executor. <a href="#a16d0f1ab276e32242685702aa10b9a3d">More...</a><br/></td></tr>
<tr class="separator:a16d0f1ab276e32242685702aa10b9a3d"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a07a3e917596ed08718ee2859283c2476"><td class="memItemLeft" align="right" valign="top">virtual const std::unordered_map< std::string, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a07a3e917596ed08718ee2859283c2476">in_arg_map</a> () const =0</td></tr>
-<tr class="memdesc:a07a3e917596ed08718ee2859283c2476"><td class="mdescLeft"> </td><td class="mdescRight">get input argument map, key is arg name, value is arg's <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a07a3e917596ed08718ee2859283c2476">More...</a><br /></td></tr>
+<tr class="memitem:a07a3e917596ed08718ee2859283c2476"><td class="memItemLeft" align="right" valign="top">virtual const <br class="typebreak"/>
+std::unordered_map<br class="typebreak"/>
+< std::string, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a07a3e917596ed08718ee2859283c2476">in_arg_map</a> () const =0</td></tr>
+<tr class="memdesc:a07a3e917596ed08718ee2859283c2476"><td class="mdescLeft"> </td><td class="mdescRight">get input argument map, key is arg name, value is arg's <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a07a3e917596ed08718ee2859283c2476">More...</a><br/></td></tr>
<tr class="separator:a07a3e917596ed08718ee2859283c2476"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a84552a918a65633678f30c00a2a0ae7f"><td class="memItemLeft" align="right" valign="top">virtual const std::unordered_map< std::string, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a84552a918a65633678f30c00a2a0ae7f">arg_grad_map</a> () const =0</td></tr>
-<tr class="memdesc:a84552a918a65633678f30c00a2a0ae7f"><td class="mdescLeft"> </td><td class="mdescRight">get input argument graident map, key is arg name, value is gradient's <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a84552a918a65633678f30c00a2a0ae7f">More...</a><br /></td></tr>
+<tr class="memitem:a84552a918a65633678f30c00a2a0ae7f"><td class="memItemLeft" align="right" valign="top">virtual const <br class="typebreak"/>
+std::unordered_map<br class="typebreak"/>
+< std::string, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a84552a918a65633678f30c00a2a0ae7f">arg_grad_map</a> () const =0</td></tr>
+<tr class="memdesc:a84552a918a65633678f30c00a2a0ae7f"><td class="mdescLeft"> </td><td class="mdescRight">get input argument graident map, key is arg name, value is gradient's <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a84552a918a65633678f30c00a2a0ae7f">More...</a><br/></td></tr>
<tr class="separator:a84552a918a65633678f30c00a2a0ae7f"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a0a38a98fc4dcddc4205b5980ee7bdc4b"><td class="memItemLeft" align="right" valign="top">virtual const std::unordered_map< std::string, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a0a38a98fc4dcddc4205b5980ee7bdc4b">aux_state_map</a> () const =0</td></tr>
-<tr class="memdesc:a0a38a98fc4dcddc4205b5980ee7bdc4b"><td class="mdescLeft"> </td><td class="mdescRight">get aux state map, key is arg name, value is aux state's <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a0a38a98fc4dcddc4205b5980ee7bdc4b">More...</a><br /></td></tr>
+<tr class="memitem:a0a38a98fc4dcddc4205b5980ee7bdc4b"><td class="memItemLeft" align="right" valign="top">virtual const <br class="typebreak"/>
+std::unordered_map<br class="typebreak"/>
+< std::string, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a0a38a98fc4dcddc4205b5980ee7bdc4b">aux_state_map</a> () const =0</td></tr>
+<tr class="memdesc:a0a38a98fc4dcddc4205b5980ee7bdc4b"><td class="mdescLeft"> </td><td class="mdescRight">get aux state map, key is arg name, value is aux state's <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a0a38a98fc4dcddc4205b5980ee7bdc4b">More...</a><br/></td></tr>
<tr class="separator:a0a38a98fc4dcddc4205b5980ee7bdc4b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3c701dda22a9c1e474d23b11edda9b0d"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a3c701dda22a9c1e474d23b11edda9b0d">SetMonitorCallback</a> (const <a class="el" href="classmxnet_1_1Executor.html#a5684c74daafc9b80681cde6a9b7b4587">MonitorCallback</a> &callback)</td></tr>
-<tr class="memdesc:a3c701dda22a9c1e474d23b11edda9b0d"><td class="mdescLeft"> </td><td class="mdescRight">Install a callback to notify the completion of operation. <a href="#a3c701dda22a9c1e474d23b11edda9b0d">More...</a><br /></td></tr>
+<tr class="memdesc:a3c701dda22a9c1e474d23b11edda9b0d"><td class="mdescLeft"> </td><td class="mdescRight">Install a callback to notify the completion of operation. <a href="#a3c701dda22a9c1e474d23b11edda9b0d">More...</a><br/></td></tr>
<tr class="separator:a3c701dda22a9c1e474d23b11edda9b0d"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:a8217aa9fbdd99d107bd836872d7d9692"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classmxnet_1_1Executor.html">Executor</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#a8217aa9fbdd99d107bd836872d7d9692">Bind</a> (nnvm::Symbol symbol, const <a class="el" href="structmxnet_1_1Context.html">Context</a> &default_ctx, const std::map< std::string, <a class="el" href="structmxnet_1_ [...]
-<tr class="memdesc:a8217aa9fbdd99d107bd836872d7d9692"><td class="mdescLeft"> </td><td class="mdescRight">Create an operator by bind symbol with context and arguments. If user do not want to compute the gradients of i-th argument, grad_req_type[i] can be kNullOp. <a href="#a8217aa9fbdd99d107bd836872d7d9692">More...</a><br /></td></tr>
+<tr class="memdesc:a8217aa9fbdd99d107bd836872d7d9692"><td class="mdescLeft"> </td><td class="mdescRight">Create an operator by bind symbol with context and arguments. If user do not want to compute the gradients of i-th argument, grad_req_type[i] can be kNullOp. <a href="#a8217aa9fbdd99d107bd836872d7d9692">More...</a><br/></td></tr>
<tr class="separator:a8217aa9fbdd99d107bd836872d7d9692"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae1a319bca5e26ef58057f8410f46e484"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classmxnet_1_1Executor.html">Executor</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html#ae1a319bca5e26ef58057f8410f46e484">SimpleBind</a> (nnvm::Symbol symbol, const <a class="el" href="structmxnet_1_1Context.html">Context</a> &default_ctx, const std::map< std::string, <a class="el" href="structmx [...]
<tr class="separator:ae1a319bca5e26ef58057f8410f46e484"><td class="memSeparator" colspan="2"> </td></tr>
@@ -132,9 +166,7 @@ Static Public Member Functions</h2></td></tr>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> of a computation graph. <a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> can be created by Binding a symbol. </p>
</div><h2 class="groupheader">Member Typedef Documentation</h2>
-<a id="a5684c74daafc9b80681cde6a9b7b4587"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a5684c74daafc9b80681cde6a9b7b4587">◆ </a></span>MonitorCallback</h2>
-
+<a class="anchor" id="a5684c74daafc9b80681cde6a9b7b4587"></a>
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@@ -149,9 +181,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
<h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="adf445ff665c954061889d6bbee3c1c2a"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#adf445ff665c954061889d6bbee3c1c2a">◆ </a></span>~Executor()</h2>
-
+<a class="anchor" id="adf445ff665c954061889d6bbee3c1c2a"></a>
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@@ -177,9 +207,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
-<a id="a84552a918a65633678f30c00a2a0ae7f"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a84552a918a65633678f30c00a2a0ae7f">◆ </a></span>arg_grad_map()</h2>
-
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@@ -205,9 +233,7 @@ Static Public Member Functions</h2></td></tr>
</div>
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-<a id="a0a38a98fc4dcddc4205b5980ee7bdc4b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a0a38a98fc4dcddc4205b5980ee7bdc4b">◆ </a></span>aux_state_map()</h2>
-
+<a class="anchor" id="a0a38a98fc4dcddc4205b5980ee7bdc4b"></a>
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@@ -233,9 +259,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
-<a id="a15157351d5770c4e5e7e652b2a0e2cbd"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a15157351d5770c4e5e7e652b2a0e2cbd">◆ </a></span>Backward()</h2>
-
+<a class="anchor" id="aa2fd32510a718aff38323e63be0d65e3"></a>
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@@ -246,8 +270,18 @@ Static Public Member Functions</h2></td></tr>
<td class="memname">virtual void mxnet::Executor::Backward </td>
<td>(</td>
<td class="paramtype">const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > & </td>
- <td class="paramname"><em>head_grads</em></td><td>)</td>
+ <td class="paramname"><em>head_grads</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
<td></td>
+ <td class="paramtype">bool </td>
+ <td class="paramname"><em>is_train</em> = <code>true</code> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
</tr>
</table>
</td>
@@ -267,9 +301,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
-<a id="a8217aa9fbdd99d107bd836872d7d9692"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a8217aa9fbdd99d107bd836872d7d9692">◆ </a></span>Bind()</h2>
-
+<a class="anchor" id="a8217aa9fbdd99d107bd836872d7d9692"></a>
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@@ -355,9 +387,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="ab3716be8e6c265b091e618f46eeb6f9b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ab3716be8e6c265b091e618f46eeb6f9b">◆ </a></span>Forward()</h2>
-
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@@ -383,9 +413,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="a07a3e917596ed08718ee2859283c2476"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a07a3e917596ed08718ee2859283c2476">◆ </a></span>in_arg_map()</h2>
-
+<a class="anchor" id="a07a3e917596ed08718ee2859283c2476"></a>
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@@ -411,9 +439,7 @@ Static Public Member Functions</h2></td></tr>
</div>
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-<a id="a16d0f1ab276e32242685702aa10b9a3d"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a16d0f1ab276e32242685702aa10b9a3d">◆ </a></span>outputs()</h2>
-
+<a class="anchor" id="a16d0f1ab276e32242685702aa10b9a3d"></a>
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@@ -439,9 +465,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="a3e261d44065848e434c527000d4390c8"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a3e261d44065848e434c527000d4390c8">◆ </a></span>PartialForward()</h2>
-
+<a class="anchor" id="a3e261d44065848e434c527000d4390c8"></a>
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@@ -491,9 +515,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="ac391ee6cf8cf8afb60acd0480db2ae86"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ac391ee6cf8cf8afb60acd0480db2ae86">◆ </a></span>Print()</h2>
-
+<a class="anchor" id="a3803afcb87e4c69940c8d3d88a03e6ee"></a>
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@@ -525,9 +547,7 @@ Static Public Member Functions</h2></td></tr>
</div>
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-<a id="a3c701dda22a9c1e474d23b11edda9b0d"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a3c701dda22a9c1e474d23b11edda9b0d">◆ </a></span>SetMonitorCallback()</h2>
-
+<a class="anchor" id="a3c701dda22a9c1e474d23b11edda9b0d"></a>
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@@ -553,9 +573,7 @@ Static Public Member Functions</h2></td></tr>
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-<a id="ae1a319bca5e26ef58057f8410f46e484"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ae1a319bca5e26ef58057f8410f46e484">◆ </a></span>SimpleBind()</h2>
-
+<a class="anchor" id="ae1a319bca5e26ef58057f8410f46e484"></a>
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@@ -673,9 +691,9 @@ Static Public Member Functions</h2></td></tr>
</div><!-- contents -->
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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index 536ba6c..57cc4da 100644
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+++ b/versions/master/doxygen/classmxnet_1_1IIterator-members.html
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<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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@@ -79,9 +103,9 @@ $(function() {
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/classmxnet_1_1IIterator.html b/versions/master/doxygen/classmxnet_1_1IIterator.html
index 1a12493..87e90c2 100644
--- a/versions/master/doxygen/classmxnet_1_1IIterator.html
+++ b/versions/master/doxygen/classmxnet_1_1IIterator.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::IIterator< DType > Class Template Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -79,42 +103,46 @@ $(function() {
Inheritance diagram for mxnet::IIterator< DType >:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1IIterator__inherit__graph.png" border="0" usemap="#mxnet_1_1IIterator_3_01DType_01_4_inherit__map" alt="Inheritance graph"/></div>
+<map name="mxnet_1_1IIterator_3_01DType_01_4_inherit__map" id="mxnet_1_1IIterator_3_01DType_01_4_inherit__map">
+</map>
</div>
<div class="dynheader">
Collaboration diagram for mxnet::IIterator< DType >:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1IIterator__coll__graph.png" border="0" usemap="#mxnet_1_1IIterator_3_01DType_01_4_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1IIterator_3_01DType_01_4_coll__map" id="mxnet_1_1IIterator_3_01DType_01_4_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a774e490e5f3eee767fdc11b779bbd1fe"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1IIterator.html#a774e490e5f3eee767fdc11b779bbd1fe">Init</a> (const std::vector< std::pair< std::string, std::string > > &kwargs)=0</td></tr>
-<tr class="memdesc:a774e490e5f3eee767fdc11b779bbd1fe"><td class="mdescLeft"> </td><td class="mdescRight">set the parameters and init iter <a href="#a774e490e5f3eee767fdc11b779bbd1fe">More...</a><br /></td></tr>
+<tr class="memdesc:a774e490e5f3eee767fdc11b779bbd1fe"><td class="mdescLeft"> </td><td class="mdescRight">set the parameters and init iter <a href="#a774e490e5f3eee767fdc11b779bbd1fe">More...</a><br/></td></tr>
<tr class="separator:a774e490e5f3eee767fdc11b779bbd1fe"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a37fc8b163cd0158686183cc0f319529a"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1IIterator.html#a37fc8b163cd0158686183cc0f319529a">BeforeFirst</a> (void)=0</td></tr>
-<tr class="memdesc:a37fc8b163cd0158686183cc0f319529a"><td class="mdescLeft"> </td><td class="mdescRight">reset the iterator <a href="#a37fc8b163cd0158686183cc0f319529a">More...</a><br /></td></tr>
+<tr class="memdesc:a37fc8b163cd0158686183cc0f319529a"><td class="mdescLeft"> </td><td class="mdescRight">reset the iterator <a href="#a37fc8b163cd0158686183cc0f319529a">More...</a><br/></td></tr>
<tr class="separator:a37fc8b163cd0158686183cc0f319529a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac02dda5d191a7bc629aaa93e5abab3d5"><td class="memItemLeft" align="right" valign="top">virtual bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1IIterator.html#ac02dda5d191a7bc629aaa93e5abab3d5">Next</a> (void)=0</td></tr>
-<tr class="memdesc:ac02dda5d191a7bc629aaa93e5abab3d5"><td class="mdescLeft"> </td><td class="mdescRight">move to next item <a href="#ac02dda5d191a7bc629aaa93e5abab3d5">More...</a><br /></td></tr>
+<tr class="memdesc:ac02dda5d191a7bc629aaa93e5abab3d5"><td class="mdescLeft"> </td><td class="mdescRight">move to next item <a href="#ac02dda5d191a7bc629aaa93e5abab3d5">More...</a><br/></td></tr>
<tr class="separator:ac02dda5d191a7bc629aaa93e5abab3d5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aef2e0b74766cd0283849300c8a081929"><td class="memItemLeft" align="right" valign="top">virtual const DType & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1IIterator.html#aef2e0b74766cd0283849300c8a081929">Value</a> (void) const =0</td></tr>
-<tr class="memdesc:aef2e0b74766cd0283849300c8a081929"><td class="mdescLeft"> </td><td class="mdescRight">get current data <a href="#aef2e0b74766cd0283849300c8a081929">More...</a><br /></td></tr>
+<tr class="memdesc:aef2e0b74766cd0283849300c8a081929"><td class="mdescLeft"> </td><td class="mdescRight">get current data <a href="#aef2e0b74766cd0283849300c8a081929">More...</a><br/></td></tr>
<tr class="separator:aef2e0b74766cd0283849300c8a081929"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6e9049dca03b889bba0d18338e286070"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1IIterator.html#a6e9049dca03b889bba0d18338e286070">~IIterator</a> (void)</td></tr>
-<tr class="memdesc:a6e9049dca03b889bba0d18338e286070"><td class="mdescLeft"> </td><td class="mdescRight">constructor <a href="#a6e9049dca03b889bba0d18338e286070">More...</a><br /></td></tr>
+<tr class="memdesc:a6e9049dca03b889bba0d18338e286070"><td class="mdescLeft"> </td><td class="mdescRight">constructor <a href="#a6e9049dca03b889bba0d18338e286070">More...</a><br/></td></tr>
<tr class="separator:a6e9049dca03b889bba0d18338e286070"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a79ed9023cf4e525c8b4a8c6fa96eea05"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1IIterator.html#a79ed9023cf4e525c8b4a8c6fa96eea05">SetDataName</a> (const std::string data_name)</td></tr>
-<tr class="memdesc:a79ed9023cf4e525c8b4a8c6fa96eea05"><td class="mdescLeft"> </td><td class="mdescRight">set data name to each attribute of data <a href="#a79ed9023cf4e525c8b4a8c6fa96eea05">More...</a><br /></td></tr>
+<tr class="memdesc:a79ed9023cf4e525c8b4a8c6fa96eea05"><td class="mdescLeft"> </td><td class="mdescRight">set data name to each attribute of data <a href="#a79ed9023cf4e525c8b4a8c6fa96eea05">More...</a><br/></td></tr>
<tr class="separator:a79ed9023cf4e525c8b4a8c6fa96eea05"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:a65cd81ecf71d54a17a94ca65d27aa014"><td class="memItemLeft" align="right" valign="top">std::vector< std::string > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1IIterator.html#a65cd81ecf71d54a17a94ca65d27aa014">data_names</a></td></tr>
-<tr class="memdesc:a65cd81ecf71d54a17a94ca65d27aa014"><td class="mdescLeft"> </td><td class="mdescRight">store the name of each data, it could be used for making NDArrays <a href="#a65cd81ecf71d54a17a94ca65d27aa014">More...</a><br /></td></tr>
+<tr class="memdesc:a65cd81ecf71d54a17a94ca65d27aa014"><td class="mdescLeft"> </td><td class="mdescRight">store the name of each data, it could be used for making NDArrays <a href="#a65cd81ecf71d54a17a94ca65d27aa014">More...</a><br/></td></tr>
<tr class="separator:a65cd81ecf71d54a17a94ca65d27aa014"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
-<div class="textblock"><h3>template<typename DType><br />
+<div class="textblock"><h3>template<typename DType><br/>
class mxnet::IIterator< DType ></h3>
<p>iterator type </p>
@@ -125,9 +153,7 @@ class mxnet::IIterator< DType ></h3>
</dd>
</dl>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a6e9049dca03b889bba0d18338e286070"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a6e9049dca03b889bba0d18338e286070">◆ </a></span>~IIterator()</h2>
-
+<a class="anchor" id="a6e9049dca03b889bba0d18338e286070"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -156,9 +182,7 @@ template<typename DType > </div>
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
-<a id="a37fc8b163cd0158686183cc0f319529a"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a37fc8b163cd0158686183cc0f319529a">◆ </a></span>BeforeFirst()</h2>
-
+<a class="anchor" id="a37fc8b163cd0158686183cc0f319529a"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -186,9 +210,7 @@ template<typename DType > </div>
</div>
</div>
-<a id="a774e490e5f3eee767fdc11b779bbd1fe"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a774e490e5f3eee767fdc11b779bbd1fe">◆ </a></span>Init()</h2>
-
+<a class="anchor" id="a774e490e5f3eee767fdc11b779bbd1fe"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -222,9 +244,7 @@ template<typename DType > </div>
</div>
</div>
-<a id="ac02dda5d191a7bc629aaa93e5abab3d5"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ac02dda5d191a7bc629aaa93e5abab3d5">◆ </a></span>Next()</h2>
-
+<a class="anchor" id="ac02dda5d191a7bc629aaa93e5abab3d5"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -252,9 +272,7 @@ template<typename DType > </div>
</div>
</div>
-<a id="a79ed9023cf4e525c8b4a8c6fa96eea05"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a79ed9023cf4e525c8b4a8c6fa96eea05">◆ </a></span>SetDataName()</h2>
-
+<a class="anchor" id="a79ed9023cf4e525c8b4a8c6fa96eea05"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -282,9 +300,7 @@ template<typename DType > </div>
</div>
</div>
-<a id="aef2e0b74766cd0283849300c8a081929"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aef2e0b74766cd0283849300c8a081929">◆ </a></span>Value()</h2>
-
+<a class="anchor" id="aef2e0b74766cd0283849300c8a081929"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -313,9 +329,7 @@ template<typename DType > </div>
</div>
</div>
<h2 class="groupheader">Member Data Documentation</h2>
-<a id="a65cd81ecf71d54a17a94ca65d27aa014"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a65cd81ecf71d54a17a94ca65d27aa014">◆ </a></span>data_names</h2>
-
+<a class="anchor" id="a65cd81ecf71d54a17a94ca65d27aa014"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -337,9 +351,9 @@ template<typename DType > </div>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/classmxnet_1_1IIterator__coll__graph.md5 b/versions/master/doxygen/classmxnet_1_1IIterator__coll__graph.md5
index b74c548..234e141 100644
--- a/versions/master/doxygen/classmxnet_1_1IIterator__coll__graph.md5
+++ b/versions/master/doxygen/classmxnet_1_1IIterator__coll__graph.md5
@@ -1 +1 @@
-896156421475453bddc37d33fc5f19b5
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+d423194d1c37b63f24eeedbeac8726a7
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diff --git a/versions/master/doxygen/classmxnet_1_1IIterator__coll__graph.png b/versions/master/doxygen/classmxnet_1_1IIterator__coll__graph.png
index 694ca89..56da596 100644
Binary files a/versions/master/doxygen/classmxnet_1_1IIterator__coll__graph.png and b/versions/master/doxygen/classmxnet_1_1IIterator__coll__graph.png differ
diff --git a/versions/master/doxygen/classmxnet_1_1IIterator__inherit__graph.md5 b/versions/master/doxygen/classmxnet_1_1IIterator__inherit__graph.md5
index d86f61a..234e141 100644
--- a/versions/master/doxygen/classmxnet_1_1IIterator__inherit__graph.md5
+++ b/versions/master/doxygen/classmxnet_1_1IIterator__inherit__graph.md5
@@ -1 +1 @@
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+d423194d1c37b63f24eeedbeac8726a7
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diff --git a/versions/master/doxygen/classmxnet_1_1IIterator__inherit__graph.png b/versions/master/doxygen/classmxnet_1_1IIterator__inherit__graph.png
index 694ca89..56da596 100644
Binary files a/versions/master/doxygen/classmxnet_1_1IIterator__inherit__graph.png and b/versions/master/doxygen/classmxnet_1_1IIterator__inherit__graph.png differ
diff --git a/versions/master/doxygen/classmxnet_1_1KVStore-members.html b/versions/master/doxygen/classmxnet_1_1KVStore-members.html
index 1346808..5e3d229 100644
--- a/versions/master/doxygen/classmxnet_1_1KVStore-members.html
+++ b/versions/master/doxygen/classmxnet_1_1KVStore-members.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -70,26 +94,26 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a>, including all inherited members.</p>
<table class="directory">
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a4f9275fb7a11589fae1dcc5a8a3354ac">Barrier</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#aa8ba7abf8334326cab19eb568cb31cfa">barrier_before_exit_</a></td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3">Controller</a> typedef</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a6d54b075d3b43577f8339b25000a0c82">Create</a>(const char *type="local")</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a2314122405a7f3ed5f9df55cb6a66f2a">get_group_size</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a9bbe72794d75a2d8d804124ee686c01c">get_num_dead_node</a>(int node_id, int timeout=60) const</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#afa281ae77e3ca58beaee6f3a5feafbc1">get_rank</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265">Init</a>(const std::vector< int > &keys, const std::vector< NDArray > &values)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a164c3314ba04dadab4fa2a7a74c440d4">Init</a>(const std::vector< std::string > &str_keys, const std::vector< NDArray > &values)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a5d919a7a4c9495e9a569c8d733d6325f">InitPSEnv</a>(const std::unordered_map< std::string, std::string > &envs)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a41113b9dfeab6c705ba064cc3f010fb4">IsSchedulerNode</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a101178924ce1d635b0c8c2a5bce0ceaf">IsServerNode</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#ab216fb292fa77e843e95f474fa671a20">IsWorkerNode</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#ab03a9d2faa1d9a1920c6fec2f74fbb72">Pull</a>(const std::vector< int > &keys, const std::vector< NDArray *> &values, int priority=0)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a168927975b39665dc1ea7bd51a0b8805">Pull</a>(const std::vector< std::string > &str_keys, const std::vector< NDArray *> &values, int priority=0)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">Push</a>(const std::vector< int > &keys, const std::vector< NDArray > &values, int priority=0)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#aa2a9cf32a8da809bb4ae4abf40159c96">Push</a>(const std::vector< std::string > &str_keys, const std::vector< NDArray > &values, int priority=0)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#ae137ab8b5407060529c18a8084946d0b">RunServer</a>(const Controller &controller)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a4ff4ca05f3c519ebbc2e46264e8b63dd">SendCommandToServers</a>(int cmd_id, const std::string &cmd_body)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#afa87753d90fc7e75234229834ea23abb">set_barrier_before_exit</a>(const bool barrier_before_exit)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a352ef7eae04211eca1ed47401a6d15d5">set_updater</a>(const Updater &updater)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3">Controller</a> typedef</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a6d54b075d3b43577f8339b25000a0c82">Create</a>(const char *type="local")</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a1fd19b2a123b6069b35e1e8b7fad9dc3">get_group_size</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#af5c48df3aade761a547a1dc6f7b8702d">get_num_dead_node</a>(int node_id, int timeout=60) const </td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a262ef45340106fdb5c2a296c3cb711b1">get_rank</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265">Init</a>(const std::vector< int > &keys, const std::vector< NDArray > &values)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a164c3314ba04dadab4fa2a7a74c440d4">Init</a>(const std::vector< std::string > &str_keys, const std::vector< NDArray > &values)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a5d919a7a4c9495e9a569c8d733d6325f">InitPSEnv</a>(const std::unordered_map< std::string, std::string > &envs)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a41113b9dfeab6c705ba064cc3f010fb4">IsSchedulerNode</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a101178924ce1d635b0c8c2a5bce0ceaf">IsServerNode</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#ab216fb292fa77e843e95f474fa671a20">IsWorkerNode</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a98e041793c133337cecc441e441ed721">Pull</a>(const std::vector< int > &keys, const std::vector< NDArray * > &values, int priority=0)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a473e95dbf4618e34ba2e519944ece2b4">Pull</a>(const std::vector< std::string > &str_keys, const std::vector< NDArray * > &values, int priority=0)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">Push</a>(const std::vector< int > &keys, const std::vector< NDArray > &values, int priority=0)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#aa2a9cf32a8da809bb4ae4abf40159c96">Push</a>(const std::vector< std::string > &str_keys, const std::vector< NDArray > &values, int priority=0)=0</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#ae137ab8b5407060529c18a8084946d0b">RunServer</a>(const Controller &controller)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a4ff4ca05f3c519ebbc2e46264e8b63dd">SendCommandToServers</a>(int cmd_id, const std::string &cmd_body)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#afa87753d90fc7e75234229834ea23abb">set_barrier_before_exit</a>(const bool barrier_before_exit)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a352ef7eae04211eca1ed47401a6d15d5">set_updater</a>(const Updater &updater)</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a265215cd3de11dd36d9e79330a535615">true</a></td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a722b2150dbf50d528fce62307809f949">type</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#ad1023ca677c6e87ccc7d92c4df48bd40">type_</a></td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64">Updater</a> typedef</td><td class="entry"><a class="el" href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></td><td class="entry"></td></tr>
@@ -98,9 +122,9 @@ $(function() {
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/classmxnet_1_1KVStore.html b/versions/master/doxygen/classmxnet_1_1KVStore.html
index 64c5f0c..595b34c 100644
--- a/versions/master/doxygen/classmxnet_1_1KVStore.html
+++ b/versions/master/doxygen/classmxnet_1_1KVStore.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::KVStore Class Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -81,71 +105,75 @@ $(function() {
Collaboration diagram for mxnet::KVStore:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1KVStore__coll__graph.png" border="0" usemap="#mxnet_1_1KVStore_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1KVStore_coll__map" id="mxnet_1_1KVStore_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
-<tr class="memitem:a6e4a76bd57f185349c58a5a658c32a64"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(int, const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *)> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64">Updater</a></td></tr>
-<tr class="memdesc:a6e4a76bd57f185349c58a5a658c32a64"><td class="mdescLeft"> </td><td class="mdescRight">the prototype of user-defined updater <a href="#a6e4a76bd57f185349c58a5a658c32a64">More...</a><br /></td></tr>
+<tr class="memitem:a6e4a76bd57f185349c58a5a658c32a64"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(int, <br class="typebreak"/>
+const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *)> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64">Updater</a></td></tr>
+<tr class="memdesc:a6e4a76bd57f185349c58a5a658c32a64"><td class="mdescLeft"> </td><td class="mdescRight">the prototype of user-defined updater <a href="#a6e4a76bd57f185349c58a5a658c32a64">More...</a><br/></td></tr>
<tr class="separator:a6e4a76bd57f185349c58a5a658c32a64"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a50db5cb1a140fb84c8e31def54270ae3"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(int, const std::string &)> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3">Controller</a></td></tr>
-<tr class="memdesc:a50db5cb1a140fb84c8e31def54270ae3"><td class="mdescLeft"> </td><td class="mdescRight">the prototype of a server controller <a href="#a50db5cb1a140fb84c8e31def54270ae3">More...</a><br /></td></tr>
+<tr class="memitem:a50db5cb1a140fb84c8e31def54270ae3"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(int, <br class="typebreak"/>
+const std::string &)> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3">Controller</a></td></tr>
+<tr class="memdesc:a50db5cb1a140fb84c8e31def54270ae3"><td class="mdescLeft"> </td><td class="mdescRight">the prototype of a server controller <a href="#a50db5cb1a140fb84c8e31def54270ae3">More...</a><br/></td></tr>
<tr class="separator:a50db5cb1a140fb84c8e31def54270ae3"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a02e23071eb46464b58f2e2bf5654d6d6"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a02e23071eb46464b58f2e2bf5654d6d6">~KVStore</a> ()</td></tr>
-<tr class="memdesc:a02e23071eb46464b58f2e2bf5654d6d6"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#a02e23071eb46464b58f2e2bf5654d6d6">More...</a><br /></td></tr>
+<tr class="memdesc:a02e23071eb46464b58f2e2bf5654d6d6"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#a02e23071eb46464b58f2e2bf5654d6d6">More...</a><br/></td></tr>
<tr class="separator:a02e23071eb46464b58f2e2bf5654d6d6"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a722b2150dbf50d528fce62307809f949"><td class="memItemLeft" align="right" valign="top">const std::string & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a722b2150dbf50d528fce62307809f949">type</a> ()</td></tr>
-<tr class="memdesc:a722b2150dbf50d528fce62307809f949"><td class="mdescLeft"> </td><td class="mdescRight">return the type <a href="#a722b2150dbf50d528fce62307809f949">More...</a><br /></td></tr>
+<tr class="memdesc:a722b2150dbf50d528fce62307809f949"><td class="mdescLeft"> </td><td class="mdescRight">return the type <a href="#a722b2150dbf50d528fce62307809f949">More...</a><br/></td></tr>
<tr class="separator:a722b2150dbf50d528fce62307809f949"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af4e56c1f2b90bdd02e2c4e7d2abf6265"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265">Init</a> (const std::vector< int > &keys, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &values)=0</td></tr>
-<tr class="memdesc:af4e56c1f2b90bdd02e2c4e7d2abf6265"><td class="mdescLeft"> </td><td class="mdescRight">Initialize a list of key-value pair to the store. <a href="#af4e56c1f2b90bdd02e2c4e7d2abf6265">More...</a><br /></td></tr>
+<tr class="memdesc:af4e56c1f2b90bdd02e2c4e7d2abf6265"><td class="mdescLeft"> </td><td class="mdescRight">Initialize a list of key-value pair to the store. <a href="#af4e56c1f2b90bdd02e2c4e7d2abf6265">More...</a><br/></td></tr>
<tr class="separator:af4e56c1f2b90bdd02e2c4e7d2abf6265"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a164c3314ba04dadab4fa2a7a74c440d4"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a164c3314ba04dadab4fa2a7a74c440d4">Init</a> (const std::vector< std::string > &str_keys, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &values)=0</td></tr>
-<tr class="memdesc:a164c3314ba04dadab4fa2a7a74c440d4"><td class="mdescLeft"> </td><td class="mdescRight">Initialize a list of key-value pair to the store. <a href="#a164c3314ba04dadab4fa2a7a74c440d4">More...</a><br /></td></tr>
+<tr class="memdesc:a164c3314ba04dadab4fa2a7a74c440d4"><td class="mdescLeft"> </td><td class="mdescRight">Initialize a list of key-value pair to the store. <a href="#a164c3314ba04dadab4fa2a7a74c440d4">More...</a><br/></td></tr>
<tr class="separator:a164c3314ba04dadab4fa2a7a74c440d4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a56a3b6d687700f7c71310cd47ab5a54c"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">Push</a> (const std::vector< int > &keys, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &values, int priority=0)=0</td></tr>
-<tr class="memdesc:a56a3b6d687700f7c71310cd47ab5a54c"><td class="mdescLeft"> </td><td class="mdescRight">push a list of key-value pairs into the store <a href="#a56a3b6d687700f7c71310cd47ab5a54c">More...</a><br /></td></tr>
+<tr class="memdesc:a56a3b6d687700f7c71310cd47ab5a54c"><td class="mdescLeft"> </td><td class="mdescRight">push a list of key-value pairs into the store <a href="#a56a3b6d687700f7c71310cd47ab5a54c">More...</a><br/></td></tr>
<tr class="separator:a56a3b6d687700f7c71310cd47ab5a54c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa2a9cf32a8da809bb4ae4abf40159c96"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#aa2a9cf32a8da809bb4ae4abf40159c96">Push</a> (const std::vector< std::string > &str_keys, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &values, int priority=0)=0</td></tr>
-<tr class="memdesc:aa2a9cf32a8da809bb4ae4abf40159c96"><td class="mdescLeft"> </td><td class="mdescRight">push a list of key-value pairs into the store <a href="#aa2a9cf32a8da809bb4ae4abf40159c96">More...</a><br /></td></tr>
+<tr class="memdesc:aa2a9cf32a8da809bb4ae4abf40159c96"><td class="mdescLeft"> </td><td class="mdescRight">push a list of key-value pairs into the store <a href="#aa2a9cf32a8da809bb4ae4abf40159c96">More...</a><br/></td></tr>
<tr class="separator:aa2a9cf32a8da809bb4ae4abf40159c96"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ab03a9d2faa1d9a1920c6fec2f74fbb72"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#ab03a9d2faa1d9a1920c6fec2f74fbb72">Pull</a> (const std::vector< int > &keys, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *> &values, int priority=0)=0</td></tr>
-<tr class="memdesc:ab03a9d2faa1d9a1920c6fec2f74fbb72"><td class="mdescLeft"> </td><td class="mdescRight">pull a list of key-value pairs from the store <a href="#ab03a9d2faa1d9a1920c6fec2f74fbb72">More...</a><br /></td></tr>
-<tr class="separator:ab03a9d2faa1d9a1920c6fec2f74fbb72"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a168927975b39665dc1ea7bd51a0b8805"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a168927975b39665dc1ea7bd51a0b8805">Pull</a> (const std::vector< std::string > &str_keys, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *> &values, int priority=0)=0</td></tr>
-<tr class="memdesc:a168927975b39665dc1ea7bd51a0b8805"><td class="mdescLeft"> </td><td class="mdescRight">pull a list of key-value pairs from the store <a href="#a168927975b39665dc1ea7bd51a0b8805">More...</a><br /></td></tr>
-<tr class="separator:a168927975b39665dc1ea7bd51a0b8805"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a98e041793c133337cecc441e441ed721"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a98e041793c133337cecc441e441ed721">Pull</a> (const std::vector< int > &keys, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * > &values, int priority=0)=0</td></tr>
+<tr class="memdesc:a98e041793c133337cecc441e441ed721"><td class="mdescLeft"> </td><td class="mdescRight">pull a list of key-value pairs from the store <a href="#a98e041793c133337cecc441e441ed721">More...</a><br/></td></tr>
+<tr class="separator:a98e041793c133337cecc441e441ed721"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a473e95dbf4618e34ba2e519944ece2b4"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a473e95dbf4618e34ba2e519944ece2b4">Pull</a> (const std::vector< std::string > &str_keys, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * > &values, int priority=0)=0</td></tr>
+<tr class="memdesc:a473e95dbf4618e34ba2e519944ece2b4"><td class="mdescLeft"> </td><td class="mdescRight">pull a list of key-value pairs from the store <a href="#a473e95dbf4618e34ba2e519944ece2b4">More...</a><br/></td></tr>
+<tr class="separator:a473e95dbf4618e34ba2e519944ece2b4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a352ef7eae04211eca1ed47401a6d15d5"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a352ef7eae04211eca1ed47401a6d15d5">set_updater</a> (const <a class="el" href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64">Updater</a> &updater)</td></tr>
-<tr class="memdesc:a352ef7eae04211eca1ed47401a6d15d5"><td class="mdescLeft"> </td><td class="mdescRight">set an updater <a href="#a352ef7eae04211eca1ed47401a6d15d5">More...</a><br /></td></tr>
+<tr class="memdesc:a352ef7eae04211eca1ed47401a6d15d5"><td class="mdescLeft"> </td><td class="mdescRight">set an updater <a href="#a352ef7eae04211eca1ed47401a6d15d5">More...</a><br/></td></tr>
<tr class="separator:a352ef7eae04211eca1ed47401a6d15d5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:afa87753d90fc7e75234229834ea23abb"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#afa87753d90fc7e75234229834ea23abb">set_barrier_before_exit</a> (const bool barrier_before_exit)</td></tr>
<tr class="separator:afa87753d90fc7e75234229834ea23abb"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:afa281ae77e3ca58beaee6f3a5feafbc1"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#afa281ae77e3ca58beaee6f3a5feafbc1">get_rank</a> () const</td></tr>
-<tr class="separator:afa281ae77e3ca58beaee6f3a5feafbc1"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2314122405a7f3ed5f9df55cb6a66f2a"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a2314122405a7f3ed5f9df55cb6a66f2a">get_group_size</a> () const</td></tr>
-<tr class="separator:a2314122405a7f3ed5f9df55cb6a66f2a"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a9bbe72794d75a2d8d804124ee686c01c"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a9bbe72794d75a2d8d804124ee686c01c">get_num_dead_node</a> (int node_id, int timeout=60) const</td></tr>
-<tr class="separator:a9bbe72794d75a2d8d804124ee686c01c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a262ef45340106fdb5c2a296c3cb711b1"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a262ef45340106fdb5c2a296c3cb711b1">get_rank</a> () const </td></tr>
+<tr class="separator:a262ef45340106fdb5c2a296c3cb711b1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1fd19b2a123b6069b35e1e8b7fad9dc3"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a1fd19b2a123b6069b35e1e8b7fad9dc3">get_group_size</a> () const </td></tr>
+<tr class="separator:a1fd19b2a123b6069b35e1e8b7fad9dc3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af5c48df3aade761a547a1dc6f7b8702d"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#af5c48df3aade761a547a1dc6f7b8702d">get_num_dead_node</a> (int node_id, int timeout=60) const </td></tr>
+<tr class="separator:af5c48df3aade761a547a1dc6f7b8702d"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4f9275fb7a11589fae1dcc5a8a3354ac"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a4f9275fb7a11589fae1dcc5a8a3354ac">Barrier</a> ()</td></tr>
-<tr class="memdesc:a4f9275fb7a11589fae1dcc5a8a3354ac"><td class="mdescLeft"> </td><td class="mdescRight">global barrier among all worker machines <a href="#a4f9275fb7a11589fae1dcc5a8a3354ac">More...</a><br /></td></tr>
+<tr class="memdesc:a4f9275fb7a11589fae1dcc5a8a3354ac"><td class="mdescLeft"> </td><td class="mdescRight">global barrier among all worker machines <a href="#a4f9275fb7a11589fae1dcc5a8a3354ac">More...</a><br/></td></tr>
<tr class="separator:a4f9275fb7a11589fae1dcc5a8a3354ac"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4ff4ca05f3c519ebbc2e46264e8b63dd"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a4ff4ca05f3c519ebbc2e46264e8b63dd">SendCommandToServers</a> (int cmd_id, const std::string &cmd_body)</td></tr>
-<tr class="memdesc:a4ff4ca05f3c519ebbc2e46264e8b63dd"><td class="mdescLeft"> </td><td class="mdescRight">Send a command to all server nodes. <a href="#a4ff4ca05f3c519ebbc2e46264e8b63dd">More...</a><br /></td></tr>
+<tr class="memdesc:a4ff4ca05f3c519ebbc2e46264e8b63dd"><td class="mdescLeft"> </td><td class="mdescRight">Send a command to all server nodes. <a href="#a4ff4ca05f3c519ebbc2e46264e8b63dd">More...</a><br/></td></tr>
<tr class="separator:a4ff4ca05f3c519ebbc2e46264e8b63dd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae137ab8b5407060529c18a8084946d0b"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#ae137ab8b5407060529c18a8084946d0b">RunServer</a> (const <a class="el" href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3">Controller</a> &controller)</td></tr>
-<tr class="memdesc:ae137ab8b5407060529c18a8084946d0b"><td class="mdescLeft"> </td><td class="mdescRight">Run as server (or scheduler) <a href="#ae137ab8b5407060529c18a8084946d0b">More...</a><br /></td></tr>
+<tr class="memdesc:ae137ab8b5407060529c18a8084946d0b"><td class="mdescLeft"> </td><td class="mdescRight">Run as server (or scheduler) <a href="#ae137ab8b5407060529c18a8084946d0b">More...</a><br/></td></tr>
<tr class="separator:ae137ab8b5407060529c18a8084946d0b"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:a6d54b075d3b43577f8339b25000a0c82"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classmxnet_1_1KVStore.html">KVStore</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a6d54b075d3b43577f8339b25000a0c82">Create</a> (const char *<a class="el" href="classmxnet_1_1KVStore.html#a722b2150dbf50d528fce62307809f949">type</a>="local")</td></tr>
-<tr class="memdesc:a6d54b075d3b43577f8339b25000a0c82"><td class="mdescLeft"> </td><td class="mdescRight">Factory function to create a new <a class="el" href="classmxnet_1_1KVStore.html" title="distributed key-value store ">KVStore</a>. <a href="#a6d54b075d3b43577f8339b25000a0c82">More...</a><br /></td></tr>
+<tr class="memdesc:a6d54b075d3b43577f8339b25000a0c82"><td class="mdescLeft"> </td><td class="mdescRight">Factory function to create a new <a class="el" href="classmxnet_1_1KVStore.html" title="distributed key-value store ">KVStore</a>. <a href="#a6d54b075d3b43577f8339b25000a0c82">More...</a><br/></td></tr>
<tr class="separator:a6d54b075d3b43577f8339b25000a0c82"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5d919a7a4c9495e9a569c8d733d6325f"><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a5d919a7a4c9495e9a569c8d733d6325f">InitPSEnv</a> (const std::unordered_map< std::string, std::string > &envs)</td></tr>
-<tr class="memdesc:a5d919a7a4c9495e9a569c8d733d6325f"><td class="mdescLeft"> </td><td class="mdescRight">initalize ps-lite environment variables <a href="#a5d919a7a4c9495e9a569c8d733d6325f">More...</a><br /></td></tr>
+<tr class="memdesc:a5d919a7a4c9495e9a569c8d733d6325f"><td class="mdescLeft"> </td><td class="mdescRight">initalize ps-lite environment variables <a href="#a5d919a7a4c9495e9a569c8d733d6325f">More...</a><br/></td></tr>
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<tr class="memitem:ab216fb292fa77e843e95f474fa671a20"><td class="memItemLeft" align="right" valign="top">static bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#ab216fb292fa77e843e95f474fa671a20">IsWorkerNode</a> ()</td></tr>
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Protected Attributes</h2></td></tr>
<tr class="memitem:a01befb768a81a8e7a366708240ee6d06"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64">Updater</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a01befb768a81a8e7a366708240ee6d06">updater_</a></td></tr>
-<tr class="memdesc:a01befb768a81a8e7a366708240ee6d06"><td class="mdescLeft"> </td><td class="mdescRight">the user-defined updater <a href="#a01befb768a81a8e7a366708240ee6d06">More...</a><br /></td></tr>
+<tr class="memdesc:a01befb768a81a8e7a366708240ee6d06"><td class="mdescLeft"> </td><td class="mdescRight">the user-defined updater <a href="#a01befb768a81a8e7a366708240ee6d06">More...</a><br/></td></tr>
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<tr class="memitem:ad1023ca677c6e87ccc7d92c4df48bd40"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#ad1023ca677c6e87ccc7d92c4df48bd40">type_</a></td></tr>
-<tr class="memdesc:ad1023ca677c6e87ccc7d92c4df48bd40"><td class="mdescLeft"> </td><td class="mdescRight">the kvstore type <a href="#ad1023ca677c6e87ccc7d92c4df48bd40">More...</a><br /></td></tr>
+<tr class="memdesc:ad1023ca677c6e87ccc7d92c4df48bd40"><td class="mdescLeft"> </td><td class="mdescRight">the kvstore type <a href="#ad1023ca677c6e87ccc7d92c4df48bd40">More...</a><br/></td></tr>
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-<tr class="memitem:aa8ba7abf8334326cab19eb568cb31cfa"><td class="memItemLeft" align="right" valign="top">std::atomic< bool > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#aa8ba7abf8334326cab19eb568cb31cfa">barrier_before_exit_</a> {true}</td></tr>
-<tr class="memdesc:aa8ba7abf8334326cab19eb568cb31cfa"><td class="mdescLeft"> </td><td class="mdescRight">whether to do barrier when finalize <a href="#aa8ba7abf8334326cab19eb568cb31cfa">More...</a><br /></td></tr>
-<tr class="separator:aa8ba7abf8334326cab19eb568cb31cfa"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a265215cd3de11dd36d9e79330a535615"><td class="memItemLeft" align="right" valign="top">std::atomic< bool ><br class="typebreak"/>
+ barrier_before_exit_ </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html#a265215cd3de11dd36d9e79330a535615">true</a></td></tr>
+<tr class="memdesc:a265215cd3de11dd36d9e79330a535615"><td class="mdescLeft"> </td><td class="mdescRight">whether to do barrier when finalize <a href="#a265215cd3de11dd36d9e79330a535615">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>distributed key-value store </p>
<p>A distributed key-value store for data synchronization over multiple devices/machines. It support user-defined updater. </p>
</div><h2 class="groupheader">Member Typedef Documentation</h2>
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<h2 class="groupheader">Member Function Documentation</h2>
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<p>global barrier among all worker machines </p>
-<p>But note that, this functions only blocks the main thread of workers until all of them are reached this point. It doesn't guarantee that all operations issued before are actually finished, such as <a class="el" href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">Push</a> and <a class="el" href="classmxnet_1_1KVStore.html#ab03a9d2faa1d9a1920c6fec2f74fbb72">Pull</a>. </p>
+<p>But note that, this functions only blocks the main thread of workers until all of them are reached this point. It doesn't guarantee that all operations issued before are actually finished, such as <a class="el" href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">Push</a> and <a class="el" href="classmxnet_1_1KVStore.html#a98e041793c133337cecc441e441ed721">Pull</a>. </p>
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<p>Initialize a list of key-value pair to the store. </p>
-<p>One must initalize the key before <a class="el" href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">Push</a> and <a class="el" href="classmxnet_1_1KVStore.html#ab03a9d2faa1d9a1920c6fec2f74fbb72">Pull</a>, and a key should be only initialized once</p>
+<p>One must initialize the key before <a class="el" href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">Push</a> and <a class="el" href="classmxnet_1_1KVStore.html#a98e041793c133337cecc441e441ed721">Pull</a>, and a key should be only initialized once</p>
<p>It returns after data have been initialized successfully.</p>
-<p>For multiple workers, all workers must call <a class="el" href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265">Init</a>. But only worker 0 (<a class="el" href="classmxnet_1_1KVStore.html#afa281ae77e3ca58beaee6f3a5feafbc1">get_rank()</a> == 0)'s values are used for initialization. So others' values can be empty (but not keys). This function blocks until all workers are finished. That means, any worker can push and pull on the keys now.</p>
+<p>For multiple workers, all workers must call <a class="el" href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265">Init</a>. But only worker 0 (<a class="el" href="classmxnet_1_1KVStore.html#a262ef45340106fdb5c2a296c3cb711b1">get_rank()</a> == 0)'s values are used for initialization. So others' values can be empty (but not keys). This function blocks until all workers are finished. That means, any worker can push and pull on the keys now.</p>
<dl class="params"><dt>Parameters</dt><dd>
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<tr><td class="paramname">keys</td><td>a list of unique keys </td></tr>
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- <td class="paramtype">const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *> & </td>
+ <td class="paramtype">const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * > & </td>
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<p>One must call <a class="el" href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265" title="Initialize a list of key-value pair to the store. ">Init()</a> on <em>key</em> before. And <em>value</em> should be pre-allocated</p>
<p>This function returns after adding a pull operator to the engine. Any following operator requiring reading value will be blocked until the actual pull is finished. One can wait the pull is finished by</p>
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-<li>when type == "local" <div class="fragment"><div class="line"><span class="keywordflow">for</span> (<span class="keyword">auto</span>& v : values) v.WaitToRead()</div></div><!-- fragment --></li>
-<li>when type == "dist" <div class="fragment"><div class="line">Wait(keys);</div></div><!-- fragment --></li>
+<li>when type == "local" <div class="fragment"><div class="line"><span class="keywordflow">for</span> (<span class="keyword">auto</span>& v : values) v.WaitToRead()</div>
+</div><!-- fragment --></li>
+<li>when type == "dist" <div class="fragment"><div class="line">Wait(keys);</div>
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<p>push a list of key-value pairs into the store </p>
<p>If a key appears mulitple times in <em>keys</em>, then the according values will be aggregated (summed) before pushing.</p>
<p>The (aggregated) values are merged into the store one by one</p>
-<div class="fragment"><div class="line">updater(key, value, &value_in_store);</div></div><!-- fragment --><p>One can set a user-defined updater by <a class="el" href="classmxnet_1_1KVStore.html#a352ef7eae04211eca1ed47401a6d15d5">set_updater</a>. The default updater is Assign.</p>
+<div class="fragment"><div class="line">updater(key, value, &value_in_store);</div>
+</div><!-- fragment --><p>One can set a user-defined updater by <a class="el" href="classmxnet_1_1KVStore.html#a352ef7eae04211eca1ed47401a6d15d5">set_updater</a>. The default updater is Assign.</p>
<p>This function returns after adding a push operator to the engine. Any following operator requiring writing value will be blocked until the actual push is finished. One can wait the push is finished by</p>
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-<li>when type == "local" <div class="fragment"><div class="line"><span class="keywordflow">for</span> (<span class="keyword">auto</span>& v : values) v.WaitToWrite()</div></div><!-- fragment --></li>
-<li>when type == "dist" <div class="fragment"><div class="line">Wait(keys);</div></div><!-- fragment --></li>
+<li>when type == "local" <div class="fragment"><div class="line"><span class="keywordflow">for</span> (<span class="keyword">auto</span>& v : values) v.WaitToWrite()</div>
+</div><!-- fragment --></li>
+<li>when type == "dist" <div class="fragment"><div class="line">Wait(keys);</div>
+</div><!-- fragment --></li>
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<p>One must call <a class="el" href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265" title="Initialize a list of key-value pair to the store. ">Init()</a> on every key before. And the value <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> should be always has the same shape as being inited.</p>
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</div><div class="memdoc">
<p>Run as server (or scheduler) </p>
-<p>The behavior of a server: </p><div class="fragment"><div class="line"><span class="keywordflow">while</span>(receive(x)) {</div><div class="line"> <span class="keywordflow">if</span> (IsCommand(x)) controller(x)</div><div class="line"> <span class="keywordflow">else</span> <span class="keywordflow">if</span> (IsKeyValue(x)) updater(x)</div><div class="line">}</div></div><!-- fragment --><dl class="params"><dt>Parameters</dt><dd>
+<p>The behavior of a server: </p>
+<div class="fragment"><div class="line"><span class="keywordflow">while</span>(receive(x)) {</div>
+<div class="line"> <span class="keywordflow">if</span> (IsCommand(x)) controller(x)</div>
+<div class="line"> else if (IsKeyValue(x)) updater(x)</div>
+<div class="line">}</div>
+</div><!-- fragment --><dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">controller</td><td>the user-defined server controller </td></tr>
</table>
@@ -862,9 +863,7 @@ Protected Attributes</h2></td></tr>
</div>
</div>
-<a id="a4ff4ca05f3c519ebbc2e46264e8b63dd"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a4ff4ca05f3c519ebbc2e46264e8b63dd">◆ </a></span>SendCommandToServers()</h2>
-
+<a class="anchor" id="a4ff4ca05f3c519ebbc2e46264e8b63dd"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -909,9 +908,7 @@ Protected Attributes</h2></td></tr>
</div>
</div>
-<a id="afa87753d90fc7e75234229834ea23abb"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#afa87753d90fc7e75234229834ea23abb">◆ </a></span>set_barrier_before_exit()</h2>
-
+<a class="anchor" id="afa87753d90fc7e75234229834ea23abb"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -935,9 +932,7 @@ Protected Attributes</h2></td></tr>
</div>
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-<a id="a352ef7eae04211eca1ed47401a6d15d5"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a352ef7eae04211eca1ed47401a6d15d5">◆ </a></span>set_updater()</h2>
-
+<a class="anchor" id="a352ef7eae04211eca1ed47401a6d15d5"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -970,9 +965,7 @@ Protected Attributes</h2></td></tr>
</div>
</div>
-<a id="a722b2150dbf50d528fce62307809f949"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a722b2150dbf50d528fce62307809f949">◆ </a></span>type()</h2>
-
+<a class="anchor" id="a722b2150dbf50d528fce62307809f949"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -998,9 +991,7 @@ Protected Attributes</h2></td></tr>
</div>
</div>
<h2 class="groupheader">Member Data Documentation</h2>
-<a id="aa8ba7abf8334326cab19eb568cb31cfa"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aa8ba7abf8334326cab19eb568cb31cfa">◆ </a></span>barrier_before_exit_</h2>
-
+<a class="anchor" id="a265215cd3de11dd36d9e79330a535615"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -1008,7 +999,7 @@ Protected Attributes</h2></td></tr>
<td class="mlabels-left">
<table class="memname">
<tr>
- <td class="memname">std::atomic<bool> mxnet::KVStore::barrier_before_exit_ {true}</td>
+ <td class="memname">std::atomic<bool> barrier_before_exit_ mxnet::KVStore::true</td>
</tr>
</table>
</td>
@@ -1022,9 +1013,7 @@ Protected Attributes</h2></td></tr>
</div>
</div>
-<a id="ad1023ca677c6e87ccc7d92c4df48bd40"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad1023ca677c6e87ccc7d92c4df48bd40">◆ </a></span>type_</h2>
-
+<a class="anchor" id="ad1023ca677c6e87ccc7d92c4df48bd40"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -1046,9 +1035,7 @@ Protected Attributes</h2></td></tr>
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-<a id="a01befb768a81a8e7a366708240ee6d06"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a01befb768a81a8e7a366708240ee6d06">◆ </a></span>updater_</h2>
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+<a class="anchor" id="a01befb768a81a8e7a366708240ee6d06"></a>
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<div class="memproto">
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@@ -1076,9 +1063,9 @@ Protected Attributes</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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</html>
diff --git a/versions/master/doxygen/classmxnet_1_1KVStore__coll__graph.md5 b/versions/master/doxygen/classmxnet_1_1KVStore__coll__graph.md5
index e7dce73..629b000 100644
--- a/versions/master/doxygen/classmxnet_1_1KVStore__coll__graph.md5
+++ b/versions/master/doxygen/classmxnet_1_1KVStore__coll__graph.md5
@@ -1 +1 @@
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+99a9ceacdb6c6231ad26d98577d1a1f2
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diff --git a/versions/master/doxygen/classmxnet_1_1KVStore__coll__graph.png b/versions/master/doxygen/classmxnet_1_1KVStore__coll__graph.png
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Binary files a/versions/master/doxygen/classmxnet_1_1KVStore__coll__graph.png and b/versions/master/doxygen/classmxnet_1_1KVStore__coll__graph.png differ
diff --git a/versions/master/doxygen/classmxnet_1_1NDArray-members.html b/versions/master/doxygen/classmxnet_1_1NDArray-members.html
index f56dbcd..7bf99cd 100644
--- a/versions/master/doxygen/classmxnet_1_1NDArray-members.html
+++ b/versions/master/doxygen/classmxnet_1_1NDArray-members.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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@@ -69,50 +93,52 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a>, including all inherited members.</p>
<table class="directory">
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ac3c424c05eabe0b7a4976191d81cf6b0">AsArray</a>(const TShape &shape, int dtype) const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#addb42a7353a4c07b2d1bae0afa8ef48e">At</a>(index_t idx) const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a3896fcce89afce4c1d2f825f7853ca0c">AsArray</a>(const TShape &shape, int dtype) const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a3b615512a8fb89b9edc99b35064ead95">At</a>(index_t idx) const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a423cc97aa03ee0dd8bc13781b77b8e50">autograd::AutogradRuntime</a> class</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a531a3437dde0dfc9b0c40a32450f4c7a">CheckAndAlloc</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a6617e1bf4242314f7ee6b6e3a66ce493">Copy</a>(Context ctx) const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a26d1ddb2bb0ed1e671ce7606498b942d">ctx</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">data</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a76646c5c7e164bd7c81854521d3bee52">Detach</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a67b0f8eb36568b4c9fbd3da2a1c4b0a3">dtype</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ad9d2fa3ffbe963fb5960375da1b557a2">fresh_out_grad</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a9c038544414ed2b992f9393a14eff6cd">grad</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a29673998ea5bec1cab7337595d907c67">is_none</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a">CheckAndAlloc</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a4a00e2868ad8b9ccb0a48079635b110f">Copy</a>(Context ctx) const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">ctx</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a34a3694141050a257dc6b40a6ad2c4ec">Detach</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">dtype</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#af5d6ed79d0346407edfc969d4d51d15c">dtype_</a>(data.type_flag_)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a09ff97852143fdd75dd1e071d0d53266">entry_</a>({nullptr, 0, 0})</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#aee74980eb87bf7c482d15458f5194cbe">fresh_out_grad</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#aed729c3bc9db04ab346fd3969b4fc982">grad</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a2a15ffdbf29d4d5455a4fbb4b607880c">is_none</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a5203aa033a53942deaecb7bb3bf46480">Load</a>(dmlc::Stream *strm)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ae45c38de80d6d4dc79ad3d24baa9e981">Load</a>(dmlc::Stream *fi, std::vector< NDArray > *data, std::vector< std::string > *keys)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a12ec978e5dbe9092d21721a657c39756">NDArray</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a8fb6d94c737c452e180650fdfe5228ad">NDArray</a>(const TShape &shape, Context ctx, bool delay_alloc=false, int dtype=mshadow::default_type_flag)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a1a7288505ac9bbb19830161f7e5c1a6f">NDArray</a>(const TBlob &data, int dev_id)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a1b47078a60bf0369ff0a7df893caf65b">operator*=</a>(const NDArray &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a7b4b2ac8b0c154ae8c53ebfd2d1d3fcf">operator*=</a>(const real_t &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a7df5a5d9956a481635fc43ca4d9a196c">operator+=</a>(const NDArray &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a1068954b69113429bab7b369300fdbe0">operator+=</a>(const real_t &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ad35ba01da6949494afe895b7429e4fc3">operator-=</a>(const NDArray &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a81a7b37678af0f23edc2beec6daca3ab">operator-=</a>(const real_t &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#aebc61006705874666423c45f161df546">operator/=</a>(const NDArray &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ab88b476b2092b30c7d456d99df9f36e3">operator/=</a>(const real_t &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#aa7a198e3e4c80a48cf92a913009aab8f">operator=</a>(real_t scalar)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a1d01269333b9226fffddc89ce3b7fb63">Reshape</a>(const TShape &shape) const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a7dd1143eeb4fbe15cebe85f28142551f">Save</a>(dmlc::Stream *strm) const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#af215b7a886e717b4bb3c911fe3c1a032">Save</a>(dmlc::Stream *fo, const std::vector< NDArray > &data, const std::vector< std::string > &names)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#aec1b90af51327c2956adf182c201de03">set_fresh_out_grad</a>(bool state) const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a2fc268d12168d39d9095829b3b9a3836">shape</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a0cfa7466a652feb2258bb990453a0a7d">Slice</a>(index_t begin, index_t end) const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ac390edfdd8a7dd99f69ea373f4d18e1a">SyncCopyFromCPU</a>(const void *data, size_t size) const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#afb3dbc357c53e1e0b980257f6c5e2cac">SyncCopyToCPU</a>(void *data, size_t size) const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a7876b8d60109a18feabe767d85f66226">T</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a168da55c01cc5c7a218d975af6b5b1f9">var</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a1cf4c49e6925d35586f75a1c9bdb51e4">WaitToRead</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ad8599450a4ba06b5890c5d06bd024d8e">WaitToWrite</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a1b47078a60bf0369ff0a7df893caf65b">operator*=</a>(const NDArray &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a7b4b2ac8b0c154ae8c53ebfd2d1d3fcf">operator*=</a>(const real_t &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a7df5a5d9956a481635fc43ca4d9a196c">operator+=</a>(const NDArray &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a1068954b69113429bab7b369300fdbe0">operator+=</a>(const real_t &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ad35ba01da6949494afe895b7429e4fc3">operator-=</a>(const NDArray &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a81a7b37678af0f23edc2beec6daca3ab">operator-=</a>(const real_t &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#aebc61006705874666423c45f161df546">operator/=</a>(const NDArray &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ab88b476b2092b30c7d456d99df9f36e3">operator/=</a>(const real_t &src)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#aa7a198e3e4c80a48cf92a913009aab8f">operator=</a>(real_t scalar)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#ada618436206b59aa3ffcdb8474d5051c">Reshape</a>(const TShape &shape) const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a524662078483f8f46c6c71714fbfde0b">Save</a>(dmlc::Stream *strm) const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#af215b7a886e717b4bb3c911fe3c1a032">Save</a>(dmlc::Stream *fo, const std::vector< NDArray > &data, const std::vector< std::string > &names)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a32ed2be4ecf94c58b19bd43b48f2b1fa">set_fresh_out_grad</a>(bool state) const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">shape</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a18e75568a3a4fc14f271793dde914b56">shape_</a>(data.shape_)</td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a7acb6462c11e071949db2b0e1fbfd66e">Slice</a>(index_t begin, index_t end) const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a422c70b16646f8a273e9b4936fbe2e37">SyncCopyFromCPU</a>(const void *data, size_t size) const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a4819d2e6431ccdf4b9f8efa8eb0bd1fd">SyncCopyToCPU</a>(void *data, size_t size) const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a3fe1974c7c2d522b09b9bf82fd2865ba">T</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">var</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#aedd79a43d5fa4173f3ddd719fb939fbc">WaitToRead</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html#a4b88b5d86ec9ddf4ca96701107337461">WaitToWrite</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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-</a> 1.8.13
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diff --git a/versions/master/doxygen/classmxnet_1_1NDArray.html b/versions/master/doxygen/classmxnet_1_1NDArray.html
index 51fbf9c..2d30359 100644
--- a/versions/master/doxygen/classmxnet_1_1NDArray.html
+++ b/versions/master/doxygen/classmxnet_1_1NDArray.html
@@ -3,15 +3,16 @@
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<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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-<meta name="generator" content="Doxygen 1.8.13"/>
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+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::NDArray Class Reference</title>
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@@ -20,7 +21,7 @@
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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@@ -29,25 +30,48 @@
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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onmouseover="return searchBox.OnSearchSelectShow()"
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -88,109 +112,112 @@ Collaboration diagram for mxnet::NDArray:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a12ec978e5dbe9092d21721a657c39756"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a12ec978e5dbe9092d21721a657c39756">NDArray</a> ()</td></tr>
-<tr class="memdesc:a12ec978e5dbe9092d21721a657c39756"><td class="mdescLeft"> </td><td class="mdescRight">default constructor <a href="#a12ec978e5dbe9092d21721a657c39756">More...</a><br /></td></tr>
+<tr class="memdesc:a12ec978e5dbe9092d21721a657c39756"><td class="mdescLeft"> </td><td class="mdescRight">default constructor <a href="#a12ec978e5dbe9092d21721a657c39756">More...</a><br/></td></tr>
<tr class="separator:a12ec978e5dbe9092d21721a657c39756"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a8fb6d94c737c452e180650fdfe5228ad"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a8fb6d94c737c452e180650fdfe5228ad">NDArray</a> (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &<a class="el" href="classmxnet_1_1NDArray.html#a2fc268d12168d39d9095829b3b9a3836">shape</a>, <a class="el" href="structmxnet_1_1Context.html">C [...]
-<tr class="memdesc:a8fb6d94c737c452e180650fdfe5228ad"><td class="mdescLeft"> </td><td class="mdescRight">constructs a new dynamic <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a8fb6d94c737c452e180650fdfe5228ad">More...</a><br /></td></tr>
+<tr class="memitem:a8fb6d94c737c452e180650fdfe5228ad"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a8fb6d94c737c452e180650fdfe5228ad">NDArray</a> (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &<a class="el" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">shape</a>, <a class="el" href="structmxnet_1_1Context.html">C [...]
+<tr class="memdesc:a8fb6d94c737c452e180650fdfe5228ad"><td class="mdescLeft"> </td><td class="mdescRight">constructs a new dynamic <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a8fb6d94c737c452e180650fdfe5228ad">More...</a><br/></td></tr>
<tr class="separator:a8fb6d94c737c452e180650fdfe5228ad"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a1a7288505ac9bbb19830161f7e5c1a6f"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a1a7288505ac9bbb19830161f7e5c1a6f">NDArray</a> (const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &<a class="el" href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">data</a>, int dev_id)</td></tr>
-<tr class="memdesc:a1a7288505ac9bbb19830161f7e5c1a6f"><td class="mdescLeft"> </td><td class="mdescRight">constructing a static <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> that shares data with <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> Use with caution: allocate ONLY ONE <a class="el" href="classmxnet_1_1NDArray.html" tit [...]
-<tr class="separator:a1a7288505ac9bbb19830161f7e5c1a6f"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2fc268d12168d39d9095829b3b9a3836"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a2fc268d12168d39d9095829b3b9a3836">shape</a> () const</td></tr>
-<tr class="separator:a2fc268d12168d39d9095829b3b9a3836"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ade5c291caf0e240892b1bffaf7e5c8b5"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">data</a> () const</td></tr>
-<tr class="separator:ade5c291caf0e240892b1bffaf7e5c8b5"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a9c038544414ed2b992f9393a14eff6cd"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a9c038544414ed2b992f9393a14eff6cd">grad</a> () const</td></tr>
-<tr class="separator:a9c038544414ed2b992f9393a14eff6cd"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a26d1ddb2bb0ed1e671ce7606498b942d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1Context.html">Context</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a26d1ddb2bb0ed1e671ce7606498b942d">ctx</a> () const</td></tr>
-<tr class="separator:a26d1ddb2bb0ed1e671ce7606498b942d"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a67b0f8eb36568b4c9fbd3da2a1c4b0a3"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a67b0f8eb36568b4c9fbd3da2a1c4b0a3">dtype</a> () const</td></tr>
-<tr class="separator:a67b0f8eb36568b4c9fbd3da2a1c4b0a3"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a29673998ea5bec1cab7337595d907c67"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a29673998ea5bec1cab7337595d907c67">is_none</a> () const</td></tr>
-<tr class="separator:a29673998ea5bec1cab7337595d907c67"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad9d2fa3ffbe963fb5960375da1b557a2"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ad9d2fa3ffbe963fb5960375da1b557a2">fresh_out_grad</a> () const</td></tr>
-<tr class="separator:ad9d2fa3ffbe963fb5960375da1b557a2"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aec1b90af51327c2956adf182c201de03"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#aec1b90af51327c2956adf182c201de03">set_fresh_out_grad</a> (bool state) const</td></tr>
-<tr class="separator:aec1b90af51327c2956adf182c201de03"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a1cf4c49e6925d35586f75a1c9bdb51e4"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a1cf4c49e6925d35586f75a1c9bdb51e4">WaitToRead</a> () const</td></tr>
-<tr class="memdesc:a1cf4c49e6925d35586f75a1c9bdb51e4"><td class="mdescLeft"> </td><td class="mdescRight">Block until all the pending write operations with respect to current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> are finished, and read can be performed. <a href="#a1cf4c49e6925d35586f75a1c9bdb51e4">More...</a><br /></td></tr>
-<tr class="separator:a1cf4c49e6925d35586f75a1c9bdb51e4"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad8599450a4ba06b5890c5d06bd024d8e"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ad8599450a4ba06b5890c5d06bd024d8e">WaitToWrite</a> () const</td></tr>
-<tr class="memdesc:ad8599450a4ba06b5890c5d06bd024d8e"><td class="mdescLeft"> </td><td class="mdescRight">Block until all the pending read/write operations with respect to current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> are finished, and write can be performed. <a href="#ad8599450a4ba06b5890c5d06bd024d8e">More...</a><br /></td></tr>
-<tr class="separator:ad8599450a4ba06b5890c5d06bd024d8e"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a168da55c01cc5c7a218d975af6b5b1f9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">Engine::VarHandle</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a168da55c01cc5c7a218d975af6b5b1f9">var</a> () const</td></tr>
-<tr class="separator:a168da55c01cc5c7a218d975af6b5b1f9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a7dd1143eeb4fbe15cebe85f28142551f"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a7dd1143eeb4fbe15cebe85f28142551f">Save</a> (dmlc::Stream *strm) const</td></tr>
-<tr class="memdesc:a7dd1143eeb4fbe15cebe85f28142551f"><td class="mdescLeft"> </td><td class="mdescRight">save the content into binary stream <a href="#a7dd1143eeb4fbe15cebe85f28142551f">More...</a><br /></td></tr>
-<tr class="separator:a7dd1143eeb4fbe15cebe85f28142551f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a18e75568a3a4fc14f271793dde914b56"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a18e75568a3a4fc14f271793dde914b56">shape_</a> (data.shape_)</td></tr>
+<tr class="separator:a18e75568a3a4fc14f271793dde914b56"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af5d6ed79d0346407edfc969d4d51d15c"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#af5d6ed79d0346407edfc969d4d51d15c">dtype_</a> (data.type_flag_)</td></tr>
+<tr class="separator:af5d6ed79d0346407edfc969d4d51d15c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a09ff97852143fdd75dd1e071d0d53266"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a09ff97852143fdd75dd1e071d0d53266">entry_</a> ({nullptr, 0, 0})</td></tr>
+<tr class="separator:a09ff97852143fdd75dd1e071d0d53266"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:abfbb85eb84be64976417ed0b13b1d8ee"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">shape</a> () const </td></tr>
+<tr class="separator:abfbb85eb84be64976417ed0b13b1d8ee"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aebd03e900ec7664a99b780a0cb1efea6"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a> () const </td></tr>
+<tr class="separator:aebd03e900ec7664a99b780a0cb1efea6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aed729c3bc9db04ab346fd3969b4fc982"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#aed729c3bc9db04ab346fd3969b4fc982">grad</a> () const </td></tr>
+<tr class="separator:aed729c3bc9db04ab346fd3969b4fc982"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5e2eb64751872f537ae3cb25f949fff1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1Context.html">Context</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">ctx</a> () const </td></tr>
+<tr class="separator:a5e2eb64751872f537ae3cb25f949fff1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1b6038a196c14d038bd3e6d6bbe36ad2"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">dtype</a> () const </td></tr>
+<tr class="separator:a1b6038a196c14d038bd3e6d6bbe36ad2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2a15ffdbf29d4d5455a4fbb4b607880c"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a2a15ffdbf29d4d5455a4fbb4b607880c">is_none</a> () const </td></tr>
+<tr class="separator:a2a15ffdbf29d4d5455a4fbb4b607880c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aee74980eb87bf7c482d15458f5194cbe"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#aee74980eb87bf7c482d15458f5194cbe">fresh_out_grad</a> () const </td></tr>
+<tr class="separator:aee74980eb87bf7c482d15458f5194cbe"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a32ed2be4ecf94c58b19bd43b48f2b1fa"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a32ed2be4ecf94c58b19bd43b48f2b1fa">set_fresh_out_grad</a> (bool state) const </td></tr>
+<tr class="separator:a32ed2be4ecf94c58b19bd43b48f2b1fa"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aedd79a43d5fa4173f3ddd719fb939fbc"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#aedd79a43d5fa4173f3ddd719fb939fbc">WaitToRead</a> () const </td></tr>
+<tr class="memdesc:aedd79a43d5fa4173f3ddd719fb939fbc"><td class="mdescLeft"> </td><td class="mdescRight">Block until all the pending write operations with respect to current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> are finished, and read can be performed. <a href="#aedd79a43d5fa4173f3ddd719fb939fbc">More...</a><br/></td></tr>
+<tr class="separator:aedd79a43d5fa4173f3ddd719fb939fbc"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4b88b5d86ec9ddf4ca96701107337461"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a4b88b5d86ec9ddf4ca96701107337461">WaitToWrite</a> () const </td></tr>
+<tr class="memdesc:a4b88b5d86ec9ddf4ca96701107337461"><td class="mdescLeft"> </td><td class="mdescRight">Block until all the pending read/write operations with respect to current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> are finished, and write can be performed. <a href="#a4b88b5d86ec9ddf4ca96701107337461">More...</a><br/></td></tr>
+<tr class="separator:a4b88b5d86ec9ddf4ca96701107337461"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a35c9b5da0852b480e0114f0865338cbc"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">Engine::VarHandle</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">var</a> () const </td></tr>
+<tr class="separator:a35c9b5da0852b480e0114f0865338cbc"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a524662078483f8f46c6c71714fbfde0b"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a524662078483f8f46c6c71714fbfde0b">Save</a> (dmlc::Stream *strm) const </td></tr>
+<tr class="memdesc:a524662078483f8f46c6c71714fbfde0b"><td class="mdescLeft"> </td><td class="mdescRight">save the content into binary stream <a href="#a524662078483f8f46c6c71714fbfde0b">More...</a><br/></td></tr>
+<tr class="separator:a524662078483f8f46c6c71714fbfde0b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5203aa033a53942deaecb7bb3bf46480"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a5203aa033a53942deaecb7bb3bf46480">Load</a> (dmlc::Stream *strm)</td></tr>
-<tr class="memdesc:a5203aa033a53942deaecb7bb3bf46480"><td class="mdescLeft"> </td><td class="mdescRight">load the content from binary stream <a href="#a5203aa033a53942deaecb7bb3bf46480">More...</a><br /></td></tr>
+<tr class="memdesc:a5203aa033a53942deaecb7bb3bf46480"><td class="mdescLeft"> </td><td class="mdescRight">load the content from binary stream <a href="#a5203aa033a53942deaecb7bb3bf46480">More...</a><br/></td></tr>
<tr class="separator:a5203aa033a53942deaecb7bb3bf46480"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa7a198e3e4c80a48cf92a913009aab8f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#aa7a198e3e4c80a48cf92a913009aab8f">operator=</a> (<a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> scalar)</td></tr>
-<tr class="memdesc:aa7a198e3e4c80a48cf92a913009aab8f"><td class="mdescLeft"> </td><td class="mdescRight">set all the elements in ndarray to be scalar <a href="#aa7a198e3e4c80a48cf92a913009aab8f">More...</a><br /></td></tr>
+<tr class="memdesc:aa7a198e3e4c80a48cf92a913009aab8f"><td class="mdescLeft"> </td><td class="mdescRight">set all the elements in ndarray to be scalar <a href="#aa7a198e3e4c80a48cf92a913009aab8f">More...</a><br/></td></tr>
<tr class="separator:aa7a198e3e4c80a48cf92a913009aab8f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7df5a5d9956a481635fc43ca4d9a196c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a7df5a5d9956a481635fc43ca4d9a196c">operator+=</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &src)</td></tr>
-<tr class="memdesc:a7df5a5d9956a481635fc43ca4d9a196c"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add to current space this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a7df5a5d9956a481635fc43ca4d9a196c">More...</a><br /></td></tr>
+<tr class="memdesc:a7df5a5d9956a481635fc43ca4d9a196c"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add to current space this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a7df5a5d9956a481635fc43ca4d9a196c">More...</a><br/></td></tr>
<tr class="separator:a7df5a5d9956a481635fc43ca4d9a196c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1068954b69113429bab7b369300fdbe0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a1068954b69113429bab7b369300fdbe0">operator+=</a> (const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &src)</td></tr>
-<tr class="memdesc:a1068954b69113429bab7b369300fdbe0"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add to current space this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a1068954b69113429bab7b369300fdbe0">More...</a><br /></td></tr>
+<tr class="memdesc:a1068954b69113429bab7b369300fdbe0"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add to current space this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a1068954b69113429bab7b369300fdbe0">More...</a><br/></td></tr>
<tr class="separator:a1068954b69113429bab7b369300fdbe0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad35ba01da6949494afe895b7429e4fc3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ad35ba01da6949494afe895b7429e4fc3">operator-=</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &src)</td></tr>
-<tr class="memdesc:ad35ba01da6949494afe895b7429e4fc3"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtract from current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#ad35ba01da6949494afe895b7429e4fc3">More...</a><br /></td></tr>
+<tr class="memdesc:ad35ba01da6949494afe895b7429e4fc3"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtract from current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#ad35ba01da6949494afe895b7429e4fc3">More...</a><br/></td></tr>
<tr class="separator:ad35ba01da6949494afe895b7429e4fc3"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a81a7b37678af0f23edc2beec6daca3ab"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a81a7b37678af0f23edc2beec6daca3ab">operator-=</a> (const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &src)</td></tr>
-<tr class="memdesc:a81a7b37678af0f23edc2beec6daca3ab"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtract from current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a81a7b37678af0f23edc2beec6daca3ab">More...</a><br /></td></tr>
+<tr class="memdesc:a81a7b37678af0f23edc2beec6daca3ab"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtract from current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a81a7b37678af0f23edc2beec6daca3ab">More...</a><br/></td></tr>
<tr class="separator:a81a7b37678af0f23edc2beec6daca3ab"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1b47078a60bf0369ff0a7df893caf65b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a1b47078a60bf0369ff0a7df893caf65b">operator*=</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &src)</td></tr>
-<tr class="memdesc:a1b47078a60bf0369ff0a7df893caf65b"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication to current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a1b47078a60bf0369ff0a7df893caf65b">More...</a><br /></td></tr>
+<tr class="memdesc:a1b47078a60bf0369ff0a7df893caf65b"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication to current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a1b47078a60bf0369ff0a7df893caf65b">More...</a><br/></td></tr>
<tr class="separator:a1b47078a60bf0369ff0a7df893caf65b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7b4b2ac8b0c154ae8c53ebfd2d1d3fcf"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a7b4b2ac8b0c154ae8c53ebfd2d1d3fcf">operator*=</a> (const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &src)</td></tr>
-<tr class="memdesc:a7b4b2ac8b0c154ae8c53ebfd2d1d3fcf"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication to current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a7b4b2ac8b0c154ae8c53ebfd2d1d3fcf">More...</a><br /></td></tr>
+<tr class="memdesc:a7b4b2ac8b0c154ae8c53ebfd2d1d3fcf"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication to current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a7b4b2ac8b0c154ae8c53ebfd2d1d3fcf">More...</a><br/></td></tr>
<tr class="separator:a7b4b2ac8b0c154ae8c53ebfd2d1d3fcf"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aebc61006705874666423c45f161df546"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#aebc61006705874666423c45f161df546">operator/=</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &src)</td></tr>
-<tr class="memdesc:aebc61006705874666423c45f161df546"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division from current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#aebc61006705874666423c45f161df546">More...</a><br /></td></tr>
+<tr class="memdesc:aebc61006705874666423c45f161df546"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division from current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#aebc61006705874666423c45f161df546">More...</a><br/></td></tr>
<tr class="separator:aebc61006705874666423c45f161df546"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab88b476b2092b30c7d456d99df9f36e3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ab88b476b2092b30c7d456d99df9f36e3">operator/=</a> (const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &src)</td></tr>
-<tr class="memdesc:ab88b476b2092b30c7d456d99df9f36e3"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division from current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#ab88b476b2092b30c7d456d99df9f36e3">More...</a><br /></td></tr>
+<tr class="memdesc:ab88b476b2092b30c7d456d99df9f36e3"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division from current ndarray this mutate the current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#ab88b476b2092b30c7d456d99df9f36e3">More...</a><br/></td></tr>
<tr class="separator:ab88b476b2092b30c7d456d99df9f36e3"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a7876b8d60109a18feabe767d85f66226"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a7876b8d60109a18feabe767d85f66226">T</a> () const</td></tr>
-<tr class="memdesc:a7876b8d60109a18feabe767d85f66226"><td class="mdescLeft"> </td><td class="mdescRight">return transpose of current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a7876b8d60109a18feabe767d85f66226">More...</a><br /></td></tr>
-<tr class="separator:a7876b8d60109a18feabe767d85f66226"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6617e1bf4242314f7ee6b6e3a66ce493"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a6617e1bf4242314f7ee6b6e3a66ce493">Copy</a> (<a class="el" href="structmxnet_1_1Context.html">Context</a> <a class="el" href="classmxnet_1_1NDArray.html#a26d1ddb2bb0ed1e671ce7606498b942d">ctx</a>) const</td></tr>
-<tr class="memdesc:a6617e1bf4242314f7ee6b6e3a66ce493"><td class="mdescLeft"> </td><td class="mdescRight">return a new copy this <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a6617e1bf4242314f7ee6b6e3a66ce493">More...</a><br /></td></tr>
-<tr class="separator:a6617e1bf4242314f7ee6b6e3a66ce493"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ac390edfdd8a7dd99f69ea373f4d18e1a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ac390edfdd8a7dd99f69ea373f4d18e1a">SyncCopyFromCPU</a> (const void *<a class="el" href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">data</a>, size_t size) const</td></tr>
-<tr class="memdesc:ac390edfdd8a7dd99f69ea373f4d18e1a"><td class="mdescLeft"> </td><td class="mdescRight">Do a synchronize copy from a continugous CPU memory region. <a href="#ac390edfdd8a7dd99f69ea373f4d18e1a">More...</a><br /></td></tr>
-<tr class="separator:ac390edfdd8a7dd99f69ea373f4d18e1a"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:afb3dbc357c53e1e0b980257f6c5e2cac"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#afb3dbc357c53e1e0b980257f6c5e2cac">SyncCopyToCPU</a> (void *<a class="el" href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">data</a>, size_t size) const</td></tr>
-<tr class="memdesc:afb3dbc357c53e1e0b980257f6c5e2cac"><td class="mdescLeft"> </td><td class="mdescRight">Do a synchronize copy to a continugous CPU memory region. <a href="#afb3dbc357c53e1e0b980257f6c5e2cac">More...</a><br /></td></tr>
-<tr class="separator:afb3dbc357c53e1e0b980257f6c5e2cac"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a0cfa7466a652feb2258bb990453a0a7d"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a0cfa7466a652feb2258bb990453a0a7d">Slice</a> (<a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> begin, <a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> en [...]
-<tr class="memdesc:a0cfa7466a652feb2258bb990453a0a7d"><td class="mdescLeft"> </td><td class="mdescRight">Slice a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a0cfa7466a652feb2258bb990453a0a7d">More...</a><br /></td></tr>
-<tr class="separator:a0cfa7466a652feb2258bb990453a0a7d"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:addb42a7353a4c07b2d1bae0afa8ef48e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#addb42a7353a4c07b2d1bae0afa8ef48e">At</a> (<a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> idx) const</td></tr>
-<tr class="memdesc:addb42a7353a4c07b2d1bae0afa8ef48e"><td class="mdescLeft"> </td><td class="mdescRight">Index a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#addb42a7353a4c07b2d1bae0afa8ef48e">More...</a><br /></td></tr>
-<tr class="separator:addb42a7353a4c07b2d1bae0afa8ef48e"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ac3c424c05eabe0b7a4976191d81cf6b0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ac3c424c05eabe0b7a4976191d81cf6b0">AsArray</a> (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &<a class="el" href="classmxnet_1_1NDArray.html#a2fc268d12168d39d9095829b3b9a3836">sh [...]
-<tr class="memdesc:ac3c424c05eabe0b7a4976191d81cf6b0"><td class="mdescLeft"> </td><td class="mdescRight">Create a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> that shares memory with current one The new array must have smaller memory size than the current array. <a href="#ac3c424c05eabe0b7a4976191d81cf6b0">More...</a><br /></td></tr>
-<tr class="separator:ac3c424c05eabe0b7a4976191d81cf6b0"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a1d01269333b9226fffddc89ce3b7fb63"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a1d01269333b9226fffddc89ce3b7fb63">Reshape</a> (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &<a class="el" href="classmxnet_1_1NDArray.html#a2fc268d12168d39d9095829b3b9a3836">sh [...]
-<tr class="memdesc:a1d01269333b9226fffddc89ce3b7fb63"><td class="mdescLeft"> </td><td class="mdescRight">Get an reshaped <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a1d01269333b9226fffddc89ce3b7fb63">More...</a><br /></td></tr>
-<tr class="separator:a1d01269333b9226fffddc89ce3b7fb63"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a76646c5c7e164bd7c81854521d3bee52"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a76646c5c7e164bd7c81854521d3bee52">Detach</a> () const</td></tr>
-<tr class="memdesc:a76646c5c7e164bd7c81854521d3bee52"><td class="mdescLeft"> </td><td class="mdescRight">Return a copy of this <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> without autograd history. <a href="#a76646c5c7e164bd7c81854521d3bee52">More...</a><br /></td></tr>
-<tr class="separator:a76646c5c7e164bd7c81854521d3bee52"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a531a3437dde0dfc9b0c40a32450f4c7a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a531a3437dde0dfc9b0c40a32450f4c7a">CheckAndAlloc</a> () const</td></tr>
-<tr class="memdesc:a531a3437dde0dfc9b0c40a32450f4c7a"><td class="mdescLeft"> </td><td class="mdescRight">Allocate the space if it is delayed allocated. This is an internal function used by system that normal user should not use. <a href="#a531a3437dde0dfc9b0c40a32450f4c7a">More...</a><br /></td></tr>
-<tr class="separator:a531a3437dde0dfc9b0c40a32450f4c7a"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3fe1974c7c2d522b09b9bf82fd2865ba"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a3fe1974c7c2d522b09b9bf82fd2865ba">T</a> () const </td></tr>
+<tr class="memdesc:a3fe1974c7c2d522b09b9bf82fd2865ba"><td class="mdescLeft"> </td><td class="mdescRight">return transpose of current <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a3fe1974c7c2d522b09b9bf82fd2865ba">More...</a><br/></td></tr>
+<tr class="separator:a3fe1974c7c2d522b09b9bf82fd2865ba"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4a00e2868ad8b9ccb0a48079635b110f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a4a00e2868ad8b9ccb0a48079635b110f">Copy</a> (<a class="el" href="structmxnet_1_1Context.html">Context</a> <a class="el" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">ctx</a>) const </td></tr>
+<tr class="memdesc:a4a00e2868ad8b9ccb0a48079635b110f"><td class="mdescLeft"> </td><td class="mdescRight">return a new copy this <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> <a href="#a4a00e2868ad8b9ccb0a48079635b110f">More...</a><br/></td></tr>
+<tr class="separator:a4a00e2868ad8b9ccb0a48079635b110f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a422c70b16646f8a273e9b4936fbe2e37"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a422c70b16646f8a273e9b4936fbe2e37">SyncCopyFromCPU</a> (const void *<a class="el" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>, size_t size) const </td></tr>
+<tr class="memdesc:a422c70b16646f8a273e9b4936fbe2e37"><td class="mdescLeft"> </td><td class="mdescRight">Do a synchronize copy from a continugous CPU memory region. <a href="#a422c70b16646f8a273e9b4936fbe2e37">More...</a><br/></td></tr>
+<tr class="separator:a422c70b16646f8a273e9b4936fbe2e37"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4819d2e6431ccdf4b9f8efa8eb0bd1fd"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a4819d2e6431ccdf4b9f8efa8eb0bd1fd">SyncCopyToCPU</a> (void *<a class="el" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>, size_t size) const </td></tr>
+<tr class="memdesc:a4819d2e6431ccdf4b9f8efa8eb0bd1fd"><td class="mdescLeft"> </td><td class="mdescRight">Do a synchronize copy to a continugous CPU memory region. <a href="#a4819d2e6431ccdf4b9f8efa8eb0bd1fd">More...</a><br/></td></tr>
+<tr class="separator:a4819d2e6431ccdf4b9f8efa8eb0bd1fd"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7acb6462c11e071949db2b0e1fbfd66e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a7acb6462c11e071949db2b0e1fbfd66e">Slice</a> (<a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> begin, <a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> en [...]
+<tr class="memdesc:a7acb6462c11e071949db2b0e1fbfd66e"><td class="mdescLeft"> </td><td class="mdescRight">Slice a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a7acb6462c11e071949db2b0e1fbfd66e">More...</a><br/></td></tr>
+<tr class="separator:a7acb6462c11e071949db2b0e1fbfd66e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3b615512a8fb89b9edc99b35064ead95"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a3b615512a8fb89b9edc99b35064ead95">At</a> (<a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> idx) const </td></tr>
+<tr class="memdesc:a3b615512a8fb89b9edc99b35064ead95"><td class="mdescLeft"> </td><td class="mdescRight">Index a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#a3b615512a8fb89b9edc99b35064ead95">More...</a><br/></td></tr>
+<tr class="separator:a3b615512a8fb89b9edc99b35064ead95"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3896fcce89afce4c1d2f825f7853ca0c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a3896fcce89afce4c1d2f825f7853ca0c">AsArray</a> (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &<a class="el" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">sh [...]
+<tr class="memdesc:a3896fcce89afce4c1d2f825f7853ca0c"><td class="mdescLeft"> </td><td class="mdescRight">Create a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> that shares memory with current one The new array must have smaller memory size than the current array. <a href="#a3896fcce89afce4c1d2f825f7853ca0c">More...</a><br/></td></tr>
+<tr class="separator:a3896fcce89afce4c1d2f825f7853ca0c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ada618436206b59aa3ffcdb8474d5051c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ada618436206b59aa3ffcdb8474d5051c">Reshape</a> (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &<a class="el" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">sh [...]
+<tr class="memdesc:ada618436206b59aa3ffcdb8474d5051c"><td class="mdescLeft"> </td><td class="mdescRight">Get an reshaped <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. <a href="#ada618436206b59aa3ffcdb8474d5051c">More...</a><br/></td></tr>
+<tr class="separator:ada618436206b59aa3ffcdb8474d5051c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a34a3694141050a257dc6b40a6ad2c4ec"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a34a3694141050a257dc6b40a6ad2c4ec">Detach</a> () const </td></tr>
+<tr class="memdesc:a34a3694141050a257dc6b40a6ad2c4ec"><td class="mdescLeft"> </td><td class="mdescRight">Return a copy of this <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> without autograd history. <a href="#a34a3694141050a257dc6b40a6ad2c4ec">More...</a><br/></td></tr>
+<tr class="separator:a34a3694141050a257dc6b40a6ad2c4ec"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a35c2a2c797f601fbc5d560481b11720a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a">CheckAndAlloc</a> () const </td></tr>
+<tr class="memdesc:a35c2a2c797f601fbc5d560481b11720a"><td class="mdescLeft"> </td><td class="mdescRight">Allocate the space if it is delayed allocated. This is an internal function used by system that normal user should not use. <a href="#a35c2a2c797f601fbc5d560481b11720a">More...</a><br/></td></tr>
+<tr class="separator:a35c2a2c797f601fbc5d560481b11720a"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
-<tr class="memitem:af215b7a886e717b4bb3c911fe3c1a032"><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#af215b7a886e717b4bb3c911fe3c1a032">Save</a> (dmlc::Stream *fo, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &<a class="el" href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">data</a>, const std::vector< std::str [...]
-<tr class="memdesc:af215b7a886e717b4bb3c911fe3c1a032"><td class="mdescLeft"> </td><td class="mdescRight">Save list of ndarray into the Stream.x. <a href="#af215b7a886e717b4bb3c911fe3c1a032">More...</a><br /></td></tr>
+<tr class="memitem:af215b7a886e717b4bb3c911fe3c1a032"><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#af215b7a886e717b4bb3c911fe3c1a032">Save</a> (dmlc::Stream *fo, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &<a class="el" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>, const std::vector< std::str [...]
+<tr class="memdesc:af215b7a886e717b4bb3c911fe3c1a032"><td class="mdescLeft"> </td><td class="mdescRight">Save list of ndarray into the Stream.x. <a href="#af215b7a886e717b4bb3c911fe3c1a032">More...</a><br/></td></tr>
<tr class="separator:af215b7a886e717b4bb3c911fe3c1a032"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ae45c38de80d6d4dc79ad3d24baa9e981"><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ae45c38de80d6d4dc79ad3d24baa9e981">Load</a> (dmlc::Stream *fi, std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > *<a class="el" href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">data</a>, std::vector< std::string > *keys)< [...]
-<tr class="memdesc:ae45c38de80d6d4dc79ad3d24baa9e981"><td class="mdescLeft"> </td><td class="mdescRight">Load list of ndarray into from the stream. <a href="#ae45c38de80d6d4dc79ad3d24baa9e981">More...</a><br /></td></tr>
+<tr class="memitem:ae45c38de80d6d4dc79ad3d24baa9e981"><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html#ae45c38de80d6d4dc79ad3d24baa9e981">Load</a> (dmlc::Stream *fi, std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > *<a class="el" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>, std::vector< std::string > *keys)< [...]
+<tr class="memdesc:ae45c38de80d6d4dc79ad3d24baa9e981"><td class="mdescLeft"> </td><td class="mdescRight">Load list of ndarray into from the stream. <a href="#ae45c38de80d6d4dc79ad3d24baa9e981">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>ndarray interface </p>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
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- <td class="memname">mxnet::NDArray::NDArray </td>
- <td>(</td>
- <td class="paramtype">const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> & </td>
- <td class="paramname"><em>data</em>, </td>
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- <td class="paramname"><em>dev_id</em> </td>
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-<p>constructing a static <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> that shares data with <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> Use with caution: allocate ONLY ONE <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> for each <a class="el" href="classmxnet_1_1TBlob.html" title="tensor bl [...]
-<dl class="params"><dt>Parameters</dt><dd>
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- <tr><td class="paramname">data</td><td>the memory content of static data </td></tr>
- <tr><td class="paramname">dev_id</td><td>the device id this tensor sits at </td></tr>
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- </dd>
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<h2 class="groupheader">Member Function Documentation</h2>
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+ <td class="memname">mxnet::NDArray::dtype_ </td>
+ <td>(</td>
+ <td class="paramtype">data. </td>
+ <td class="paramname"><em>type_flag_</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
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+ <td>(</td>
+ <td class="paramtype">{nullptr, 0, 0} </td>
+ <td class="paramname"></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+ </td>
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+ <td>(</td>
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<table class="directory">
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
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+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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@@ -86,28 +110,26 @@ Collaboration diagram for mxnet::OpStatePtr:</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
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+<tr class="memitem:aa2ea1d7ae06008faf44a92c009b2b26e"><td class="memTemplParams" colspan="2">template<typename T > </td></tr>
+<tr class="memitem:aa2ea1d7ae06008faf44a92c009b2b26e"><td class="memTemplItemLeft" align="right" valign="top">T & </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OpStatePtr.html#aa2ea1d7ae06008faf44a92c009b2b26e">get_state</a> () const </td></tr>
+<tr class="separator:aa2ea1d7ae06008faf44a92c009b2b26e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae43213b3a65073a87bf489350a58d78e"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OpStatePtr.html#ae43213b3a65073a87bf489350a58d78e">reset</a> ()</td></tr>
<tr class="separator:ae43213b3a65073a87bf489350a58d78e"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ae233fba51851ddd1704de60c9c860ee3"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OpStatePtr.html#ae233fba51851ddd1704de60c9c860ee3">operator bool</a> () const</td></tr>
-<tr class="separator:ae233fba51851ddd1704de60c9c860ee3"><td class="memSeparator" colspan="2"> </td></tr>
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+<tr class="separator:a6f407d1c8d461dc474cd1c50aa53e3f8"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
-<tr class="memitem:a2484d61830b33181bf24c536ba91b790"><td class="memTemplParams" colspan="2">template<typename T , typename... Args> </td></tr>
-<tr class="memitem:a2484d61830b33181bf24c536ba91b790"><td class="memTemplItemLeft" align="right" valign="top">static <a class="el" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a> </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OpStatePtr.html#a2484d61830b33181bf24c536ba91b790">Create</a> (Args &&... args)</td></tr>
-<tr class="separator:a2484d61830b33181bf24c536ba91b790"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae0f821c7efce61a11730ef8b93070e3e"><td class="memTemplParams" colspan="2">template<typename T , typename... Args> </td></tr>
+<tr class="memitem:ae0f821c7efce61a11730ef8b93070e3e"><td class="memTemplItemLeft" align="right" valign="top">static <a class="el" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a> </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OpStatePtr.html#ae0f821c7efce61a11730ef8b93070e3e">Create</a> (Args &&...args)</td></tr>
+<tr class="separator:ae0f821c7efce61a11730ef8b93070e3e"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> state. This is a pointer type, its content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is const. </p>
</div><h2 class="groupheader">Member Function Documentation</h2>
-<a id="a2484d61830b33181bf24c536ba91b790"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a2484d61830b33181bf24c536ba91b790">◆ </a></span>Create()</h2>
-
+<a class="anchor" id="ae0f821c7efce61a11730ef8b93070e3e"></a>
<div class="memitem">
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<div class="memtemplate">
@@ -133,9 +155,7 @@ template<typename T , typename... Args> </div>
</div>
</div>
-<a id="a6d720b8843340fa4d9a7ff7937f2e141"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a6d720b8843340fa4d9a7ff7937f2e141">◆ </a></span>get_state()</h2>
-
+<a class="anchor" id="aa2ea1d7ae06008faf44a92c009b2b26e"></a>
<div class="memitem">
<div class="memproto">
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@@ -160,9 +180,7 @@ template<typename T > </div>
</div>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#af2082fb6224b78b2aac9e05f4b5365a4">◆ </a></span>get_var()</h2>
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<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -185,9 +203,7 @@ template<typename T > </div>
</div>
</div>
-<a id="ae233fba51851ddd1704de60c9c860ee3"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ae233fba51851ddd1704de60c9c860ee3">◆ </a></span>operator bool()</h2>
-
+<a class="anchor" id="a6f407d1c8d461dc474cd1c50aa53e3f8"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -210,9 +226,7 @@ template<typename T > </div>
</div>
</div>
-<a id="ae43213b3a65073a87bf489350a58d78e"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ae43213b3a65073a87bf489350a58d78e">◆ </a></span>reset()</h2>
-
+<a class="anchor" id="ae43213b3a65073a87bf489350a58d78e"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -241,9 +255,9 @@ template<typename T > </div>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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index 2403899..40c7204 100644
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-<script type="text/javascript" src="search/searchdata.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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@@ -20,7 +21,7 @@
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+ <td style="padding-left: 0.5em;">
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <span class="left">
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+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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</table></div><!-- contents -->
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/classmxnet_1_1Operator.html b/versions/master/doxygen/classmxnet_1_1Operator.html
index 252d276..69008f2 100644
--- a/versions/master/doxygen/classmxnet_1_1Operator.html
+++ b/versions/master/doxygen/classmxnet_1_1Operator.html
@@ -3,15 +3,16 @@
<head>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::Operator Class Reference</title>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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@@ -29,25 +30,48 @@
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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@@ -78,18 +102,20 @@ $(function() {
Collaboration diagram for mxnet::Operator:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1Operator__coll__graph.png" border="0" usemap="#mxnet_1_1Operator_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1Operator_coll__map" id="mxnet_1_1Operator_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a4d34cc792d7271bd8b5b60eaa0b55767"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Operator.html#a4d34cc792d7271bd8b5b60eaa0b55767">~Operator</a> ()</td></tr>
-<tr class="memdesc:a4d34cc792d7271bd8b5b60eaa0b55767"><td class="mdescLeft"> </td><td class="mdescRight">destructor <a href="#a4d34cc792d7271bd8b5b60eaa0b55767">More...</a><br /></td></tr>
+<tr class="memdesc:a4d34cc792d7271bd8b5b60eaa0b55767"><td class="mdescLeft"> </td><td class="mdescRight">destructor <a href="#a4d34cc792d7271bd8b5b60eaa0b55767">More...</a><br/></td></tr>
<tr class="separator:a4d34cc792d7271bd8b5b60eaa0b55767"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3dbe23d985df86da51eeb868439872dd"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd">Forward</a> (const <a class="el" href="structmxnet_1_1OpContext.html">OpContext</a> &ctx, const std::vector< <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> > &in_data, const std::vector< <a class="el" href="namespa [...]
-<tr class="memdesc:a3dbe23d985df86da51eeb868439872dd"><td class="mdescLeft"> </td><td class="mdescRight">perform a forward operation of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>, save the output to <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob< [...]
+<tr class="memdesc:a3dbe23d985df86da51eeb868439872dd"><td class="mdescLeft"> </td><td class="mdescRight">perform a forward operation of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>, save the output to <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob< [...]
<tr class="separator:a3dbe23d985df86da51eeb868439872dd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad32d74b9bf56caf920b5e6379d2bb259"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Operator.html#ad32d74b9bf56caf920b5e6379d2bb259">Backward</a> (const <a class="el" href="structmxnet_1_1OpContext.html">OpContext</a> &ctx, const std::vector< <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> > &out_grad, const std::vector< <a class="el" href="class [...]
-<tr class="memdesc:ad32d74b9bf56caf920b5e6379d2bb259"><td class="mdescLeft"> </td><td class="mdescRight">Perform a Backward Operation, write gradient to the in_grad. <a href="#ad32d74b9bf56caf920b5e6379d2bb259">More...</a><br /></td></tr>
+<tr class="memdesc:ad32d74b9bf56caf920b5e6379d2bb259"><td class="mdescLeft"> </td><td class="mdescRight">Perform a Backward Operation, write gradient to the in_grad. <a href="#ad32d74b9bf56caf920b5e6379d2bb259">More...</a><br/></td></tr>
<tr class="separator:ad32d74b9bf56caf920b5e6379d2bb259"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa6b4c247a914b4659c773965248019c7"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">ExecType</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Operator.html#aa6b4c247a914b4659c773965248019c7">exec_type</a> () const final</td></tr>
<tr class="separator:aa6b4c247a914b4659c773965248019c7"><td class="memSeparator" colspan="2"> </td></tr>
@@ -97,11 +123,9 @@ Public Member Functions</h2></td></tr>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> interface. <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> defines basic operation unit of optimized computation graph in mxnet. This interface relies on pre-allocated memo [...]
<p><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> is generated by <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a>. To add new operator(aka. layers of neural nets) to mxnet, developer need to create a new <a class="el" href="c [...]
-<dl class="section see"><dt>See also</dt><dd><a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a>, <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687" title="Shape data structure used to record shape information. ">TShape</a>, <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Oper [...]
+<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a>, <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687" title="Shape data structure used to record shape information. ">TShape</a>, <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Oper [...]
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a4d34cc792d7271bd8b5b60eaa0b55767"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a4d34cc792d7271bd8b5b60eaa0b55767">◆ </a></span>~Operator()</h2>
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+<a class="anchor" id="a4d34cc792d7271bd8b5b60eaa0b55767"></a>
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@@ -127,9 +151,7 @@ Public Member Functions</h2></td></tr>
</div>
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<h2 class="groupheader">Member Function Documentation</h2>
-<a id="ad32d74b9bf56caf920b5e6379d2bb259"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad32d74b9bf56caf920b5e6379d2bb259">◆ </a></span>Backward()</h2>
-
+<a class="anchor" id="ad32d74b9bf56caf920b5e6379d2bb259"></a>
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@@ -192,7 +214,7 @@ Public Member Functions</h2></td></tr>
</div><div class="memdoc">
<p>Perform a Backward Operation, write gradient to the in_grad. </p>
-<dl class="section note"><dt>Note</dt><dd>Convention: out_grad.size() == <a class="el" href="classmxnet_1_1OperatorProperty.html#a006f0e2c9b3f934e1f73b242b7e8b062" title="get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...">OperatorProperty.NumVisibleOutputs()</a> out_data.size() == <a class="el" href="classmxnet_1_1OperatorProperty.html#a9fc6e9f04f52b1c05f62fb7357bb95c4">OperatorProperty.NumOutputs()</a> out_data can contain additional invisible ret [...]
+<dl class="section note"><dt>Note</dt><dd>Convention: out_grad.size() == <a class="el" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b" title="get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...">OperatorProperty.NumVisibleOutputs()</a> out_data.size() == <a class="el" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">OperatorProperty.NumOutputs()</a> out_data can contain additional invisible ret [...]
<dl class="section user"><dt></dt><dd>Not all the TBlobs in the arguments will be available if you override the DeclareBackwardDependency of corresponding <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> class. Only the dependencies you declared will be available at corresponding position, the rest of the parameters are simply dummy where you will [...]
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
@@ -206,13 +228,11 @@ Public Member Functions</h2></td></tr>
</table>
</dd>
</dl>
-<dl class="section see"><dt>See also</dt><dd><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a>, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398" title="operation request type to Forward and Backward ">OpReqType</a>, <a class="el" href="structmxnet_1_1OpContext.html" title="All the possible information needed by Operator.For [...]
+<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a>, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398" title="operation request type to Forward and Backward ">OpReqType</a>, <a class="el" href="structmxnet_1_1OpContext.html" title="All the possible information needed by Operator.For [...]
</div>
</div>
-<a id="aa6b4c247a914b4659c773965248019c7"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aa6b4c247a914b4659c773965248019c7">◆ </a></span>exec_type()</h2>
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@@ -236,9 +256,7 @@ Public Member Functions</h2></td></tr>
</div>
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-<a id="a3dbe23d985df86da51eeb868439872dd"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a3dbe23d985df86da51eeb868439872dd">◆ </a></span>Forward()</h2>
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@@ -299,7 +317,7 @@ Public Member Functions</h2></td></tr>
</table>
</dd>
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-<dl class="section see"><dt>See also</dt><dd><a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398" title="operation request type to Forward and Backward ">OpReqType</a>, <a class="el" href="structmxnet_1_1OpContext.html" title="All the possible information needed by Operator.Forward and Backward This is the superset of RunConte...">OpContext</a> </dd></dl>
+<dl class="section see"><dt>See Also</dt><dd><a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398" title="operation request type to Forward and Backward ">OpReqType</a>, <a class="el" href="structmxnet_1_1OpContext.html" title="All the possible information needed by Operator.Forward and Backward This is the superset of RunConte...">OpContext</a> </dd></dl>
</div>
</div>
@@ -309,9 +327,9 @@ Public Member Functions</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/classmxnet_1_1OperatorProperty-members.html b/versions/master/doxygen/classmxnet_1_1OperatorProperty-members.html
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<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
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+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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@@ -20,7 +21,7 @@
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<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+ <li><a href="index.html"><span>Main Page</span></a></li>
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ <div id="navrow2" class="tabs2">
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+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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onmouseover="return searchBox.OnSearchSelectShow()"
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -69,34 +93,34 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a>, including all inherited members.</p>
<table class="directory">
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a0255256ac4e0e76b3fff917560203efe">BackwardInplaceOption</a>(const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data, const std::vector< void *> &in_grad) const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel [...]
- <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a6c5c8df10748a7eee873d4b7eb8d684b">BackwardInputs</a>(const std::vector< T > &out_grad, const std::vector< T > &in_data, const std::vector< T > &out_data) const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#acec41540dde2c49ff2f2a8fc425890e8">BackwardResource</a>(const std::vector< TShape > &in_shape) const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a67231a5a1525ec7480de98a79dce0194">BackwardInplaceOption</a>(const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data, const std::vector< void * > &in_grad) const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlab [...]
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a9f488509e132094ce7e79125ce853042">BackwardInputs</a>(const std::vector< T > &out_grad, const std::vector< T > &in_data, const std::vector< T > &out_data) const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#ade7720a3e0ebba67d89978925a77609b">BackwardResource</a>(const std::vector< TShape > &in_shape) const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#ab38f2f877555ba34b548efd2575cac99">Copy</a>() const =0</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a15cf1f905b08a008ff38556ef6a35dd0">Create</a>(const char *type_name)</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a546ab25ddaae590ca98705ae9abc7ac0">CreateOperator</a>(Context ctx) const =0</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#af07a7f946cfadb9bea19fa3d2b621ab4">CreateOperatorEx</a>(Context ctx, std::vector< TShape > *in_shape, std::vector< int > *in_type) const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a8aa205ea6ce962bfa3fcbe73a8f3db5d">DeclareBackwardDependency</a>(const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data) const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a149c52bd285e4fa78109a535765a9b7c">exec_type</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a78989f905d04bedb41896876fd4dbac9">ForwardInplaceOption</a>(const std::vector< int > &in_data, const std::vector< void *> &out_data) const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a1f48e07aa4979935b8f4b9dd7564db18">ForwardResource</a>(const std::vector< TShape > &in_shape) const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a7985004f99a931b4a6ff69df733bae72">CreateOperatorEx</a>(Context ctx, std::vector< TShape > *in_shape, std::vector< int > *in_type) const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#abf9e6a8d40750f3ee81fe30cbe3e2aae">DeclareBackwardDependency</a>(const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data) const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#aa5d6354ebe3e2a0c51f7f7ce4a60f664">exec_type</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#aad7a3e87866515a2b7100e849a2fe966">ForwardInplaceOption</a>(const std::vector< int > &in_data, const std::vector< void * > &out_data) const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a623210dd1b9495ca73f223e0b06f998c">ForwardResource</a>(const std::vector< TShape > &in_shape) const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a704ae43181c8a88bfd0a47e8005f7cb8">GetParams</a>() const =0</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a543cbf36f0e4d8dc99109db71705a11c">InferShape</a>(std::vector< TShape > *in_shape, std::vector< TShape > *out_shape, std::vector< TShape > *aux_shape) const =0</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a819b81e14761e9e8b9c888a63c7df1fa">InferType</a>(std::vector< int > *in_type, std::vector< int > *out_type, std::vector< int > *aux_type) const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#ad5c9f53ea70f3eeb1bf27b983f420c0b">InferType</a>(std::vector< int > *in_type, std::vector< int > *out_type, std::vector< int > *aux_type) const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a0fb32e0ab03a86517473db0f0630856c">Init</a>(const std::vector< std::pair< std::string, std::string > > &kwargs)=0</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#ad01be3a84423800af7e818b45afc748f">ListArguments</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#aa6d3a955ffa452a368a9dd4d33ce5a25">ListAuxiliaryStates</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#ab9e52badb93fe965ce961803f2101aa7">ListOutputs</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a9fc6e9f04f52b1c05f62fb7357bb95c4">NumOutputs</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a006f0e2c9b3f934e1f73b242b7e8b062">NumVisibleOutputs</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a24cc6a3763ff5f0881a2de0652810596">ListArguments</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094">ListAuxiliaryStates</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">ListOutputs</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">NumOutputs</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b">NumVisibleOutputs</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#ac6b7df90c0285a1823b463c21e926698">TypeString</a>() const =0</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html#a610af8e9cb560104301ccd1efa2a96fd">~OperatorProperty</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
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diff --git a/versions/master/doxygen/classmxnet_1_1OperatorProperty.html b/versions/master/doxygen/classmxnet_1_1OperatorProperty.html
index 936c18f..ac95778 100644
--- a/versions/master/doxygen/classmxnet_1_1OperatorProperty.html
+++ b/versions/master/doxygen/classmxnet_1_1OperatorProperty.html
@@ -3,15 +3,16 @@
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+ <td style="padding-left: 0.5em;">
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@@ -29,25 +30,48 @@
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@@ -86,81 +110,84 @@ Collaboration diagram for mxnet::OperatorProperty:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a610af8e9cb560104301ccd1efa2a96fd"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a610af8e9cb560104301ccd1efa2a96fd">~OperatorProperty</a> ()</td></tr>
-<tr class="memdesc:a610af8e9cb560104301ccd1efa2a96fd"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#a610af8e9cb560104301ccd1efa2a96fd">More...</a><br /></td></tr>
+<tr class="memdesc:a610af8e9cb560104301ccd1efa2a96fd"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#a610af8e9cb560104301ccd1efa2a96fd">More...</a><br/></td></tr>
<tr class="separator:a610af8e9cb560104301ccd1efa2a96fd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0fb32e0ab03a86517473db0f0630856c"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a0fb32e0ab03a86517473db0f0630856c">Init</a> (const std::vector< std::pair< std::string, std::string > > &kwargs)=0</td></tr>
-<tr class="memdesc:a0fb32e0ab03a86517473db0f0630856c"><td class="mdescLeft"> </td><td class="mdescRight">Initialize the <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> by setting the parameters This function need to be called before all other functions. <a href="#a0fb32e0ab03a86517473db0f0630856c">More...</a><br /></td></tr>
+<tr class="memdesc:a0fb32e0ab03a86517473db0f0630856c"><td class="mdescLeft"> </td><td class="mdescRight">Initialize the <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> by setting the parameters This function need to be called before all other functions. <a href="#a0fb32e0ab03a86517473db0f0630856c">More...</a><br/></td></tr>
<tr class="separator:a0fb32e0ab03a86517473db0f0630856c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a704ae43181c8a88bfd0a47e8005f7cb8"><td class="memItemLeft" align="right" valign="top">virtual std::map< std::string, std::string > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a704ae43181c8a88bfd0a47e8005f7cb8">GetParams</a> () const =0</td></tr>
-<tr class="memdesc:a704ae43181c8a88bfd0a47e8005f7cb8"><td class="mdescLeft"> </td><td class="mdescRight">Get a map representation of internal parameters. This can be used by Init to recover the state of <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a>. <a href="#a704ae43181c8a88bfd0a47e8005f7cb8">More...</a><br /></td></tr>
+<tr class="memitem:a704ae43181c8a88bfd0a47e8005f7cb8"><td class="memItemLeft" align="right" valign="top">virtual std::map< std::string, <br class="typebreak"/>
+std::string > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a704ae43181c8a88bfd0a47e8005f7cb8">GetParams</a> () const =0</td></tr>
+<tr class="memdesc:a704ae43181c8a88bfd0a47e8005f7cb8"><td class="mdescLeft"> </td><td class="mdescRight">Get a map representation of internal parameters. This can be used by Init to recover the state of <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a>. <a href="#a704ae43181c8a88bfd0a47e8005f7cb8">More...</a><br/></td></tr>
<tr class="separator:a704ae43181c8a88bfd0a47e8005f7cb8"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad01be3a84423800af7e818b45afc748f"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::string > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#ad01be3a84423800af7e818b45afc748f">ListArguments</a> () const</td></tr>
-<tr class="memdesc:ad01be3a84423800af7e818b45afc748f"><td class="mdescLeft"> </td><td class="mdescRight">Get input arguments of the <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. <a href="#ad01be3a84423800af7e818b45afc748f">More...</a><br /></td></tr>
-<tr class="separator:ad01be3a84423800af7e818b45afc748f"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ab9e52badb93fe965ce961803f2101aa7"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::string > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#ab9e52badb93fe965ce961803f2101aa7">ListOutputs</a> () const</td></tr>
-<tr class="memdesc:ab9e52badb93fe965ce961803f2101aa7"><td class="mdescLeft"> </td><td class="mdescRight">Get name of output values of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. <a href="#ab9e52badb93fe965ce961803f2101aa7">More...</a><br /></td></tr>
-<tr class="separator:ab9e52badb93fe965ce961803f2101aa7"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aa6d3a955ffa452a368a9dd4d33ce5a25"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::string > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#aa6d3a955ffa452a368a9dd4d33ce5a25">ListAuxiliaryStates</a> () const</td></tr>
-<tr class="memdesc:aa6d3a955ffa452a368a9dd4d33ce5a25"><td class="mdescLeft"> </td><td class="mdescRight">Get name of auxiliary states of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. <a href="#aa6d3a955ffa452a368a9dd4d33ce5a25">More...</a><br /></td></tr>
-<tr class="separator:aa6d3a955ffa452a368a9dd4d33ce5a25"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a9fc6e9f04f52b1c05f62fb7357bb95c4"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a9fc6e9f04f52b1c05f62fb7357bb95c4">NumOutputs</a> () const</td></tr>
-<tr class="separator:a9fc6e9f04f52b1c05f62fb7357bb95c4"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a006f0e2c9b3f934e1f73b242b7e8b062"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a006f0e2c9b3f934e1f73b242b7e8b062">NumVisibleOutputs</a> () const</td></tr>
-<tr class="memdesc:a006f0e2c9b3f934e1f73b242b7e8b062"><td class="mdescLeft"> </td><td class="mdescRight">get number of visible return values during Symbol creation. If <a class="el" href="classmxnet_1_1OperatorProperty.html#a006f0e2c9b3f934e1f73b242b7e8b062" title="get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...">NumVisibleOutputs()</a> = k, and <a class="el" href="classmxnet_1_1OperatorProperty.html#a9fc6e9f04f52b1c05f62fb7357bb95c4">NumOut [...]
-<tr class="separator:a006f0e2c9b3f934e1f73b242b7e8b062"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a24cc6a3763ff5f0881a2de0652810596"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::string > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a24cc6a3763ff5f0881a2de0652810596">ListArguments</a> () const </td></tr>
+<tr class="memdesc:a24cc6a3763ff5f0881a2de0652810596"><td class="mdescLeft"> </td><td class="mdescRight">Get input arguments of the <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. <a href="#a24cc6a3763ff5f0881a2de0652810596">More...</a><br/></td></tr>
+<tr class="separator:a24cc6a3763ff5f0881a2de0652810596"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1b37fb362748fa5027d4b5c925131892"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::string > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">ListOutputs</a> () const </td></tr>
+<tr class="memdesc:a1b37fb362748fa5027d4b5c925131892"><td class="mdescLeft"> </td><td class="mdescRight">Get name of output values of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. <a href="#a1b37fb362748fa5027d4b5c925131892">More...</a><br/></td></tr>
+<tr class="separator:a1b37fb362748fa5027d4b5c925131892"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0e20dead67dad0cd76c147a997ef5094"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::string > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094">ListAuxiliaryStates</a> () const </td></tr>
+<tr class="memdesc:a0e20dead67dad0cd76c147a997ef5094"><td class="mdescLeft"> </td><td class="mdescRight">Get name of auxiliary states of <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. <a href="#a0e20dead67dad0cd76c147a997ef5094">More...</a><br/></td></tr>
+<tr class="separator:a0e20dead67dad0cd76c147a997ef5094"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a49e25d85c38199338a9fa8150bc86df1"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">NumOutputs</a> () const </td></tr>
+<tr class="separator:a49e25d85c38199338a9fa8150bc86df1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6dc5ecd089ef01dcc154c02b20ad2f4b"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b">NumVisibleOutputs</a> () const </td></tr>
+<tr class="memdesc:a6dc5ecd089ef01dcc154c02b20ad2f4b"><td class="mdescLeft"> </td><td class="mdescRight">get number of visible return values during Symbol creation. If <a class="el" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b" title="get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...">NumVisibleOutputs()</a> = k, and <a class="el" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">NumOut [...]
+<tr class="separator:a6dc5ecd089ef01dcc154c02b20ad2f4b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a543cbf36f0e4d8dc99109db71705a11c"><td class="memItemLeft" align="right" valign="top">virtual bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a543cbf36f0e4d8dc99109db71705a11c">InferShape</a> (std::vector< <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> > *in_shape, std::vector< <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape< [...]
-<tr class="memdesc:a543cbf36f0e4d8dc99109db71705a11c"><td class="mdescLeft"> </td><td class="mdescRight">infer the shapes of outputs and unknown input arguments <a href="#a543cbf36f0e4d8dc99109db71705a11c">More...</a><br /></td></tr>
+<tr class="memdesc:a543cbf36f0e4d8dc99109db71705a11c"><td class="mdescLeft"> </td><td class="mdescRight">infer the shapes of outputs and unknown input arguments <a href="#a543cbf36f0e4d8dc99109db71705a11c">More...</a><br/></td></tr>
<tr class="separator:a543cbf36f0e4d8dc99109db71705a11c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a819b81e14761e9e8b9c888a63c7df1fa"><td class="memItemLeft" align="right" valign="top">virtual bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a819b81e14761e9e8b9c888a63c7df1fa">InferType</a> (std::vector< int > *in_type, std::vector< int > *out_type, std::vector< int > *aux_type) const</td></tr>
-<tr class="memdesc:a819b81e14761e9e8b9c888a63c7df1fa"><td class="mdescLeft"> </td><td class="mdescRight">infer the data types of outputs and unknown input arguments <a href="#a819b81e14761e9e8b9c888a63c7df1fa">More...</a><br /></td></tr>
-<tr class="separator:a819b81e14761e9e8b9c888a63c7df1fa"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad5c9f53ea70f3eeb1bf27b983f420c0b"><td class="memItemLeft" align="right" valign="top">virtual bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#ad5c9f53ea70f3eeb1bf27b983f420c0b">InferType</a> (std::vector< int > *in_type, std::vector< int > *out_type, std::vector< int > *aux_type) const </td></tr>
+<tr class="memdesc:ad5c9f53ea70f3eeb1bf27b983f420c0b"><td class="mdescLeft"> </td><td class="mdescRight">infer the data types of outputs and unknown input arguments <a href="#ad5c9f53ea70f3eeb1bf27b983f420c0b">More...</a><br/></td></tr>
+<tr class="separator:ad5c9f53ea70f3eeb1bf27b983f420c0b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab38f2f877555ba34b548efd2575cac99"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#ab38f2f877555ba34b548efd2575cac99">Copy</a> () const =0</td></tr>
-<tr class="memdesc:ab38f2f877555ba34b548efd2575cac99"><td class="mdescLeft"> </td><td class="mdescRight">Copy this <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a>. <a href="#ab38f2f877555ba34b548efd2575cac99">More...</a><br /></td></tr>
+<tr class="memdesc:ab38f2f877555ba34b548efd2575cac99"><td class="mdescLeft"> </td><td class="mdescRight">Copy this <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a>. <a href="#ab38f2f877555ba34b548efd2575cac99">More...</a><br/></td></tr>
<tr class="separator:ab38f2f877555ba34b548efd2575cac99"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a546ab25ddaae590ca98705ae9abc7ac0"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1Operator.html">Operator</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a546ab25ddaae590ca98705ae9abc7ac0">CreateOperator</a> (<a class="el" href="structmxnet_1_1Context.html">Context</a> ctx) const =0</td></tr>
-<tr class="memdesc:a546ab25ddaae590ca98705ae9abc7ac0"><td class="mdescLeft"> </td><td class="mdescRight">Create a <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> on specific context. <a href="#a546ab25ddaae590ca98705ae9abc7ac0">More...</a><br /></td></tr>
+<tr class="memdesc:a546ab25ddaae590ca98705ae9abc7ac0"><td class="mdescLeft"> </td><td class="mdescRight">Create a <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> on specific context. <a href="#a546ab25ddaae590ca98705ae9abc7ac0">More...</a><br/></td></tr>
<tr class="separator:a546ab25ddaae590ca98705ae9abc7ac0"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:af07a7f946cfadb9bea19fa3d2b621ab4"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1Operator.html">Operator</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#af07a7f946cfadb9bea19fa3d2b621ab4">CreateOperatorEx</a> (<a class="el" href="structmxnet_1_1Context.html">Context</a> ctx, std::vector< <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da65 [...]
-<tr class="memdesc:af07a7f946cfadb9bea19fa3d2b621ab4"><td class="mdescLeft"> </td><td class="mdescRight">Create a <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> on specific context and input shape/type. <a href="#af07a7f946cfadb9bea19fa3d2b621ab4">More...</a><br /></td></tr>
-<tr class="separator:af07a7f946cfadb9bea19fa3d2b621ab4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7985004f99a931b4a6ff69df733bae72"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1Operator.html">Operator</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a7985004f99a931b4a6ff69df733bae72">CreateOperatorEx</a> (<a class="el" href="structmxnet_1_1Context.html">Context</a> ctx, std::vector< <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da65 [...]
+<tr class="memdesc:a7985004f99a931b4a6ff69df733bae72"><td class="mdescLeft"> </td><td class="mdescRight">Create a <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> on specific context and input shape/type. <a href="#a7985004f99a931b4a6ff69df733bae72">More...</a><br/></td></tr>
+<tr class="separator:a7985004f99a931b4a6ff69df733bae72"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac6b7df90c0285a1823b463c21e926698"><td class="memItemLeft" align="right" valign="top">virtual std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#ac6b7df90c0285a1823b463c21e926698">TypeString</a> () const =0</td></tr>
-<tr class="memdesc:ac6b7df90c0285a1823b463c21e926698"><td class="mdescLeft"> </td><td class="mdescRight">return the type string of the <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> subclasses override this function. <a href="#ac6b7df90c0285a1823b463c21e926698">More...</a><br /></td></tr>
+<tr class="memdesc:ac6b7df90c0285a1823b463c21e926698"><td class="mdescLeft"> </td><td class="mdescRight">return the type string of the <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> subclasses override this function. <a href="#ac6b7df90c0285a1823b463c21e926698">More...</a><br/></td></tr>
<tr class="separator:ac6b7df90c0285a1823b463c21e926698"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a1f48e07aa4979935b8f4b9dd7564db18"><td class="memItemLeft" align="right" valign="top">virtual std::vector< <a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a1f48e07aa4979935b8f4b9dd7564db18">ForwardResource</a> (const std::vector< <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> > & [...]
-<tr class="memdesc:a1f48e07aa4979935b8f4b9dd7564db18"><td class="mdescLeft"> </td><td class="mdescRight">Declare additional resource required in forward pass. These additional resources will be presented in <a class="el" href="structmxnet_1_1OpContext.html#a833e95f95cf5e3af9c193b74b500fc5b" title="Resources requested by the operator. ">OpContext.requested</a> in the same order of the returned <a class="el" href="structmxnet_1_1Resource.html" title="Resources used by mxnet operations [...]
-<tr class="separator:a1f48e07aa4979935b8f4b9dd7564db18"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:acec41540dde2c49ff2f2a8fc425890e8"><td class="memItemLeft" align="right" valign="top">virtual std::vector< <a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#acec41540dde2c49ff2f2a8fc425890e8">BackwardResource</a> (const std::vector< <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> > & [...]
-<tr class="memdesc:acec41540dde2c49ff2f2a8fc425890e8"><td class="mdescLeft"> </td><td class="mdescRight">Declare additional resource required in backward pass. These additional resources will be presented in <a class="el" href="structmxnet_1_1OpContext.html#a833e95f95cf5e3af9c193b74b500fc5b" title="Resources requested by the operator. ">OpContext.requested</a> in the same order of the returned <a class="el" href="structmxnet_1_1Resource.html" title="Resources used by mxnet operation [...]
-<tr class="separator:acec41540dde2c49ff2f2a8fc425890e8"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a8aa205ea6ce962bfa3fcbe73a8f3db5d"><td class="memItemLeft" align="right" valign="top">virtual std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a8aa205ea6ce962bfa3fcbe73a8f3db5d">DeclareBackwardDependency</a> (const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data) const</td></tr>
-<tr class="memdesc:a8aa205ea6ce962bfa3fcbe73a8f3db5d"><td class="mdescLeft"> </td><td class="mdescRight">Declare the input requirement of Backward pass. <a href="#a8aa205ea6ce962bfa3fcbe73a8f3db5d">More...</a><br /></td></tr>
-<tr class="separator:a8aa205ea6ce962bfa3fcbe73a8f3db5d"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a78989f905d04bedb41896876fd4dbac9"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::pair< int, void * > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a78989f905d04bedb41896876fd4dbac9">ForwardInplaceOption</a> (const std::vector< int > &in_data, const std::vector< void *> &out_data) const</td></tr>
-<tr class="memdesc:a78989f905d04bedb41896876fd4dbac9"><td class="mdescLeft"> </td><td class="mdescRight">Get possible forward inplace options. This function enables optimization to reuse memory of inputs in output. Only override when necessary, by default in-place is disabled. <a href="#a78989f905d04bedb41896876fd4dbac9">More...</a><br /></td></tr>
-<tr class="separator:a78989f905d04bedb41896876fd4dbac9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a0255256ac4e0e76b3fff917560203efe"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::pair< int, void * > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a0255256ac4e0e76b3fff917560203efe">BackwardInplaceOption</a> (const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data, const std::vector< [...]
-<tr class="memdesc:a0255256ac4e0e76b3fff917560203efe"><td class="mdescLeft"> </td><td class="mdescRight">Get possible backward inplace options. This function enables optimization to reuse memory of inputs in output. Only override when necessary, by default in-place is disabled. <a href="#a0255256ac4e0e76b3fff917560203efe">More...</a><br /></td></tr>
-<tr class="separator:a0255256ac4e0e76b3fff917560203efe"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6c5c8df10748a7eee873d4b7eb8d684b"><td class="memTemplParams" colspan="2">template<typename T > </td></tr>
-<tr class="memitem:a6c5c8df10748a7eee873d4b7eb8d684b"><td class="memTemplItemLeft" align="right" valign="top">std::vector< T > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a6c5c8df10748a7eee873d4b7eb8d684b">BackwardInputs</a> (const std::vector< T > &out_grad, const std::vector< T > &in_data, const std::vector< T > &out_data) const</td></tr>
-<tr class="memdesc:a6c5c8df10748a7eee873d4b7eb8d684b"><td class="mdescLeft"> </td><td class="mdescRight">Get Backward Input Dependency for generic types of data. Normally T can be pointer of Symbol::DataEntry, or <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. This function will select the result list of T according to DeclareBackwardDependency. <a href="#a6c5c8df10748a7eee873d4b7eb8d684b">More...</a><br /></td></tr>
-<tr class="separator:a6c5c8df10748a7eee873d4b7eb8d684b"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a149c52bd285e4fa78109a535765a9b7c"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">ExecType</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a149c52bd285e4fa78109a535765a9b7c">exec_type</a> () const</td></tr>
-<tr class="separator:a149c52bd285e4fa78109a535765a9b7c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a623210dd1b9495ca73f223e0b06f998c"><td class="memItemLeft" align="right" valign="top">virtual std::vector<br class="typebreak"/>
+< <a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a623210dd1b9495ca73f223e0b06f998c">ForwardResource</a> (const std::vector< <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> > &in_shape) const </td></tr>
+<tr class="memdesc:a623210dd1b9495ca73f223e0b06f998c"><td class="mdescLeft"> </td><td class="mdescRight">Declare additional resource required in forward pass. These additional resources will be presented in <a class="el" href="structmxnet_1_1OpContext.html#a833e95f95cf5e3af9c193b74b500fc5b" title="Resources requested by the operator. ">OpContext.requested</a> in the same order of the returned <a class="el" href="structmxnet_1_1Resource.html" title="Resources used by mxnet operations [...]
+<tr class="separator:a623210dd1b9495ca73f223e0b06f998c"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ade7720a3e0ebba67d89978925a77609b"><td class="memItemLeft" align="right" valign="top">virtual std::vector<br class="typebreak"/>
+< <a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#ade7720a3e0ebba67d89978925a77609b">BackwardResource</a> (const std::vector< <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> > &in_shape) const </td></tr>
+<tr class="memdesc:ade7720a3e0ebba67d89978925a77609b"><td class="mdescLeft"> </td><td class="mdescRight">Declare additional resource required in backward pass. These additional resources will be presented in <a class="el" href="structmxnet_1_1OpContext.html#a833e95f95cf5e3af9c193b74b500fc5b" title="Resources requested by the operator. ">OpContext.requested</a> in the same order of the returned <a class="el" href="structmxnet_1_1Resource.html" title="Resources used by mxnet operation [...]
+<tr class="separator:ade7720a3e0ebba67d89978925a77609b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:abf9e6a8d40750f3ee81fe30cbe3e2aae"><td class="memItemLeft" align="right" valign="top">virtual std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#abf9e6a8d40750f3ee81fe30cbe3e2aae">DeclareBackwardDependency</a> (const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data) const </td></tr>
+<tr class="memdesc:abf9e6a8d40750f3ee81fe30cbe3e2aae"><td class="mdescLeft"> </td><td class="mdescRight">Declare the input requirement of Backward pass. <a href="#abf9e6a8d40750f3ee81fe30cbe3e2aae">More...</a><br/></td></tr>
+<tr class="separator:abf9e6a8d40750f3ee81fe30cbe3e2aae"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aad7a3e87866515a2b7100e849a2fe966"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::pair<br class="typebreak"/>
+< int, void * > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#aad7a3e87866515a2b7100e849a2fe966">ForwardInplaceOption</a> (const std::vector< int > &in_data, const std::vector< void * > &out_data) const </td></tr>
+<tr class="memdesc:aad7a3e87866515a2b7100e849a2fe966"><td class="mdescLeft"> </td><td class="mdescRight">Get possible forward inplace options. This function enables optimization to reuse memory of inputs in output. Only override when necessary, by default in-place is disabled. <a href="#aad7a3e87866515a2b7100e849a2fe966">More...</a><br/></td></tr>
+<tr class="separator:aad7a3e87866515a2b7100e849a2fe966"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a67231a5a1525ec7480de98a79dce0194"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::pair<br class="typebreak"/>
+< int, void * > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a67231a5a1525ec7480de98a79dce0194">BackwardInplaceOption</a> (const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data, const std::vector< void * > &in_grad) const </td></tr>
+<tr class="memdesc:a67231a5a1525ec7480de98a79dce0194"><td class="mdescLeft"> </td><td class="mdescRight">Get possible backward inplace options. This function enables optimization to reuse memory of inputs in output. Only override when necessary, by default in-place is disabled. <a href="#a67231a5a1525ec7480de98a79dce0194">More...</a><br/></td></tr>
+<tr class="separator:a67231a5a1525ec7480de98a79dce0194"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9f488509e132094ce7e79125ce853042"><td class="memTemplParams" colspan="2">template<typename T > </td></tr>
+<tr class="memitem:a9f488509e132094ce7e79125ce853042"><td class="memTemplItemLeft" align="right" valign="top">std::vector< T > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a9f488509e132094ce7e79125ce853042">BackwardInputs</a> (const std::vector< T > &out_grad, const std::vector< T > &in_data, const std::vector< T > &out_data) const </td></tr>
+<tr class="memdesc:a9f488509e132094ce7e79125ce853042"><td class="mdescLeft"> </td><td class="mdescRight">Get Backward Input Dependency for generic types of data. Normally T can be pointer of Symbol::DataEntry, or <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>. This function will select the result list of T according to DeclareBackwardDependency. <a href="#a9f488509e132094ce7e79125ce853042">More...</a><br/></td></tr>
+<tr class="separator:a9f488509e132094ce7e79125ce853042"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa5d6354ebe3e2a0c51f7f7ce4a60f664"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">ExecType</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#aa5d6354ebe3e2a0c51f7f7ce4a60f664">exec_type</a> () const </td></tr>
+<tr class="separator:aa5d6354ebe3e2a0c51f7f7ce4a60f664"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:a15cf1f905b08a008ff38556ef6a35dd0"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html#a15cf1f905b08a008ff38556ef6a35dd0">Create</a> (const char *type_name)</td></tr>
-<tr class="memdesc:a15cf1f905b08a008ff38556ef6a35dd0"><td class="mdescLeft"> </td><td class="mdescRight">create <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> <a href="#a15cf1f905b08a008ff38556ef6a35dd0">More...</a><br /></td></tr>
+<tr class="memdesc:a15cf1f905b08a008ff38556ef6a35dd0"><td class="mdescLeft"> </td><td class="mdescRight">create <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> <a href="#a15cf1f905b08a008ff38556ef6a35dd0">More...</a><br/></td></tr>
<tr class="separator:a15cf1f905b08a008ff38556ef6a35dd0"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> is a object that stores all information about <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. It also contains method to generate context(device) speci [...]
<p>It also contains various functions that can be optimally overriden to provide optimization chance for computation engine. </p>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a610af8e9cb560104301ccd1efa2a96fd"></a>
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<h2 class="groupheader">Member Function Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a0255256ac4e0e76b3fff917560203efe">◆ </a></span>BackwardInplaceOption()</h2>
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<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype">const std::vector< void *> & </td>
+ <td class="paramtype">const std::vector< void * > & </td>
<td class="paramname"><em>in_grad</em> </td>
</tr>
<tr>
@@ -234,7 +259,15 @@ Static Public Member Functions</h2></td></tr>
<p>Get possible backward inplace options. This function enables optimization to reuse memory of inputs in output. Only override when necessary, by default in-place is disabled. </p>
<p>The reason for void* type in the in_grad is to distinguish the order of mappings between the two, compiler will report error when in_data and out_data's order in the pair get reversed.</p>
-<div class="fragment"><div class="line"><span class="comment">// The following code says in_grad[0] can share data with in_data[0]</span></div><div class="line">vector<pair<int,int> > <a class="code" href="classmxnet_1_1OperatorProperty.html#a0255256ac4e0e76b3fff917560203efe">BackwardInplaceOption</a>(</div><div class="line"> <span class="keyword">const</span> std::vector<int> &out_grad,</div><div class="line"> <span class="keyword">const [...]
+<div class="fragment"><div class="line"><span class="comment">// The following code says in_grad[0] can share data with in_data[0]</span></div>
+<div class="line">vector<pair<int,int> > <a class="code" href="classmxnet_1_1OperatorProperty.html#a67231a5a1525ec7480de98a79dce0194">BackwardInplaceOption</a>(</div>
+<div class="line"> <span class="keyword">const</span> std::vector<int> &out_grad,</div>
+<div class="line"> <span class="keyword">const</span> std::vector<int> &in_data,</div>
+<div class="line"> <span class="keyword">const</span> std::vector<int> &out_data,</div>
+<div class="line"> <span class="keyword">const</span> std::vector<int> &in_grad)<span class="keyword"> const </span>{</div>
+<div class="line"> <span class="keywordflow">return</span> {in_data[0], in_grad[0]}};</div>
+<div class="line">}</div>
+</div><!-- fragment --> <dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">in_data</td><td>The input data in forward pass. </td></tr>
<tr><td class="paramname">out_data</td><td>The output data in forward pass. </td></tr>
@@ -247,9 +280,7 @@ Static Public Member Functions</h2></td></tr>
</div>
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</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>vector of inputs the Backward Operation depends on. </dd></dl>
-<dl class="section see"><dt>See also</dt><dd><a class="el" href="classmxnet_1_1OperatorProperty.html#a8aa205ea6ce962bfa3fcbe73a8f3db5d" title="Declare the input requirement of Backward pass. ">DeclareBackwardDependency</a> </dd></dl>
+<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classmxnet_1_1OperatorProperty.html#abf9e6a8d40750f3ee81fe30cbe3e2aae" title="Declare the input requirement of Backward pass. ">DeclareBackwardDependency</a> </dd></dl>
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-<h2 class="memtitle"><span class="permalink"><a href="#a8aa205ea6ce962bfa3fcbe73a8f3db5d">◆ </a></span>DeclareBackwardDependency()</h2>
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@@ -530,7 +549,13 @@ template<typename T > </div>
<p>Declare the input requirement of Backward pass. </p>
<p>Only the returned list of variables will be used in Backward. This function is used for memory optimization. It is advised to override and only return what is actually needed. If this function is not overriden, all the variables will be valid in Backward.</p>
-<div class="fragment"><div class="line"><span class="comment">// The following code declares Backward need out_grad[0], in_data[0],in_data[1]</span></div><div class="line">vector<int> <a class="code" href="classmxnet_1_1OperatorProperty.html#a6c5c8df10748a7eee873d4b7eb8d684b">BackwardInputs</a>(<span class="keyword">const</span> vector<int> &out_grad,</div><div class="line"> <span class="keyword">const</span> vector<int> &in_data,</div> [...]
+<div class="fragment"><div class="line"><span class="comment">// The following code declares Backward need out_grad[0], in_data[0],in_data[1]</span></div>
+<div class="line">vector<int> <a class="code" href="classmxnet_1_1OperatorProperty.html#a9f488509e132094ce7e79125ce853042">BackwardInputs</a>(<span class="keyword">const</span> vector<int> &out_grad,</div>
+<div class="line"> <span class="keyword">const</span> vector<int> &in_data,</div>
+<div class="line"> <span class="keyword">const</span> vector<int> &out_data)<span class="keyword"> const </span>{</div>
+<div class="line"> <span class="keywordflow">return</span> {out_grad[0], in_data[0], in_data[1]};</div>
+<div class="line">}</div>
+</div><!-- fragment --> <dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">out_grad</td><td>gradient of outputs in backward pass. </td></tr>
<tr><td class="paramname">in_data</td><td>the input data in forward pass. </td></tr>
@@ -539,13 +564,11 @@ template<typename T > </div>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>an integer vector indicating the input requirments </dd></dl>
-<dl class="section see"><dt>See also</dt><dd><a class="el" href="classmxnet_1_1OperatorProperty.html#a6c5c8df10748a7eee873d4b7eb8d684b" title="Get Backward Input Dependency for generic types of data. Normally T can be pointer of Symbol::DataEnt...">BackwardInputs</a> </dd></dl>
+<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classmxnet_1_1OperatorProperty.html#a9f488509e132094ce7e79125ce853042" title="Get Backward Input Dependency for generic types of data. Normally T can be pointer of Symbol::DataEnt...">BackwardInputs</a> </dd></dl>
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-<h2 class="memtitle"><span class="permalink"><a href="#a78989f905d04bedb41896876fd4dbac9">◆ </a></span>ForwardInplaceOption()</h2>
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<td></td>
- <td class="paramtype">const std::vector< void *> & </td>
+ <td class="paramtype">const std::vector< void * > & </td>
<td class="paramname"><em>out_data</em> </td>
</tr>
<tr>
@@ -605,7 +626,12 @@ template<typename T > </div>
<p>Get possible forward inplace options. This function enables optimization to reuse memory of inputs in output. Only override when necessary, by default in-place is disabled. </p>
<p>The reason for void* type in the out_data is to distinguish the order of mappings between the two, compiler will report error when in_data and out_data's order in the pair get reversed.</p>
-<div class="fragment"><div class="line"><span class="comment">// The following code says out_data[0] can share data with in_data[0]</span></div><div class="line">vector<pair<int, void*> > <a class="code" href="classmxnet_1_1OperatorProperty.html#a78989f905d04bedb41896876fd4dbac9">ForwardInplaceOption</a>(<span class="keyword">const</span> vector<int> &in_data,</div><div class="line"> <span class="keyword">const</span> ve [...]
+<div class="fragment"><div class="line"><span class="comment">// The following code says out_data[0] can share data with in_data[0]</span></div>
+<div class="line">vector<pair<int, void*> > <a class="code" href="classmxnet_1_1OperatorProperty.html#aad7a3e87866515a2b7100e849a2fe966">ForwardInplaceOption</a>(<span class="keyword">const</span> vector<int> &in_data,</div>
+<div class="line"> <span class="keyword">const</span> vector<void*> &out_data)<span class="keyword"> const </span>{</div>
+<div class="line"> <span class="keywordflow">return</span> {{in_data[0], out_data[0]}};</div>
+<div class="line">}</div>
+</div><!-- fragment --> <dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">in_data</td><td>The input data in forward pass. </td></tr>
<tr><td class="paramname">out_data</td><td>The output data in forward pass. </td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a006f0e2c9b3f934e1f73b242b7e8b062">◆ </a></span>NumVisibleOutputs()</h2>
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</table>
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-<p>get number of visible return values during Symbol creation. If <a class="el" href="classmxnet_1_1OperatorProperty.html#a006f0e2c9b3f934e1f73b242b7e8b062" title="get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...">NumVisibleOutputs()</a> = k, and <a class="el" href="classmxnet_1_1OperatorProperty.html#a9fc6e9f04f52b1c05f62fb7357bb95c4">NumOutputs()</a> = n. The first k returns will be presented in the resulting symbol. </p>
-<p>The rest of the returns can be used for auxiliary states for Backward. For example, Dropout will return [data, mask], with <a class="el" href="classmxnet_1_1OperatorProperty.html#a006f0e2c9b3f934e1f73b242b7e8b062" title="get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...">NumVisibleOutputs()</a> == 1. So when user call sym = Dropout(input), only data is presented in sym. But all the returns will be presented in out_data parameter of Backward if r [...]
+<p>get number of visible return values during Symbol creation. If <a class="el" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b" title="get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...">NumVisibleOutputs()</a> = k, and <a class="el" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">NumOutputs()</a> = n. The first k returns will be presented in the resulting symbol. </p>
+<p>The rest of the returns can be used for auxiliary states for Backward. For example, Dropout will return [data, mask], with <a class="el" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b" title="get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...">NumVisibleOutputs()</a> == 1. So when user call sym = Dropout(input), only data is presented in sym. But all the returns will be presented in out_data parameter of Backward if r [...]
<dl class="section return"><dt>Returns</dt><dd>number of default return values </dd></dl>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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+ <ul class="tablist">
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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diff --git a/versions/master/doxygen/classmxnet_1_1ResourceManager.html b/versions/master/doxygen/classmxnet_1_1ResourceManager.html
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<title>mxnet: mxnet::ResourceManager Class Reference</title>
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- $(document).ready(function() { init_search(); });
-});
-</script>
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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Collaboration diagram for mxnet::ResourceManager:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1ResourceManager__coll__graph.png" border="0" usemap="#mxnet_1_1ResourceManager_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1ResourceManager_coll__map" id="mxnet_1_1ResourceManager_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a276e4e3ae229a118e50a03955f3f1a08"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="structmxnet_1_1Resource.html">Resource</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1ResourceManager.html#a276e4e3ae229a118e50a03955f3f1a08">Request</a> (<a class="el" href="structmxnet_1_1Context.html">Context</a> ctx, const <a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> &req)=0</ [...]
-<tr class="memdesc:a276e4e3ae229a118e50a03955f3f1a08"><td class="mdescLeft"> </td><td class="mdescRight">Get resource of requested type. <a href="#a276e4e3ae229a118e50a03955f3f1a08">More...</a><br /></td></tr>
+<tr class="memdesc:a276e4e3ae229a118e50a03955f3f1a08"><td class="mdescLeft"> </td><td class="mdescRight">Get resource of requested type. <a href="#a276e4e3ae229a118e50a03955f3f1a08">More...</a><br/></td></tr>
<tr class="separator:a276e4e3ae229a118e50a03955f3f1a08"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7aa335bc9231a8eb7cd04de22e033603"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1ResourceManager.html#a7aa335bc9231a8eb7cd04de22e033603">SeedRandom</a> (uint32_t seed)=0</td></tr>
-<tr class="memdesc:a7aa335bc9231a8eb7cd04de22e033603"><td class="mdescLeft"> </td><td class="mdescRight">Seed all the allocated random numbers. <a href="#a7aa335bc9231a8eb7cd04de22e033603">More...</a><br /></td></tr>
+<tr class="memdesc:a7aa335bc9231a8eb7cd04de22e033603"><td class="mdescLeft"> </td><td class="mdescRight">Seed all the allocated random numbers. <a href="#a7aa335bc9231a8eb7cd04de22e033603">More...</a><br/></td></tr>
<tr class="separator:a7aa335bc9231a8eb7cd04de22e033603"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2b53c0843ddbaf7ccb9e06f448b20cac"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1ResourceManager.html#a2b53c0843ddbaf7ccb9e06f448b20cac">~ResourceManager</a> () DMLC_THROW_EXCEPTION</td></tr>
-<tr class="memdesc:a2b53c0843ddbaf7ccb9e06f448b20cac"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#a2b53c0843ddbaf7ccb9e06f448b20cac">More...</a><br /></td></tr>
+<tr class="memdesc:a2b53c0843ddbaf7ccb9e06f448b20cac"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#a2b53c0843ddbaf7ccb9e06f448b20cac">More...</a><br/></td></tr>
<tr class="separator:a2b53c0843ddbaf7ccb9e06f448b20cac"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
@@ -101,9 +127,7 @@ Static Public Member Functions</h2></td></tr>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Global resource manager. </p>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a2b53c0843ddbaf7ccb9e06f448b20cac"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a2b53c0843ddbaf7ccb9e06f448b20cac">◆ </a></span>~ResourceManager()</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -129,9 +153,7 @@ Static Public Member Functions</h2></td></tr>
</div>
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<h2 class="groupheader">Member Function Documentation</h2>
-<a id="a5712bb055045ba9fb45dd3cdb812abb8"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a5712bb055045ba9fb45dd3cdb812abb8">◆ </a></span>Get()</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -155,9 +177,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
-<a id="a276e4e3ae229a118e50a03955f3f1a08"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a276e4e3ae229a118e50a03955f3f1a08">◆ </a></span>Request()</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -202,9 +222,7 @@ Static Public Member Functions</h2></td></tr>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a7aa335bc9231a8eb7cd04de22e033603">◆ </a></span>SeedRandom()</h2>
-
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<div class="memproto">
<table class="mlabels">
@@ -242,9 +260,9 @@ Static Public Member Functions</h2></td></tr>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
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- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
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-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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+<!-- Generated by Doxygen 1.8.6 -->
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var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -80,27 +104,29 @@ $(function() {
Collaboration diagram for mxnet::Storage:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1Storage__coll__graph.png" border="0" usemap="#mxnet_1_1Storage_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1Storage_coll__map" id="mxnet_1_1Storage_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Storage_1_1Handle.html">Handle</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> handle. <a href="structmxnet_1_1Storage_1_1Handle.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> handle. <a href="structmxnet_1_1Storage_1_1Handle.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:aaa387e9ae8d66a63438543d640f6cfff"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="structmxnet_1_1Storage_1_1Handle.html">Handle</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Storage.html#aaa387e9ae8d66a63438543d640f6cfff">Alloc</a> (size_t size, <a class="el" href="structmxnet_1_1Context.html">Context</a> ctx)=0</td></tr>
-<tr class="memdesc:aaa387e9ae8d66a63438543d640f6cfff"><td class="mdescLeft"> </td><td class="mdescRight">Allocate a new contiguous memory for a given size. <a href="#aaa387e9ae8d66a63438543d640f6cfff">More...</a><br /></td></tr>
+<tr class="memdesc:aaa387e9ae8d66a63438543d640f6cfff"><td class="mdescLeft"> </td><td class="mdescRight">Allocate a new contiguous memory for a given size. <a href="#aaa387e9ae8d66a63438543d640f6cfff">More...</a><br/></td></tr>
<tr class="separator:aaa387e9ae8d66a63438543d640f6cfff"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5bdbd7b3ab712cd9e087ab007bdc64dd"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Storage.html#a5bdbd7b3ab712cd9e087ab007bdc64dd">Free</a> (<a class="el" href="structmxnet_1_1Storage_1_1Handle.html">Handle</a> handle)=0</td></tr>
-<tr class="memdesc:a5bdbd7b3ab712cd9e087ab007bdc64dd"><td class="mdescLeft"> </td><td class="mdescRight">Free storage. <a href="#a5bdbd7b3ab712cd9e087ab007bdc64dd">More...</a><br /></td></tr>
+<tr class="memdesc:a5bdbd7b3ab712cd9e087ab007bdc64dd"><td class="mdescLeft"> </td><td class="mdescRight">Free storage. <a href="#a5bdbd7b3ab712cd9e087ab007bdc64dd">More...</a><br/></td></tr>
<tr class="separator:a5bdbd7b3ab712cd9e087ab007bdc64dd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a098f8ebacd25f5203b2dc4c1796aec64"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Storage.html#a098f8ebacd25f5203b2dc4c1796aec64">DirectFree</a> (<a class="el" href="structmxnet_1_1Storage_1_1Handle.html">Handle</a> handle)=0</td></tr>
-<tr class="memdesc:a098f8ebacd25f5203b2dc4c1796aec64"><td class="mdescLeft"> </td><td class="mdescRight">Free storage directly, without putting it into memory pool. This can synchronization of all previous runned device functions. <a href="#a098f8ebacd25f5203b2dc4c1796aec64">More...</a><br /></td></tr>
+<tr class="memdesc:a098f8ebacd25f5203b2dc4c1796aec64"><td class="mdescLeft"> </td><td class="mdescRight">Free storage directly, without putting it into memory pool. This can synchronization of all previous runned device functions. <a href="#a098f8ebacd25f5203b2dc4c1796aec64">More...</a><br/></td></tr>
<tr class="separator:a098f8ebacd25f5203b2dc4c1796aec64"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af1e1bfea9236ccfba64344226416b04a"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Storage.html#af1e1bfea9236ccfba64344226416b04a">~Storage</a> ()</td></tr>
-<tr class="memdesc:af1e1bfea9236ccfba64344226416b04a"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <a href="#af1e1bfea9236ccfba64344226416b04a">More...</a><br /></td></tr>
+<tr class="memdesc:af1e1bfea9236ccfba64344226416b04a"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <a href="#af1e1bfea9236ccfba64344226416b04a">More...</a><br/></td></tr>
<tr class="separator:af1e1bfea9236ccfba64344226416b04a"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
@@ -108,15 +134,13 @@ Static Public Member Functions</h2></td></tr>
<tr class="memitem:a96abc072908d72d3271360a7e3e4bbc2"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classmxnet_1_1Storage.html">Storage</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Storage.html#a96abc072908d72d3271360a7e3e4bbc2">Get</a> ()</td></tr>
<tr class="separator:a96abc072908d72d3271360a7e3e4bbc2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4c5e9c239447ee223ab912716d53d7c7"><td class="memItemLeft" align="right" valign="top">static std::shared_ptr< <a class="el" href="classmxnet_1_1Storage.html">Storage</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Storage.html#a4c5e9c239447ee223ab912716d53d7c7">_GetSharedRef</a> ()</td></tr>
-<tr class="memdesc:a4c5e9c239447ee223ab912716d53d7c7"><td class="mdescLeft"> </td><td class="mdescRight">Get shared pointer reference to storage singleton. Most user should not call this function. This function is called by another singleton X who requires <a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> to be destructed after X. <a href="#a4c5e9c239447ee223ab912716d53d7c7">More...</a><br /></td></tr>
+<tr class="memdesc:a4c5e9c239447ee223ab912716d53d7c7"><td class="mdescLeft"> </td><td class="mdescRight">Get shared pointer reference to storage singleton. Most user should not call this function. This function is called by another singleton X who requires <a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> to be destructed after X. <a href="#a4c5e9c239447ee223ab912716d53d7c7">More...</a><br/></td></tr>
<tr class="separator:a4c5e9c239447ee223ab912716d53d7c7"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> manager across multiple devices. </p>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="af1e1bfea9236ccfba64344226416b04a"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#af1e1bfea9236ccfba64344226416b04a">◆ </a></span>~Storage()</h2>
-
+<a class="anchor" id="af1e1bfea9236ccfba64344226416b04a"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -142,9 +166,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
-<a id="a4c5e9c239447ee223ab912716d53d7c7"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a4c5e9c239447ee223ab912716d53d7c7">◆ </a></span>_GetSharedRef()</h2>
-
+<a class="anchor" id="a4c5e9c239447ee223ab912716d53d7c7"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -170,9 +192,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
-<a id="aaa387e9ae8d66a63438543d640f6cfff"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aaa387e9ae8d66a63438543d640f6cfff">◆ </a></span>Alloc()</h2>
-
+<a class="anchor" id="aaa387e9ae8d66a63438543d640f6cfff"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -216,9 +236,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
-<a id="a098f8ebacd25f5203b2dc4c1796aec64"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a098f8ebacd25f5203b2dc4c1796aec64">◆ </a></span>DirectFree()</h2>
-
+<a class="anchor" id="a098f8ebacd25f5203b2dc4c1796aec64"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -251,9 +269,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
-<a id="a5bdbd7b3ab712cd9e087ab007bdc64dd"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a5bdbd7b3ab712cd9e087ab007bdc64dd">◆ </a></span>Free()</h2>
-
+<a class="anchor" id="a5bdbd7b3ab712cd9e087ab007bdc64dd"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -285,9 +301,7 @@ Static Public Member Functions</h2></td></tr>
</div>
</div>
-<a id="a96abc072908d72d3271360a7e3e4bbc2"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a96abc072908d72d3271360a7e3e4bbc2">◆ </a></span>Get()</h2>
-
+<a class="anchor" id="a96abc072908d72d3271360a7e3e4bbc2"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -317,9 +331,9 @@ Static Public Member Functions</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/classmxnet_1_1Storage__coll__graph.md5 b/versions/master/doxygen/classmxnet_1_1Storage__coll__graph.md5
index ffca71b..3f83e34 100644
--- a/versions/master/doxygen/classmxnet_1_1Storage__coll__graph.md5
+++ b/versions/master/doxygen/classmxnet_1_1Storage__coll__graph.md5
@@ -1 +1 @@
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diff --git a/versions/master/doxygen/classmxnet_1_1Storage__coll__graph.png b/versions/master/doxygen/classmxnet_1_1Storage__coll__graph.png
index 2ff9533..c534c5c 100644
Binary files a/versions/master/doxygen/classmxnet_1_1Storage__coll__graph.png and b/versions/master/doxygen/classmxnet_1_1Storage__coll__graph.png differ
diff --git a/versions/master/doxygen/classmxnet_1_1TBlob-members.html b/versions/master/doxygen/classmxnet_1_1TBlob-members.html
index 2e5e298..4143e01 100644
--- a/versions/master/doxygen/classmxnet_1_1TBlob-members.html
+++ b/versions/master/doxygen/classmxnet_1_1TBlob-members.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -69,26 +93,26 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a>, including all inherited members.</p>
<table class="directory">
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#ac3abff8bace8f3afb3143d85551a18ac">CheckContiguous</a>(void) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a2caa6723ffda5908eb5c2e062ac77ebd">dev_id</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#ad8bcf56e13082fc0607e21f08b14758f">dev_mask</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a4b0a53821dec20c558a030ed3df14dda">dltensor</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#ad931186405f2b40a327278a8f5ff57cd">dptr</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#aa6cfdf8823a601602b6b56011af4b874">CheckContiguous</a>(void) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd">dev_id</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a519111d4450efa8e58cc10cfc1dbeca7">dltensor</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">dptr</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a></td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#add31f43a251cecde199e245f3ff8383a">FlatTo1D</a>(mshadow::Stream< Device > *stream=NULL) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a3e4b61ee072b4d875a90e8153ef50721">FlatTo2D</a>(mshadow::Stream< Device > *stream=NULL) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#aefabf95412878b86b1130035386beee8">FlatTo3D</a>(int axis, mshadow::Stream< Device > *stream=NULL) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#ae1dfdf1a1bc6ca78e4e06fe4aa71e074">FlatTo3D</a>(int axis_begin, int axis_end, mshadow::Stream< Device > *stream=NULL) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a5be50f29087afef8d48942ea8fe39cbd">FlatToKD</a>(mshadow::Stream< Device > *stream=NULL) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a3acb7675c6a1f682fe28d0d62aeb338a">get</a>(mshadow::Stream< Device > *stream=NULL) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#ae3601040650637a7ac05b3f00f0adbee">get_with_shape</a>(const mshadow::Shape< dim > &shape, mshadow::Stream< Device > *stream=NULL) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a95bf7032c2b90627510a89ac8bb2946f">FlatTo1D</a>(mshadow::Stream< Device > *stream=NULL) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a23cc57be3b2c40a269c8c9f242fa0532">FlatTo2D</a>(mshadow::Stream< Device > *stream=NULL) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#ac5cd4953869cb66ed374854a34da40f8">FlatTo3D</a>(int axis, mshadow::Stream< Device > *stream=NULL) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#ada83447eb80b913592e9ac2bf8c5740f">FlatTo3D</a>(int axis_begin, int axis_end, mshadow::Stream< Device > *stream=NULL) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#aa2f8e15eb1fc21e159c496ec0d499b6f">FlatToKD</a>(mshadow::Stream< Device > *stream=NULL) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#acb2e058394548527844e219c7e3c480d">get</a>(mshadow::Stream< Device > *stream=NULL) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#abbdbdc8d02c15e854cc7f5fe5dc09f61">get_with_shape</a>(const mshadow::Shape< dim > &shape, mshadow::Stream< Device > *stream=NULL) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#ad41557e205cf8f4d6ef70b60587c37cb">NDArray</a> class</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a77a8929e46d06e5d6a0026bcc9e48851">ndim</a>(void) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">ndim</a>(void) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#ad3263d9d1ef40678cd592be822d1c7be">operator=</a>(const mshadow::Tensor< Device, dim, DType > &src)</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a3b89df7835e39c652889b5cdb39235fa">reshape</a>(const TShape &shape) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a0c66b10f5a17528cc858762b60125789">reshape</a>(const TShape &shape) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a></td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a7b45f281d954cc716267a7b3ec0f514c">Size</a>(void) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a44c93b8a96203a1a85fa9d4b85e123c7">size</a>(index_t idx) const</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a3b04db1348834e1576cfb2ed54959703">Size</a>(void) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a523fd58883de4b9cd0b1a2852daa64ae">size</a>(index_t idx) const </td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#adcdc6de67f81454f0a142bfc9fd38c9e">TBlob</a>(void)</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#af89975f6a6de34624a1ee7fbc43df8fa">TBlob</a>(DType *dptr, const TShape &shape, int dev_mask, int dev_id=-1)</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html#a660f447fbe40782e1db6a993ab1e37b4">TBlob</a>(void *dptr, const TShape &shape, int dev_mask, int type_flag, int dev_id=-1)</td><td class="entry"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
@@ -97,9 +121,9 @@ $(function() {
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/classmxnet_1_1TBlob.html b/versions/master/doxygen/classmxnet_1_1TBlob.html
index d762112..32ee660 100644
--- a/versions/master/doxygen/classmxnet_1_1TBlob.html
+++ b/versions/master/doxygen/classmxnet_1_1TBlob.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::TBlob Class Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -80,94 +104,99 @@ $(function() {
Collaboration diagram for mxnet::TBlob:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1TBlob__coll__graph.png" border="0" usemap="#mxnet_1_1TBlob_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1TBlob_coll__map" id="mxnet_1_1TBlob_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:adcdc6de67f81454f0a142bfc9fd38c9e"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#adcdc6de67f81454f0a142bfc9fd38c9e">TBlob</a> (void)</td></tr>
-<tr class="memdesc:adcdc6de67f81454f0a142bfc9fd38c9e"><td class="mdescLeft"> </td><td class="mdescRight">storing mkl chunk buffer blob, use for experimental only <a href="#adcdc6de67f81454f0a142bfc9fd38c9e">More...</a><br /></td></tr>
+<tr class="memdesc:adcdc6de67f81454f0a142bfc9fd38c9e"><td class="mdescLeft"> </td><td class="mdescRight">storing mkl chunk buffer blob, use for experimental only <a href="#adcdc6de67f81454f0a142bfc9fd38c9e">More...</a><br/></td></tr>
<tr class="separator:adcdc6de67f81454f0a142bfc9fd38c9e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af89975f6a6de34624a1ee7fbc43df8fa"><td class="memTemplParams" colspan="2">template<typename DType > </td></tr>
-<tr class="memitem:af89975f6a6de34624a1ee7fbc43df8fa"><td class="memTemplItemLeft" align="right" valign="top"> </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#af89975f6a6de34624a1ee7fbc43df8fa">TBlob</a> (DType *<a class="el" href="classmxnet_1_1TBlob.html#ad931186405f2b40a327278a8f5ff57cd">dptr</a>, const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &shape, int <a class="el" href="classmxne [...]
-<tr class="memdesc:af89975f6a6de34624a1ee7fbc43df8fa"><td class="mdescLeft"> </td><td class="mdescRight">constructor that construct <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> from contiguous memory <a href="#af89975f6a6de34624a1ee7fbc43df8fa">More...</a><br /></td></tr>
+<tr class="memitem:af89975f6a6de34624a1ee7fbc43df8fa"><td class="memTemplItemLeft" align="right" valign="top"> </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#af89975f6a6de34624a1ee7fbc43df8fa">TBlob</a> (DType *<a class="el" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">dptr</a>, const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &shape, int <a class="el" href="classmxne [...]
+<tr class="memdesc:af89975f6a6de34624a1ee7fbc43df8fa"><td class="mdescLeft"> </td><td class="mdescRight">constructor that construct <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> from contiguous memory <a href="#af89975f6a6de34624a1ee7fbc43df8fa">More...</a><br/></td></tr>
<tr class="separator:af89975f6a6de34624a1ee7fbc43df8fa"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a660f447fbe40782e1db6a993ab1e37b4"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a660f447fbe40782e1db6a993ab1e37b4">TBlob</a> (void *<a class="el" href="classmxnet_1_1TBlob.html#ad931186405f2b40a327278a8f5ff57cd">dptr</a>, const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &shape, int <a class="el" href="classmxnet_1_1TBlob. [...]
-<tr class="memdesc:a660f447fbe40782e1db6a993ab1e37b4"><td class="mdescLeft"> </td><td class="mdescRight">constructor that construct <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> from contiguous memory <a href="#a660f447fbe40782e1db6a993ab1e37b4">More...</a><br /></td></tr>
+<tr class="memitem:a660f447fbe40782e1db6a993ab1e37b4"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a660f447fbe40782e1db6a993ab1e37b4">TBlob</a> (void *<a class="el" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">dptr</a>, const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &shape, int <a class="el" href="classmxnet_1_1TBlob. [...]
+<tr class="memdesc:a660f447fbe40782e1db6a993ab1e37b4"><td class="mdescLeft"> </td><td class="mdescRight">constructor that construct <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> from contiguous memory <a href="#a660f447fbe40782e1db6a993ab1e37b4">More...</a><br/></td></tr>
<tr class="separator:a660f447fbe40782e1db6a993ab1e37b4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aae92a89345aad79c8c3b451b9e3b84a5"><td class="memTemplParams" colspan="2">template<typename Device , int dim, typename DType > </td></tr>
<tr class="memitem:aae92a89345aad79c8c3b451b9e3b84a5"><td class="memTemplItemLeft" align="right" valign="top"> </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#aae92a89345aad79c8c3b451b9e3b84a5">TBlob</a> (const mshadow::Tensor< Device, dim, DType > &src)</td></tr>
-<tr class="memdesc:aae92a89345aad79c8c3b451b9e3b84a5"><td class="mdescLeft"> </td><td class="mdescRight">constructor from tensor <a href="#aae92a89345aad79c8c3b451b9e3b84a5">More...</a><br /></td></tr>
+<tr class="memdesc:aae92a89345aad79c8c3b451b9e3b84a5"><td class="mdescLeft"> </td><td class="mdescRight">constructor from tensor <a href="#aae92a89345aad79c8c3b451b9e3b84a5">More...</a><br/></td></tr>
<tr class="separator:aae92a89345aad79c8c3b451b9e3b84a5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad3263d9d1ef40678cd592be822d1c7be"><td class="memTemplParams" colspan="2">template<typename Device , int dim, typename DType > </td></tr>
<tr class="memitem:ad3263d9d1ef40678cd592be822d1c7be"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> & </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#ad3263d9d1ef40678cd592be822d1c7be">operator=</a> (const mshadow::Tensor< Device, dim, DType > &src)</td></tr>
-<tr class="memdesc:ad3263d9d1ef40678cd592be822d1c7be"><td class="mdescLeft"> </td><td class="mdescRight">assignment from tensor <a href="#ad3263d9d1ef40678cd592be822d1c7be">More...</a><br /></td></tr>
+<tr class="memdesc:ad3263d9d1ef40678cd592be822d1c7be"><td class="mdescLeft"> </td><td class="mdescRight">assignment from tensor <a href="#ad3263d9d1ef40678cd592be822d1c7be">More...</a><br/></td></tr>
<tr class="separator:ad3263d9d1ef40678cd592be822d1c7be"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ac3abff8bace8f3afb3143d85551a18ac"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#ac3abff8bace8f3afb3143d85551a18ac">CheckContiguous</a> (void) const</td></tr>
-<tr class="separator:ac3abff8bace8f3afb3143d85551a18ac"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a3b89df7835e39c652889b5cdb39235fa"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a3b89df7835e39c652889b5cdb39235fa">reshape</a> (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &shape) const</td></tr>
-<tr class="memdesc:a3b89df7835e39c652889b5cdb39235fa"><td class="mdescLeft"> </td><td class="mdescRight">reshape to shape <a href="#a3b89df7835e39c652889b5cdb39235fa">More...</a><br /></td></tr>
-<tr class="separator:a3b89df7835e39c652889b5cdb39235fa"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a3e4b61ee072b4d875a90e8153ef50721"><td class="memTemplParams" colspan="2">template<typename Device , typename DType > </td></tr>
-<tr class="memitem:a3e4b61ee072b4d875a90e8153ef50721"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, 2, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a3e4b61ee072b4d875a90e8153ef50721">FlatTo2D</a> (mshadow::Stream< Device > *stream=NULL) const</td></tr>
-<tr class="memdesc:a3e4b61ee072b4d875a90e8153ef50721"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to 2 dimension, collapse the higher dimensions together <a href="#a3e4b61ee072b4d875a90e8153ef50721">More...</a><br /></td></tr>
-<tr class="separator:a3e4b61ee072b4d875a90e8153ef50721"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:add31f43a251cecde199e245f3ff8383a"><td class="memTemplParams" colspan="2">template<typename Device , typename DType > </td></tr>
-<tr class="memitem:add31f43a251cecde199e245f3ff8383a"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, 1, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#add31f43a251cecde199e245f3ff8383a">FlatTo1D</a> (mshadow::Stream< Device > *stream=NULL) const</td></tr>
-<tr class="memdesc:add31f43a251cecde199e245f3ff8383a"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to 1 dimension, collapse all the dimensions together. <a href="#add31f43a251cecde199e245f3ff8383a">More...</a><br /></td></tr>
-<tr class="separator:add31f43a251cecde199e245f3ff8383a"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a77a8929e46d06e5d6a0026bcc9e48851"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a77a8929e46d06e5d6a0026bcc9e48851">ndim</a> (void) const</td></tr>
-<tr class="memdesc:a77a8929e46d06e5d6a0026bcc9e48851"><td class="mdescLeft"> </td><td class="mdescRight">return number of dimension of the tensor inside <a href="#a77a8929e46d06e5d6a0026bcc9e48851">More...</a><br /></td></tr>
-<tr class="separator:a77a8929e46d06e5d6a0026bcc9e48851"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a44c93b8a96203a1a85fa9d4b85e123c7"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a44c93b8a96203a1a85fa9d4b85e123c7">size</a> (<a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> idx) const</td></tr>
-<tr class="memdesc:a44c93b8a96203a1a85fa9d4b85e123c7"><td class="mdescLeft"> </td><td class="mdescRight">return size of i-th dimension, start counting from highest dimension <a href="#a44c93b8a96203a1a85fa9d4b85e123c7">More...</a><br /></td></tr>
-<tr class="separator:a44c93b8a96203a1a85fa9d4b85e123c7"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a7b45f281d954cc716267a7b3ec0f514c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a7b45f281d954cc716267a7b3ec0f514c">Size</a> (void) const</td></tr>
-<tr class="memdesc:a7b45f281d954cc716267a7b3ec0f514c"><td class="mdescLeft"> </td><td class="mdescRight">total number of elements in the tensor <a href="#a7b45f281d954cc716267a7b3ec0f514c">More...</a><br /></td></tr>
-<tr class="separator:a7b45f281d954cc716267a7b3ec0f514c"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad931186405f2b40a327278a8f5ff57cd"><td class="memTemplParams" colspan="2">template<typename DType > </td></tr>
-<tr class="memitem:ad931186405f2b40a327278a8f5ff57cd"><td class="memTemplItemLeft" align="right" valign="top">DType * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#ad931186405f2b40a327278a8f5ff57cd">dptr</a> () const</td></tr>
-<tr class="memdesc:ad931186405f2b40a327278a8f5ff57cd"><td class="mdescLeft"> </td><td class="mdescRight">get pointer in dtype <a href="#ad931186405f2b40a327278a8f5ff57cd">More...</a><br /></td></tr>
-<tr class="separator:ad931186405f2b40a327278a8f5ff57cd"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad8bcf56e13082fc0607e21f08b14758f"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#ad8bcf56e13082fc0607e21f08b14758f">dev_mask</a> () const</td></tr>
-<tr class="memdesc:ad8bcf56e13082fc0607e21f08b14758f"><td class="mdescLeft"> </td><td class="mdescRight">device mask of the corresponding device <a href="#ad8bcf56e13082fc0607e21f08b14758f">More...</a><br /></td></tr>
-<tr class="separator:ad8bcf56e13082fc0607e21f08b14758f"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2caa6723ffda5908eb5c2e062ac77ebd"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a2caa6723ffda5908eb5c2e062ac77ebd">dev_id</a> () const</td></tr>
-<tr class="memdesc:a2caa6723ffda5908eb5c2e062ac77ebd"><td class="mdescLeft"> </td><td class="mdescRight">device index of the corresponding device <a href="#a2caa6723ffda5908eb5c2e062ac77ebd">More...</a><br /></td></tr>
-<tr class="separator:a2caa6723ffda5908eb5c2e062ac77ebd"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a4b0a53821dec20c558a030ed3df14dda"><td class="memItemLeft" align="right" valign="top">const DLTensor & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a4b0a53821dec20c558a030ed3df14dda">dltensor</a> () const</td></tr>
-<tr class="memdesc:a4b0a53821dec20c558a030ed3df14dda"><td class="mdescLeft"> </td><td class="mdescRight">return the corresponding DLTensor <a href="#a4b0a53821dec20c558a030ed3df14dda">More...</a><br /></td></tr>
-<tr class="separator:a4b0a53821dec20c558a030ed3df14dda"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a3acb7675c6a1f682fe28d0d62aeb338a"><td class="memTemplParams" colspan="2">template<typename Device , int dim, typename DType > </td></tr>
-<tr class="memitem:a3acb7675c6a1f682fe28d0d62aeb338a"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, dim, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a3acb7675c6a1f682fe28d0d62aeb338a">get</a> (mshadow::Stream< Device > *stream=NULL) const</td></tr>
-<tr class="memdesc:a3acb7675c6a1f682fe28d0d62aeb338a"><td class="mdescLeft"> </td><td class="mdescRight">fetch the tensor, with respect to specific dimension if dim do not match the stored dimension, an error will be issued <a href="#a3acb7675c6a1f682fe28d0d62aeb338a">More...</a><br /></td></tr>
-<tr class="separator:a3acb7675c6a1f682fe28d0d62aeb338a"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ae3601040650637a7ac05b3f00f0adbee"><td class="memTemplParams" colspan="2">template<typename Device , int dim, typename DType > </td></tr>
-<tr class="memitem:ae3601040650637a7ac05b3f00f0adbee"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, dim, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#ae3601040650637a7ac05b3f00f0adbee">get_with_shape</a> (const mshadow::Shape< dim > &shape, mshadow::Stream< Device > *stream=NULL) const</td></tr>
-<tr class="memdesc:ae3601040650637a7ac05b3f00f0adbee"><td class="mdescLeft"> </td><td class="mdescRight">fetch a tensor in given shape If size do not match the stored size, an error will be issued <a href="#ae3601040650637a7ac05b3f00f0adbee">More...</a><br /></td></tr>
-<tr class="separator:ae3601040650637a7ac05b3f00f0adbee"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aefabf95412878b86b1130035386beee8"><td class="memTemplParams" colspan="2">template<typename Device , typename DType > </td></tr>
-<tr class="memitem:aefabf95412878b86b1130035386beee8"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, 3, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#aefabf95412878b86b1130035386beee8">FlatTo3D</a> (int axis, mshadow::Stream< Device > *stream=NULL) const</td></tr>
-<tr class="memdesc:aefabf95412878b86b1130035386beee8"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to 3 dimension, collapse the dimension before and after specified axis. <a href="#aefabf95412878b86b1130035386beee8">More...</a><br /></td></tr>
-<tr class="separator:aefabf95412878b86b1130035386beee8"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ae1dfdf1a1bc6ca78e4e06fe4aa71e074"><td class="memTemplParams" colspan="2">template<typename Device , typename DType > </td></tr>
-<tr class="memitem:ae1dfdf1a1bc6ca78e4e06fe4aa71e074"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, 3, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#ae1dfdf1a1bc6ca78e4e06fe4aa71e074">FlatTo3D</a> (int axis_begin, int axis_end, mshadow::Stream< Device > *stream=NULL) const</td></tr>
-<tr class="memdesc:ae1dfdf1a1bc6ca78e4e06fe4aa71e074"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to 3 dimension, collapse the dimension: [0, axis_begin), [axis_begin, axis_end], (axis_end, ndim). <a href="#ae1dfdf1a1bc6ca78e4e06fe4aa71e074">More...</a><br /></td></tr>
-<tr class="separator:ae1dfdf1a1bc6ca78e4e06fe4aa71e074"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a5be50f29087afef8d48942ea8fe39cbd"><td class="memTemplParams" colspan="2">template<typename Device , int dim, typename DType > </td></tr>
-<tr class="memitem:a5be50f29087afef8d48942ea8fe39cbd"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, dim, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a5be50f29087afef8d48942ea8fe39cbd">FlatToKD</a> (mshadow::Stream< Device > *stream=NULL) const</td></tr>
-<tr class="memdesc:a5be50f29087afef8d48942ea8fe39cbd"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to specified number of dimensions, collapse the highest dimensions or pad with higher dimensions <a href="#a5be50f29087afef8d48942ea8fe39cbd">More...</a><br /></td></tr>
-<tr class="separator:a5be50f29087afef8d48942ea8fe39cbd"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa6cfdf8823a601602b6b56011af4b874"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#aa6cfdf8823a601602b6b56011af4b874">CheckContiguous</a> (void) const </td></tr>
+<tr class="separator:aa6cfdf8823a601602b6b56011af4b874"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0c66b10f5a17528cc858762b60125789"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a0c66b10f5a17528cc858762b60125789">reshape</a> (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &shape) const </td></tr>
+<tr class="memdesc:a0c66b10f5a17528cc858762b60125789"><td class="mdescLeft"> </td><td class="mdescRight">reshape to shape <a href="#a0c66b10f5a17528cc858762b60125789">More...</a><br/></td></tr>
+<tr class="separator:a0c66b10f5a17528cc858762b60125789"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a23cc57be3b2c40a269c8c9f242fa0532"><td class="memTemplParams" colspan="2">template<typename Device , typename DType > </td></tr>
+<tr class="memitem:a23cc57be3b2c40a269c8c9f242fa0532"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, 2, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a23cc57be3b2c40a269c8c9f242fa0532">FlatTo2D</a> (mshadow::Stream< Device > *stream=NULL) const </td></tr>
+<tr class="memdesc:a23cc57be3b2c40a269c8c9f242fa0532"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to 2 dimension, collapse the higher dimensions together <a href="#a23cc57be3b2c40a269c8c9f242fa0532">More...</a><br/></td></tr>
+<tr class="separator:a23cc57be3b2c40a269c8c9f242fa0532"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a95bf7032c2b90627510a89ac8bb2946f"><td class="memTemplParams" colspan="2">template<typename Device , typename DType > </td></tr>
+<tr class="memitem:a95bf7032c2b90627510a89ac8bb2946f"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, 1, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a95bf7032c2b90627510a89ac8bb2946f">FlatTo1D</a> (mshadow::Stream< Device > *stream=NULL) const </td></tr>
+<tr class="memdesc:a95bf7032c2b90627510a89ac8bb2946f"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to 1 dimension, collapse all the dimensions together. <a href="#a95bf7032c2b90627510a89ac8bb2946f">More...</a><br/></td></tr>
+<tr class="separator:a95bf7032c2b90627510a89ac8bb2946f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3be3b640e59a480b8ac5369d73b117de"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">ndim</a> (void) const </td></tr>
+<tr class="memdesc:a3be3b640e59a480b8ac5369d73b117de"><td class="mdescLeft"> </td><td class="mdescRight">return number of dimension of the tensor inside <a href="#a3be3b640e59a480b8ac5369d73b117de">More...</a><br/></td></tr>
+<tr class="separator:a3be3b640e59a480b8ac5369d73b117de"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a523fd58883de4b9cd0b1a2852daa64ae"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a523fd58883de4b9cd0b1a2852daa64ae">size</a> (<a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> idx) const </td></tr>
+<tr class="memdesc:a523fd58883de4b9cd0b1a2852daa64ae"><td class="mdescLeft"> </td><td class="mdescRight">return size of i-th dimension, start counting from highest dimension <a href="#a523fd58883de4b9cd0b1a2852daa64ae">More...</a><br/></td></tr>
+<tr class="separator:a523fd58883de4b9cd0b1a2852daa64ae"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3b04db1348834e1576cfb2ed54959703"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a3b04db1348834e1576cfb2ed54959703">Size</a> (void) const </td></tr>
+<tr class="memdesc:a3b04db1348834e1576cfb2ed54959703"><td class="mdescLeft"> </td><td class="mdescRight">total number of elements in the tensor <a href="#a3b04db1348834e1576cfb2ed54959703">More...</a><br/></td></tr>
+<tr class="separator:a3b04db1348834e1576cfb2ed54959703"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3449ca0a9130e87c343130b041b68c53"><td class="memTemplParams" colspan="2">template<typename DType > </td></tr>
+<tr class="memitem:a3449ca0a9130e87c343130b041b68c53"><td class="memTemplItemLeft" align="right" valign="top">DType * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">dptr</a> () const </td></tr>
+<tr class="memdesc:a3449ca0a9130e87c343130b041b68c53"><td class="mdescLeft"> </td><td class="mdescRight">get pointer in dtype <a href="#a3449ca0a9130e87c343130b041b68c53">More...</a><br/></td></tr>
+<tr class="separator:a3449ca0a9130e87c343130b041b68c53"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a613ad1f5ba31efcbecbd041e23cc61b2"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a> () const </td></tr>
+<tr class="memdesc:a613ad1f5ba31efcbecbd041e23cc61b2"><td class="mdescLeft"> </td><td class="mdescRight">device mask of the corresponding device <a href="#a613ad1f5ba31efcbecbd041e23cc61b2">More...</a><br/></td></tr>
+<tr class="separator:a613ad1f5ba31efcbecbd041e23cc61b2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5dc3a399b0119175643da0d35fde7edd"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd">dev_id</a> () const </td></tr>
+<tr class="memdesc:a5dc3a399b0119175643da0d35fde7edd"><td class="mdescLeft"> </td><td class="mdescRight">device index of the corresponding device <a href="#a5dc3a399b0119175643da0d35fde7edd">More...</a><br/></td></tr>
+<tr class="separator:a5dc3a399b0119175643da0d35fde7edd"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a519111d4450efa8e58cc10cfc1dbeca7"><td class="memItemLeft" align="right" valign="top">const DLTensor & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a519111d4450efa8e58cc10cfc1dbeca7">dltensor</a> () const </td></tr>
+<tr class="memdesc:a519111d4450efa8e58cc10cfc1dbeca7"><td class="mdescLeft"> </td><td class="mdescRight">return the corresponding DLTensor <a href="#a519111d4450efa8e58cc10cfc1dbeca7">More...</a><br/></td></tr>
+<tr class="separator:a519111d4450efa8e58cc10cfc1dbeca7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:acb2e058394548527844e219c7e3c480d"><td class="memTemplParams" colspan="2">template<typename Device , int dim, typename DType > </td></tr>
+<tr class="memitem:acb2e058394548527844e219c7e3c480d"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, dim, <br class="typebreak"/>
+DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#acb2e058394548527844e219c7e3c480d">get</a> (mshadow::Stream< Device > *stream=NULL) const </td></tr>
+<tr class="memdesc:acb2e058394548527844e219c7e3c480d"><td class="mdescLeft"> </td><td class="mdescRight">fetch the tensor, with respect to specific dimension if dim do not match the stored dimension, an error will be issued <a href="#acb2e058394548527844e219c7e3c480d">More...</a><br/></td></tr>
+<tr class="separator:acb2e058394548527844e219c7e3c480d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:abbdbdc8d02c15e854cc7f5fe5dc09f61"><td class="memTemplParams" colspan="2">template<typename Device , int dim, typename DType > </td></tr>
+<tr class="memitem:abbdbdc8d02c15e854cc7f5fe5dc09f61"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, dim, <br class="typebreak"/>
+DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#abbdbdc8d02c15e854cc7f5fe5dc09f61">get_with_shape</a> (const mshadow::Shape< dim > &shape, mshadow::Stream< Device > *stream=NULL) const </td></tr>
+<tr class="memdesc:abbdbdc8d02c15e854cc7f5fe5dc09f61"><td class="mdescLeft"> </td><td class="mdescRight">fetch a tensor in given shape If size do not match the stored size, an error will be issued <a href="#abbdbdc8d02c15e854cc7f5fe5dc09f61">More...</a><br/></td></tr>
+<tr class="separator:abbdbdc8d02c15e854cc7f5fe5dc09f61"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac5cd4953869cb66ed374854a34da40f8"><td class="memTemplParams" colspan="2">template<typename Device , typename DType > </td></tr>
+<tr class="memitem:ac5cd4953869cb66ed374854a34da40f8"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, 3, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#ac5cd4953869cb66ed374854a34da40f8">FlatTo3D</a> (int axis, mshadow::Stream< Device > *stream=NULL) const </td></tr>
+<tr class="memdesc:ac5cd4953869cb66ed374854a34da40f8"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to 3 dimension, collapse the dimension before and after specified axis. <a href="#ac5cd4953869cb66ed374854a34da40f8">More...</a><br/></td></tr>
+<tr class="separator:ac5cd4953869cb66ed374854a34da40f8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ada83447eb80b913592e9ac2bf8c5740f"><td class="memTemplParams" colspan="2">template<typename Device , typename DType > </td></tr>
+<tr class="memitem:ada83447eb80b913592e9ac2bf8c5740f"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, 3, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#ada83447eb80b913592e9ac2bf8c5740f">FlatTo3D</a> (int axis_begin, int axis_end, mshadow::Stream< Device > *stream=NULL) const </td></tr>
+<tr class="memdesc:ada83447eb80b913592e9ac2bf8c5740f"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to 3 dimension, collapse the dimension: [0, axis_begin), [axis_begin, axis_end], (axis_end, ndim). <a href="#ada83447eb80b913592e9ac2bf8c5740f">More...</a><br/></td></tr>
+<tr class="separator:ada83447eb80b913592e9ac2bf8c5740f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa2f8e15eb1fc21e159c496ec0d499b6f"><td class="memTemplParams" colspan="2">template<typename Device , int dim, typename DType > </td></tr>
+<tr class="memitem:aa2f8e15eb1fc21e159c496ec0d499b6f"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< Device, dim, <br class="typebreak"/>
+DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#aa2f8e15eb1fc21e159c496ec0d499b6f">FlatToKD</a> (mshadow::Stream< Device > *stream=NULL) const </td></tr>
+<tr class="memdesc:aa2f8e15eb1fc21e159c496ec0d499b6f"><td class="mdescLeft"> </td><td class="mdescRight">flatten the tensor to specified number of dimensions, collapse the highest dimensions or pad with higher dimensions <a href="#aa2f8e15eb1fc21e159c496ec0d499b6f">More...</a><br/></td></tr>
+<tr class="separator:aa2f8e15eb1fc21e159c496ec0d499b6f"><td class="memSeparator" colspan="2"> </td></tr>
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Public Attributes</h2></td></tr>
<tr class="memitem:a4e5f4953be2c16724ecff0afc5ea2e10"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a></td></tr>
-<tr class="memdesc:a4e5f4953be2c16724ecff0afc5ea2e10"><td class="mdescLeft"> </td><td class="mdescRight">pointer to the data <a href="#a4e5f4953be2c16724ecff0afc5ea2e10">More...</a><br /></td></tr>
+<tr class="memdesc:a4e5f4953be2c16724ecff0afc5ea2e10"><td class="mdescLeft"> </td><td class="mdescRight">pointer to the data <a href="#a4e5f4953be2c16724ecff0afc5ea2e10">More...</a><br/></td></tr>
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<tr class="memitem:a6cc7a57d9c33fefc08630e81951a004f"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a></td></tr>
-<tr class="memdesc:a6cc7a57d9c33fefc08630e81951a004f"><td class="mdescLeft"> </td><td class="mdescRight">shape of the tensor <a href="#a6cc7a57d9c33fefc08630e81951a004f">More...</a><br /></td></tr>
+<tr class="memdesc:a6cc7a57d9c33fefc08630e81951a004f"><td class="mdescLeft"> </td><td class="mdescRight">shape of the tensor <a href="#a6cc7a57d9c33fefc08630e81951a004f">More...</a><br/></td></tr>
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<tr class="memitem:a4649b740b80a6045754cb2389cfcca61"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a></td></tr>
-<tr class="memdesc:a4649b740b80a6045754cb2389cfcca61"><td class="mdescLeft"> </td><td class="mdescRight">type flag of the tensor blob <a href="#a4649b740b80a6045754cb2389cfcca61">More...</a><br /></td></tr>
+<tr class="memdesc:a4649b740b80a6045754cb2389cfcca61"><td class="mdescLeft"> </td><td class="mdescRight">type flag of the tensor blob <a href="#a4649b740b80a6045754cb2389cfcca61">More...</a><br/></td></tr>
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<div class="textblock"><p>tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> itself do not involve any arithmentic operations, but it can be converted to tensor of fixed dimension for further operations </p>
<p>Like tensor, this data structure is like a pointer class and do not implicit allocated, de-allocate space. This data structure can be helpful to hold tensors of different dimensions and wait for further processing </p>
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@@ -1118,9 +1097,9 @@ template<typename Device , int dim, typename DType > </div>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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index 5401471..c0caf7a 100644
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<head>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
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<script type="text/javascript" src="jquery.js"></script>
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<table cellspacing="0" cellpadding="0">
<tbody>
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+ <td style="padding-left: 0.5em;">
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -72,15 +96,15 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ada6b942a22f14e3d61fccb645f9e9a27">ag_node</a></td><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a8edff5e8fa169924abe520edcae76c45">clear</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ad0fccdc6fc5d492306c0eaf6df11880a">index</a></td><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a74e885929385a8b5a5632da4f69ef35e">is_none</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a3dc6bbdfcf766bd4db8756001f5278f5">nn_entry</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#adf925c9b87cbd5969e142123da6c7e30">is_none</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a7dbd6c573b3bc3eda29b9ce9a2d1283a">nn_entry</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ae5aa3f0c4136f8e2ca4d5e1e47f1877f">version</a></td><td class="entry"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></td><td class="entry"></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/classmxnet_1_1autograd_1_1AGNodeEntry.html b/versions/master/doxygen/classmxnet_1_1autograd_1_1AGNodeEntry.html
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+<script type="text/javascript">
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
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+<!-- Generated by Doxygen 1.8.6 -->
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var searchBox = new SearchBox("searchBox", "search",false,'Search');
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
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@@ -76,16 +100,18 @@ $(function() {
Collaboration diagram for mxnet::autograd::AGNodeEntry:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1autograd_1_1AGNodeEntry__coll__graph.png" border="0" usemap="#mxnet_1_1autograd_1_1AGNodeEntry_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1autograd_1_1AGNodeEntry_coll__map" id="mxnet_1_1autograd_1_1AGNodeEntry_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a8edff5e8fa169924abe520edcae76c45"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a8edff5e8fa169924abe520edcae76c45">clear</a> ()</td></tr>
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-<tr class="memitem:a74e885929385a8b5a5632da4f69ef35e"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a74e885929385a8b5a5632da4f69ef35e">is_none</a> () const</td></tr>
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+<tr class="memitem:a7dbd6c573b3bc3eda29b9ce9a2d1283a"><td class="memItemLeft" align="right" valign="top">nnvm::NodeEntry </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a7dbd6c573b3bc3eda29b9ce9a2d1283a">nn_entry</a> () const </td></tr>
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Public Attributes</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a8edff5e8fa169924abe520edcae76c45">◆ </a></span>clear()</h2>
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<div class="memproto">
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-<h2 class="memtitle"><span class="permalink"><a href="#a74e885929385a8b5a5632da4f69ef35e">◆ </a></span>is_none()</h2>
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<div class="memproto">
<table class="memname">
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-<h2 class="memtitle"><span class="permalink"><a href="#a3dc6bbdfcf766bd4db8756001f5278f5">◆ </a></span>nn_entry()</h2>
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<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -157,9 +177,7 @@ Public Attributes</h2></td></tr>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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index a18372e..56e2e54 100644
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <span class="left">
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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diff --git a/versions/master/doxygen/classmxnet_1_1common_1_1LazyAllocArray.html b/versions/master/doxygen/classmxnet_1_1common_1_1LazyAllocArray.html
index b3cc1c8..4eb902a 100644
--- a/versions/master/doxygen/classmxnet_1_1common_1_1LazyAllocArray.html
+++ b/versions/master/doxygen/classmxnet_1_1common_1_1LazyAllocArray.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::common::LazyAllocArray< TElem > Class Template Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
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+<!-- Generated by Doxygen 1.8.6 -->
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
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+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -76,6 +100,8 @@ $(function() {
Collaboration diagram for mxnet::common::LazyAllocArray< TElem >:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1common_1_1LazyAllocArray__coll__graph.png" border="0" usemap="#mxnet_1_1common_1_1LazyAllocArray_3_01TElem_01_4_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1common_1_1LazyAllocArray_3_01TElem_01_4_coll__map" id="mxnet_1_1common_1_1LazyAllocArray_3_01TElem_01_4_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
@@ -84,22 +110,20 @@ Public Member Functions</h2></td></tr>
<tr class="separator:a1dd703a9eabfe691793a8398706bcc09"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac4528ab581eda5b0aba35faa3687c201"><td class="memTemplParams" colspan="2">template<typename FCreate > </td></tr>
<tr class="memitem:ac4528ab581eda5b0aba35faa3687c201"><td class="memTemplItemLeft" align="right" valign="top">std::shared_ptr< TElem > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#ac4528ab581eda5b0aba35faa3687c201">Get</a> (int index, FCreate creator)</td></tr>
-<tr class="memdesc:ac4528ab581eda5b0aba35faa3687c201"><td class="mdescLeft"> </td><td class="mdescRight">Get element of corresponding index, if it is not created create by creator. <a href="#ac4528ab581eda5b0aba35faa3687c201">More...</a><br /></td></tr>
+<tr class="memdesc:ac4528ab581eda5b0aba35faa3687c201"><td class="mdescLeft"> </td><td class="mdescRight">Get element of corresponding index, if it is not created create by creator. <a href="#ac4528ab581eda5b0aba35faa3687c201">More...</a><br/></td></tr>
<tr class="separator:ac4528ab581eda5b0aba35faa3687c201"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a43a89c8e7b4e1a09f346694073a2d998"><td class="memTemplParams" colspan="2">template<typename FVisit > </td></tr>
<tr class="memitem:a43a89c8e7b4e1a09f346694073a2d998"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#a43a89c8e7b4e1a09f346694073a2d998">ForEach</a> (FVisit fvisit)</td></tr>
-<tr class="memdesc:a43a89c8e7b4e1a09f346694073a2d998"><td class="mdescLeft"> </td><td class="mdescRight">for each not null element of the array, call fvisit <a href="#a43a89c8e7b4e1a09f346694073a2d998">More...</a><br /></td></tr>
+<tr class="memdesc:a43a89c8e7b4e1a09f346694073a2d998"><td class="mdescLeft"> </td><td class="mdescRight">for each not null element of the array, call fvisit <a href="#a43a89c8e7b4e1a09f346694073a2d998">More...</a><br/></td></tr>
<tr class="separator:a43a89c8e7b4e1a09f346694073a2d998"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3af1ab75feb0ba07db5af7e6ef5717b1"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#a3af1ab75feb0ba07db5af7e6ef5717b1">Clear</a> ()</td></tr>
-<tr class="memdesc:a3af1ab75feb0ba07db5af7e6ef5717b1"><td class="mdescLeft"> </td><td class="mdescRight">clear all the allocated elements in array <a href="#a3af1ab75feb0ba07db5af7e6ef5717b1">More...</a><br /></td></tr>
+<tr class="memdesc:a3af1ab75feb0ba07db5af7e6ef5717b1"><td class="mdescLeft"> </td><td class="mdescRight">clear all the allocated elements in array <a href="#a3af1ab75feb0ba07db5af7e6ef5717b1">More...</a><br/></td></tr>
<tr class="separator:a3af1ab75feb0ba07db5af7e6ef5717b1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a000e31ad914a20415519a2b39a1d08c3"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#a000e31ad914a20415519a2b39a1d08c3">SignalForKill</a> ()</td></tr>
<tr class="separator:a000e31ad914a20415519a2b39a1d08c3"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a1dd703a9eabfe691793a8398706bcc09"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a1dd703a9eabfe691793a8398706bcc09">◆ </a></span>LazyAllocArray()</h2>
-
+<a class="anchor" id="a1dd703a9eabfe691793a8398706bcc09"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -125,9 +149,7 @@ template<typename TElem > </div>
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
-<a id="a3af1ab75feb0ba07db5af7e6ef5717b1"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a3af1ab75feb0ba07db5af7e6ef5717b1">◆ </a></span>Clear()</h2>
-
+<a class="anchor" id="a3af1ab75feb0ba07db5af7e6ef5717b1"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -154,9 +176,7 @@ template<typename TElem > </div>
</div>
</div>
-<a id="a43a89c8e7b4e1a09f346694073a2d998"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a43a89c8e7b4e1a09f346694073a2d998">◆ </a></span>ForEach()</h2>
-
+<a class="anchor" id="a43a89c8e7b4e1a09f346694073a2d998"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -192,9 +212,7 @@ template<typename FVisit > </div>
</div>
</div>
-<a id="ac4528ab581eda5b0aba35faa3687c201"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ac4528ab581eda5b0aba35faa3687c201">◆ </a></span>Get()</h2>
-
+<a class="anchor" id="ac4528ab581eda5b0aba35faa3687c201"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -241,9 +259,7 @@ template<typename FCreate > </div>
</div>
</div>
-<a id="a000e31ad914a20415519a2b39a1d08c3"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a000e31ad914a20415519a2b39a1d08c3">◆ </a></span>SignalForKill()</h2>
-
+<a class="anchor" id="a000e31ad914a20415519a2b39a1d08c3"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -274,9 +290,9 @@ template<typename TElem > </div>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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onmouseover="return searchBox.OnSearchSelectShow()"
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@@ -72,14 +96,14 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#ac6e5e05a710c3f094efb4ba13d36ab89">_GetSharedRef</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">mxnet::common::ObjectPool< T ></a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a7dcdcbe9b6764cedc28a1de2fbc8dd6f">Delete</a>(T *ptr)</td><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">mxnet::common::ObjectPool< T ></a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a82b4d0c3577560a4f6f62f482b125471">Get</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">mxnet::common::ObjectPool< T ></a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#aaadcbc99eaec88191b201bad9da441c6">New</a>(Args &&... args)</td><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">mxnet::common::ObjectPool< T ></a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a5d711031f61dbd06d8c6975fd49e5632">New</a>(Args &&...args)</td><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">mxnet::common::ObjectPool< T ></a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#aa5ff3f223210e5f3d0881c272462aedb">~ObjectPool</a>()</td><td class="entry"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">mxnet::common::ObjectPool< T ></a></td><td class="entry"></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/classmxnet_1_1common_1_1ObjectPool.html b/versions/master/doxygen/classmxnet_1_1common_1_1ObjectPool.html
index b462d9f..e7e28ef 100644
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::common::ObjectPool< T > Class Template Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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@@ -80,39 +104,40 @@ $(function() {
Collaboration diagram for mxnet::common::ObjectPool< T >:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1common_1_1ObjectPool__coll__graph.png" border="0" usemap="#mxnet_1_1common_1_1ObjectPool_3_01T_01_4_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1common_1_1ObjectPool_3_01T_01_4_coll__map" id="mxnet_1_1common_1_1ObjectPool_3_01T_01_4_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:aa5ff3f223210e5f3d0881c272462aedb"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#aa5ff3f223210e5f3d0881c272462aedb">~ObjectPool</a> ()</td></tr>
-<tr class="memdesc:aa5ff3f223210e5f3d0881c272462aedb"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <a href="#aa5ff3f223210e5f3d0881c272462aedb">More...</a><br /></td></tr>
+<tr class="memdesc:aa5ff3f223210e5f3d0881c272462aedb"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <a href="#aa5ff3f223210e5f3d0881c272462aedb">More...</a><br/></td></tr>
<tr class="separator:aa5ff3f223210e5f3d0881c272462aedb"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aaadcbc99eaec88191b201bad9da441c6"><td class="memTemplParams" colspan="2">template<typename... Args> </td></tr>
-<tr class="memitem:aaadcbc99eaec88191b201bad9da441c6"><td class="memTemplItemLeft" align="right" valign="top">T * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#aaadcbc99eaec88191b201bad9da441c6">New</a> (Args &&... args)</td></tr>
-<tr class="memdesc:aaadcbc99eaec88191b201bad9da441c6"><td class="mdescLeft"> </td><td class="mdescRight">Create new object. <a href="#aaadcbc99eaec88191b201bad9da441c6">More...</a><br /></td></tr>
-<tr class="separator:aaadcbc99eaec88191b201bad9da441c6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5d711031f61dbd06d8c6975fd49e5632"><td class="memTemplParams" colspan="2">template<typename... Args> </td></tr>
+<tr class="memitem:a5d711031f61dbd06d8c6975fd49e5632"><td class="memTemplItemLeft" align="right" valign="top">T * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a5d711031f61dbd06d8c6975fd49e5632">New</a> (Args &&...args)</td></tr>
+<tr class="memdesc:a5d711031f61dbd06d8c6975fd49e5632"><td class="mdescLeft"> </td><td class="mdescRight">Create new object. <a href="#a5d711031f61dbd06d8c6975fd49e5632">More...</a><br/></td></tr>
+<tr class="separator:a5d711031f61dbd06d8c6975fd49e5632"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7dcdcbe9b6764cedc28a1de2fbc8dd6f"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a7dcdcbe9b6764cedc28a1de2fbc8dd6f">Delete</a> (T *ptr)</td></tr>
-<tr class="memdesc:a7dcdcbe9b6764cedc28a1de2fbc8dd6f"><td class="mdescLeft"> </td><td class="mdescRight">Delete an existing object. <a href="#a7dcdcbe9b6764cedc28a1de2fbc8dd6f">More...</a><br /></td></tr>
+<tr class="memdesc:a7dcdcbe9b6764cedc28a1de2fbc8dd6f"><td class="mdescLeft"> </td><td class="mdescRight">Delete an existing object. <a href="#a7dcdcbe9b6764cedc28a1de2fbc8dd6f">More...</a><br/></td></tr>
<tr class="separator:a7dcdcbe9b6764cedc28a1de2fbc8dd6f"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:a82b4d0c3577560a4f6f62f482b125471"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a82b4d0c3577560a4f6f62f482b125471">Get</a> ()</td></tr>
-<tr class="memdesc:a82b4d0c3577560a4f6f62f482b125471"><td class="mdescLeft"> </td><td class="mdescRight">Get singleton instance of pool. <a href="#a82b4d0c3577560a4f6f62f482b125471">More...</a><br /></td></tr>
+<tr class="memdesc:a82b4d0c3577560a4f6f62f482b125471"><td class="mdescLeft"> </td><td class="mdescRight">Get singleton instance of pool. <a href="#a82b4d0c3577560a4f6f62f482b125471">More...</a><br/></td></tr>
<tr class="separator:a82b4d0c3577560a4f6f62f482b125471"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ac6e5e05a710c3f094efb4ba13d36ab89"><td class="memItemLeft" align="right" valign="top">static std::shared_ptr< <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#ac6e5e05a710c3f094efb4ba13d36ab89">_GetSharedRef</a> ()</td></tr>
-<tr class="memdesc:ac6e5e05a710c3f094efb4ba13d36ab89"><td class="mdescLeft"> </td><td class="mdescRight">Get a shared ptr of the singleton instance of pool. <a href="#ac6e5e05a710c3f094efb4ba13d36ab89">More...</a><br /></td></tr>
+<tr class="memitem:ac6e5e05a710c3f094efb4ba13d36ab89"><td class="memItemLeft" align="right" valign="top">static std::shared_ptr<br class="typebreak"/>
+< <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#ac6e5e05a710c3f094efb4ba13d36ab89">_GetSharedRef</a> ()</td></tr>
+<tr class="memdesc:ac6e5e05a710c3f094efb4ba13d36ab89"><td class="mdescLeft"> </td><td class="mdescRight">Get a shared ptr of the singleton instance of pool. <a href="#ac6e5e05a710c3f094efb4ba13d36ab89">More...</a><br/></td></tr>
<tr class="separator:ac6e5e05a710c3f094efb4ba13d36ab89"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
-<div class="textblock"><h3>template<typename T><br />
+<div class="textblock"><h3>template<typename T><br/>
class mxnet::common::ObjectPool< T ></h3>
<p>Object pool for fast allocation and deallocation. </p>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#aa5ff3f223210e5f3d0881c272462aedb">◆ </a></span>~ObjectPool()</h2>
-
+<a class="anchor" id="aa5ff3f223210e5f3d0881c272462aedb"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -132,9 +157,7 @@ template<typename T > </div>
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
-<a id="ac6e5e05a710c3f094efb4ba13d36ab89"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ac6e5e05a710c3f094efb4ba13d36ab89">◆ </a></span>_GetSharedRef()</h2>
-
+<a class="anchor" id="ac6e5e05a710c3f094efb4ba13d36ab89"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -162,9 +185,7 @@ template<typename T > </div>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a7dcdcbe9b6764cedc28a1de2fbc8dd6f">◆ </a></span>Delete()</h2>
-
+<a class="anchor" id="a7dcdcbe9b6764cedc28a1de2fbc8dd6f"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -191,9 +212,7 @@ template<typename T > </div>
</div>
</div>
-<a id="a82b4d0c3577560a4f6f62f482b125471"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a82b4d0c3577560a4f6f62f482b125471">◆ </a></span>Get()</h2>
-
+<a class="anchor" id="a82b4d0c3577560a4f6f62f482b125471"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -221,9 +240,7 @@ template<typename T > </div>
</div>
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-<a id="aaadcbc99eaec88191b201bad9da441c6"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aaadcbc99eaec88191b201bad9da441c6">◆ </a></span>New()</h2>
-
+<a class="anchor" id="a5d711031f61dbd06d8c6975fd49e5632"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -252,9 +269,9 @@ template<typename... Args> </div>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<p>This is the complete list of members for <a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">mxnet::engine::CallbackOnComplete</a>, including all inherited members.</p>
<table class="directory">
<tr class="even"><td class="entry"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#aa365d0be0a3a369cdc5da59787f93297">::mxnet::Engine</a> class</td><td class="entry"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">mxnet::engine::CallbackOnComplete</a></td><td class="entry"><span class="mlabel">friend</span></td></tr>
- <tr><td class="entry"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a71e8c5a29d9e103f6471f35f30e05727">operator()</a>() const</td><td class="entry"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">mxnet::engine::CallbackOnComplete</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a97abc3d705870669f303ee22a1342d4a">operator()</a>() const </td><td class="entry"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">mxnet::engine::CallbackOnComplete</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::engine::CallbackOnComplete Class Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<table cellspacing="0" cellpadding="0">
<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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@@ -29,25 +30,48 @@
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <img id="MSearchSelect" src="search/mag_sel.png"
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -79,26 +103,26 @@ $(function() {
Collaboration diagram for mxnet::engine::CallbackOnComplete:</div>
<div class="dyncontent">
<div class="center"><img src="classmxnet_1_1engine_1_1CallbackOnComplete__coll__graph.png" border="0" usemap="#mxnet_1_1engine_1_1CallbackOnComplete_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1engine_1_1CallbackOnComplete_coll__map" id="mxnet_1_1engine_1_1CallbackOnComplete_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
-<tr class="memitem:a71e8c5a29d9e103f6471f35f30e05727"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a71e8c5a29d9e103f6471f35f30e05727">operator()</a> () const</td></tr>
-<tr class="memdesc:a71e8c5a29d9e103f6471f35f30e05727"><td class="mdescLeft"> </td><td class="mdescRight">involve the callback <a href="#a71e8c5a29d9e103f6471f35f30e05727">More...</a><br /></td></tr>
-<tr class="separator:a71e8c5a29d9e103f6471f35f30e05727"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a97abc3d705870669f303ee22a1342d4a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a97abc3d705870669f303ee22a1342d4a">operator()</a> () const </td></tr>
+<tr class="memdesc:a97abc3d705870669f303ee22a1342d4a"><td class="mdescLeft"> </td><td class="mdescRight">involve the callback <a href="#a97abc3d705870669f303ee22a1342d4a">More...</a><br/></td></tr>
+<tr class="separator:a97abc3d705870669f303ee22a1342d4a"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="friends"></a>
Friends</h2></td></tr>
<tr class="memitem:aa365d0be0a3a369cdc5da59787f93297"><td class="memItemLeft" align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#aa365d0be0a3a369cdc5da59787f93297">::mxnet::Engine</a></td></tr>
-<tr class="memdesc:aa365d0be0a3a369cdc5da59787f93297"><td class="mdescLeft"> </td><td class="mdescRight">engine can see content of callback <a href="#aa365d0be0a3a369cdc5da59787f93297">More...</a><br /></td></tr>
+<tr class="memdesc:aa365d0be0a3a369cdc5da59787f93297"><td class="mdescLeft"> </td><td class="mdescRight">engine can see content of callback <a href="#aa365d0be0a3a369cdc5da59787f93297">More...</a><br/></td></tr>
<tr class="separator:aa365d0be0a3a369cdc5da59787f93297"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>OnComplete Callback to the engine, called by AsyncFn when action completes. </p>
</div><h2 class="groupheader">Member Function Documentation</h2>
-<a id="a71e8c5a29d9e103f6471f35f30e05727"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a71e8c5a29d9e103f6471f35f30e05727">◆ </a></span>operator()()</h2>
-
+<a class="anchor" id="a97abc3d705870669f303ee22a1342d4a"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -124,9 +148,7 @@ Friends</h2></td></tr>
</div>
</div>
<h2 class="groupheader">Friends And Related Function Documentation</h2>
-<a id="aa365d0be0a3a369cdc5da59787f93297"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aa365d0be0a3a369cdc5da59787f93297">◆ </a></span>::mxnet::Engine</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -154,9 +176,9 @@ Friends</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
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+<!-- Generated by Doxygen 1.8.6 -->
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -93,9 +117,9 @@ $(function() {
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::op::SimpleOpRegEntry Class Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -80,86 +104,86 @@ $(function() {
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<div class="center"><img src="classmxnet_1_1op_1_1SimpleOpRegEntry__coll__graph.png" border="0" usemap="#mxnet_1_1op_1_1SimpleOpRegEntry_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1op_1_1SimpleOpRegEntry_coll__map" id="mxnet_1_1op_1_1SimpleOpRegEntry_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:ac2fd6ca9171b44a1ab131430cc5866a3"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">SimpleOpRegEntry</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a></td></tr>
-<tr class="memdesc:ac2fd6ca9171b44a1ab131430cc5866a3"><td class="mdescLeft"> </td><td class="mdescRight">declare self type <a href="#ac2fd6ca9171b44a1ab131430cc5866a3">More...</a><br /></td></tr>
+<tr class="memdesc:ac2fd6ca9171b44a1ab131430cc5866a3"><td class="mdescLeft"> </td><td class="mdescRight">declare self type <a href="#ac2fd6ca9171b44a1ab131430cc5866a3">More...</a><br/></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:ac258ea0f99d31b36e7dd682c25b3aeca"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac258ea0f99d31b36e7dd682c25b3aeca">set_symbol_op_name</a> (char const *symbol_name)=0</td></tr>
-<tr class="memdesc:ac258ea0f99d31b36e7dd682c25b3aeca"><td class="mdescLeft"> </td><td class="mdescRight">set a seperate name for symbol This must be called before set_function. Default: this is set to be same as the name of operator. <a href="#ac258ea0f99d31b36e7dd682c25b3aeca">More...</a><br /></td></tr>
+<tr class="memdesc:ac258ea0f99d31b36e7dd682c25b3aeca"><td class="mdescLeft"> </td><td class="mdescRight">set a seperate name for symbol This must be called before set_function. Default: this is set to be same as the name of operator. <a href="#ac258ea0f99d31b36e7dd682c25b3aeca">More...</a><br/></td></tr>
<tr class="separator:ac258ea0f99d31b36e7dd682c25b3aeca"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0725f1b670044149a088355ffeb6977f"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a0725f1b670044149a088355ffeb6977f">set_enable_scalar</a> (bool enable_scalar, <a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd697 [...]
-<tr class="memdesc:a0725f1b670044149a088355ffeb6977f"><td class="mdescLeft"> </td><td class="mdescRight">set number of scalar arguments needed to be passed in env A function cannot have both kwargs and scalar arguments. Default: this is set to false <a href="#a0725f1b670044149a088355ffeb6977f">More...</a><br /></td></tr>
+<tr class="memdesc:a0725f1b670044149a088355ffeb6977f"><td class="mdescLeft"> </td><td class="mdescRight">set number of scalar arguments needed to be passed in env A function cannot have both kwargs and scalar arguments. Default: this is set to false <a href="#a0725f1b670044149a088355ffeb6977f">More...</a><br/></td></tr>
<tr class="separator:a0725f1b670044149a088355ffeb6977f"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4cdffb8337b41a643c05632b29e65a2b"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a4cdffb8337b41a643c05632b29e65a2b">set_enable_kwargs</a> (bool enable_kwargs)=0</td></tr>
-<tr class="memdesc:a4cdffb8337b41a643c05632b29e65a2b"><td class="mdescLeft"> </td><td class="mdescRight">set whether to enable kwargs A function cannot have both kwargs and scalar arguments. Default: this is set to false <a href="#a4cdffb8337b41a643c05632b29e65a2b">More...</a><br /></td></tr>
+<tr class="memdesc:a4cdffb8337b41a643c05632b29e65a2b"><td class="mdescLeft"> </td><td class="mdescRight">set whether to enable kwargs A function cannot have both kwargs and scalar arguments. Default: this is set to false <a href="#a4cdffb8337b41a643c05632b29e65a2b">More...</a><br/></td></tr>
<tr class="separator:a4cdffb8337b41a643c05632b29e65a2b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af67ac155e01e797e7f633789453fb5f3"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#af67ac155e01e797e7f633789453fb5f3">set_resource_request</a> (const std::vector< <a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceReque [...]
-<tr class="memdesc:af67ac155e01e797e7f633789453fb5f3"><td class="mdescLeft"> </td><td class="mdescRight">set resource request By default there is no resource request. The resource will be presented in both forward and backward. <a href="#af67ac155e01e797e7f633789453fb5f3">More...</a><br /></td></tr>
+<tr class="memdesc:af67ac155e01e797e7f633789453fb5f3"><td class="mdescLeft"> </td><td class="mdescRight">set resource request By default there is no resource request. The resource will be presented in both forward and backward. <a href="#af67ac155e01e797e7f633789453fb5f3">More...</a><br/></td></tr>
<tr class="separator:af67ac155e01e797e7f633789453fb5f3"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7b615f5c6e5edc53a7ba008e631dcd52"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a7b615f5c6e5edc53a7ba008e631dcd52">set_resource_request</a> (<a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> req)=0</td></tr>
-<tr class="memdesc:a7b615f5c6e5edc53a7ba008e631dcd52"><td class="mdescLeft"> </td><td class="mdescRight">set resource request By default there is no resource request. The resource will be presented in both forward and backward. <a href="#a7b615f5c6e5edc53a7ba008e631dcd52">More...</a><br /></td></tr>
+<tr class="memdesc:a7b615f5c6e5edc53a7ba008e631dcd52"><td class="mdescLeft"> </td><td class="mdescRight">set resource request By default there is no resource request. The resource will be presented in both forward and backward. <a href="#a7b615f5c6e5edc53a7ba008e631dcd52">More...</a><br/></td></tr>
<tr class="separator:a7b615f5c6e5edc53a7ba008e631dcd52"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a3be9c65fc5f747b6a05e3c2889d8f72b"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a3be9c65fc5f747b6a05e3c2889d8f72b">set_shape_function</a> (<a class="el" href="namespacemxnet_1_1op.html#a3419f1ab0d1a62068a5e6c59efdc0fc9">SourceShapeFu [...]
-<tr class="memdesc:a3be9c65fc5f747b6a05e3c2889d8f72b"><td class="mdescLeft"> </td><td class="mdescRight">set source inference function. <a href="#a3be9c65fc5f747b6a05e3c2889d8f72b">More...</a><br /></td></tr>
+<tr class="memitem:a3be9c65fc5f747b6a05e3c2889d8f72b"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a3be9c65fc5f747b6a05e3c2889d8f72b">set_shape_function</a> (<a class="el" href="namespacemxnet_1_1op.html#af71f289b0df41398bcbac75d37435a07">SourceShapeFu [...]
+<tr class="memdesc:a3be9c65fc5f747b6a05e3c2889d8f72b"><td class="mdescLeft"> </td><td class="mdescRight">set source inference function. <a href="#a3be9c65fc5f747b6a05e3c2889d8f72b">More...</a><br/></td></tr>
<tr class="separator:a3be9c65fc5f747b6a05e3c2889d8f72b"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a1cc9dca87282218902486f66c98acf8b"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1cc9dca87282218902486f66c98acf8b">set_shape_function</a> (<a class="el" href="namespacemxnet_1_1op.html#a2d6fbc6ef1e22ea4433b3cf44ad3cef7">UnaryShapeFun [...]
-<tr class="memdesc:a1cc9dca87282218902486f66c98acf8b"><td class="mdescLeft"> </td><td class="mdescRight">set shape inference function. Default: out_shape = in_shape <a href="#a1cc9dca87282218902486f66c98acf8b">More...</a><br /></td></tr>
+<tr class="memitem:a1cc9dca87282218902486f66c98acf8b"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1cc9dca87282218902486f66c98acf8b">set_shape_function</a> (<a class="el" href="namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311">UnaryShapeFun [...]
+<tr class="memdesc:a1cc9dca87282218902486f66c98acf8b"><td class="mdescLeft"> </td><td class="mdescRight">set shape inference function. Default: out_shape = in_shape <a href="#a1cc9dca87282218902486f66c98acf8b">More...</a><br/></td></tr>
<tr class="separator:a1cc9dca87282218902486f66c98acf8b"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a8a67eaf7bab4a18d5b46f3a1639f426f"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a8a67eaf7bab4a18d5b46f3a1639f426f">set_shape_function</a> (<a class="el" href="namespacemxnet_1_1op.html#a78c7bd2075894ac41351feca1b1e5c94">BinaryShapeFu [...]
-<tr class="memdesc:a8a67eaf7bab4a18d5b46f3a1639f426f"><td class="mdescLeft"> </td><td class="mdescRight">set shape inference function to be the binary inference function Default: out_shape = lhs_shape, and lhs_shape must equal rhs_shape. <a href="#a8a67eaf7bab4a18d5b46f3a1639f426f">More...</a><br /></td></tr>
+<tr class="memitem:a8a67eaf7bab4a18d5b46f3a1639f426f"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a8a67eaf7bab4a18d5b46f3a1639f426f">set_shape_function</a> (<a class="el" href="namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5">BinaryShapeFu [...]
+<tr class="memdesc:a8a67eaf7bab4a18d5b46f3a1639f426f"><td class="mdescLeft"> </td><td class="mdescRight">set shape inference function to be the binary inference function Default: out_shape = lhs_shape, and lhs_shape must equal rhs_shape. <a href="#a8a67eaf7bab4a18d5b46f3a1639f426f">More...</a><br/></td></tr>
<tr class="separator:a8a67eaf7bab4a18d5b46f3a1639f426f"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a1f9bcff5039f065f73c7ec3a2558d799"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1f9bcff5039f065f73c7ec3a2558d799">set_function</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a65e8bade5ae0539689b705b921ed9a9d">Sourc [...]
-<tr class="memdesc:a1f9bcff5039f065f73c7ec3a2558d799"><td class="mdescLeft"> </td><td class="mdescRight">set function of the function to be fsource <a href="#a1f9bcff5039f065f73c7ec3a2558d799">More...</a><br /></td></tr>
+<tr class="memitem:a1f9bcff5039f065f73c7ec3a2558d799"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1f9bcff5039f065f73c7ec3a2558d799">set_function</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a8dc66fcca4ca52343570931503fca2c3">Sourc [...]
+<tr class="memdesc:a1f9bcff5039f065f73c7ec3a2558d799"><td class="mdescLeft"> </td><td class="mdescRight">set function of the function to be fsource <a href="#a1f9bcff5039f065f73c7ec3a2558d799">More...</a><br/></td></tr>
<tr class="separator:a1f9bcff5039f065f73c7ec3a2558d799"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2960b7ef7ff755d06336b9d87a4a0ced"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a2960b7ef7ff755d06336b9d87a4a0ced">set_function</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#aa37d5216112b72e8110fc8654860910b">Unary [...]
-<tr class="memdesc:a2960b7ef7ff755d06336b9d87a4a0ced"><td class="mdescLeft"> </td><td class="mdescRight">set function of the function to be funary <a href="#a2960b7ef7ff755d06336b9d87a4a0ced">More...</a><br /></td></tr>
+<tr class="memitem:a2960b7ef7ff755d06336b9d87a4a0ced"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a2960b7ef7ff755d06336b9d87a4a0ced">set_function</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11">Unary [...]
+<tr class="memdesc:a2960b7ef7ff755d06336b9d87a4a0ced"><td class="mdescLeft"> </td><td class="mdescRight">set function of the function to be funary <a href="#a2960b7ef7ff755d06336b9d87a4a0ced">More...</a><br/></td></tr>
<tr class="separator:a2960b7ef7ff755d06336b9d87a4a0ced"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a52c14c9d53298c63c9776fc2b7095149"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a52c14c9d53298c63c9776fc2b7095149">set_function</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a880eff3ebc68810ef133a6d8b77facc9">Binar [...]
-<tr class="memdesc:a52c14c9d53298c63c9776fc2b7095149"><td class="mdescLeft"> </td><td class="mdescRight">set function of the function to be funary <a href="#a52c14c9d53298c63c9776fc2b7095149">More...</a><br /></td></tr>
+<tr class="memitem:a52c14c9d53298c63c9776fc2b7095149"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a52c14c9d53298c63c9776fc2b7095149">set_function</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0">Binar [...]
+<tr class="memdesc:a52c14c9d53298c63c9776fc2b7095149"><td class="mdescLeft"> </td><td class="mdescRight">set function of the function to be funary <a href="#a52c14c9d53298c63c9776fc2b7095149">More...</a><br/></td></tr>
<tr class="separator:a52c14c9d53298c63c9776fc2b7095149"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ab8fe122e15142dc85c679071acf53302"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ab8fe122e15142dc85c679071acf53302">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a1e5767bffea568a1c38379060cbb68b4">Unary [...]
-<tr class="memdesc:ab8fe122e15142dc85c679071acf53302"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#ab8fe122e15142dc85c679071acf53302">More...</a><br /></td></tr>
+<tr class="memitem:ab8fe122e15142dc85c679071acf53302"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ab8fe122e15142dc85c679071acf53302">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b">Unary [...]
+<tr class="memdesc:ab8fe122e15142dc85c679071acf53302"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#ab8fe122e15142dc85c679071acf53302">More...</a><br/></td></tr>
<tr class="separator:ab8fe122e15142dc85c679071acf53302"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:af586bed58bb245dab4c4f1ffd6aeede4"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#af586bed58bb245dab4c4f1ffd6aeede4">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a6ad11641816278f8fdb6fe87c7fd3740">Unary [...]
-<tr class="memdesc:af586bed58bb245dab4c4f1ffd6aeede4"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#af586bed58bb245dab4c4f1ffd6aeede4">More...</a><br /></td></tr>
+<tr class="memitem:af586bed58bb245dab4c4f1ffd6aeede4"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#af586bed58bb245dab4c4f1ffd6aeede4">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3">Unary [...]
+<tr class="memdesc:af586bed58bb245dab4c4f1ffd6aeede4"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#af586bed58bb245dab4c4f1ffd6aeede4">More...</a><br/></td></tr>
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-<tr class="memitem:ac6e523d1552de21667e4b5bb4d783cc1"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac6e523d1552de21667e4b5bb4d783cc1">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#ac29102128f7e7785493bbb5b7db80997">Unary [...]
-<tr class="memdesc:ac6e523d1552de21667e4b5bb4d783cc1"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#ac6e523d1552de21667e4b5bb4d783cc1">More...</a><br /></td></tr>
+<tr class="memitem:ac6e523d1552de21667e4b5bb4d783cc1"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac6e523d1552de21667e4b5bb4d783cc1">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11">Unary [...]
+<tr class="memdesc:ac6e523d1552de21667e4b5bb4d783cc1"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#ac6e523d1552de21667e4b5bb4d783cc1">More...</a><br/></td></tr>
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-<tr class="memitem:a15d795e1aa76b5ed12ca5a9b7b091cd2"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a15d795e1aa76b5ed12ca5a9b7b091cd2">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#abcef841f759a7874ef50f21ae0226c8a">Binar [...]
-<tr class="memdesc:a15d795e1aa76b5ed12ca5a9b7b091cd2"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#a15d795e1aa76b5ed12ca5a9b7b091cd2">More...</a><br /></td></tr>
+<tr class="memitem:a15d795e1aa76b5ed12ca5a9b7b091cd2"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a15d795e1aa76b5ed12ca5a9b7b091cd2">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3">Binar [...]
+<tr class="memdesc:a15d795e1aa76b5ed12ca5a9b7b091cd2"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#a15d795e1aa76b5ed12ca5a9b7b091cd2">More...</a><br/></td></tr>
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-<tr class="memitem:a4df35e5bab55c3b2e742455d598e2e90"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a4df35e5bab55c3b2e742455d598e2e90">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a027d1f190d964f7494816733aff6c3f2">Binar [...]
-<tr class="memdesc:a4df35e5bab55c3b2e742455d598e2e90"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#a4df35e5bab55c3b2e742455d598e2e90">More...</a><br /></td></tr>
+<tr class="memitem:a4df35e5bab55c3b2e742455d598e2e90"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a4df35e5bab55c3b2e742455d598e2e90">set_gradient</a> (int dev_mask, <a class="el" href="namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849">Binar [...]
+<tr class="memdesc:a4df35e5bab55c3b2e742455d598e2e90"><td class="mdescLeft"> </td><td class="mdescRight">set gradient of the function of this function. <a href="#a4df35e5bab55c3b2e742455d598e2e90">More...</a><br/></td></tr>
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<tr class="memitem:a1cce9a6aaa175711b6be5b864b055f6a"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1cce9a6aaa175711b6be5b864b055f6a">describe</a> (const std::string &description)=0</td></tr>
-<tr class="memdesc:a1cce9a6aaa175711b6be5b864b055f6a"><td class="mdescLeft"> </td><td class="mdescRight">Describe the function. <a href="#a1cce9a6aaa175711b6be5b864b055f6a">More...</a><br /></td></tr>
+<tr class="memdesc:a1cce9a6aaa175711b6be5b864b055f6a"><td class="mdescLeft"> </td><td class="mdescRight">Describe the function. <a href="#a1cce9a6aaa175711b6be5b864b055f6a">More...</a><br/></td></tr>
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<tr class="memitem:a405bfb5929d459d45d93c2eaa645bfb5"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a405bfb5929d459d45d93c2eaa645bfb5">add_arguments</a> (const std::vector< dmlc::ParamFieldInfo > &args)=0</td></tr>
-<tr class="memdesc:a405bfb5929d459d45d93c2eaa645bfb5"><td class="mdescLeft"> </td><td class="mdescRight">Describe the function. <a href="#a405bfb5929d459d45d93c2eaa645bfb5">More...</a><br /></td></tr>
+<tr class="memdesc:a405bfb5929d459d45d93c2eaa645bfb5"><td class="mdescLeft"> </td><td class="mdescRight">Describe the function. <a href="#a405bfb5929d459d45d93c2eaa645bfb5">More...</a><br/></td></tr>
<tr class="separator:a405bfb5929d459d45d93c2eaa645bfb5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a94815080350681ad70e7815da1bc0eae"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a94815080350681ad70e7815da1bc0eae">~SimpleOpRegEntry</a> ()</td></tr>
-<tr class="memdesc:a94815080350681ad70e7815da1bc0eae"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#a94815080350681ad70e7815da1bc0eae">More...</a><br /></td></tr>
+<tr class="memdesc:a94815080350681ad70e7815da1bc0eae"><td class="mdescLeft"> </td><td class="mdescRight">virtual destructor <a href="#a94815080350681ad70e7815da1bc0eae">More...</a><br/></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:a02c180f08b215e9553d3c93fa783f9a4"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a02c180f08b215e9553d3c93fa783f9a4">name</a></td></tr>
-<tr class="memdesc:a02c180f08b215e9553d3c93fa783f9a4"><td class="mdescLeft"> </td><td class="mdescRight">name of the operator <a href="#a02c180f08b215e9553d3c93fa783f9a4">More...</a><br /></td></tr>
+<tr class="memdesc:a02c180f08b215e9553d3c93fa783f9a4"><td class="mdescLeft"> </td><td class="mdescRight">name of the operator <a href="#a02c180f08b215e9553d3c93fa783f9a4">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>registry entry to register simple operators via functions. </p>
</div><h2 class="groupheader">Member Typedef Documentation</h2>
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<h2 class="groupheader">Constructor & Destructor Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a94815080350681ad70e7815da1bc0eae">◆ </a></span>~SimpleOpRegEntry()</h2>
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- <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#a65e8bade5ae0539689b705b921ed9a9d">SourceFunction</a> </td>
+ <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#a8dc66fcca4ca52343570931503fca2c3">SourceFunction</a> </td>
<td class="paramname"><em>fsource</em>, </td>
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- <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#aa37d5216112b72e8110fc8654860910b">UnaryFunction</a> </td>
+ <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11">UnaryFunction</a> </td>
<td class="paramname"><em>funary</em>, </td>
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- <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#a880eff3ebc68810ef133a6d8b77facc9">BinaryFunction</a> </td>
+ <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0">BinaryFunction</a> </td>
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- <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#a1e5767bffea568a1c38379060cbb68b4">UnaryGradFunctionT0</a> </td>
+ <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b">UnaryGradFunctionT0</a> </td>
<td class="paramname"><em>fgrad</em>, </td>
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+ <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3">UnaryGradFunctionT1</a> </td>
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+ <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11">UnaryGradFunctionT2</a> </td>
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+ <td class="paramtype"><a class="el" href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3">BinaryGradFunctionT0</a> </td>
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<td class="memname">virtual <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a>& mxnet::op::SimpleOpRegEntry::set_shape_function </td>
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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diff --git a/versions/master/doxygen/classmxnet_1_1op_1_1SimpleOpRegistry.html b/versions/master/doxygen/classmxnet_1_1op_1_1SimpleOpRegistry.html
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<table class="memberdecls">
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Public Member Functions</h2></td></tr>
<tr class="memitem:a376e3412e9f425db998c384e5d3f0523"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">SimpleOpRegEntry</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a376e3412e9f425db998c384e5d3f0523">__REGISTER_OR_FIND__</a> (char const *name)</td></tr>
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+<tr class="memdesc:a376e3412e9f425db998c384e5d3f0523"><td class="mdescLeft"> </td><td class="mdescRight">Internal function to register a name function under name. <a href="#a376e3412e9f425db998c384e5d3f0523">More...</a><br/></td></tr>
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Static Public Member Functions</h2></td></tr>
<tr class="memitem:a8ee596c99eef39fbbfffca181bf7f26e"><td class="memItemLeft" align="right" valign="top">static const <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">SimpleOpRegEntry</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a8ee596c99eef39fbbfffca181bf7f26e">Find</a> (const std::string &name)</td></tr>
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+<tr class="memdesc:a8ee596c99eef39fbbfffca181bf7f26e"><td class="mdescLeft"> </td><td class="mdescRight">Find the entry with corresponding name. <a href="#a8ee596c99eef39fbbfffca181bf7f26e">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>registry for <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> functions </p>
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<div class="memproto">
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@@ -192,9 +212,9 @@ Static Public Member Functions</h2></td></tr>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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+ <li><a href="index.html"><span>Main Page</span></a></li>
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@@ -73,61 +95,61 @@ $(function() {
<p>CUDA debugging utilities.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/logging.h></code><br />
-<code>#include <mshadow/base.h></code><br />
-<code>#include <cuda_runtime.h></code><br />
-<code>#include <cublas_v2.h></code><br />
-<code>#include <curand.h></code><br />
+<div class="textblock"><code>#include <dmlc/logging.h></code><br/>
+<code>#include <mshadow/base.h></code><br/>
+<code>#include <cuda_runtime.h></code><br/>
+<code>#include <cublas_v2.h></code><br/>
+<code>#include <curand.h></code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for cuda_utils.h:</div>
<div class="dyncontent">
<div class="center"><img src="cuda__utils_8h__incl.png" border="0" usemap="#src_2common_2cuda__utils_8h" alt=""/></div>
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</div>
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<p><a href="cuda__utils_8h_source.html">Go to the source code of this file.</a></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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-<tr class="memdesc:namespacemxnet_1_1common_1_1cuda"><td class="mdescLeft"> </td><td class="mdescRight">common utils for cuda <br /></td></tr>
+<tr class="memitem:namespacemxnet_1_1common_1_1cuda"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common_1_1cuda.html">mxnet::common::cuda</a></td></tr>
+<tr class="memdesc:namespacemxnet_1_1common_1_1cuda"><td class="mdescLeft"> </td><td class="mdescRight">common utils for cuda <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="define-members"></a>
Macros</h2></td></tr>
<tr class="memitem:afc69a418242c5b851993bc2307b1c897"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="cuda__utils_8h.html#afc69a418242c5b851993bc2307b1c897">CHECK_CUDA_ERROR</a>(msg)</td></tr>
-<tr class="memdesc:afc69a418242c5b851993bc2307b1c897"><td class="mdescLeft"> </td><td class="mdescRight">Check CUDA error. <a href="#afc69a418242c5b851993bc2307b1c897">More...</a><br /></td></tr>
+<tr class="memdesc:afc69a418242c5b851993bc2307b1c897"><td class="mdescLeft"> </td><td class="mdescRight">Check CUDA error. <a href="#afc69a418242c5b851993bc2307b1c897">More...</a><br/></td></tr>
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<tr class="memitem:a06cc7d24ca66505e69f5ad40009f5e8d"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="cuda__utils_8h.html#a06cc7d24ca66505e69f5ad40009f5e8d">CUDA_CALL</a>(func)</td></tr>
-<tr class="memdesc:a06cc7d24ca66505e69f5ad40009f5e8d"><td class="mdescLeft"> </td><td class="mdescRight">Protected CUDA call. <a href="#a06cc7d24ca66505e69f5ad40009f5e8d">More...</a><br /></td></tr>
+<tr class="memdesc:a06cc7d24ca66505e69f5ad40009f5e8d"><td class="mdescLeft"> </td><td class="mdescRight">Protected CUDA call. <a href="#a06cc7d24ca66505e69f5ad40009f5e8d">More...</a><br/></td></tr>
<tr class="separator:a06cc7d24ca66505e69f5ad40009f5e8d"><td class="memSeparator" colspan="2"> </td></tr>
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-<tr class="memdesc:a685d7ca3c9370ff471665abcacdeb381"><td class="mdescLeft"> </td><td class="mdescRight">Protected cuBLAS call. <a href="#a685d7ca3c9370ff471665abcacdeb381">More...</a><br /></td></tr>
+<tr class="memdesc:a685d7ca3c9370ff471665abcacdeb381"><td class="mdescLeft"> </td><td class="mdescRight">Protected cuBLAS call. <a href="#a685d7ca3c9370ff471665abcacdeb381">More...</a><br/></td></tr>
<tr class="separator:a685d7ca3c9370ff471665abcacdeb381"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a82d7233550780a8c186e79c24aed8406"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="cuda__utils_8h.html#a82d7233550780a8c186e79c24aed8406">CURAND_CALL</a>(func)</td></tr>
-<tr class="memdesc:a82d7233550780a8c186e79c24aed8406"><td class="mdescLeft"> </td><td class="mdescRight">Protected cuRAND call. <a href="#a82d7233550780a8c186e79c24aed8406">More...</a><br /></td></tr>
+<tr class="memdesc:a82d7233550780a8c186e79c24aed8406"><td class="mdescLeft"> </td><td class="mdescRight">Protected cuRAND call. <a href="#a82d7233550780a8c186e79c24aed8406">More...</a><br/></td></tr>
<tr class="separator:a82d7233550780a8c186e79c24aed8406"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:a9feee613a4f16a954dd68e55345a72ac"><td class="memItemLeft" align="right" valign="top">const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common_1_1cuda.html#a9feee613a4f16a954dd68e55345a72ac">mxnet::common::cuda::CublasGetErrorString</a> (cublasStatus_t error)</td></tr>
-<tr class="memdesc:a9feee613a4f16a954dd68e55345a72ac"><td class="mdescLeft"> </td><td class="mdescRight">Get string representation of cuBLAS errors. <a href="namespacemxnet_1_1common_1_1cuda.html#a9feee613a4f16a954dd68e55345a72ac">More...</a><br /></td></tr>
+<tr class="memdesc:a9feee613a4f16a954dd68e55345a72ac"><td class="mdescLeft"> </td><td class="mdescRight">Get string representation of cuBLAS errors. <a href="#a9feee613a4f16a954dd68e55345a72ac">More...</a><br/></td></tr>
<tr class="separator:a9feee613a4f16a954dd68e55345a72ac"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a97c06b2f4d26445a7386b0f54fae1feb"><td class="memItemLeft" align="right" valign="top">const char * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common_1_1cuda.html#a97c06b2f4d26445a7386b0f54fae1feb">mxnet::common::cuda::CurandGetErrorString</a> (curandStatus_t status)</td></tr>
-<tr class="memdesc:a97c06b2f4d26445a7386b0f54fae1feb"><td class="mdescLeft"> </td><td class="mdescRight">Get string representation of cuRAND errors. <a href="namespacemxnet_1_1common_1_1cuda.html#a97c06b2f4d26445a7386b0f54fae1feb">More...</a><br /></td></tr>
+<tr class="memdesc:a97c06b2f4d26445a7386b0f54fae1feb"><td class="mdescLeft"> </td><td class="mdescRight">Get string representation of cuRAND errors. <a href="#a97c06b2f4d26445a7386b0f54fae1feb">More...</a><br/></td></tr>
<tr class="separator:a97c06b2f4d26445a7386b0f54fae1feb"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>CUDA debugging utilities. </p>
<p>Copyright (c) 2015 by Contributors </p>
</div><h2 class="groupheader">Macro Definition Documentation</h2>
-<a id="afc69a418242c5b851993bc2307b1c897"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#afc69a418242c5b851993bc2307b1c897">◆ </a></span>CHECK_CUDA_ERROR</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -140,7 +162,11 @@ Functions</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<b>Value:</b><div class="fragment"><div class="line">{ \</div><div class="line"> cudaError_t e = cudaGetLastError(); \</div><div class="line"> CHECK_EQ(e, cudaSuccess) << (msg) << <span class="stringliteral">" CUDA: "</span> << cudaGetErrorString(e); \</div><div class="line"> }</div></div><!-- fragment -->
+<b>Value:</b><div class="fragment"><div class="line">{ \</div>
+<div class="line"> cudaError_t e = cudaGetLastError(); \</div>
+<div class="line"> CHECK_EQ(e, cudaSuccess) << (msg) << <span class="stringliteral">" CUDA: "</span> << cudaGetErrorString(e); \</div>
+<div class="line"> }</div>
+</div><!-- fragment -->
<p>Check CUDA error. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
@@ -151,9 +177,7 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a685d7ca3c9370ff471665abcacdeb381"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a685d7ca3c9370ff471665abcacdeb381">◆ </a></span>CUBLAS_CALL</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -166,7 +190,12 @@ Functions</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<b>Value:</b><div class="fragment"><div class="line">{ \</div><div class="line"> cublasStatus_t e = (func); \</div><div class="line"> CHECK_EQ(e, CUBLAS_STATUS_SUCCESS) \</div><div class="line"> << <span class="stringliteral">"cuBLAS: "</span> << <a class="code" href="namespacemxnet_1_1common_1_1cuda.html#a9feee613a4f16a954dd68e55345a72a [...]
+<b>Value:</b><div class="fragment"><div class="line">{ \</div>
+<div class="line"> cublasStatus_t e = (func); \</div>
+<div class="line"> CHECK_EQ(e, CUBLAS_STATUS_SUCCESS) \</div>
+<div class="line"> << <span class="stringliteral">"cuBLAS: "</span> << <a class="code" href="namespacemxnet_1_1common_1_1cuda.html#a9feee613a4f16a954dd68e55345a72ac">common::cuda::CublasGetErrorString</a>(e); \</div>
+<div class="line"> }</div>
+<div class="ttc" id="namespacemxnet_1_1common_1_1cuda_html_a9feee613a4f16a954dd68e55345a72ac"><div class="ttname"><a href="namespacemxnet_1_1common_1_1cuda.html#a9feee613a4f16a954dd68e55345a72ac">mxnet::common::cuda::CublasGetErrorString</a></div><div class="ttdeci">const char * CublasGetErrorString(cublasStatus_t error)</div><div class="ttdoc">Get string representation of cuBLAS errors. </div><div class="ttdef"><b>Definition:</b> cuda_utils.h:44</div></div>
</div><!-- fragment -->
<p>Protected cuBLAS call. </p>
<dl class="params"><dt>Parameters</dt><dd>
@@ -179,9 +208,7 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a06cc7d24ca66505e69f5ad40009f5e8d"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a06cc7d24ca66505e69f5ad40009f5e8d">◆ </a></span>CUDA_CALL</h2>
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@@ -194,7 +221,12 @@ Functions</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<b>Value:</b><div class="fragment"><div class="line">{ \</div><div class="line"> cudaError_t e = (func); \</div><div class="line"> CHECK(e == cudaSuccess || e == cudaErrorCudartUnloading) \</div><div class="line"> << <span class="stringliteral">"CUDA: "</span> << cudaGetErrorString(e); \</div><div class="line"> }</div></div>< [...]
+<b>Value:</b><div class="fragment"><div class="line">{ \</div>
+<div class="line"> cudaError_t e = (func); \</div>
+<div class="line"> CHECK(e == cudaSuccess || e == cudaErrorCudartUnloading) \</div>
+<div class="line"> << <span class="stringliteral">"CUDA: "</span> << cudaGetErrorString(e); \</div>
+<div class="line"> }</div>
+</div><!-- fragment -->
<p>Protected CUDA call. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
@@ -206,9 +238,7 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a82d7233550780a8c186e79c24aed8406"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a82d7233550780a8c186e79c24aed8406">◆ </a></span>CURAND_CALL</h2>
-
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@@ -221,7 +251,12 @@ Functions</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<b>Value:</b><div class="fragment"><div class="line">{ \</div><div class="line"> curandStatus_t e = (func); \</div><div class="line"> CHECK_EQ(e, CURAND_STATUS_SUCCESS) \</div><div class="line"> << <span class="stringliteral">"cuRAND: "</span> << <a class="code" href="namespacemxnet_1_1common_1_1cuda.html#a97c06b2f4d26445a7386b0f54fae1fe [...]
+<b>Value:</b><div class="fragment"><div class="line">{ \</div>
+<div class="line"> curandStatus_t e = (func); \</div>
+<div class="line"> CHECK_EQ(e, CURAND_STATUS_SUCCESS) \</div>
+<div class="line"> << <span class="stringliteral">"cuRAND: "</span> << <a class="code" href="namespacemxnet_1_1common_1_1cuda.html#a97c06b2f4d26445a7386b0f54fae1feb">common::cuda::CurandGetErrorString</a>(e); \</div>
+<div class="line"> }</div>
+<div class="ttc" id="namespacemxnet_1_1common_1_1cuda_html_a97c06b2f4d26445a7386b0f54fae1feb"><div class="ttname"><a href="namespacemxnet_1_1common_1_1cuda.html#a97c06b2f4d26445a7386b0f54fae1feb">mxnet::common::cuda::CurandGetErrorString</a></div><div class="ttdeci">const char * CurandGetErrorString(curandStatus_t status)</div><div class="ttdoc">Get string representation of cuRAND errors. </div><div class="ttdef"><b>Definition:</b> cuda_utils.h:75</div></div>
</div><!-- fragment -->
<p>Protected cuRAND call. </p>
<dl class="params"><dt>Parameters</dt><dd>
@@ -237,9 +272,9 @@ Functions</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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+++ b/versions/master/doxygen/cuda__utils_8h_source.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: src/common/cuda_utils.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
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-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -66,15 +88,196 @@ $(function() {
<div class="title">cuda_utils.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="cuda__utils_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_COMMON_CUDA_UTILS_H_</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#define MXNET_COMMON_CUDA_UTILS_H_</span></div><div class= [...]
+<a href="cuda__utils_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_COMMON_CUDA_UTILS_H_</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_COMMON_CUDA_UTILS_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <dmlc/logging.h></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <mshadow/base.h></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#ifdef __JETBRAINS_IDE__</span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor"></span><span class="preprocessor">#define __CUDACC__ 1</span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor"></span><span class="preprocessor">#define __host__</span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor"></span><span class="preprocessor">#define __device__</span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor"></span><span class="preprocessor">#define __global__</span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor"></span><span class="preprocessor">#define __forceinline__</span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor"></span><span class="preprocessor">#define __shared__</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor"></span><span class="keyword">inline</span> <span class="keywordtype">void</span> __syncthreads() {}</div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> __threadfence_block() {}</div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="keyword">template</span><<span class="keyword">class</span> T> <span class="keyword">inline</span> T __clz(<span class="keyword">const</span> T val) { <span class="keywordflow">return</span> val; }</div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">struct </span>__cuda_fake_struct { <span class="keywordtype">int</span> x; <span class="keywordtype">int</span> y; <span class="keywordtype">int</span> z; };</div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">extern</span> __cuda_fake_struct blockDim;</div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="keyword">extern</span> __cuda_fake_struct threadIdx;</div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">extern</span> __cuda_fake_struct blockIdx;</div>
+<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#if MXNET_USE_CUDA</span></div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include <cuda_runtime.h></span></div>
+<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <cublas_v2.h></span></div>
+<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <curand.h></span></div>
+<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div>
+<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common.html"> 36</a></span> <span class="keyword">namespace </span>common {</div>
+<div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common_1_1cuda.html"> 38</a></span> <span class="keyword">namespace </span>cuda {</div>
+<div class="line"><a name="l00044"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common_1_1cuda.html#a9feee613a4f16a954dd68e55345a72ac"> 44</a></span> <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacemxnet_1_1common_1_1cuda.html#a9feee613a4f16a954dd68e55345a72ac">CublasGetErrorString</a>(cublasStatus_t error) {</div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordflow">switch</span> (error) {</div>
+<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordflow">case</span> CUBLAS_STATUS_SUCCESS:</div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CUBLAS_STATUS_SUCCESS"</span>;</div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">case</span> CUBLAS_STATUS_NOT_INITIALIZED:</div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CUBLAS_STATUS_NOT_INITIALIZED"</span>;</div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordflow">case</span> CUBLAS_STATUS_ALLOC_FAILED:</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CUBLAS_STATUS_ALLOC_FAILED"</span>;</div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">case</span> CUBLAS_STATUS_INVALID_VALUE:</div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CUBLAS_STATUS_INVALID_VALUE"</span>;</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">case</span> CUBLAS_STATUS_ARCH_MISMATCH:</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CUBLAS_STATUS_ARCH_MISMATCH"</span>;</div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">case</span> CUBLAS_STATUS_MAPPING_ERROR:</div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CUBLAS_STATUS_MAPPING_ERROR"</span>;</div>
+<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">case</span> CUBLAS_STATUS_EXECUTION_FAILED:</div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CUBLAS_STATUS_EXECUTION_FAILED"</span>;</div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">case</span> CUBLAS_STATUS_INTERNAL_ERROR:</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CUBLAS_STATUS_INTERNAL_ERROR"</span>;</div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">case</span> CUBLAS_STATUS_NOT_SUPPORTED:</div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CUBLAS_STATUS_NOT_SUPPORTED"</span>;</div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">default</span>:</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div>
+<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordflow">return</span> <span class="stringliteral">"Unknown cuBLAS status"</span>;</div>
+<div class="line"><a name="l00068"></a><span class="lineno"> 68</span> }</div>
+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div>
+<div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common_1_1cuda.html#a97c06b2f4d26445a7386b0f54fae1feb"> 75</a></span> <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="namespacemxnet_1_1common_1_1cuda.html#a97c06b2f4d26445a7386b0f54fae1feb">CurandGetErrorString</a>(curandStatus_t status) {</div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">switch</span> (status) {</div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">case</span> CURAND_STATUS_SUCCESS:</div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_SUCCESS"</span>;</div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">case</span> CURAND_STATUS_VERSION_MISMATCH:</div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_VERSION_MISMATCH"</span>;</div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordflow">case</span> CURAND_STATUS_NOT_INITIALIZED:</div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_NOT_INITIALIZED"</span>;</div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">case</span> CURAND_STATUS_ALLOCATION_FAILED:</div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_ALLOCATION_FAILED"</span>;</div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">case</span> CURAND_STATUS_TYPE_ERROR:</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_TYPE_ERROR"</span>;</div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">case</span> CURAND_STATUS_OUT_OF_RANGE:</div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_OUT_OF_RANGE"</span>;</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">case</span> CURAND_STATUS_LENGTH_NOT_MULTIPLE:</div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_LENGTH_NOT_MULTIPLE"</span>;</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">case</span> CURAND_STATUS_DOUBLE_PRECISION_REQUIRED:</div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_DOUBLE_PRECISION_REQUIRED"</span>;</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">case</span> CURAND_STATUS_LAUNCH_FAILURE:</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_LAUNCH_FAILURE"</span>;</div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">case</span> CURAND_STATUS_PREEXISTING_FAILURE:</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_PREEXISTING_FAILURE"</span>;</div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">case</span> CURAND_STATUS_INITIALIZATION_FAILED:</div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_INITIALIZATION_FAILED"</span>;</div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">case</span> CURAND_STATUS_ARCH_MISMATCH:</div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_ARCH_MISMATCH"</span>;</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">case</span> CURAND_STATUS_INTERNAL_ERROR:</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">return</span> <span class="stringliteral">"CURAND_STATUS_INTERNAL_ERROR"</span>;</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  }</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordflow">return</span> <span class="stringliteral">"Unknown cuRAND status"</span>;</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span> }</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span> } <span class="comment">// namespace cuda</span></div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span> } <span class="comment">// namespace common</span></div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div>
+<div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="cuda__utils_8h.html#afc69a418242c5b851993bc2307b1c897"> 115</a></span> <span class="preprocessor">#define CHECK_CUDA_ERROR(msg) \</span></div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="preprocessor"> { \</span></div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="preprocessor"> cudaError_t e = cudaGetLastError(); \</span></div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="preprocessor"> CHECK_EQ(e, cudaSuccess) << (msg) << " CUDA: " << cudaGetErrorString(e); \</span></div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="preprocessor"> }</span></div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00127"></a><span class="lineno"><a class="line" href="cuda__utils_8h.html#a06cc7d24ca66505e69f5ad40009f5e8d"> 127</a></span> <span class="preprocessor">#define CUDA_CALL(func) \</span></div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="preprocessor"> { \</span></div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="preprocessor"> cudaError_t e = (func); \</span></div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="preprocessor"> CHECK(e == cudaSuccess || e == cudaErrorCudartUnloading) \</span></div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="preprocessor"> << "CUDA: " << cudaGetErrorString(e); \</span></div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="preprocessor"> }</span></div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00140"></a><span class="lineno"><a class="line" href="cuda__utils_8h.html#a685d7ca3c9370ff471665abcacdeb381"> 140</a></span> <span class="preprocessor">#define CUBLAS_CALL(func) \</span></div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span> <span class="preprocessor"> { \</span></div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="preprocessor"> cublasStatus_t e = (func); \</span></div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="preprocessor"> CHECK_EQ(e, CUBLAS_STATUS_SUCCESS) \</span></div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="preprocessor"> << "cuBLAS: " << common::cuda::CublasGetErrorString(e); \</span></div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="preprocessor"> }</span></div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00153"></a><span class="lineno"><a class="line" href="cuda__utils_8h.html#a82d7233550780a8c186e79c24aed8406"> 153</a></span> <span class="preprocessor">#define CURAND_CALL(func) \</span></div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span> <span class="preprocessor"> { \</span></div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="preprocessor"> curandStatus_t e = (func); \</span></div>
+<div class="line"><a name="l00156"></a><span class="lineno"> 156</span> <span class="preprocessor"> CHECK_EQ(e, CURAND_STATUS_SUCCESS) \</span></div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span> <span class="preprocessor"> << "cuRAND: " << common::cuda::CurandGetErrorString(e); \</span></div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <span class="preprocessor"> }</span></div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <span class="preprocessor">#endif // MXNET_USE_CUDA</span></div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="preprocessor">#if MXNET_USE_CUDNN</span></div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span> <span class="preprocessor">#include <cudnn.h></span></div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="preprocessor">#define CUDNN_CALL(func) \</span></div>
+<div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="preprocessor"> { \</span></div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="preprocessor"> cudnnStatus_t e = (func); \</span></div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <span class="preprocessor"> CHECK_EQ(e, CUDNN_STATUS_SUCCESS) << "cuDNN: " << cudnnGetErrorString(e); \</span></div>
+<div class="line"><a name="l00170"></a><span class="lineno"> 170</span> <span class="preprocessor"> }</span></div>
+<div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="preprocessor">#endif // MXNET_USE_CUDNN</span></div>
+<div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="comment">// Overload atomicAdd to work for floats on all architectures</span></div>
+<div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="preprocessor">#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 600</span></div>
+<div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="preprocessor"></span><span class="comment">// From CUDA Programming Guide</span></div>
+<div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="keyword">static</span> <span class="keyword">inline</span> __device__ <span class="keywordtype">void</span> atomicAdd(<span class="keywordtype">double</span> *address, <span class="keywordtype">double</span> val) {</div>
+<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> <span class="keywordtype">long</span>* address_as_ull = <span class="comment">// NOLINT(*)</span></div>
+<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keyword">reinterpret_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> <span class="keywordtype">long</span>*<span class="keyword">></span>(address); <span class="comment">// NOLINT(*)</span></div>
+<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> <span class="keywordtype">long</span> old = *address_as_ull; <span class="comment">// NOLINT(*)</span></div>
+<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> <span class="keywordtype">long</span> assumed; <span class="comment">// NOLINT(*)</span></div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div>
+<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">do</span> {</div>
+<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  assumed = old;</div>
+<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  old = atomicCAS(address_as_ull, assumed,</div>
+<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  __double_as_longlong(val +</div>
+<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  __longlong_as_double(assumed)));</div>
+<div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div>
+<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">// Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN)</span></div>
+<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  } <span class="keywordflow">while</span> (assumed != old);</div>
+<div class="line"><a name="l00191"></a><span class="lineno"> 191</span> }</div>
+<div class="line"><a name="l00192"></a><span class="lineno"> 192</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="comment">// Overload atomicAdd for half precision</span></div>
+<div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="comment">// Taken from:</span></div>
+<div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="comment">// https://github.com/torch/cutorch/blob/master/lib/THC/THCAtomics.cuh</span></div>
+<div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="preprocessor">#if defined(__CUDA_ARCH__)</span></div>
+<div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="preprocessor"></span><span class="keyword">static</span> <span class="keyword">inline</span> __device__ <span class="keywordtype">void</span> atomicAdd(mshadow::half::half_t *address,</div>
+<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  mshadow::half::half_t val) {</div>
+<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> *address_as_ui =</div>
+<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keyword">reinterpret_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> *<span class="keyword">></span>(<span class="keyword">reinterpret_cast<</span><span class="keywordtype">char</span> *<span class="keyword">></span>(address) -</div>
+<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  (reinterpret_cast<size_t>(address) & 2));</div>
+<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> old = *address_as_ui;</div>
+<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> assumed;</div>
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">do</span> {</div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  assumed = old;</div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  mshadow::half::half_t hsum;</div>
+<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  hsum.half_ =</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">reinterpret_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(address) & 2 ? (old >> 16) : (old & 0xffff);</div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  hsum += val;</div>
+<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  old = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(address) & 2</div>
+<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  ? (old & 0xffff) | (hsum.half_ << 16)</div>
+<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  : (old & 0xffff0000) | hsum.half_;</div>
+<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  old = atomicCAS(address_as_ui, assumed, old);</div>
+<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  } <span class="keywordflow">while</span> (assumed != old);</div>
+<div class="line"><a name="l00217"></a><span class="lineno"> 217</span> }</div>
+<div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00220"></a><span class="lineno"> 220</span> <span class="preprocessor">#endif // MXNET_COMMON_CUDA_UTILS_H_</span></div>
<div class="ttc" id="namespacemxnet_1_1common_1_1cuda_html_a97c06b2f4d26445a7386b0f54fae1feb"><div class="ttname"><a href="namespacemxnet_1_1common_1_1cuda.html#a97c06b2f4d26445a7386b0f54fae1feb">mxnet::common::cuda::CurandGetErrorString</a></div><div class="ttdeci">const char * CurandGetErrorString(curandStatus_t status)</div><div class="ttdoc">Get string representation of cuRAND errors. </div><div class="ttdef"><b>Definition:</b> cuda_utils.h:75</div></div>
<div class="ttc" id="namespacemxnet_1_1common_1_1cuda_html_a9feee613a4f16a954dd68e55345a72ac"><div class="ttname"><a href="namespacemxnet_1_1common_1_1cuda.html#a9feee613a4f16a954dd68e55345a72ac">mxnet::common::cuda::CublasGetErrorString</a></div><div class="ttdeci">const char * CublasGetErrorString(cublasStatus_t error)</div><div class="ttdoc">Get string representation of cuBLAS errors. </div><div class="ttdef"><b>Definition:</b> cuda_utils.h:44</div></div>
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+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/dir_000003_000000.html b/versions/master/doxygen/dir_000003_000000.html
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: src/common -> include Relation</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,40 @@
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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var searchBox = new SearchBox("searchBox", "search",false,'Search');
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
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+ </div>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -65,9 +81,9 @@ $(function() {
<h3>common → include Relation</h3><table class="dirtab"><tr class="dirtab"><th class="dirtab">File in src/common</th><th class="dirtab">Includes file in include</th></tr><tr class="dirtab"><td class="dirtab"><a class="el" href="utils_8h.html">utils.h</a></td><td class="dirtab"><a class="el" href="dir_1143c7affb9ebd026cb6818dd282def7.html">mxnet</a> / <a class="el" href="engine_8h.html">engine.h</a></td></tr></table></div><!-- contents -->
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/dir_1143c7affb9ebd026cb6818dd282def7.html b/versions/master/doxygen/dir_1143c7affb9ebd026cb6818dd282def7.html
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--- a/versions/master/doxygen/dir_1143c7affb9ebd026cb6818dd282def7.html
+++ b/versions/master/doxygen/dir_1143c7affb9ebd026cb6818dd282def7.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet Directory Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
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@@ -20,7 +21,7 @@
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<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,40 @@
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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+ <img id="MSearchSelect" src="search/mag_sel.png"
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+ onfocus="searchBox.OnSearchFieldFocus(true)"
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@@ -66,64 +82,71 @@ $(function() {
<div class="title">mxnet Directory Reference</div> </div>
</div><!--header-->
<div class="contents">
+<div class="dynheader">
+Directory dependency graph for mxnet:</div>
+<div class="dyncontent">
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="files"></a>
Files</h2></td></tr>
<tr class="memitem:base_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="base_8h.html">base.h</a> <a href="base_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:base_8h"><td class="mdescLeft"> </td><td class="mdescRight">configuation of mxnet as well as basic data structure. <br /></td></tr>
+<tr class="memdesc:base_8h"><td class="mdescLeft"> </td><td class="mdescRight">configuation of mxnet as well as basic data structure. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:c__api_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="c__api_8h.html">c_api.h</a> <a href="c__api_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:c__api_8h"><td class="mdescLeft"> </td><td class="mdescRight">C API of mxnet. <br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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-<tr class="memdesc:c__lapack__api_8h"><td class="mdescLeft"> </td><td class="mdescRight">Unified interface for LAPACK calls from within mxnet. Purpose is to hide the platform specific differences. <br /></td></tr>
+<tr class="memdesc:c__lapack__api_8h"><td class="mdescLeft"> </td><td class="mdescRight">Unified interface for LAPACK calls from within mxnet. Purpose is to hide the platform specific differences. <br/></td></tr>
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<tr class="memitem:c__predict__api_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="c__predict__api_8h.html">c_predict_api.h</a> <a href="c__predict__api_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:c__predict__api_8h"><td class="mdescLeft"> </td><td class="mdescRight">C predict API of mxnet, contains a minimum API to run prediction. This file is self-contained, and do not dependent on any other files. <br /></td></tr>
+<tr class="memdesc:c__predict__api_8h"><td class="mdescLeft"> </td><td class="mdescRight">C predict API of mxnet, contains a minimum API to run prediction. This file is self-contained, and do not dependent on any other files. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:engine_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="engine_8h.html">engine.h</a> <a href="engine_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:engine_8h"><td class="mdescLeft"> </td><td class="mdescRight">Engine that schedules all the operations according to dependency. <br /></td></tr>
+<tr class="memdesc:engine_8h"><td class="mdescLeft"> </td><td class="mdescRight">Engine that schedules all the operations according to dependency. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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-<tr class="memdesc:executor_8h"><td class="mdescLeft"> </td><td class="mdescRight">Symbolic executor interface of mxnet. <br /></td></tr>
+<tr class="memdesc:executor_8h"><td class="mdescLeft"> </td><td class="mdescRight">Symbolic executor interface of mxnet. <br/></td></tr>
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-<tr class="memdesc:io_8h"><td class="mdescLeft"> </td><td class="mdescRight">mxnet io data structure and data iterator <br /></td></tr>
+<tr class="memdesc:io_8h"><td class="mdescLeft"> </td><td class="mdescRight">mxnet io data structure and data iterator <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:kvstore_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="kvstore_8h.html">kvstore.h</a> <a href="kvstore_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:kvstore_8h"><td class="mdescLeft"> </td><td class="mdescRight">key-value store interface for mxnet <br /></td></tr>
+<tr class="memdesc:kvstore_8h"><td class="mdescLeft"> </td><td class="mdescRight">key-value store interface for mxnet <br/></td></tr>
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-<tr class="memdesc:mxrtc_8h"><td class="mdescLeft"> </td><td class="mdescRight">Wrapper for NVRTC. <br /></td></tr>
+<tr class="memdesc:mxrtc_8h"><td class="mdescLeft"> </td><td class="mdescRight">Wrapper for NVRTC. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ndarray_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="ndarray_8h.html">ndarray.h</a> <a href="ndarray_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:ndarray_8h"><td class="mdescLeft"> </td><td class="mdescRight">NDArray interface that handles array arithematics. <br /></td></tr>
+<tr class="memdesc:ndarray_8h"><td class="mdescLeft"> </td><td class="mdescRight">NDArray interface that handles array arithematics. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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-<tr class="memdesc:op__attr__types_8h"><td class="mdescLeft"> </td><td class="mdescRight">Additional operator attributes beside the ones provided by NNVM. <br /></td></tr>
+<tr class="memdesc:op__attr__types_8h"><td class="mdescLeft"> </td><td class="mdescRight">Additional operator attributes beside the ones provided by NNVM. <br/></td></tr>
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-<tr class="memdesc:operator_8h"><td class="mdescLeft"> </td><td class="mdescRight">Operator interface of mxnet. <br /></td></tr>
+<tr class="memdesc:operator_8h"><td class="mdescLeft"> </td><td class="mdescRight">Operator interface of mxnet. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:operator__util_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="operator__util_8h.html">operator_util.h</a> <a href="operator__util_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:operator__util_8h"><td class="mdescLeft"> </td><td class="mdescRight">Utility functions and registries to help quickly build new operators. [Deprecated] Use the register functions in this file when possible to simplify operator creations. Operators registered in this file will be exposed to both NDArray API and symbolic API. <br /></td></tr>
+<tr class="memdesc:operator__util_8h"><td class="mdescLeft"> </td><td class="mdescRight">Utility functions and registries to help quickly build new operators. [Deprecated] Use the register functions in this file when possible to simplify operator creations. Operators registered in this file will be exposed to both NDArray API and symbolic API. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:resource_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="resource_8h.html">resource.h</a> <a href="resource_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:resource_8h"><td class="mdescLeft"> </td><td class="mdescRight">Global resource allocation handling. <br /></td></tr>
+<tr class="memdesc:resource_8h"><td class="mdescLeft"> </td><td class="mdescRight">Global resource allocation handling. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:storage_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="storage_8h.html">storage.h</a> <a href="storage_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:storage_8h"><td class="mdescLeft"> </td><td class="mdescRight">Storage manager across multiple devices. <br /></td></tr>
+<tr class="memdesc:storage_8h"><td class="mdescLeft"> </td><td class="mdescRight">Storage manager across multiple devices. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:tensor__blob_8h"><td class="memItemLeft" align="right" valign="top">file  </td><td class="memItemRight" valign="bottom"><a class="el" href="tensor__blob_8h.html">tensor_blob.h</a> <a href="tensor__blob_8h_source.html">[code]</a></td></tr>
-<tr class="memdesc:tensor__blob_8h"><td class="mdescLeft"> </td><td class="mdescRight">TBlob class that holds common representation of arbirary dimension tensor, can be used to transformed to normal fixed dimenson tensor. <br /></td></tr>
+<tr class="memdesc:tensor__blob_8h"><td class="mdescLeft"> </td><td class="mdescRight">TBlob class that holds common representation of arbirary dimension tensor, can be used to transformed to normal fixed dimenson tensor. <br/></td></tr>
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</table>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<tr style="height: 56px;">
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{
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-#projectalign
-{
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@@ -1259,7 +1108,7 @@ div.toc {
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float: right;
height: auto;
- margin: 0 8px 10px 10px;
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diff --git a/versions/master/doxygen/dynsections.js b/versions/master/doxygen/dynsections.js
index 85e1836..ed092c7 100644
--- a/versions/master/doxygen/dynsections.js
+++ b/versions/master/doxygen/dynsections.js
@@ -24,20 +24,19 @@ function updateStripes()
$('table.directory tr').
removeClass('even').filter(':visible:even').addClass('even');
}
-
function toggleLevel(level)
{
- $('table.directory tr').each(function() {
+ $('table.directory tr').each(function(){
var l = this.id.split('_').length-1;
var i = $('#img'+this.id.substring(3));
var a = $('#arr'+this.id.substring(3));
if (l<level+1) {
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- a.html('▼');
+ i.attr('src','ftv2folderopen.png');
+ a.attr('src','ftv2mnode.png');
$(this).show();
} else if (l==level+1) {
- i.removeClass('iconfclosed iconfopen').addClass('iconfclosed');
- a.html('►');
+ i.attr('src','ftv2folderclosed.png');
+ a.attr('src','ftv2pnode.png');
$(this).show();
} else {
$(this).hide();
@@ -48,33 +47,34 @@ function toggleLevel(level)
function toggleFolder(id)
{
- // the clicked row
+ //The clicked row
var currentRow = $('#row_'+id);
+ var currentRowImages = currentRow.find("img");
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var rows = currentRow.nextAll("tr");
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-
- // only match elements AFTER this one (can't hide elements before)
- var childRows = rows.filter(function() { return this.id.match(re); });
+ //Only match elements AFTER this one (can't hide elements before)
+ var childRows = rows.filter(function() {
+ var re = new RegExp('^row_'+id+'\\d+_$', "i"); //only one sub
+ return this.id.match(re);
+ });
- // first row is visible we are HIDING
+ //First row is visible we are HIDING
if (childRows.filter(':first').is(':visible')===true) {
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+ //All sub images
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+
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+ currentRow.find("[id^=img]").attr('src', 'ftv2folderopen.png'); //open row
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diff --git a/versions/master/doxygen/engine_8h.html b/versions/master/doxygen/engine_8h.html
index de8cf41..40c7cf7 100644
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+++ b/versions/master/doxygen/engine_8h.html
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<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/engine.h File Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
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@@ -74,35 +96,25 @@ $(function() {
<p>Engine that schedules all the operations according to dependency.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/base.h></code><br />
-<code>#include <algorithm></code><br />
-<code>#include <memory></code><br />
-<code>#include <functional></code><br />
-<code>#include <vector></code><br />
-<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br />
+<div class="textblock"><code>#include <dmlc/base.h></code><br/>
+<code>#include <algorithm></code><br/>
+<code>#include <memory></code><br/>
+<code>#include <functional></code><br/>
+<code>#include <vector></code><br/>
+<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for engine.h:</div>
<div class="dyncontent">
<div class="center"><img src="engine_8h__incl.png" border="0" usemap="#include_2mxnet_2engine_8h" alt=""/></div>
<map name="include_2mxnet_2engine_8h" id="include_2mxnet_2engine_8h">
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-</map>
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</div>
</div><div class="textblock"><div class="dynheader">
This graph shows which files directly or indirectly include this file:</div>
<div class="dyncontent">
<div class="center"><img src="engine_8h__dep__incl.png" border="0" usemap="#include_2mxnet_2engine_8hdep" alt=""/></div>
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<p><a href="engine_8h_source.html">Go to the source code of this file.</a></p>
@@ -110,42 +122,42 @@ This graph shows which files directly or indirectly include this file:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">mxnet::engine::CallbackOnComplete</a></td></tr>
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">OnComplete Callback to the engine, called by AsyncFn when action completes. <a href="classmxnet_1_1engine_1_1CallbackOnComplete.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html">mxnet::Engine</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Dependency engine that schedules operations. <a href="classmxnet_1_1Engine.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Dependency engine that schedules operations. <a href="classmxnet_1_1Engine.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
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-<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
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+<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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-<tr class="memdesc:namespacemxnet_1_1engine"><td class="mdescLeft"> </td><td class="mdescRight">namespace of engine internal types. <br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
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<tr class="memitem:a9d36c4f33eae8531586dc2edf83ae7cf"><td class="memItemLeft" align="right" valign="top">typedef Var * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">mxnet::engine::VarHandle</a></td></tr>
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+<tr class="memdesc:a9d36c4f33eae8531586dc2edf83ae7cf"><td class="mdescLeft"> </td><td class="mdescRight">Variable pointer type, usually hold by user used to specify dependencies. <a href="#a9d36c4f33eae8531586dc2edf83ae7cf">More...</a><br/></td></tr>
<tr class="separator:a9d36c4f33eae8531586dc2edf83ae7cf"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2d9b14b658e3f3c4e03ca49cd38ace94"><td class="memItemLeft" align="right" valign="top">typedef Opr * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1engine.html#a2d9b14b658e3f3c4e03ca49cd38ace94">mxnet::engine::OprHandle</a></td></tr>
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+<tr class="memdesc:a2d9b14b658e3f3c4e03ca49cd38ace94"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> pointer type, usually hold by user. <a href="#a2d9b14b658e3f3c4e03ca49cd38ace94">More...</a><br/></td></tr>
<tr class="separator:a2d9b14b658e3f3c4e03ca49cd38ace94"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="enum-members"></a>
Enumerations</h2></td></tr>
-<tr class="memitem:a998b74220fab2b012cf8a179650e1b3b"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">mxnet::FnProperty</a> { <br />
+<tr class="memitem:a998b74220fab2b012cf8a179650e1b3b"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">mxnet::FnProperty</a> { <br/>
  <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416">mxnet::FnProperty::kNormal</a>,
<a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba739f2f416f05f4728c217f09e93958d1">mxnet::FnProperty::kCopyFromGPU</a>,
<a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba6cd75f41e0ec8d61b0a2f0e20ef6d1e8">mxnet::FnProperty::kCopyToGPU</a>,
<a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3bac41ceb98eeb9b2e208e3e242a7357142">mxnet::FnProperty::kCPUPrioritized</a>,
-<br />
+<br/>
  <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7">mxnet::FnProperty::kAsync</a>
-<br />
- }<tr class="memdesc:a998b74220fab2b012cf8a179650e1b3b"><td class="mdescLeft"> </td><td class="mdescRight">Function property, used to hint what action is pushed to engine. <a href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">More...</a><br /></td></tr>
-</td></tr>
+<br/>
+ }</td></tr>
+<tr class="memdesc:a998b74220fab2b012cf8a179650e1b3b"><td class="mdescLeft"> </td><td class="mdescRight">Function property, used to hint what action is pushed to engine. <a href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">More...</a><br/></td></tr>
<tr class="separator:a998b74220fab2b012cf8a179650e1b3b"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
@@ -154,9 +166,9 @@ Enumerations</h2></td></tr>
</div></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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index 242f5ba..8eed33f 100644
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+++ b/versions/master/doxygen/engine_8h_source.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/engine.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
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-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
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+ </span>
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+ </li>
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -66,19 +88,141 @@ $(function() {
<div class="title">engine.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="engine_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_ENGINE_H_</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#define MXNET_ENGINE_H_</span></div><div class="line"><a name="l00008"></a [...]
+<a href="engine_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_ENGINE_H_</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_ENGINE_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <dmlc/base.h></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#if DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor"></span><span class="preprocessor">#include <algorithm></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <memory></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <functional></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor"></span><span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment">// forward declare engine</span></div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">class </span>Engine;</div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div>
+<div class="line"><a name="l00024"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1engine.html"> 24</a></span> <span class="keyword">namespace </span>engine {</div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">struct </span>Var;</div>
+<div class="line"><a name="l00028"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf"> 28</a></span> <span class="keyword">struct </span>Opr;</div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">typedef</span> Var* <a class="code" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">VarHandle</a>;</div>
+<div class="line"><a name="l00032"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1engine.html#a2d9b14b658e3f3c4e03ca49cd38ace94"> 32</a></span> <span class="keyword">typedef</span> Opr* <a class="code" href="namespacemxnet_1_1engine.html#a2d9b14b658e3f3c4e03ca49cd38ace94">OprHandle</a>;</div>
+<div class="line"><a name="l00037"></a><span class="lineno"><a class="line" href="classmxnet_1_1engine_1_1CallbackOnComplete.html"> 37</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">CallbackOnComplete</a> {</div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="comment">// use implicit copy and assign</span></div>
+<div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a97abc3d705870669f303ee22a1342d4a"> 41</a></span> <span class="comment"></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a97abc3d705870669f303ee22a1342d4a">operator()</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  (*callback_)(engine_, param_);</div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  }</div>
+<div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00047"></a><span class="lineno"><a class="line" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#aa365d0be0a3a369cdc5da59787f93297"> 47</a></span>  <span class="keyword">friend</span> class ::mxnet::Engine;</div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  void (*callback_)(<a class="code" href="classmxnet_1_1Engine.html">Engine</a> *, <span class="keywordtype">void</span> *);</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="classmxnet_1_1Engine.html">Engine</a>* engine_;</div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordtype">void</span>* param_;</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> };</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> } <span class="comment">// namespace engine</span></div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="preprocessor">#if DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00059"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b"> 59</a></span> <span class="keyword">enum class</span> <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">FnProperty</a> {</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416">kNormal</a>,</div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba739f2f416f05f4728c217f09e93958d1">kCopyFromGPU</a>,</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba6cd75f41e0ec8d61b0a2f0e20ef6d1e8">kCopyToGPU</a>,</div>
+<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3bac41ceb98eeb9b2e208e3e242a7357142">kCPUPrioritized</a>,</div>
+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7">kAsync</a></div>
+<div class="line"><a name="l00070"></a><span class="lineno"> 70</span> }; <span class="comment">// enum class FnProperty</span></div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div>
+<div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html"> 75</a></span> <span class="keyword">class </span><a class="code" href="base_8h.html#a91a09948aaaffa1eb64508db79009a05">MXNET_API</a> <a class="code" href="classmxnet_1_1Engine.html">Engine</a> {</div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00078"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html#a16b757432556f835d27f1b5e1dbe1b06"> 78</a></span>  <span class="keyword">typedef</span> <a class="code" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">engine::CallbackOnComplete</a> <a class="code" href="classmxnet_1_1Engine.html#a16b757432556f835d27f1b5e1dbe1b06">CallbackOnComplete</a>;</div>
+<div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html#a07f30ab85fca436e1bbcc72cd4d8bb35"> 80</a></span>  <span class="keyword">typedef</span> std::function<void(RunContext)> <a class="code" href="classmxnet_1_1Engine.html#a07f30ab85fca436e1bbcc72cd4d8bb35">SyncFn</a>;</div>
+<div class="line"><a name="l00082"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c"> 82</a></span>  <span class="keyword">typedef</span> std::function<void(RunContext, CallbackOnComplete)> <a class="code" href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c">AsyncFn</a>;</div>
+<div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491"> 84</a></span>  <span class="keyword">typedef</span> <a class="code" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">engine::VarHandle</a> <a class="code" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">VarHandle</a>;</div>
+<div class="line"><a name="l00086"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01"> 86</a></span>  <span class="keyword">typedef</span> <a class="code" href="namespacemxnet_1_1engine.html#a2d9b14b658e3f3c4e03ca49cd38ace94">engine::OprHandle</a> <a class="code" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">OprHandle</a>;</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> NotifyShutdown() = 0;</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">VarHandle</a> NewVariable() = 0;</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">OprHandle</a> NewOperator(<a class="code" href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c">AsyncFn</a> fn,</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  std::vector<VarHandle> <span class="keyword">const</span>& const_vars,</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  std::vector<VarHandle> <span class="keyword">const</span>& mutable_vars,</div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">FnProperty</a> prop = <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416">FnProperty::kNormal</a>,</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* opr_name = <span class="keyword">nullptr</span>) = 0;</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> DeleteOperator(<a class="code" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">OprHandle</a> op) = 0;</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> Push(<a class="code" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">OprHandle</a> op, <a class="code" href="structmxnet_1_1Context.html">Context</a> exec_ctx, <span class="keywordtype">int</span> priority = 0, <span class="keywordtype">bool</span> profiling = <span class="keyword">false</span>) = 0;</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> PushAsync(<a class="code" href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c">AsyncFn</a> exec_fun, <a class="code" href="structmxnet_1_1Context.html">Context</a> exec_ctx,</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  std::vector<VarHandle> <span class="keyword">const</span>& const_vars,</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  std::vector<VarHandle> <span class="keyword">const</span>& mutable_vars,</div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">FnProperty</a> prop = <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416">FnProperty::kNormal</a>,</div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordtype">int</span> priority = 0,</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* opr_name = <span class="keyword">nullptr</span>) = 0;</div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> DeleteVariable(<a class="code" href="classmxnet_1_1Engine.html#a07f30ab85fca436e1bbcc72cd4d8bb35">SyncFn</a> delete_fn,</div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="structmxnet_1_1Context.html">Context</a> exec_ctx,</div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">VarHandle</a> var) = 0;</div>
+<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> WaitForVar(<a class="code" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">VarHandle</a> var) = 0;</div>
+<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> WaitForAll() = 0;</div>
+<div class="line"><a name="l00178"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html#aff025321827e15096c02342225f2395b"> 178</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1Engine.html#aff025321827e15096c02342225f2395b">~Engine</a>() noexcept(false) {}</div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1Engine.html">Engine</a>* Get();</div>
+<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">static</span> std::shared_ptr<Engine> _GetSharedRef();</div>
+<div class="line"><a name="l00204"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html#a1c2f38927e4bf7a62e23353b0bd3d619"> 204</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Engine.html#a1c2f38927e4bf7a62e23353b0bd3d619">PushSync</a>(<a class="code" href="classmxnet_1_1Engine.html#a07f30ab85fca436e1bbcc72cd4d8bb35">SyncFn</a> exec_fn, <a class="code" href="structmxnet_1_1Context.html">C [...]
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  std::vector<VarHandle> <span class="keyword">const</span>& const_vars,</div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  std::vector<VarHandle> <span class="keyword">const</span>& mutable_vars,</div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">FnProperty</a> prop = <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416">FnProperty::kNormal</a>,</div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordtype">int</span> priority = 0,</div>
+<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* opr_name = <span class="keyword">nullptr</span>) {</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  this->PushAsync([exec_fn](<a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> ctx, <a class="code" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">CallbackOnComplete</a> on_complete) {</div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  exec_fn(ctx);</div>
+<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  on_complete();</div>
+<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }, exec_ctx, const_vars, mutable_vars, prop, priority, opr_name);</div>
+<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  }</div>
+<div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div>
+<div class="line"><a name="l00221"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html#acf25be815b4200b48ee6e4e169bf95b8"> 221</a></span>  <span class="keyword">inline</span> <a class="code" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">CallbackOnComplete</a> <a class="code" href="classmxnet_1_1Engine.html#acf25be815b4200b48ee6e4e169bf95b8">CreateCallback</a>(</div>
+<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordtype">void</span> (*callback)(<a class="code" href="classmxnet_1_1Engine.html">Engine</a> *, <span class="keywordtype">void</span> *), <span class="keywordtype">void</span> *param) {</div>
+<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <a class="code" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">CallbackOnComplete</a> ret;</div>
+<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  ret.callback_ = callback;</div>
+<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  ret.engine_ = <span class="keyword">this</span>;</div>
+<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  ret.param_ = param;</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">return</span> ret;</div>
+<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  }</div>
+<div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="comment">// For each var vector, sort it and remove the duplicated vars.</span></div>
+<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="comment">// Also remove vars from read_vars if it also appears in write_vars</span></div>
+<div class="line"><a name="l00231"></a><span class="lineno"><a class="line" href="classmxnet_1_1Engine.html#a9d8ac1987a6844dba9b0463030fb3430"> 231</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Engine.html#a9d8ac1987a6844dba9b0463030fb3430">DeduplicateVarHandle</a>(std::vector<engine::VarHandle> *read_vars,</div>
+<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  std::vector<engine::VarHandle> *write_vars) {</div>
+<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  std::sort(write_vars->begin(), write_vars->end());</div>
+<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  write_vars->resize(std::unique(write_vars->begin(), write_vars->end()) -</div>
+<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  write_vars->begin());</div>
+<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  std::sort(read_vars->begin(), read_vars->end());</div>
+<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  read_vars->resize(std::unique(read_vars->begin(), read_vars->end()) -</div>
+<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  read_vars->begin());</div>
+<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">auto</span> wit = write_vars->begin();</div>
+<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keyword">auto</span> rtop = read_vars->begin();</div>
+<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> rit = read_vars->begin(); rit != read_vars->end(); ++rit) {</div>
+<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">while</span> (wit != write_vars->end() && *wit < *rit) ++wit;</div>
+<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordflow">if</span> (wit == write_vars->end() || *wit != *rit) {</div>
+<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  *rtop = *rit;</div>
+<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  ++rtop;</div>
+<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  }</div>
+<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  }</div>
+<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  read_vars->resize(rtop - read_vars->begin());</div>
+<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div>
+<div class="line"><a name="l00250"></a><span class="lineno"> 250</span> }; <span class="comment">// class Engine</span></div>
+<div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="preprocessor">#endif // DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="preprocessor"></span>} <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="preprocessor">#endif // MXNET_ENGINE_H_</span></div>
+<div class="ttc" id="classmxnet_1_1Engine_html_a9d8ac1987a6844dba9b0463030fb3430"><div class="ttname"><a href="classmxnet_1_1Engine.html#a9d8ac1987a6844dba9b0463030fb3430">mxnet::Engine::DeduplicateVarHandle</a></div><div class="ttdeci">void DeduplicateVarHandle(std::vector< engine::VarHandle > *read_vars, std::vector< engine::VarHandle > *write_vars)</div><div class="ttdef"><b>Definition:</b> engine.h:231</div></div>
<div class="ttc" id="namespacemxnet_html_a998b74220fab2b012cf8a179650e1b3b"><div class="ttname"><a href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">mxnet::FnProperty</a></div><div class="ttdeci">FnProperty</div><div class="ttdoc">Function property, used to hint what action is pushed to engine. </div><div class="ttdef"><b>Definition:</b> engine.h:59</div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_a07f30ab85fca436e1bbcc72cd4d8bb35"><div class="ttname"><a href="classmxnet_1_1Engine.html#a07f30ab85fca436e1bbcc72cd4d8bb35">mxnet::Engine::SyncFn</a></div><div class="ttdeci">std::function< void(RunContext)> SyncFn</div><div class="ttdoc">Synchronous operation to pass to engine. </div><div class="ttdef"><b>Definition:</b> engine.h:80</div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_ad41feff70bba0f29fc24f60b5381984c"><div class="ttname"><a href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c">mxnet::Engine::AsyncFn</a></div><div class="ttdeci">std::function< void(RunContext, CallbackOnComplete)> AsyncFn</div><div class="ttdoc">Asynchronous operation to pass to engine. </div><div class="ttdef"><b>Definition:</b> engine.h:82</div></div>
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
<div class="ttc" id="namespacemxnet_html_a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7"><div class="ttname"><a href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7">mxnet::FnProperty::kAsync</a></div><div class="ttdoc">Asynchronous function call. </div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_acf25be815b4200b48ee6e4e169bf95b8"><div class="ttname"><a href="classmxnet_1_1Engine.html#acf25be815b4200b48ee6e4e169bf95b8">mxnet::Engine::CreateCallback</a></div><div class="ttdeci">CallbackOnComplete CreateCallback(void(*callback)(Engine *, void *), void *param)</div><div class="ttdoc">factory function to create OnComplete callback. </div><div class="ttdef"><b>Definition:</b> engine.h:221</div></div>
+<div class="ttc" id="classmxnet_1_1engine_1_1CallbackOnComplete_html_a97abc3d705870669f303ee22a1342d4a"><div class="ttname"><a href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a97abc3d705870669f303ee22a1342d4a">mxnet::engine::CallbackOnComplete::operator()</a></div><div class="ttdeci">void operator()() const </div><div class="ttdoc">involve the callback </div><div class="ttdef"><b>Definition:</b> engine.h:41</div></div>
<div class="ttc" id="structmxnet_1_1RunContext_html"><div class="ttname"><a href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></div><div class="ttdoc">execution time context. The information needed in runtime for actual execution. </div><div class="ttdef"><b>Definition:</b> base.h:213</div></div>
<div class="ttc" id="namespacemxnet_html_a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416"><div class="ttname"><a href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416">mxnet::FnProperty::kNormal</a></div><div class="ttdoc">Normal operation. </div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_aff025321827e15096c02342225f2395b"><div class="ttname"><a href="classmxnet_1_1Engine.html#aff025321827e15096c02342225f2395b">mxnet::Engine::~Engine</a></div><div class="ttdeci">virtual ~Engine() noexcept(false)</div><div class="ttdoc">virtual destructor </div><div class="ttdef"><b>Definition:</b> engine.h:178</div></div>
<div class="ttc" id="base_8h_html_a91a09948aaaffa1eb64508db79009a05"><div class="ttname"><a href="base_8h.html#a91a09948aaaffa1eb64508db79009a05">MXNET_API</a></div><div class="ttdeci">#define MXNET_API</div><div class="ttdoc">define compatible keywords in g++ Used to support g++-4.6 and g++4.7 </div><div class="ttdef"><b>Definition:</b> base.h:66</div></div>
<div class="ttc" id="namespacemxnet_html_a998b74220fab2b012cf8a179650e1b3ba739f2f416f05f4728c217f09e93958d1"><div class="ttname"><a href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba739f2f416f05f4728c217f09e93958d1">mxnet::FnProperty::kCopyFromGPU</a></div><div class="ttdoc">Copy operation from GPU to other devices. </div></div>
-<div class="ttc" id="classmxnet_1_1engine_1_1CallbackOnComplete_html_a71e8c5a29d9e103f6471f35f30e05727"><div class="ttname"><a href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a71e8c5a29d9e103f6471f35f30e05727">mxnet::engine::CallbackOnComplete::operator()</a></div><div class="ttdeci">void operator()() const</div><div class="ttdoc">involve the callback </div><div class="ttdef"><b>Definition:</b> engine.h:41</div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_a832436e413a075291aa1a631942c3f01"><div class="ttname"><a href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">mxnet::Engine::OprHandle</a></div><div class="ttdeci">engine::OprHandle OprHandle</div><div class="ttdoc">Operator pointer. </div><div class="ttdef"><b>Definition:</b> engine.h:86</div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_aac31510c793a12944c33f9cac6150491"><div class="ttname"><a href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">mxnet::Engine::VarHandle</a></div><div class="ttdeci">engine::VarHandle VarHandle</div><div class="ttdoc">Variable pointer. </div><div class="ttdef"><b>Definition:</b> engine.h:84</div></div>
<div class="ttc" id="namespacemxnet_1_1engine_html_a9d36c4f33eae8531586dc2edf83ae7cf"><div class="ttname"><a href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">mxnet::engine::VarHandle</a></div><div class="ttdeci">Var * VarHandle</div><div class="ttdoc">Variable pointer type, usually hold by user used to specify dependencies. </div><div class="ttdef"><b>Definition:</b> engine.h:28</div></div>
@@ -94,9 +238,9 @@ $(function() {
</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/executor_8h.html b/versions/master/doxygen/executor_8h.html
index 3f672bd..147dfd8 100644
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<title>mxnet: include/mxnet/executor.h File Reference</title>
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<script type="text/javascript" src="jquery.js"></script>
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+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
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- $(document).ready(function() { init_search(); });
-});
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
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@@ -72,30 +94,22 @@ $(function() {
<p>Symbolic executor interface of mxnet.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/base.h></code><br />
-<code>#include <vector></code><br />
-<code>#include <memory></code><br />
-<code>#include <map></code><br />
-<code>#include <string></code><br />
-<code>#include <utility></code><br />
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-<code>#include "<a class="el" href="ndarray_8h_source.html">./ndarray.h</a>"</code><br />
-<code>#include "<a class="el" href="operator_8h_source.html">./operator.h</a>"</code><br />
+<div class="textblock"><code>#include <dmlc/base.h></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <memory></code><br/>
+<code>#include <map></code><br/>
+<code>#include <string></code><br/>
+<code>#include <utility></code><br/>
+<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br/>
+<code>#include "<a class="el" href="c__api_8h_source.html">./c_api.h</a>"</code><br/>
+<code>#include "<a class="el" href="ndarray_8h_source.html">./ndarray.h</a>"</code><br/>
+<code>#include "<a class="el" href="operator_8h_source.html">./operator.h</a>"</code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for executor.h:</div>
<div class="dyncontent">
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<p><a href="executor_8h_source.html">Go to the source code of this file.</a></p>
@@ -103,13 +117,13 @@ Include dependency graph for executor.h:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html">mxnet::Executor</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> of a computation graph. <a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> can be created by Binding a symbol. <a href="classmxnet_1_1Executor.html#details">More...</a [...]
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> of a computation graph. <a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> can be created by Binding a symbol. <a href="classmxnet_1_1Executor.html#details">More...</a [...]
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
+<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
+<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
@@ -119,9 +133,9 @@ Namespaces</h2></td></tr>
</div></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<head>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/executor.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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@@ -66,13 +88,78 @@ $(function() {
<div class="title">executor.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="executor_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_EXECUTOR_H_</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#define MXNET_EXECUTOR_H_</span></div><div class="line"><a name="l0000 [...]
+<a href="executor_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_EXECUTOR_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_EXECUTOR_H_</span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <dmlc/base.h></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <memory></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <map></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <string></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <utility></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include "<a class="code" href="c__api_8h.html">./c_api.h</a>"</span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include "<a class="code" href="ndarray_8h.html">./ndarray.h</a>"</span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "<a class="code" href="operator_8h.html">./operator.h</a>"</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment">// check c++11</span></div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#if DMLC_USE_CXX11 == 0</span></div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor"></span><span class="preprocessor">#error "CXX11 was required for symbolic module"</span></div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="keyword">using</span> nnvm::Symbol;</div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div>
+<div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="classmxnet_1_1Executor.html"> 34</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1Executor.html">Executor</a> {</div>
+<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00037"></a><span class="lineno"><a class="line" href="classmxnet_1_1Executor.html#adf445ff665c954061889d6bbee3c1c2a"> 37</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1Executor.html#adf445ff665c954061889d6bbee3c1c2a">~Executor</a>() {}</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Executor.html#ab3716be8e6c265b091e618f46eeb6f9b">Forward</a>(<span class="keywordtype">bool</span> is_train) = 0;</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Executor.html#a3e261d44065848e434c527000d4390c8">PartialForward</a>(<span class="keywordtype">bool</span> is_train, <span class="keywordtype">int</span> step, <span class="keywordtype">int</span> *step_left) = 0;</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Executor.html#aa2fd32510a718aff38323e63be0d65e3">Backward</a>(<span class="keyword">const</span> std::vector<NDArray> &head_grads, <span class="keywordtype">bool</span> is_train = <span class="keyword">true</span>) = 0;</div>
+<div class="line"><a name="l00066"></a><span class="lineno"><a class="line" href="classmxnet_1_1Executor.html#a3803afcb87e4c69940c8d3d88a03e6ee"> 66</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Executor.html#a3803afcb87e4c69940c8d3d88a03e6ee">Print</a>(std::ostream &os)<span class="keyword"> const </span>{} <span class="comment">// NOLINT(*)</span></div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="comment"></span> <span class="keyword">virtual</span> <span class="keyword">const</span> std::vector<NDArray> &<a class="code" href="classmxnet_1_1Executor.html#a16d0f1ab276e32242685702aa10b9a3d">outputs</a>() <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">virtual</span> <span class="keyword">const</span> std::unordered_map<std::string, NDArray>& <a class="code" href="classmxnet_1_1Executor.html#a07a3e917596ed08718ee2859283c2476">in_arg_map</a>() <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keyword">virtual</span> <span class="keyword">const</span> std::unordered_map<std::string, NDArray>& <a class="code" href="classmxnet_1_1Executor.html#a84552a918a65633678f30c00a2a0ae7f">arg_grad_map</a>() <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">virtual</span> <span class="keyword">const</span> std::unordered_map<std::string, NDArray>& <a class="code" href="classmxnet_1_1Executor.html#a0a38a98fc4dcddc4205b5980ee7bdc4b">aux_state_map</a>() <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1Executor.html">Executor</a> *<a class="code" href="classmxnet_1_1Executor.html#a8217aa9fbdd99d107bd836872d7d9692">Bind</a>(nnvm::Symbol symbol,</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1Context.html">Context</a>& default_ctx,</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">const</span> std::map<std::string, Context>& group2ctx,</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keyword">const</span> std::vector<NDArray> &in_args,</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keyword">const</span> std::vector<NDArray> &arg_grad_store,</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keyword">const</span> std::vector<OpReqType> &grad_req_type,</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keyword">const</span> std::vector<NDArray> &aux_states,</div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <a class="code" href="classmxnet_1_1Executor.html">Executor</a>* shared_exec = NULL);</div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1Executor.html">Executor</a>* <a class="code" href="classmxnet_1_1Executor.html#ae1a319bca5e26ef58057f8410f46e484">SimpleBind</a>(nnvm::Symbol symbol,</div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1Context.html">Context</a>& default_ctx,</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> std::map<std::string, Context>& group2ctx,</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keyword">const</span> std::vector<Context>& in_arg_ctxes,</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keyword">const</span> std::vector<Context>& arg_grad_ctxes,</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keyword">const</span> std::vector<Context>& aux_state_ctxes,</div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">const</span> std::unordered_map<std::string, TShape>& arg_shape_map,</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">const</span> std::unordered_map<std::string, int>& arg_dtype_map,</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">const</span> std::vector<OpReqType>& grad_req_types,</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">const</span> std::unordered_set<std::string>& param_names,</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  std::vector<NDArray>* in_args,</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  std::vector<NDArray>* arg_grads,</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  std::vector<NDArray>* aux_states,</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  std::unordered_map<std::string, NDArray>*</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  shared_data_arrays = <span class="keyword">nullptr</span>,</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="classmxnet_1_1Executor.html">Executor</a>* shared_exec = <span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00129"></a><span class="lineno"><a class="line" href="classmxnet_1_1Executor.html#a5684c74daafc9b80681cde6a9b7b4587"> 129</a></span>  <span class="keyword">typedef</span> std::function<void(const char*, void*)> <a class="code" href="classmxnet_1_1Executor.html#a5684c74daafc9b80681cde6a9b7b4587">MonitorCallback</a>;</div>
+<div class="line"><a name="l00133"></a><span class="lineno"><a class="line" href="classmxnet_1_1Executor.html#a3c701dda22a9c1e474d23b11edda9b0d"> 133</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Executor.html#a3c701dda22a9c1e474d23b11edda9b0d">SetMonitorCallback</a>(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1Executor.html#a5684c74daafc9b80681cde6a9b7b4587">MonitorCallback</a>& [...]
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span> }; <span class="comment">// class executor</span></div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="preprocessor">#endif // MXNET_EXECUTOR_H_</span></div>
+<div class="ttc" id="classmxnet_1_1Executor_html"><div class="ttname"><a href="classmxnet_1_1Executor.html">mxnet::Executor</a></div><div class="ttdoc">Executor of a computation graph. Executor can be created by Binding a symbol. </div><div class="ttdef"><b>Definition:</b> executor.h:34</div></div>
<div class="ttc" id="c__api_8h_html"><div class="ttname"><a href="c__api_8h.html">c_api.h</a></div><div class="ttdoc">C API of mxnet. </div></div>
<div class="ttc" id="classmxnet_1_1Executor_html_adf445ff665c954061889d6bbee3c1c2a"><div class="ttname"><a href="classmxnet_1_1Executor.html#adf445ff665c954061889d6bbee3c1c2a">mxnet::Executor::~Executor</a></div><div class="ttdeci">virtual ~Executor()</div><div class="ttdoc">destructor </div><div class="ttdef"><b>Definition:</b> executor.h:37</div></div>
<div class="ttc" id="classmxnet_1_1Executor_html_a5684c74daafc9b80681cde6a9b7b4587"><div class="ttname"><a href="classmxnet_1_1Executor.html#a5684c74daafc9b80681cde6a9b7b4587">mxnet::Executor::MonitorCallback</a></div><div class="ttdeci">std::function< void(const char *, void *)> MonitorCallback</div><div class="ttdoc">the prototype of user-defined monitor callback </div><div class="ttdef"><b>Definition:</b> executor.h:129</div></div>
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
+<div class="ttc" id="classmxnet_1_1Executor_html_aa2fd32510a718aff38323e63be0d65e3"><div class="ttname"><a href="classmxnet_1_1Executor.html#aa2fd32510a718aff38323e63be0d65e3">mxnet::Executor::Backward</a></div><div class="ttdeci">virtual void Backward(const std::vector< NDArray > &head_grads, bool is_train=true)=0</div><div class="ttdoc">Perform a Backward operation of the Operator. This must be called after Forward. After this operation...</div></div>
<div class="ttc" id="classmxnet_1_1Executor_html_a8217aa9fbdd99d107bd836872d7d9692"><div class="ttname"><a href="classmxnet_1_1Executor.html#a8217aa9fbdd99d107bd836872d7d9692">mxnet::Executor::Bind</a></div><div class="ttdeci">static Executor * Bind(nnvm::Symbol symbol, const Context &default_ctx, const std::map< std::string, Context > &group2ctx, const std::vector< NDArray > &in_args, const std::vector< NDArray > &arg_grad_store, const std::vector< O [...]
-<div class="ttc" id="classmxnet_1_1Executor_html_a07a3e917596ed08718ee2859283c2476"><div class="ttname"><a href="classmxnet_1_1Executor.html#a07a3e917596ed08718ee2859283c2476">mxnet::Executor::in_arg_map</a></div><div class="ttdeci">virtual const std::unordered_map< std::string, NDArray > & in_arg_map() const =0</div><div class="ttdoc">get input argument map, key is arg name, value is arg&#39;s NDArray. </div></div>
+<div class="ttc" id="classmxnet_1_1Executor_html_a3803afcb87e4c69940c8d3d88a03e6ee"><div class="ttname"><a href="classmxnet_1_1Executor.html#a3803afcb87e4c69940c8d3d88a03e6ee">mxnet::Executor::Print</a></div><div class="ttdeci">virtual void Print(std::ostream &os) const </div><div class="ttdoc">print the execution plan info to output stream. </div><div class="ttdef"><b>Definition:</b> executor.h:66</div></div>
+<div class="ttc" id="classmxnet_1_1Executor_html_a07a3e917596ed08718ee2859283c2476"><div class="ttname"><a href="classmxnet_1_1Executor.html#a07a3e917596ed08718ee2859283c2476">mxnet::Executor::in_arg_map</a></div><div class="ttdeci">virtual const std::unordered_map< std::string, NDArray > & in_arg_map() const =0</div><div class="ttdoc">get input argument map, key is arg name, value is arg's NDArray. </div></div>
<div class="ttc" id="classmxnet_1_1Executor_html_a16d0f1ab276e32242685702aa10b9a3d"><div class="ttname"><a href="classmxnet_1_1Executor.html#a16d0f1ab276e32242685702aa10b9a3d">mxnet::Executor::outputs</a></div><div class="ttdeci">virtual const std::vector< NDArray > & outputs() const =0</div><div class="ttdoc">get array of outputs in the executor. </div></div>
<div class="ttc" id="classmxnet_1_1Executor_html_a3c701dda22a9c1e474d23b11edda9b0d"><div class="ttname"><a href="classmxnet_1_1Executor.html#a3c701dda22a9c1e474d23b11edda9b0d">mxnet::Executor::SetMonitorCallback</a></div><div class="ttdeci">virtual void SetMonitorCallback(const MonitorCallback &callback)</div><div class="ttdoc">Install a callback to notify the completion of operation. </div><div class="ttdef"><b>Definition:</b> executor.h:133</div></div>
<div class="ttc" id="ndarray_8h_html"><div class="ttname"><a href="ndarray_8h.html">ndarray.h</a></div><div class="ttdoc">NDArray interface that handles array arithematics. </div></div>
@@ -80,18 +167,16 @@ $(function() {
<div class="ttc" id="operator_8h_html"><div class="ttname"><a href="operator_8h.html">operator.h</a></div><div class="ttdoc">Operator interface of mxnet. </div></div>
<div class="ttc" id="classmxnet_1_1Executor_html_a3e261d44065848e434c527000d4390c8"><div class="ttname"><a href="classmxnet_1_1Executor.html#a3e261d44065848e434c527000d4390c8">mxnet::Executor::PartialForward</a></div><div class="ttdeci">virtual void PartialForward(bool is_train, int step, int *step_left)=0</div><div class="ttdoc">Perform a Partial Forward operation of Operator. Only issue operation specified by step...</div></div>
<div class="ttc" id="classmxnet_1_1Executor_html_ae1a319bca5e26ef58057f8410f46e484"><div class="ttname"><a href="classmxnet_1_1Executor.html#ae1a319bca5e26ef58057f8410f46e484">mxnet::Executor::SimpleBind</a></div><div class="ttdeci">static Executor * SimpleBind(nnvm::Symbol symbol, const Context &default_ctx, const std::map< std::string, Context > &group2ctx, const std::vector< Context > &in_arg_ctxes, const std::vector< Context > &arg_grad_ctxes, const [...]
-<div class="ttc" id="classmxnet_1_1Executor_html_a15157351d5770c4e5e7e652b2a0e2cbd"><div class="ttname"><a href="classmxnet_1_1Executor.html#a15157351d5770c4e5e7e652b2a0e2cbd">mxnet::Executor::Backward</a></div><div class="ttdeci">virtual void Backward(const std::vector< NDArray > &head_grads)=0</div><div class="ttdoc">Perform a Backward operation of the Operator. This must be called after Forward. After this operation...</div></div>
<div class="ttc" id="base_8h_html"><div class="ttname"><a href="base_8h.html">base.h</a></div><div class="ttdoc">configuation of mxnet as well as basic data structure. </div></div>
-<div class="ttc" id="classmxnet_1_1Executor_html_a84552a918a65633678f30c00a2a0ae7f"><div class="ttname"><a href="classmxnet_1_1Executor.html#a84552a918a65633678f30c00a2a0ae7f">mxnet::Executor::arg_grad_map</a></div><div class="ttdeci">virtual const std::unordered_map< std::string, NDArray > & arg_grad_map() const =0</div><div class="ttdoc">get input argument graident map, key is arg name, value is gradient&#39;s NDArray. </div></div>
-<div class="ttc" id="classmxnet_1_1Executor_html_a0a38a98fc4dcddc4205b5980ee7bdc4b"><div class="ttname"><a href="classmxnet_1_1Executor.html#a0a38a98fc4dcddc4205b5980ee7bdc4b">mxnet::Executor::aux_state_map</a></div><div class="ttdeci">virtual const std::unordered_map< std::string, NDArray > & aux_state_map() const =0</div><div class="ttdoc">get aux state map, key is arg name, value is aux state&#39;s NDArray. </div></div>
+<div class="ttc" id="classmxnet_1_1Executor_html_a84552a918a65633678f30c00a2a0ae7f"><div class="ttname"><a href="classmxnet_1_1Executor.html#a84552a918a65633678f30c00a2a0ae7f">mxnet::Executor::arg_grad_map</a></div><div class="ttdeci">virtual const std::unordered_map< std::string, NDArray > & arg_grad_map() const =0</div><div class="ttdoc">get input argument graident map, key is arg name, value is gradient's NDArray. </div></div>
+<div class="ttc" id="classmxnet_1_1Executor_html_a0a38a98fc4dcddc4205b5980ee7bdc4b"><div class="ttname"><a href="classmxnet_1_1Executor.html#a0a38a98fc4dcddc4205b5980ee7bdc4b">mxnet::Executor::aux_state_map</a></div><div class="ttdeci">virtual const std::unordered_map< std::string, NDArray > & aux_state_map() const =0</div><div class="ttdoc">get aux state map, key is arg name, value is aux state's NDArray. </div></div>
<div class="ttc" id="structmxnet_1_1Context_html"><div class="ttname"><a href="structmxnet_1_1Context.html">mxnet::Context</a></div><div class="ttdoc">Context information about the execution environment. </div><div class="ttdef"><b>Definition:</b> base.h:116</div></div>
-<div class="ttc" id="classmxnet_1_1Executor_html_ac391ee6cf8cf8afb60acd0480db2ae86"><div class="ttname"><a href="classmxnet_1_1Executor.html#ac391ee6cf8cf8afb60acd0480db2ae86">mxnet::Executor::Print</a></div><div class="ttdeci">virtual void Print(std::ostream &os) const</div><div class="ttdoc">print the execution plan info to output stream. </div><div class="ttdef"><b>Definition:</b> executor.h:66</div></div>
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+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/files.html b/versions/master/doxygen/files.html
index 44f55e5..2bf0ca0 100644
--- a/versions/master/doxygen/files.html
+++ b/versions/master/doxygen/files.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: File List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,47 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
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onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -64,38 +86,38 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all files with brief descriptions:</div><div class="directory">
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-<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">▼</span><span id="img_0_" class="iconfopen" onclick="toggleFolder('0_')"> </span><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html" target="_self">include</a></td><td class="desc"></td></tr>
-<tr id="row_0_0_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_0_0_" class="arrow" onclick="toggleFolder('0_0_')">▼</span><span id="img_0_0_" class="iconfopen" onclick="toggleFolder('0_0_')"> </span><a class="el" href="dir_1143c7affb9ebd026cb6818dd282def7.html" target="_self">mxnet</a></td><td class="desc"></td></tr>
-<tr id="row_0_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="base_8h_source.html"><span class="icondoc"></span></a><a class="el" href="base_8h.html" target="_self">base.h</a></td><td class="desc">Configuation of mxnet as well as basic data structure </td></tr>
-<tr id="row_0_0_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="c__api_8h_source.html"><span class="icondoc"></span></a><a class="el" href="c__api_8h.html" target="_self">c_api.h</a></td><td class="desc">C API of mxnet </td></tr>
-<tr id="row_0_0_2_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="c__lapack__api_8h_source.html"><span class="icondoc"></span></a><a class="el" href="c__lapack__api_8h.html" target="_self">c_lapack_api.h</a></td><td class="desc">Unified interface for LAPACK calls from within mxnet. Purpose is to hide the platform specific differences </td></tr>
-<tr id="row_0_0_3_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="c__predict__api_8h_source.html"><span class="icondoc"></span></a><a class="el" href="c__predict__api_8h.html" target="_self">c_predict_api.h</a></td><td class="desc">C predict API of mxnet, contains a minimum API to run prediction. This file is self-contained, and do not dependent on any other files </td></tr>
-<tr id="row_0_0_4_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="engine_8h_source.html"><span class="icondoc"></span></a><a class="el" href="engine_8h.html" target="_self">engine.h</a></td><td class="desc">Engine that schedules all the operations according to dependency </td></tr>
-<tr id="row_0_0_5_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="executor_8h_source.html"><span class="icondoc"></span></a><a class="el" href="executor_8h.html" target="_self">executor.h</a></td><td class="desc">Symbolic executor interface of mxnet </td></tr>
-<tr id="row_0_0_6_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="io_8h_source.html"><span class="icondoc"></span></a><a class="el" href="io_8h.html" target="_self">io.h</a></td><td class="desc">Mxnet io data structure and data iterator </td></tr>
-<tr id="row_0_0_7_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="kvstore_8h_source.html"><span class="icondoc"></span></a><a class="el" href="kvstore_8h.html" target="_self">kvstore.h</a></td><td class="desc">Key-value store interface for mxnet </td></tr>
-<tr id="row_0_0_8_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="mxrtc_8h_source.html"><span class="icondoc"></span></a><a class="el" href="mxrtc_8h.html" target="_self">mxrtc.h</a></td><td class="desc">Wrapper for NVRTC </td></tr>
-<tr id="row_0_0_9_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="ndarray_8h_source.html"><span class="icondoc"></span></a><a class="el" href="ndarray_8h.html" target="_self">ndarray.h</a></td><td class="desc">NDArray interface that handles array arithematics </td></tr>
-<tr id="row_0_0_10_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="op__attr__types_8h_source.html"><span class="icondoc"></span></a><a class="el" href="op__attr__types_8h.html" target="_self">op_attr_types.h</a></td><td class="desc">Additional operator attributes beside the ones provided by NNVM </td></tr>
-<tr id="row_0_0_11_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="operator_8h_source.html"><span class="icondoc"></span></a><a class="el" href="operator_8h.html" target="_self">operator.h</a></td><td class="desc">Operator interface of mxnet </td></tr>
-<tr id="row_0_0_12_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="operator__util_8h_source.html"><span class="icondoc"></span></a><a class="el" href="operator__util_8h.html" target="_self">operator_util.h</a></td><td class="desc">Utility functions and registries to help quickly build new operators. [Deprecated] Use the register functions in this file when possible to simplify operator creations. Operators registered in this file will [...]
-<tr id="row_0_0_13_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="resource_8h_source.html"><span class="icondoc"></span></a><a class="el" href="resource_8h.html" target="_self">resource.h</a></td><td class="desc">Global resource allocation handling </td></tr>
-<tr id="row_0_0_14_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="storage_8h_source.html"><span class="icondoc"></span></a><a class="el" href="storage_8h.html" target="_self">storage.h</a></td><td class="desc">Storage manager across multiple devices </td></tr>
-<tr id="row_0_0_15_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="tensor__blob_8h_source.html"><span class="icondoc"></span></a><a class="el" href="tensor__blob_8h.html" target="_self">tensor_blob.h</a></td><td class="desc">TBlob class that holds common representation of arbirary dimension tensor, can be used to transformed to normal fixed dimenson tensor </td></tr>
-<tr id="row_1_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_1_" class="arrow" onclick="toggleFolder('1_')">▼</span><span id="img_1_" class="iconfopen" onclick="toggleFolder('1_')"> </span><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html" target="_self">src</a></td><td class="desc"></td></tr>
-<tr id="row_1_0_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_0_" class="arrow" onclick="toggleFolder('1_0_')">▼</span><span id="img_1_0_" class="iconfopen" onclick="toggleFolder('1_0_')"> </span><a class="el" href="dir_fdedb0aba14d44ce9d99bc100e026e6a.html" target="_self">common</a></td><td class="desc"></td></tr>
-<tr id="row_1_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="cuda__utils_8h_source.html"><span class="icondoc"></span></a><a class="el" href="cuda__utils_8h.html" target="_self">cuda_utils.h</a></td><td class="desc">CUDA debugging utilities </td></tr>
-<tr id="row_1_0_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="lazy__alloc__array_8h_source.html"><span class="icondoc"></span></a><a class="el" href="lazy__alloc__array_8h.html" target="_self">lazy_alloc_array.h</a></td><td class="desc">An array that lazily allocate elements as First time the cell get visited </td></tr>
-<tr id="row_1_0_2_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="object__pool_8h_source.html"><span class="icondoc"></span></a><a class="el" href="object__pool_8h.html" target="_self">object_pool.h</a></td><td class="desc"></td></tr>
-<tr id="row_1_0_3_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a href="utils_8h_source.html"><span class="icondoc"></span></a><a class="el" href="utils_8h.html" target="_self">utils.h</a></td><td class="desc">Basic utilility functions </td></tr>
+<tr id="row_0_" class="even"><td class="entry"><img id="arr_0_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('0_')"/><img id="img_0_" src="ftv2folderopen.png" alt="-" width="24" height="22" onclick="toggleFolder('0_')"/><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html" target="_self">include</a></td><td class="desc"></td></tr>
+<tr id="row_0_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img id="arr_0_0_" src="ftv2mlastnode.png" alt="\" width="16" height="22" onclick="toggleFolder('0_0_')"/><img id="img_0_0_" src="ftv2folderopen.png" alt="-" width="24" height="22" onclick="toggleFolder('0_0_')"/><a class="el" href="dir_1143c7affb9ebd026cb6818dd282def7.html" target="_self">mxnet</a></td><td class="desc"></td></tr>
+<tr id="row_0_0_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="base_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="base_8h.html" target="_self">base.h</a></td><td class="desc">Configuation of mxnet as well as basic data structure </td></tr>
+<tr id="row_0_0_1_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="c__api_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="c__api_8h.html" target="_self">c_api.h</a></td><td class="desc">C API of mxnet </td></tr>
+<tr id="row_0_0_2_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="c__lapack__api_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="c__lapack__api_8h.html" target="_self">c_lapack_api.h</a></td><td class="desc">Unified interface for LAPACK calls from within mxnet. Purpose [...]
+<tr id="row_0_0_3_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="c__predict__api_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="c__predict__api_8h.html" target="_self">c_predict_api.h</a></td><td class="desc">C predict API of mxnet, contains a minimum API to run prediction. This [...]
+<tr id="row_0_0_4_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="engine_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="engine_8h.html" target="_self">engine.h</a></td><td class="desc">Engine that schedules all the operations according to dependency </td></tr>
+<tr id="row_0_0_5_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="executor_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="executor_8h.html" target="_self">executor.h</a></td><td class="desc">Symbolic executor interface of mxnet </td></tr>
+<tr id="row_0_0_6_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="io_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="io_8h.html" target="_self">io.h</a></td><td class="desc">Mxnet io data structure and data iterator </td></tr>
+<tr id="row_0_0_7_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="kvstore_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="kvstore_8h.html" target="_self">kvstore.h</a></td><td class="desc">Key-value store interface for mxnet </td></tr>
+<tr id="row_0_0_8_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="mxrtc_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="mxrtc_8h.html" target="_self">mxrtc.h</a></td><td class="desc">Wrapper for NVRTC </td></tr>
+<tr id="row_0_0_9_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="ndarray_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="ndarray_8h.html" target="_self">ndarray.h</a></td><td class="desc">NDArray interface that handles array arithematics </td></tr>
+<tr id="row_0_0_10_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="op__attr__types_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="op__attr__types_8h.html" target="_self">op_attr_types.h</a></td><td class="desc">Additional operator attributes beside the ones provided b [...]
+<tr id="row_0_0_11_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="operator_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="operator_8h.html" target="_self">operator.h</a></td><td class="desc">Operator interface of mxnet </td></tr>
+<tr id="row_0_0_12_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="operator__util_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="operator__util_8h.html" target="_self">operator_util.h</a></td><td class="desc">Utility functions and registries to help quickly build new [...]
+<tr id="row_0_0_13_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="resource_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="resource_8h.html" target="_self">resource.h</a></td><td class="desc">Global resource allocation handling </td></tr>
+<tr id="row_0_0_14_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="storage_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="storage_8h.html" target="_self">storage.h</a></td><td class="desc">Storage manager across multiple devices </td></tr>
+<tr id="row_0_0_15_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><a href="tensor__blob_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="tensor__blob_8h.html" target="_self">tensor_blob.h</a></td><td class="desc">TBlob class that holds common representation of arbirary dimension tensor, [...]
+<tr id="row_1_" class="even"><td class="entry"><img id="arr_1_" src="ftv2mlastnode.png" alt="\" width="16" height="22" onclick="toggleFolder('1_')"/><img id="img_1_" src="ftv2folderopen.png" alt="-" width="24" height="22" onclick="toggleFolder('1_')"/><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html" target="_self">src</a></td><td class="desc"></td></tr>
+<tr id="row_1_0_"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img id="arr_1_0_" src="ftv2mlastnode.png" alt="\" width="16" height="22" onclick="toggleFolder('1_0_')"/><img id="img_1_0_" src="ftv2folderopen.png" alt="-" width="24" height="22" onclick="toggleFolder('1_0_')"/><a class="el" href="dir_fdedb0aba14d44ce9d99bc100e026e6a.html" target="_self">common</a></td><td class="desc"></td></tr>
+<tr id="row_1_0_0_" class="even"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="cuda__utils_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="cuda__utils_8h.html" target="_self">cuda_utils.h</a></td><td class="desc">CUDA debugging utilities </td></tr>
+<tr id="row_1_0_1_"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="lazy__alloc__array_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="lazy__alloc__array_8h.html" target="_self">lazy_alloc_array.h</a></td><td class="desc">An array that lazily allocate elements as First time the cel [...]
+<tr id="row_1_0_2_" class="even"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a href="object__pool_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="object__pool_8h.html" target="_self">object_pool.h</a></td><td class="desc"></td></tr>
+<tr id="row_1_0_3_"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><a href="utils_8h_source.html"><img src="ftv2doc.png" alt="*" width="24" height="22" /></a><a class="el" href="utils_8h.html" target="_self">utils.h</a></td><td class="desc">Basic utilility functions </td></tr>
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
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+ <div id="navrow4" class="tabs3">
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+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
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+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
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onmouseover="return searchBox.OnSearchSelectShow()"
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-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
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<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
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</div>
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+ <ul class="tablist">
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+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <div id="navrow3" class="tabs2">
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+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
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+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
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+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li class="current"><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
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+ </div>
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onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a376e3412e9f425db998c384e5d3f0523">mxnet::op::SimpleOpRegistry</a>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
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-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
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+ <li class="current"><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,7 +122,7 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_a"></a>- a -</h3><ul>
+<h3><a class="anchor" id="index_a"></a>- a -</h3><ul>
<li>add_arguments()
: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a405bfb5929d459d45d93c2eaa645bfb5">mxnet::op::SimpleOpRegEntry</a>
</li>
@@ -74,7 +136,7 @@ $(function() {
: <a class="el" href="classmxnet_1_1Executor.html#a84552a918a65633678f30c00a2a0ae7f">mxnet::Executor</a>
</li>
<li>AsArray()
-: <a class="el" href="classmxnet_1_1NDArray.html#ac3c424c05eabe0b7a4976191d81cf6b0">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a3896fcce89afce4c1d2f825f7853ca0c">mxnet::NDArray</a>
</li>
<li>async_on_complete
: <a class="el" href="structmxnet_1_1OpContext.html#a40ce0e3d4ac21412cfafafc5b1fd32ef">mxnet::OpContext</a>
@@ -83,7 +145,7 @@ $(function() {
: <a class="el" href="classmxnet_1_1Engine.html#ad41feff70bba0f29fc24f60b5381984c">mxnet::Engine</a>
</li>
<li>At()
-: <a class="el" href="classmxnet_1_1NDArray.html#addb42a7353a4c07b2d1bae0afa8ef48e">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a3b615512a8fb89b9edc99b35064ead95">mxnet::NDArray</a>
</li>
<li>autograd::AutogradRuntime
: <a class="el" href="classmxnet_1_1NDArray.html#a423cc97aa03ee0dd8bc13781b77b8e50">mxnet::NDArray</a>
@@ -95,9 +157,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_b.html b/versions/master/doxygen/functions_b.html
index dd24f65..18e164c 100644
--- a/versions/master/doxygen/functions_b.html
+++ b/versions/master/doxygen/functions_b.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li class="current"><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,30 +122,27 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_b"></a>- b -</h3><ul>
+<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
<li>Backward()
-: <a class="el" href="classmxnet_1_1Executor.html#a15157351d5770c4e5e7e652b2a0e2cbd">mxnet::Executor</a>
+: <a class="el" href="classmxnet_1_1Executor.html#aa2fd32510a718aff38323e63be0d65e3">mxnet::Executor</a>
, <a class="el" href="classmxnet_1_1Operator.html#ad32d74b9bf56caf920b5e6379d2bb259">mxnet::Operator</a>
</li>
<li>backward
-: <a class="el" href="structNativeOpInfo.html#a2faec6cfc4510b455de950a4b104ea9f">NativeOpInfo</a>
-, <a class="el" href="structNDArrayOpInfo.html#ac8a4bb0b4a71f372e5abb8ded36dd61c">NDArrayOpInfo</a>
+: <a class="el" href="structNativeOpInfo.html#a2d571a373ff0ca0da5d96295cc03f465">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#a6cafb44b639ed5256fe80a976e351cc1">NDArrayOpInfo</a>
</li>
<li>BackwardInplaceOption()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a0255256ac4e0e76b3fff917560203efe">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a67231a5a1525ec7480de98a79dce0194">mxnet::OperatorProperty</a>
</li>
<li>BackwardInputs()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a6c5c8df10748a7eee873d4b7eb8d684b">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a9f488509e132094ce7e79125ce853042">mxnet::OperatorProperty</a>
</li>
<li>BackwardResource()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#acec41540dde2c49ff2f2a8fc425890e8">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#ade7720a3e0ebba67d89978925a77609b">mxnet::OperatorProperty</a>
</li>
<li>Barrier()
: <a class="el" href="classmxnet_1_1KVStore.html#a4f9275fb7a11589fae1dcc5a8a3354ac">mxnet::KVStore</a>
</li>
-<li>barrier_before_exit_
-: <a class="el" href="classmxnet_1_1KVStore.html#aa8ba7abf8334326cab19eb568cb31cfa">mxnet::KVStore</a>
-</li>
<li>BeforeFirst()
: <a class="el" href="classmxnet_1_1IIterator.html#a37fc8b163cd0158686183cc0f319529a">mxnet::IIterator< DType ></a>
</li>
@@ -94,9 +153,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_c.html b/versions/master/doxygen/functions_c.html
index 97334a7..18fe522 100644
--- a/versions/master/doxygen/functions_c.html
+++ b/versions/master/doxygen/functions_c.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li class="current"><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,24 +122,24 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_c"></a>- c -</h3><ul>
+<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
<li>CallbackOnComplete
: <a class="el" href="classmxnet_1_1Engine.html#a16b757432556f835d27f1b5e1dbe1b06">mxnet::Engine</a>
</li>
<li>callbacks
-: <a class="el" href="structMXCallbackList.html#a3957dba7b237dc6baff2ba578c652f76">MXCallbackList</a>
+: <a class="el" href="structMXCallbackList.html#ad56325e57f5fffc2b920eb3f8f378199">MXCallbackList</a>
</li>
<li>Check()
-: <a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a6795bc6e9e883adf946f69e16c644788">dmlc::parameter::FieldEntry< mxnet::TShape ></a>
+: <a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a63affafc149cac4c39702c55cf94fdcf">dmlc::parameter::FieldEntry< mxnet::TShape ></a>
</li>
<li>check_name()
: <a class="el" href="structmxnet_1_1OperatorPropertyReg.html#ad959fdbefb6dd807ee1961a2fec7a038">mxnet::OperatorPropertyReg</a>
</li>
<li>CheckAndAlloc()
-: <a class="el" href="classmxnet_1_1NDArray.html#a531a3437dde0dfc9b0c40a32450f4c7a">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a">mxnet::NDArray</a>
</li>
<li>CheckContiguous()
-: <a class="el" href="classmxnet_1_1TBlob.html#ac3abff8bace8f3afb3143d85551a18ac">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#aa6cfdf8823a601602b6b56011af4b874">mxnet::TBlob</a>
</li>
<li>clear()
: <a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a8edff5e8fa169924abe520edcae76c45">mxnet::autograd::AGNodeEntry</a>
@@ -95,7 +157,7 @@ $(function() {
: <a class="el" href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3">mxnet::KVStore</a>
</li>
<li>Copy()
-: <a class="el" href="classmxnet_1_1NDArray.html#a6617e1bf4242314f7ee6b6e3a66ce493">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a4a00e2868ad8b9ccb0a48079635b110f">mxnet::NDArray</a>
, <a class="el" href="classmxnet_1_1OperatorProperty.html#ab38f2f877555ba34b548efd2575cac99">mxnet::OperatorProperty</a>
</li>
<li>CPU()
@@ -108,7 +170,7 @@ $(function() {
: <a class="el" href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">mxnet::Context</a>
, <a class="el" href="classmxnet_1_1KVStore.html#a6d54b075d3b43577f8339b25000a0c82">mxnet::KVStore</a>
, <a class="el" href="classmxnet_1_1OperatorProperty.html#a15cf1f905b08a008ff38556ef6a35dd0">mxnet::OperatorProperty</a>
-, <a class="el" href="classmxnet_1_1OpStatePtr.html#a2484d61830b33181bf24c536ba91b790">mxnet::OpStatePtr</a>
+, <a class="el" href="classmxnet_1_1OpStatePtr.html#ae0f821c7efce61a11730ef8b93070e3e">mxnet::OpStatePtr</a>
</li>
<li>CreateCallback()
: <a class="el" href="classmxnet_1_1Engine.html#acf25be815b4200b48ee6e4e169bf95b8">mxnet::Engine</a>
@@ -117,10 +179,10 @@ $(function() {
: <a class="el" href="classmxnet_1_1OperatorProperty.html#a546ab25ddaae590ca98705ae9abc7ac0">mxnet::OperatorProperty</a>
</li>
<li>CreateOperatorEx()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#af07a7f946cfadb9bea19fa3d2b621ab4">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a7985004f99a931b4a6ff69df733bae72">mxnet::OperatorProperty</a>
</li>
<li>ctx()
-: <a class="el" href="classmxnet_1_1NDArray.html#a26d1ddb2bb0ed1e671ce7606498b942d">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">mxnet::NDArray</a>
, <a class="el" href="structmxnet_1_1RunContext.html#ace5970ed7c25ccc98ffd0dcf35fd3c88">mxnet::RunContext</a>
, <a class="el" href="structmxnet_1_1Storage_1_1Handle.html#ae0a4202582aa12a598f5c55ef51ad4c8">mxnet::Storage::Handle</a>
</li>
@@ -128,9 +190,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_d.html b/versions/master/doxygen/functions_d.html
index 801b6e1..c60debd 100644
--- a/versions/master/doxygen/functions_d.html
+++ b/versions/master/doxygen/functions_d.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li class="current"><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,21 +122,21 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_d"></a>- d -</h3><ul>
+<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
<li>data
: <a class="el" href="structmxnet_1_1DataBatch.html#acc98dd6f48816ca7462352a929e48959">mxnet::DataBatch</a>
, <a class="el" href="structmxnet_1_1DataInst.html#a3511bbb64add286c9cd1b03c70d4b111">mxnet::DataInst</a>
-, <a class="el" href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">mxnet::NDArray</a>
+, <a class="el" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">mxnet::NDArray</a>
, <a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html#ab9214ef3cfcf53b375a2e8f716e66929">mxnet::op::GradFunctionArgument</a>
</li>
<li>data_names
: <a class="el" href="classmxnet_1_1IIterator.html#a65cd81ecf71d54a17a94ca65d27aa014">mxnet::IIterator< DType ></a>
</li>
<li>declare_backward_dependency
-: <a class="el" href="structNDArrayOpInfo.html#af73bb5dbfdc64e2442b98398f0b63ce0">NDArrayOpInfo</a>
+: <a class="el" href="structNDArrayOpInfo.html#a4327dd20a65eb3a7241f67ede18f9950">NDArrayOpInfo</a>
</li>
<li>DeclareBackwardDependency()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a8aa205ea6ce962bfa3fcbe73a8f3db5d">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#abf9e6a8d40750f3ee81fe30cbe3e2aae">mxnet::OperatorProperty</a>
</li>
<li>DeduplicateVarHandle()
: <a class="el" href="classmxnet_1_1Engine.html#a9d8ac1987a6844dba9b0463030fb3430">mxnet::Engine</a>
@@ -93,15 +155,15 @@ $(function() {
: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1cce9a6aaa175711b6be5b864b055f6a">mxnet::op::SimpleOpRegEntry</a>
</li>
<li>Detach()
-: <a class="el" href="classmxnet_1_1NDArray.html#a76646c5c7e164bd7c81854521d3bee52">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a34a3694141050a257dc6b40a6ad2c4ec">mxnet::NDArray</a>
</li>
<li>dev_id
: <a class="el" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">mxnet::Context</a>
-, <a class="el" href="classmxnet_1_1TBlob.html#a2caa6723ffda5908eb5c2e062ac77ebd">mxnet::TBlob</a>
+, <a class="el" href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd">mxnet::TBlob</a>
</li>
<li>dev_mask()
-: <a class="el" href="structmxnet_1_1Context.html#a4182c1a2f910064d058832c57489d8f5">mxnet::Context</a>
-, <a class="el" href="classmxnet_1_1TBlob.html#ad8bcf56e13082fc0607e21f08b14758f">mxnet::TBlob</a>
+: <a class="el" href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe">mxnet::Context</a>
+, <a class="el" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">mxnet::TBlob</a>
</li>
<li>dev_type
: <a class="el" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">mxnet::Context</a>
@@ -113,25 +175,28 @@ $(function() {
: <a class="el" href="classmxnet_1_1Storage.html#a098f8ebacd25f5203b2dc4c1796aec64">mxnet::Storage</a>
</li>
<li>dltensor()
-: <a class="el" href="classmxnet_1_1TBlob.html#a4b0a53821dec20c558a030ed3df14dda">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a519111d4450efa8e58cc10cfc1dbeca7">mxnet::TBlob</a>
</li>
<li>dptr
: <a class="el" href="structmxnet_1_1Storage_1_1Handle.html#a428e25a6317e50c8b55307c96f8ec595">mxnet::Storage::Handle</a>
-, <a class="el" href="classmxnet_1_1TBlob.html#ad931186405f2b40a327278a8f5ff57cd">mxnet::TBlob</a>
+, <a class="el" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">mxnet::TBlob</a>
</li>
<li>dptr_
: <a class="el" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">mxnet::TBlob</a>
</li>
<li>dtype()
-: <a class="el" href="classmxnet_1_1NDArray.html#a67b0f8eb36568b4c9fbd3da2a1c4b0a3">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">mxnet::NDArray</a>
+</li>
+<li>dtype_()
+: <a class="el" href="classmxnet_1_1NDArray.html#af5d6ed79d0346407edfc969d4d51d15c">mxnet::NDArray</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_e.html b/versions/master/doxygen/functions_e.html
index 0668ca9..0ebbeca 100644
--- a/versions/master/doxygen/functions_e.html
+++ b/versions/master/doxygen/functions_e.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li class="current"><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,13 +122,16 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_e"></a>- e -</h3><ul>
+<h3><a class="anchor" id="index_e"></a>- e -</h3><ul>
<li>enforce_nonzero()
: <a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#afa1da539bf9bcb64d3b5a55aec23e8c2">dmlc::parameter::FieldEntry< mxnet::TShape ></a>
</li>
+<li>entry_()
+: <a class="el" href="classmxnet_1_1NDArray.html#a09ff97852143fdd75dd1e071d0d53266">mxnet::NDArray</a>
+</li>
<li>exec_type()
: <a class="el" href="classmxnet_1_1Operator.html#aa6b4c247a914b4659c773965248019c7">mxnet::Operator</a>
-, <a class="el" href="classmxnet_1_1OperatorProperty.html#a149c52bd285e4fa78109a535765a9b7c">mxnet::OperatorProperty</a>
+, <a class="el" href="classmxnet_1_1OperatorProperty.html#aa5d6354ebe3e2a0c51f7f7ce4a60f664">mxnet::OperatorProperty</a>
</li>
<li>extra_data
: <a class="el" href="structmxnet_1_1DataBatch.html#a2e9faeff00dd061f04a88c3ea5758aa2">mxnet::DataBatch</a>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
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-$(function() {
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li class="current"><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
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onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Enumerator</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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@@ -29,26 +30,60 @@
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li class="current"><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/functions_f.html b/versions/master/doxygen/functions_f.html
index 8130e3b..aa71fb8 100644
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@@ -3,15 +3,16 @@
<head>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li class="current"><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,7 +122,7 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_f"></a>- f -</h3><ul>
+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
<li>FieldEntry()
: <a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a0755f0abe443e4b57da234ec7b49fde0">dmlc::parameter::FieldEntry< mxnet::TShape ></a>
</li>
@@ -68,16 +130,16 @@ $(function() {
: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a8ee596c99eef39fbbfffca181bf7f26e">mxnet::op::SimpleOpRegistry</a>
</li>
<li>FlatTo1D()
-: <a class="el" href="classmxnet_1_1TBlob.html#add31f43a251cecde199e245f3ff8383a">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a95bf7032c2b90627510a89ac8bb2946f">mxnet::TBlob</a>
</li>
<li>FlatTo2D()
-: <a class="el" href="classmxnet_1_1TBlob.html#a3e4b61ee072b4d875a90e8153ef50721">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a23cc57be3b2c40a269c8c9f242fa0532">mxnet::TBlob</a>
</li>
<li>FlatTo3D()
-: <a class="el" href="classmxnet_1_1TBlob.html#aefabf95412878b86b1130035386beee8">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#ac5cd4953869cb66ed374854a34da40f8">mxnet::TBlob</a>
</li>
<li>FlatToKD()
-: <a class="el" href="classmxnet_1_1TBlob.html#a5be50f29087afef8d48942ea8fe39cbd">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#aa2f8e15eb1fc21e159c496ec0d499b6f">mxnet::TBlob</a>
</li>
<li>ForEach()
: <a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#a43a89c8e7b4e1a09f346694073a2d998">mxnet::common::LazyAllocArray< TElem ></a>
@@ -87,20 +149,20 @@ $(function() {
, <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd">mxnet::Operator</a>
</li>
<li>forward
-: <a class="el" href="structNativeOpInfo.html#ae4deb88375716334d58f3dc28e693d2d">NativeOpInfo</a>
-, <a class="el" href="structNDArrayOpInfo.html#ad39c62943775f93fd5cb1fdc39aa38a5">NDArrayOpInfo</a>
+: <a class="el" href="structNativeOpInfo.html#a65f675dec9ef798a96122e91d4cf3fa8">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#ac11f40fb25fa4abd275b6f1c7b3d3213">NDArrayOpInfo</a>
</li>
<li>ForwardInplaceOption()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a78989f905d04bedb41896876fd4dbac9">mxnet::OperatorProperty</a>
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<li>fresh_out_grad()
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+: <a class="el" href="classmxnet_1_1NDArray.html#aee74980eb87bf7c482d15458f5194cbe">mxnet::NDArray</a>
</li>
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<hr class="footer"/><address class="footer"><small>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
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+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
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+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
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<li>__REGISTER_OR_FIND__()
: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a376e3412e9f425db998c384e5d3f0523">mxnet::op::SimpleOpRegistry</a>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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- $(document).ready(function() { init_search(); });
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <div id="navrow3" class="tabs2">
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+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
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+ <div id="navrow4" class="tabs3">
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+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li class="current"><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
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+ </div>
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onmouseover="return searchBox.OnSearchSelectShow()"
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-<h3><a id="index_a"></a>- a -</h3><ul>
+<h3><a class="anchor" id="index_a"></a>- a -</h3><ul>
<li>add_arguments()
: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a405bfb5929d459d45d93c2eaa645bfb5">mxnet::op::SimpleOpRegEntry</a>
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-: <a class="el" href="classmxnet_1_1NDArray.html#ac3c424c05eabe0b7a4976191d81cf6b0">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a3896fcce89afce4c1d2f825f7853ca0c">mxnet::NDArray</a>
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<li>At()
-: <a class="el" href="classmxnet_1_1NDArray.html#addb42a7353a4c07b2d1bae0afa8ef48e">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a3b615512a8fb89b9edc99b35064ead95">mxnet::NDArray</a>
</li>
<li>aux_state_map()
: <a class="el" href="classmxnet_1_1Executor.html#a0a38a98fc4dcddc4205b5980ee7bdc4b">mxnet::Executor</a>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<script type="text/javascript" src="search/searchdata.js"></script>
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+<script type="text/javascript">
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<div id="projectname">mxnet
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+ <li><a href="files.html"><span>Files</span></a></li>
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+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
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+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li class="current"><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
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<div class="contents">
 
-<h3><a id="index_b"></a>- b -</h3><ul>
+<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
<li>Backward()
-: <a class="el" href="classmxnet_1_1Executor.html#a15157351d5770c4e5e7e652b2a0e2cbd">mxnet::Executor</a>
+: <a class="el" href="classmxnet_1_1Executor.html#aa2fd32510a718aff38323e63be0d65e3">mxnet::Executor</a>
, <a class="el" href="classmxnet_1_1Operator.html#ad32d74b9bf56caf920b5e6379d2bb259">mxnet::Operator</a>
</li>
<li>BackwardInplaceOption()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a0255256ac4e0e76b3fff917560203efe">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a67231a5a1525ec7480de98a79dce0194">mxnet::OperatorProperty</a>
</li>
<li>BackwardInputs()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a6c5c8df10748a7eee873d4b7eb8d684b">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a9f488509e132094ce7e79125ce853042">mxnet::OperatorProperty</a>
</li>
<li>BackwardResource()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#acec41540dde2c49ff2f2a8fc425890e8">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#ade7720a3e0ebba67d89978925a77609b">mxnet::OperatorProperty</a>
</li>
<li>Barrier()
: <a class="el" href="classmxnet_1_1KVStore.html#a4f9275fb7a11589fae1dcc5a8a3354ac">mxnet::KVStore</a>
@@ -87,9 +145,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_func_c.html b/versions/master/doxygen/functions_func_c.html
index 5b602d0..d072ba5 100644
--- a/versions/master/doxygen/functions_func_c.html
+++ b/versions/master/doxygen/functions_func_c.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li class="current"><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,18 +118,18 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_c"></a>- c -</h3><ul>
+<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
<li>Check()
-: <a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a6795bc6e9e883adf946f69e16c644788">dmlc::parameter::FieldEntry< mxnet::TShape ></a>
+: <a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a63affafc149cac4c39702c55cf94fdcf">dmlc::parameter::FieldEntry< mxnet::TShape ></a>
</li>
<li>check_name()
: <a class="el" href="structmxnet_1_1OperatorPropertyReg.html#ad959fdbefb6dd807ee1961a2fec7a038">mxnet::OperatorPropertyReg</a>
</li>
<li>CheckAndAlloc()
-: <a class="el" href="classmxnet_1_1NDArray.html#a531a3437dde0dfc9b0c40a32450f4c7a">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a">mxnet::NDArray</a>
</li>
<li>CheckContiguous()
-: <a class="el" href="classmxnet_1_1TBlob.html#ac3abff8bace8f3afb3143d85551a18ac">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#aa6cfdf8823a601602b6b56011af4b874">mxnet::TBlob</a>
</li>
<li>clear()
: <a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a8edff5e8fa169924abe520edcae76c45">mxnet::autograd::AGNodeEntry</a>
@@ -83,7 +141,7 @@ $(function() {
: <a class="el" href="structmxnet_1_1Context.html#a290642a21363bef82bcb32cf7f98b884">mxnet::Context</a>
</li>
<li>Copy()
-: <a class="el" href="classmxnet_1_1NDArray.html#a6617e1bf4242314f7ee6b6e3a66ce493">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a4a00e2868ad8b9ccb0a48079635b110f">mxnet::NDArray</a>
, <a class="el" href="classmxnet_1_1OperatorProperty.html#ab38f2f877555ba34b548efd2575cac99">mxnet::OperatorProperty</a>
</li>
<li>CPU()
@@ -96,7 +154,7 @@ $(function() {
: <a class="el" href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">mxnet::Context</a>
, <a class="el" href="classmxnet_1_1KVStore.html#a6d54b075d3b43577f8339b25000a0c82">mxnet::KVStore</a>
, <a class="el" href="classmxnet_1_1OperatorProperty.html#a15cf1f905b08a008ff38556ef6a35dd0">mxnet::OperatorProperty</a>
-, <a class="el" href="classmxnet_1_1OpStatePtr.html#a2484d61830b33181bf24c536ba91b790">mxnet::OpStatePtr</a>
+, <a class="el" href="classmxnet_1_1OpStatePtr.html#ae0f821c7efce61a11730ef8b93070e3e">mxnet::OpStatePtr</a>
</li>
<li>CreateCallback()
: <a class="el" href="classmxnet_1_1Engine.html#acf25be815b4200b48ee6e4e169bf95b8">mxnet::Engine</a>
@@ -105,18 +163,18 @@ $(function() {
: <a class="el" href="classmxnet_1_1OperatorProperty.html#a546ab25ddaae590ca98705ae9abc7ac0">mxnet::OperatorProperty</a>
</li>
<li>CreateOperatorEx()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#af07a7f946cfadb9bea19fa3d2b621ab4">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a7985004f99a931b4a6ff69df733bae72">mxnet::OperatorProperty</a>
</li>
<li>ctx()
-: <a class="el" href="classmxnet_1_1NDArray.html#a26d1ddb2bb0ed1e671ce7606498b942d">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">mxnet::NDArray</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_func_d.html b/versions/master/doxygen/functions_func_d.html
index 7bcbfd2..434623f 100644
--- a/versions/master/doxygen/functions_func_d.html
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@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li class="current"><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,12 +118,12 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_d"></a>- d -</h3><ul>
+<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
<li>data()
-: <a class="el" href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">mxnet::NDArray</a>
</li>
<li>DeclareBackwardDependency()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a8aa205ea6ce962bfa3fcbe73a8f3db5d">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#abf9e6a8d40750f3ee81fe30cbe3e2aae">mxnet::OperatorProperty</a>
</li>
<li>DeduplicateVarHandle()
: <a class="el" href="classmxnet_1_1Engine.html#a9d8ac1987a6844dba9b0463030fb3430">mxnet::Engine</a>
@@ -84,34 +142,37 @@ $(function() {
: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1cce9a6aaa175711b6be5b864b055f6a">mxnet::op::SimpleOpRegEntry</a>
</li>
<li>Detach()
-: <a class="el" href="classmxnet_1_1NDArray.html#a76646c5c7e164bd7c81854521d3bee52">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a34a3694141050a257dc6b40a6ad2c4ec">mxnet::NDArray</a>
</li>
<li>dev_id()
-: <a class="el" href="classmxnet_1_1TBlob.html#a2caa6723ffda5908eb5c2e062ac77ebd">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd">mxnet::TBlob</a>
</li>
<li>dev_mask()
-: <a class="el" href="structmxnet_1_1Context.html#a4182c1a2f910064d058832c57489d8f5">mxnet::Context</a>
-, <a class="el" href="classmxnet_1_1TBlob.html#ad8bcf56e13082fc0607e21f08b14758f">mxnet::TBlob</a>
+: <a class="el" href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe">mxnet::Context</a>
+, <a class="el" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">mxnet::TBlob</a>
</li>
<li>DirectFree()
: <a class="el" href="classmxnet_1_1Storage.html#a098f8ebacd25f5203b2dc4c1796aec64">mxnet::Storage</a>
</li>
<li>dltensor()
-: <a class="el" href="classmxnet_1_1TBlob.html#a4b0a53821dec20c558a030ed3df14dda">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a519111d4450efa8e58cc10cfc1dbeca7">mxnet::TBlob</a>
</li>
<li>dptr()
-: <a class="el" href="classmxnet_1_1TBlob.html#ad931186405f2b40a327278a8f5ff57cd">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">mxnet::TBlob</a>
</li>
<li>dtype()
-: <a class="el" href="classmxnet_1_1NDArray.html#a67b0f8eb36568b4c9fbd3da2a1c4b0a3">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">mxnet::NDArray</a>
+</li>
+<li>dtype_()
+: <a class="el" href="classmxnet_1_1NDArray.html#af5d6ed79d0346407edfc969d4d51d15c">mxnet::NDArray</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_func_e.html b/versions/master/doxygen/functions_func_e.html
index 83ba2f0..93d7c5b 100644
--- a/versions/master/doxygen/functions_func_e.html
+++ b/versions/master/doxygen/functions_func_e.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li class="current"><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,21 +118,24 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_e"></a>- e -</h3><ul>
+<h3><a class="anchor" id="index_e"></a>- e -</h3><ul>
<li>enforce_nonzero()
: <a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#afa1da539bf9bcb64d3b5a55aec23e8c2">dmlc::parameter::FieldEntry< mxnet::TShape ></a>
</li>
+<li>entry_()
+: <a class="el" href="classmxnet_1_1NDArray.html#a09ff97852143fdd75dd1e071d0d53266">mxnet::NDArray</a>
+</li>
<li>exec_type()
: <a class="el" href="classmxnet_1_1Operator.html#aa6b4c247a914b4659c773965248019c7">mxnet::Operator</a>
-, <a class="el" href="classmxnet_1_1OperatorProperty.html#a149c52bd285e4fa78109a535765a9b7c">mxnet::OperatorProperty</a>
+, <a class="el" href="classmxnet_1_1OperatorProperty.html#aa5d6354ebe3e2a0c51f7f7ce4a60f664">mxnet::OperatorProperty</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_func_f.html b/versions/master/doxygen/functions_func_f.html
index 4f2a7d0..588a426 100644
--- a/versions/master/doxygen/functions_func_f.html
+++ b/versions/master/doxygen/functions_func_f.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li class="current"><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,7 +118,7 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_f"></a>- f -</h3><ul>
+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
<li>FieldEntry()
: <a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a0755f0abe443e4b57da234ec7b49fde0">dmlc::parameter::FieldEntry< mxnet::TShape ></a>
</li>
@@ -68,16 +126,16 @@ $(function() {
: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a8ee596c99eef39fbbfffca181bf7f26e">mxnet::op::SimpleOpRegistry</a>
</li>
<li>FlatTo1D()
-: <a class="el" href="classmxnet_1_1TBlob.html#add31f43a251cecde199e245f3ff8383a">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a95bf7032c2b90627510a89ac8bb2946f">mxnet::TBlob</a>
</li>
<li>FlatTo2D()
-: <a class="el" href="classmxnet_1_1TBlob.html#a3e4b61ee072b4d875a90e8153ef50721">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a23cc57be3b2c40a269c8c9f242fa0532">mxnet::TBlob</a>
</li>
<li>FlatTo3D()
-: <a class="el" href="classmxnet_1_1TBlob.html#aefabf95412878b86b1130035386beee8">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#ac5cd4953869cb66ed374854a34da40f8">mxnet::TBlob</a>
</li>
<li>FlatToKD()
-: <a class="el" href="classmxnet_1_1TBlob.html#a5be50f29087afef8d48942ea8fe39cbd">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#aa2f8e15eb1fc21e159c496ec0d499b6f">mxnet::TBlob</a>
</li>
<li>ForEach()
: <a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#a43a89c8e7b4e1a09f346694073a2d998">mxnet::common::LazyAllocArray< TElem ></a>
@@ -87,16 +145,16 @@ $(function() {
, <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd">mxnet::Operator</a>
</li>
<li>ForwardInplaceOption()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a78989f905d04bedb41896876fd4dbac9">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#aad7a3e87866515a2b7100e849a2fe966">mxnet::OperatorProperty</a>
</li>
<li>ForwardResource()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a1f48e07aa4979935b8f4b9dd7564db18">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a623210dd1b9495ca73f223e0b06f998c">mxnet::OperatorProperty</a>
</li>
<li>Free()
: <a class="el" href="classmxnet_1_1Storage.html#a5bdbd7b3ab712cd9e087ab007bdc64dd">mxnet::Storage</a>
</li>
<li>fresh_out_grad()
-: <a class="el" href="classmxnet_1_1NDArray.html#ad9d2fa3ffbe963fb5960375da1b557a2">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#aee74980eb87bf7c482d15458f5194cbe">mxnet::NDArray</a>
</li>
<li>FromString()
: <a class="el" href="structmxnet_1_1Context.html#ac58071fc1c0647bcab7aca4a39ae6a29">mxnet::Context</a>
@@ -105,9 +163,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_func_g.html b/versions/master/doxygen/functions_func_g.html
index 41f1bbe..eabd1c5 100644
--- a/versions/master/doxygen/functions_func_g.html
+++ b/versions/master/doxygen/functions_func_g.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li class="current"><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,7 +118,7 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_g"></a>- g -</h3><ul>
+<h3><a class="anchor" id="index_g"></a>- g -</h3><ul>
<li>Get()
: <a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#ac4528ab581eda5b0aba35faa3687c201">mxnet::common::LazyAllocArray< TElem ></a>
, <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a82b4d0c3577560a4f6f62f482b125471">mxnet::common::ObjectPool< T ></a>
@@ -70,53 +128,53 @@ $(function() {
, <a class="el" href="classmxnet_1_1Storage.html#a96abc072908d72d3271360a7e3e4bbc2">mxnet::Storage</a>
</li>
<li>get()
-: <a class="el" href="classmxnet_1_1TBlob.html#a3acb7675c6a1f682fe28d0d62aeb338a">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#acb2e058394548527844e219c7e3c480d">mxnet::TBlob</a>
</li>
<li>get_ctx()
-: <a class="el" href="structmxnet_1_1RunContext.html#a323fa88ecd7bfb7d5f7d9ca727101d3e">mxnet::RunContext</a>
+: <a class="el" href="structmxnet_1_1RunContext.html#a5da6c1a11d0e6ea09dbe718cf6a4ef8d">mxnet::RunContext</a>
</li>
<li>get_group_size()
-: <a class="el" href="classmxnet_1_1KVStore.html#a2314122405a7f3ed5f9df55cb6a66f2a">mxnet::KVStore</a>
+: <a class="el" href="classmxnet_1_1KVStore.html#a1fd19b2a123b6069b35e1e8b7fad9dc3">mxnet::KVStore</a>
</li>
<li>get_host_space()
-: <a class="el" href="structmxnet_1_1Resource.html#a71ac977a7b8dd6abbee947df43c85ec1">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#a92724193fad2a6c5e2a63687e63e3d80">mxnet::Resource</a>
</li>
<li>get_host_space_internal()
-: <a class="el" href="structmxnet_1_1Resource.html#a220125a23ff2a0666ec0ec498a71c19b">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#af6e0224f60b5e9afd3c883972863537f">mxnet::Resource</a>
</li>
<li>get_host_space_typed()
-: <a class="el" href="structmxnet_1_1Resource.html#a3dc6c8f2b200e2e045fe0ff74c18e5f7">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#a7c6dff75a9e05a7394b02c84b79a14b2">mxnet::Resource</a>
</li>
<li>get_num_dead_node()
-: <a class="el" href="classmxnet_1_1KVStore.html#a9bbe72794d75a2d8d804124ee686c01c">mxnet::KVStore</a>
+: <a class="el" href="classmxnet_1_1KVStore.html#af5c48df3aade761a547a1dc6f7b8702d">mxnet::KVStore</a>
</li>
<li>get_random()
-: <a class="el" href="structmxnet_1_1Resource.html#a18ed29b378bdfad93358c275967afe60">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#a86039963ae5d682ffc287aefd520f6f9">mxnet::Resource</a>
</li>
<li>get_rank()
-: <a class="el" href="classmxnet_1_1KVStore.html#afa281ae77e3ca58beaee6f3a5feafbc1">mxnet::KVStore</a>
+: <a class="el" href="classmxnet_1_1KVStore.html#a262ef45340106fdb5c2a296c3cb711b1">mxnet::KVStore</a>
</li>
<li>get_space()
-: <a class="el" href="structmxnet_1_1Resource.html#a3c3ced58b1ed0e4fdb0f8e62c6c4a362">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#ab2be3ebdd3c5f5fa181ac4f6cdd69508">mxnet::Resource</a>
</li>
<li>get_space_internal()
-: <a class="el" href="structmxnet_1_1Resource.html#a9e78ffb2885f9411ecb52683d935e9c1">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#af57d32e959227f49d54d6eb6a5c62875">mxnet::Resource</a>
</li>
<li>get_space_typed()
-: <a class="el" href="structmxnet_1_1Resource.html#a95f1c70975a646f9db28140798ae87d0">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#a84e66895d72f9e99ae994de86539a77e">mxnet::Resource</a>
</li>
<li>get_state()
-: <a class="el" href="classmxnet_1_1OpStatePtr.html#a6d720b8843340fa4d9a7ff7937f2e141">mxnet::OpStatePtr</a>
+: <a class="el" href="classmxnet_1_1OpStatePtr.html#aa2ea1d7ae06008faf44a92c009b2b26e">mxnet::OpStatePtr</a>
</li>
<li>get_stream()
-: <a class="el" href="structmxnet_1_1OpContext.html#a0c48c2419d23551a626ae3de6ee1f6da">mxnet::OpContext</a>
-, <a class="el" href="structmxnet_1_1RunContext.html#a9901db00632774aef42081ceda0d23f1">mxnet::RunContext</a>
+: <a class="el" href="structmxnet_1_1OpContext.html#aa700c5c28e024b42d41e15f46615f1b8">mxnet::OpContext</a>
+, <a class="el" href="structmxnet_1_1RunContext.html#a8bcd2fc6392f95a1fdc3d98f74dbfcb9">mxnet::RunContext</a>
</li>
<li>get_var()
-: <a class="el" href="classmxnet_1_1OpStatePtr.html#af2082fb6224b78b2aac9e05f4b5365a4">mxnet::OpStatePtr</a>
+: <a class="el" href="classmxnet_1_1OpStatePtr.html#a77a1636269466569c0b6e32941ed7264">mxnet::OpStatePtr</a>
</li>
<li>get_with_shape()
-: <a class="el" href="classmxnet_1_1TBlob.html#ae3601040650637a7ac05b3f00f0adbee">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#abbdbdc8d02c15e854cc7f5fe5dc09f61">mxnet::TBlob</a>
</li>
<li>GetParams()
: <a class="el" href="classmxnet_1_1OperatorProperty.html#a704ae43181c8a88bfd0a47e8005f7cb8">mxnet::OperatorProperty</a>
@@ -125,15 +183,15 @@ $(function() {
: <a class="el" href="structmxnet_1_1Context.html#a39d1a2b59bce135da127475995a58671">mxnet::Context</a>
</li>
<li>grad()
-: <a class="el" href="classmxnet_1_1NDArray.html#a9c038544414ed2b992f9393a14eff6cd">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#aed729c3bc9db04ab346fd3969b4fc982">mxnet::NDArray</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_func_i.html b/versions/master/doxygen/functions_func_i.html
index 3264b25..4f1b42e 100644
--- a/versions/master/doxygen/functions_func_i.html
+++ b/versions/master/doxygen/functions_func_i.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li class="current"><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,7 +118,7 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_i"></a>- i -</h3><ul>
+<h3><a class="anchor" id="index_i"></a>- i -</h3><ul>
<li>in_arg_map()
: <a class="el" href="classmxnet_1_1Executor.html#a07a3e917596ed08718ee2859283c2476">mxnet::Executor</a>
</li>
@@ -68,7 +126,7 @@ $(function() {
: <a class="el" href="classmxnet_1_1OperatorProperty.html#a543cbf36f0e4d8dc99109db71705a11c">mxnet::OperatorProperty</a>
</li>
<li>InferType()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a819b81e14761e9e8b9c888a63c7df1fa">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#ad5c9f53ea70f3eeb1bf27b983f420c0b">mxnet::OperatorProperty</a>
</li>
<li>Init()
: <a class="el" href="classmxnet_1_1IIterator.html#a774e490e5f3eee767fdc11b779bbd1fe">mxnet::IIterator< DType ></a>
@@ -79,8 +137,8 @@ $(function() {
: <a class="el" href="classmxnet_1_1KVStore.html#a5d919a7a4c9495e9a569c8d733d6325f">mxnet::KVStore</a>
</li>
<li>is_none()
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-, <a class="el" href="classmxnet_1_1NDArray.html#a29673998ea5bec1cab7337595d907c67">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#adf925c9b87cbd5969e142123da6c7e30">mxnet::autograd::AGNodeEntry</a>
+, <a class="el" href="classmxnet_1_1NDArray.html#a2a15ffdbf29d4d5455a4fbb4b607880c">mxnet::NDArray</a>
</li>
<li>IsSchedulerNode()
: <a class="el" href="classmxnet_1_1KVStore.html#a41113b9dfeab6c705ba064cc3f010fb4">mxnet::KVStore</a>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/functions_func_l.html b/versions/master/doxygen/functions_func_l.html
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li class="current"><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,18 +118,18 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_l"></a>- l -</h3><ul>
+<h3><a class="anchor" id="index_l"></a>- l -</h3><ul>
<li>LazyAllocArray()
: <a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#a1dd703a9eabfe691793a8398706bcc09">mxnet::common::LazyAllocArray< TElem ></a>
</li>
<li>ListArguments()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#ad01be3a84423800af7e818b45afc748f">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a24cc6a3763ff5f0881a2de0652810596">mxnet::OperatorProperty</a>
</li>
<li>ListAuxiliaryStates()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#aa6d3a955ffa452a368a9dd4d33ce5a25">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094">mxnet::OperatorProperty</a>
</li>
<li>ListOutputs()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#ab9e52badb93fe965ce961803f2101aa7">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">mxnet::OperatorProperty</a>
</li>
<li>Load()
: <a class="el" href="structmxnet_1_1Context.html#a882305327a53b9b9a82f5802b4b013e3">mxnet::Context</a>
@@ -81,9 +139,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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diff --git a/versions/master/doxygen/functions_func_n.html b/versions/master/doxygen/functions_func_n.html
index 0d5109c..4b2ca0a 100644
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li class="current"><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,7 +118,7 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_n"></a>- n -</h3><ul>
+<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
<li>NDArray()
: <a class="el" href="classmxnet_1_1NDArray.html#a12ec978e5dbe9092d21721a657c39756">mxnet::NDArray</a>
</li>
@@ -68,11 +126,11 @@ $(function() {
: <a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#abd540887b52ba89572ad6a18b4ac739c">mxnet::NDArrayFunctionReg</a>
</li>
<li>ndim()
-: <a class="el" href="classmxnet_1_1TBlob.html#a77a8929e46d06e5d6a0026bcc9e48851">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">mxnet::TBlob</a>
</li>
<li>New()
-: <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#aaadcbc99eaec88191b201bad9da441c6">mxnet::common::ObjectPool< T ></a>
-, <a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#aa08281412ac1a31d485884fa5a7544ce">mxnet::common::ObjectPoolAllocatable< T ></a>
+: <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a5d711031f61dbd06d8c6975fd49e5632">mxnet::common::ObjectPool< T ></a>
+, <a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#aaf92ecb0cb6542b5c3f70ec31a31db4e">mxnet::common::ObjectPoolAllocatable< T ></a>
</li>
<li>NewOperator()
: <a class="el" href="classmxnet_1_1Engine.html#aff3332258a158ef33a9a4b7bcdc2fe6f">mxnet::Engine</a>
@@ -84,24 +142,24 @@ $(function() {
: <a class="el" href="classmxnet_1_1IIterator.html#ac02dda5d191a7bc629aaa93e5abab3d5">mxnet::IIterator< DType ></a>
</li>
<li>nn_entry()
-: <a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a3dc6bbdfcf766bd4db8756001f5278f5">mxnet::autograd::AGNodeEntry</a>
+: <a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a7dbd6c573b3bc3eda29b9ce9a2d1283a">mxnet::autograd::AGNodeEntry</a>
</li>
<li>NotifyShutdown()
: <a class="el" href="classmxnet_1_1Engine.html#a3c0e2989538b5369c1592eddbcf0181c">mxnet::Engine</a>
</li>
<li>NumOutputs()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a9fc6e9f04f52b1c05f62fb7357bb95c4">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">mxnet::OperatorProperty</a>
</li>
<li>NumVisibleOutputs()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a006f0e2c9b3f934e1f73b242b7e8b062">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b">mxnet::OperatorProperty</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
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+ <ul class="tablist">
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+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <div id="navrow3" class="tabs2">
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+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
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+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
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<li>operator<()
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</li>
<li>operator==()
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
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+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <div id="navrow3" class="tabs2">
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+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
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+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
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onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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<li>PartialForward()
: <a class="el" href="classmxnet_1_1Executor.html#a3e261d44065848e434c527000d4390c8">mxnet::Executor</a>
</li>
<li>Print()
-: <a class="el" href="classmxnet_1_1Executor.html#ac391ee6cf8cf8afb60acd0480db2ae86">mxnet::Executor</a>
+: <a class="el" href="classmxnet_1_1Executor.html#a3803afcb87e4c69940c8d3d88a03e6ee">mxnet::Executor</a>
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<li>Pull()
-: <a class="el" href="classmxnet_1_1KVStore.html#a168927975b39665dc1ea7bd51a0b8805">mxnet::KVStore</a>
+: <a class="el" href="classmxnet_1_1KVStore.html#a473e95dbf4618e34ba2e519944ece2b4">mxnet::KVStore</a>
</li>
<li>Push()
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<script type="text/javascript" src="search/searchdata.js"></script>
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+<script type="text/javascript">
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<tbody>
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</div>
</td>
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- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ </div>
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
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+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
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+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li class="current"><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<h3><a class="anchor" id="index_r"></a>- r -</h3><ul>
<li>Request()
: <a class="el" href="classmxnet_1_1ResourceManager.html#a276e4e3ae229a118e50a03955f3f1a08">mxnet::ResourceManager</a>
</li>
@@ -68,10 +126,10 @@ $(function() {
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</li>
<li>Reshape()
-: <a class="el" href="classmxnet_1_1NDArray.html#a1d01269333b9226fffddc89ce3b7fb63">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#ada618436206b59aa3ffcdb8474d5051c">mxnet::NDArray</a>
</li>
<li>reshape()
-: <a class="el" href="classmxnet_1_1TBlob.html#a3b89df7835e39c652889b5cdb39235fa">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a0c66b10f5a17528cc858762b60125789">mxnet::TBlob</a>
</li>
<li>Resource()
: <a class="el" href="structmxnet_1_1Resource.html#a76a2cc8f2a94b67b3533a048a80396aa">mxnet::Resource</a>
@@ -86,9 +144,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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diff --git a/versions/master/doxygen/functions_func_s.html b/versions/master/doxygen/functions_func_s.html
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li class="current"><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -60,10 +118,10 @@ $(function() {
<div class="contents">
 
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+<h3><a class="anchor" id="index_s"></a>- s -</h3><ul>
<li>Save()
-: <a class="el" href="structmxnet_1_1Context.html#a9ea910ddb7e15d51320806b1666bf6c5">mxnet::Context</a>
-, <a class="el" href="classmxnet_1_1NDArray.html#a7dd1143eeb4fbe15cebe85f28142551f">mxnet::NDArray</a>
+: <a class="el" href="structmxnet_1_1Context.html#a90542e0930244392f9a81ed0f206b612">mxnet::Context</a>
+, <a class="el" href="classmxnet_1_1NDArray.html#a524662078483f8f46c6c71714fbfde0b">mxnet::NDArray</a>
</li>
<li>SeedRandom()
: <a class="el" href="classmxnet_1_1ResourceManager.html#a7aa335bc9231a8eb7cd04de22e033603">mxnet::ResourceManager</a>
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</li>
<li>set_fresh_out_grad()
-: <a class="el" href="classmxnet_1_1NDArray.html#aec1b90af51327c2956adf182c201de03">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a32ed2be4ecf94c58b19bd43b48f2b1fa">mxnet::NDArray</a>
</li>
<li>set_function()
: <a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#a220e1866bea35e103adbe23ff92855d9">mxnet::NDArrayFunctionReg</a>
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</li>
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+: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a3be9c65fc5f747b6a05e3c2889d8f72b">mxnet::op::SimpleOpRegEntry</a>
</li>
<li>set_symbol_op_name()
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</li>
<li>shape()
-: <a class="el" href="classmxnet_1_1NDArray.html#a2fc268d12168d39d9095829b3b9a3836">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">mxnet::NDArray</a>
+</li>
+<li>shape_()
+: <a class="el" href="classmxnet_1_1NDArray.html#a18e75568a3a4fc14f271793dde914b56">mxnet::NDArray</a>
</li>
<li>SignalForKill()
: <a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#a000e31ad914a20415519a2b39a1d08c3">mxnet::common::LazyAllocArray< TElem ></a>
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<li>SimpleBind()
: <a class="el" href="classmxnet_1_1Executor.html#ae1a319bca5e26ef58057f8410f46e484">mxnet::Executor</a>
</li>
-<li>Size()
-: <a class="el" href="classmxnet_1_1TBlob.html#a7b45f281d954cc716267a7b3ec0f514c">mxnet::TBlob</a>
-</li>
<li>size()
-: <a class="el" href="classmxnet_1_1TBlob.html#a44c93b8a96203a1a85fa9d4b85e123c7">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a523fd58883de4b9cd0b1a2852daa64ae">mxnet::TBlob</a>
+</li>
+<li>Size()
+: <a class="el" href="classmxnet_1_1TBlob.html#a3b04db1348834e1576cfb2ed54959703">mxnet::TBlob</a>
</li>
<li>Slice()
-: <a class="el" href="classmxnet_1_1NDArray.html#a0cfa7466a652feb2258bb990453a0a7d">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a7acb6462c11e071949db2b0e1fbfd66e">mxnet::NDArray</a>
</li>
<li>SyncCopyFromCPU()
-: <a class="el" href="classmxnet_1_1NDArray.html#ac390edfdd8a7dd99f69ea373f4d18e1a">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a422c70b16646f8a273e9b4936fbe2e37">mxnet::NDArray</a>
</li>
<li>SyncCopyToCPU()
-: <a class="el" href="classmxnet_1_1NDArray.html#afb3dbc357c53e1e0b980257f6c5e2cac">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a4819d2e6431ccdf4b9f8efa8eb0bd1fd">mxnet::NDArray</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_func_t.html b/versions/master/doxygen/functions_func_t.html
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Functions</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,83 @@
</table>
</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li class="current"><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions_func.html#index__"><span>_</span></a></li>
+ <li><a href="functions_func_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_func_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_func_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_func_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_func_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_func_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_func_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_func_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_func_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_func_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_func_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_func_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_func_s.html#index_s"><span>s</span></a></li>
+ <li class="current"><a href="functions_func_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_func_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_func_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_func_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,9 +118,9 @@ $(function() {
<div class="contents">
 
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<li>T()
-: <a class="el" href="classmxnet_1_1NDArray.html#a7876b8d60109a18feabe767d85f66226">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a3fe1974c7c2d522b09b9bf82fd2865ba">mxnet::NDArray</a>
</li>
<li>TBlob()
: <a class="el" href="classmxnet_1_1TBlob.html#adcdc6de67f81454f0a142bfc9fd38c9e">mxnet::TBlob</a>
@@ -77,9 +135,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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</html>
diff --git a/versions/master/doxygen/functions_func_v.html b/versions/master/doxygen/functions_func_v.html
index f75331f..687af42 100644
--- a/versions/master/doxygen/functions_func_v.html
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<hr class="footer"/><address class="footer"><small>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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+ <li><a href="functions_func_c.html#index_c"><span>c</span></a></li>
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+ <li class="current"><a href="functions_func_~.html#index_~"><span>~</span></a></li>
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+</li>
+<li>~IIterator()
+: <a class="el" href="classmxnet_1_1IIterator.html#a6e9049dca03b889bba0d18338e286070">mxnet::IIterator< DType ></a>
+</li>
+<li>~KVStore()
+: <a class="el" href="classmxnet_1_1KVStore.html#a02e23071eb46464b58f2e2bf5654d6d6">mxnet::KVStore</a>
+</li>
+<li>~ObjectPool()
+: <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#aa5ff3f223210e5f3d0881c272462aedb">mxnet::common::ObjectPool< T ></a>
+</li>
+<li>~Operator()
+: <a class="el" href="classmxnet_1_1Operator.html#a4d34cc792d7271bd8b5b60eaa0b55767">mxnet::Operator</a>
+</li>
+<li>~OperatorProperty()
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a610af8e9cb560104301ccd1efa2a96fd">mxnet::OperatorProperty</a>
+</li>
+<li>~ResourceManager()
+: <a class="el" href="classmxnet_1_1ResourceManager.html#a2b53c0843ddbaf7ccb9e06f448b20cac">mxnet::ResourceManager</a>
+</li>
+<li>~SimpleOpRegEntry()
+: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a94815080350681ad70e7815da1bc0eae">mxnet::op::SimpleOpRegEntry</a>
+</li>
+<li>~Storage()
+: <a class="el" href="classmxnet_1_1Storage.html#af1e1bfea9236ccfba64344226416b04a">mxnet::Storage</a>
+</li>
+</ul>
+</div><!-- contents -->
+<!-- start footer part -->
+<hr class="footer"/><address class="footer"><small>
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
+<img class="footer" src="doxygen.png" alt="doxygen"/>
+</a> 1.8.6
+</small></address>
+</body>
+</html>
diff --git a/versions/master/doxygen/functions_g.html b/versions/master/doxygen/functions_g.html
index 8479bc3..2064771 100644
--- a/versions/master/doxygen/functions_g.html
+++ b/versions/master/doxygen/functions_g.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li class="current"><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,7 +122,7 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_g"></a>- g -</h3><ul>
+<h3><a class="anchor" id="index_g"></a>- g -</h3><ul>
<li>Get()
: <a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#ac4528ab581eda5b0aba35faa3687c201">mxnet::common::LazyAllocArray< TElem ></a>
, <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a82b4d0c3577560a4f6f62f482b125471">mxnet::common::ObjectPool< T ></a>
@@ -70,53 +132,53 @@ $(function() {
, <a class="el" href="classmxnet_1_1Storage.html#a96abc072908d72d3271360a7e3e4bbc2">mxnet::Storage</a>
</li>
<li>get()
-: <a class="el" href="classmxnet_1_1TBlob.html#a3acb7675c6a1f682fe28d0d62aeb338a">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#acb2e058394548527844e219c7e3c480d">mxnet::TBlob</a>
</li>
<li>get_ctx()
-: <a class="el" href="structmxnet_1_1RunContext.html#a323fa88ecd7bfb7d5f7d9ca727101d3e">mxnet::RunContext</a>
+: <a class="el" href="structmxnet_1_1RunContext.html#a5da6c1a11d0e6ea09dbe718cf6a4ef8d">mxnet::RunContext</a>
</li>
<li>get_group_size()
-: <a class="el" href="classmxnet_1_1KVStore.html#a2314122405a7f3ed5f9df55cb6a66f2a">mxnet::KVStore</a>
+: <a class="el" href="classmxnet_1_1KVStore.html#a1fd19b2a123b6069b35e1e8b7fad9dc3">mxnet::KVStore</a>
</li>
<li>get_host_space()
-: <a class="el" href="structmxnet_1_1Resource.html#a71ac977a7b8dd6abbee947df43c85ec1">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#a92724193fad2a6c5e2a63687e63e3d80">mxnet::Resource</a>
</li>
<li>get_host_space_internal()
-: <a class="el" href="structmxnet_1_1Resource.html#a220125a23ff2a0666ec0ec498a71c19b">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#af6e0224f60b5e9afd3c883972863537f">mxnet::Resource</a>
</li>
<li>get_host_space_typed()
-: <a class="el" href="structmxnet_1_1Resource.html#a3dc6c8f2b200e2e045fe0ff74c18e5f7">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#a7c6dff75a9e05a7394b02c84b79a14b2">mxnet::Resource</a>
</li>
<li>get_num_dead_node()
-: <a class="el" href="classmxnet_1_1KVStore.html#a9bbe72794d75a2d8d804124ee686c01c">mxnet::KVStore</a>
+: <a class="el" href="classmxnet_1_1KVStore.html#af5c48df3aade761a547a1dc6f7b8702d">mxnet::KVStore</a>
</li>
<li>get_random()
-: <a class="el" href="structmxnet_1_1Resource.html#a18ed29b378bdfad93358c275967afe60">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#a86039963ae5d682ffc287aefd520f6f9">mxnet::Resource</a>
</li>
<li>get_rank()
-: <a class="el" href="classmxnet_1_1KVStore.html#afa281ae77e3ca58beaee6f3a5feafbc1">mxnet::KVStore</a>
+: <a class="el" href="classmxnet_1_1KVStore.html#a262ef45340106fdb5c2a296c3cb711b1">mxnet::KVStore</a>
</li>
<li>get_space()
-: <a class="el" href="structmxnet_1_1Resource.html#a3c3ced58b1ed0e4fdb0f8e62c6c4a362">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#ab2be3ebdd3c5f5fa181ac4f6cdd69508">mxnet::Resource</a>
</li>
<li>get_space_internal()
-: <a class="el" href="structmxnet_1_1Resource.html#a9e78ffb2885f9411ecb52683d935e9c1">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#af57d32e959227f49d54d6eb6a5c62875">mxnet::Resource</a>
</li>
<li>get_space_typed()
-: <a class="el" href="structmxnet_1_1Resource.html#a95f1c70975a646f9db28140798ae87d0">mxnet::Resource</a>
+: <a class="el" href="structmxnet_1_1Resource.html#a84e66895d72f9e99ae994de86539a77e">mxnet::Resource</a>
</li>
<li>get_state()
-: <a class="el" href="classmxnet_1_1OpStatePtr.html#a6d720b8843340fa4d9a7ff7937f2e141">mxnet::OpStatePtr</a>
+: <a class="el" href="classmxnet_1_1OpStatePtr.html#aa2ea1d7ae06008faf44a92c009b2b26e">mxnet::OpStatePtr</a>
</li>
<li>get_stream()
-: <a class="el" href="structmxnet_1_1OpContext.html#a0c48c2419d23551a626ae3de6ee1f6da">mxnet::OpContext</a>
-, <a class="el" href="structmxnet_1_1RunContext.html#a9901db00632774aef42081ceda0d23f1">mxnet::RunContext</a>
+: <a class="el" href="structmxnet_1_1OpContext.html#aa700c5c28e024b42d41e15f46615f1b8">mxnet::OpContext</a>
+, <a class="el" href="structmxnet_1_1RunContext.html#a8bcd2fc6392f95a1fdc3d98f74dbfcb9">mxnet::RunContext</a>
</li>
<li>get_var()
-: <a class="el" href="classmxnet_1_1OpStatePtr.html#af2082fb6224b78b2aac9e05f4b5365a4">mxnet::OpStatePtr</a>
+: <a class="el" href="classmxnet_1_1OpStatePtr.html#a77a1636269466569c0b6e32941ed7264">mxnet::OpStatePtr</a>
</li>
<li>get_with_shape()
-: <a class="el" href="classmxnet_1_1TBlob.html#ae3601040650637a7ac05b3f00f0adbee">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#abbdbdc8d02c15e854cc7f5fe5dc09f61">mxnet::TBlob</a>
</li>
<li>GetParams()
: <a class="el" href="classmxnet_1_1OperatorProperty.html#a704ae43181c8a88bfd0a47e8005f7cb8">mxnet::OperatorProperty</a>
@@ -125,15 +187,15 @@ $(function() {
: <a class="el" href="structmxnet_1_1Context.html#a39d1a2b59bce135da127475995a58671">mxnet::Context</a>
</li>
<li>grad()
-: <a class="el" href="classmxnet_1_1NDArray.html#a9c038544414ed2b992f9393a14eff6cd">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#aed729c3bc9db04ab346fd3969b4fc982">mxnet::NDArray</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_i.html b/versions/master/doxygen/functions_i.html
index b44f5dc..b9173b7 100644
--- a/versions/master/doxygen/functions_i.html
+++ b/versions/master/doxygen/functions_i.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li class="current"><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_i"></a>- i -</h3><ul>
+<h3><a class="anchor" id="index_i"></a>- i -</h3><ul>
<li>id
: <a class="el" href="structmxnet_1_1Resource.html#aa1fc8578c0fa849afe7da29c1cf1308b">mxnet::Resource</a>
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</li>
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-: <a class="el" href="structNativeOpInfo.html#a2d8f03e8586a1a1cb6b46ab8872a214a">NativeOpInfo</a>
-, <a class="el" href="structNDArrayOpInfo.html#a526a6092e581aea808b180758c2c4610">NDArrayOpInfo</a>
+: <a class="el" href="structNativeOpInfo.html#af4c12b25f9d708835619b768db5805ff">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#a139351ad186cde67a376d2f9d6682baf">NDArrayOpInfo</a>
</li>
<li>InferShape()
: <a class="el" href="classmxnet_1_1OperatorProperty.html#a543cbf36f0e4d8dc99109db71705a11c">mxnet::OperatorProperty</a>
</li>
<li>InferType()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a819b81e14761e9e8b9c888a63c7df1fa">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#ad5c9f53ea70f3eeb1bf27b983f420c0b">mxnet::OperatorProperty</a>
</li>
<li>Init()
: <a class="el" href="classmxnet_1_1IIterator.html#a774e490e5f3eee767fdc11b779bbd1fe">mxnet::IIterator< DType ></a>
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</li>
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-, <a class="el" href="classmxnet_1_1NDArray.html#a29673998ea5bec1cab7337595d907c67">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#adf925c9b87cbd5969e142123da6c7e30">mxnet::autograd::AGNodeEntry</a>
+, <a class="el" href="classmxnet_1_1NDArray.html#a2a15ffdbf29d4d5455a4fbb4b607880c">mxnet::NDArray</a>
</li>
<li>is_train
: <a class="el" href="structmxnet_1_1OpContext.html#abd7405c065167e4e15af032b1e0a5c42">mxnet::OpContext</a>
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</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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diff --git a/versions/master/doxygen/functions_k.html b/versions/master/doxygen/functions_k.html
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li class="current"><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,7 +122,7 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_k"></a>- k -</h3><ul>
+<h3><a class="anchor" id="index_k"></a>- k -</h3><ul>
<li>kCPU
: <a class="el" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140">mxnet::Context</a>
</li>
@@ -95,9 +157,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_l.html b/versions/master/doxygen/functions_l.html
index de51c7a..c2c06a1 100644
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<head>
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li class="current"><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,26 +122,26 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_l"></a>- l -</h3><ul>
+<h3><a class="anchor" id="index_l"></a>- l -</h3><ul>
<li>LazyAllocArray()
: <a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html#a1dd703a9eabfe691793a8398706bcc09">mxnet::common::LazyAllocArray< TElem ></a>
</li>
<li>list_arguments
-: <a class="el" href="structNativeOpInfo.html#af25c58d65a2350cab76403f1ce21922e">NativeOpInfo</a>
-, <a class="el" href="structNDArrayOpInfo.html#abc701d8c032a1c5b9358de8ab060d9c2">NDArrayOpInfo</a>
+: <a class="el" href="structNativeOpInfo.html#ab1f5d01bbb1d6507c23e640ac3eea834">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#ad6bb579606e6bd83569d035fa92a857c">NDArrayOpInfo</a>
</li>
<li>list_outputs
-: <a class="el" href="structNativeOpInfo.html#a6a324c4b74a64b295c404ff235fac1e1">NativeOpInfo</a>
-, <a class="el" href="structNDArrayOpInfo.html#ab99abece52b77824dde5df98837f4f7b">NDArrayOpInfo</a>
+: <a class="el" href="structNativeOpInfo.html#a1dfb6e3206e76dcf755ae0bca655081b">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#acd6f9dbac22ba35f0f9ac5062dc5c8ec">NDArrayOpInfo</a>
</li>
<li>ListArguments()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#ad01be3a84423800af7e818b45afc748f">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a24cc6a3763ff5f0881a2de0652810596">mxnet::OperatorProperty</a>
</li>
<li>ListAuxiliaryStates()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#aa6d3a955ffa452a368a9dd4d33ce5a25">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094">mxnet::OperatorProperty</a>
</li>
<li>ListOutputs()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#ab9e52badb93fe965ce961803f2101aa7">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">mxnet::OperatorProperty</a>
</li>
<li>Load()
: <a class="el" href="structmxnet_1_1Context.html#a882305327a53b9b9a82f5802b4b013e3">mxnet::Context</a>
@@ -89,9 +151,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<table cellspacing="0" cellpadding="0">
<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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@@ -29,26 +30,87 @@
</table>
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+<!-- Generated by Doxygen 1.8.6 -->
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
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+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
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+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li class="current"><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_m"></a>- m -</h3><ul>
+<h3><a class="anchor" id="index_m"></a>- m -</h3><ul>
<li>MonitorCallback
: <a class="el" href="classmxnet_1_1Executor.html#a5684c74daafc9b80681cde6a9b7b4587">mxnet::Executor</a>
</li>
@@ -68,9 +130,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/functions_n.html b/versions/master/doxygen/functions_n.html
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@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li class="current"><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_n"></a>- n -</h3><ul>
+<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
<li>name
: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a02c180f08b215e9553d3c93fa783f9a4">mxnet::op::SimpleOpRegEntry</a>
</li>
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</li>
<li>ndim()
-: <a class="el" href="classmxnet_1_1TBlob.html#a77a8929e46d06e5d6a0026bcc9e48851">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">mxnet::TBlob</a>
</li>
<li>New()
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+: <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#a5d711031f61dbd06d8c6975fd49e5632">mxnet::common::ObjectPool< T ></a>
+, <a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#aaf92ecb0cb6542b5c3f70ec31a31db4e">mxnet::common::ObjectPoolAllocatable< T ></a>
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<li>NewOperator()
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<li>nn_entry()
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+: <a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a7dbd6c573b3bc3eda29b9ce9a2d1283a">mxnet::autograd::AGNodeEntry</a>
</li>
<li>NotifyShutdown()
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<li>NumOutputs()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a9fc6e9f04f52b1c05f62fb7357bb95c4">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">mxnet::OperatorProperty</a>
</li>
<li>NumVisibleOutputs()
-: <a class="el" href="classmxnet_1_1OperatorProperty.html#a006f0e2c9b3f934e1f73b242b7e8b062">mxnet::OperatorProperty</a>
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b">mxnet::OperatorProperty</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_o.html b/versions/master/doxygen/functions_o.html
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li class="current"><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,15 +122,15 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_o"></a>- o -</h3><ul>
+<h3><a class="anchor" id="index_o"></a>- o -</h3><ul>
<li>operator bool()
-: <a class="el" href="classmxnet_1_1OpStatePtr.html#ae233fba51851ddd1704de60c9c860ee3">mxnet::OpStatePtr</a>
+: <a class="el" href="classmxnet_1_1OpStatePtr.html#a6f407d1c8d461dc474cd1c50aa53e3f8">mxnet::OpStatePtr</a>
</li>
<li>operator!=()
-: <a class="el" href="structmxnet_1_1Context.html#a7e84dc72147472e72b50e8545780fd1e">mxnet::Context</a>
+: <a class="el" href="structmxnet_1_1Context.html#a10fded12664afacb95f75c07bb77bfe6">mxnet::Context</a>
</li>
<li>operator()()
-: <a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a71e8c5a29d9e103f6471f35f30e05727">mxnet::engine::CallbackOnComplete</a>
+: <a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html#a97abc3d705870669f303ee22a1342d4a">mxnet::engine::CallbackOnComplete</a>
</li>
<li>operator*=()
: <a class="el" href="classmxnet_1_1NDArray.html#a1b47078a60bf0369ff0a7df893caf65b">mxnet::NDArray</a>
@@ -83,14 +145,14 @@ $(function() {
: <a class="el" href="classmxnet_1_1NDArray.html#ab88b476b2092b30c7d456d99df9f36e3">mxnet::NDArray</a>
</li>
<li>operator<()
-: <a class="el" href="structmxnet_1_1Context.html#afd07473e20810635cb3fffc216d76494">mxnet::Context</a>
+: <a class="el" href="structmxnet_1_1Context.html#a1862b54dcfd20332eb2365451f47c678">mxnet::Context</a>
</li>
<li>operator=()
: <a class="el" href="classmxnet_1_1NDArray.html#aa7a198e3e4c80a48cf92a913009aab8f">mxnet::NDArray</a>
, <a class="el" href="classmxnet_1_1TBlob.html#ad3263d9d1ef40678cd592be822d1c7be">mxnet::TBlob</a>
</li>
<li>operator==()
-: <a class="el" href="structmxnet_1_1Context.html#a8b8d414a429c12ab55cfa9ed13539393">mxnet::Context</a>
+: <a class="el" href="structmxnet_1_1Context.html#ad59267de7b56e3aebe86a6d0e9fca2f0">mxnet::Context</a>
</li>
<li>OprHandle
: <a class="el" href="classmxnet_1_1Engine.html#a832436e413a075291aa1a631942c3f01">mxnet::Engine</a>
@@ -102,9 +164,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_p.html b/versions/master/doxygen/functions_p.html
index d7ad8ac..e06fe68 100644
--- a/versions/master/doxygen/functions_p.html
+++ b/versions/master/doxygen/functions_p.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li class="current"><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,7 +122,7 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_p"></a>- p -</h3><ul>
+<h3><a class="anchor" id="index_p"></a>- p -</h3><ul>
<li>p_backward
: <a class="el" href="structNativeOpInfo.html#a60beae73be72aeb308b55aeed8cbd3c1">NativeOpInfo</a>
, <a class="el" href="structNDArrayOpInfo.html#afb5ee1e890c2499a5a845b21f4df518f">NDArrayOpInfo</a>
@@ -91,13 +153,13 @@ $(function() {
: <a class="el" href="classmxnet_1_1Executor.html#a3e261d44065848e434c527000d4390c8">mxnet::Executor</a>
</li>
<li>Print()
-: <a class="el" href="classmxnet_1_1Executor.html#ac391ee6cf8cf8afb60acd0480db2ae86">mxnet::Executor</a>
+: <a class="el" href="classmxnet_1_1Executor.html#a3803afcb87e4c69940c8d3d88a03e6ee">mxnet::Executor</a>
</li>
<li>ptr_
: <a class="el" href="structmxnet_1_1Resource.html#a7e9576783b7e9023375ffebd76a11806">mxnet::Resource</a>
</li>
<li>Pull()
-: <a class="el" href="classmxnet_1_1KVStore.html#ab03a9d2faa1d9a1920c6fec2f74fbb72">mxnet::KVStore</a>
+: <a class="el" href="classmxnet_1_1KVStore.html#a98e041793c133337cecc441e441ed721">mxnet::KVStore</a>
</li>
<li>Push()
: <a class="el" href="classmxnet_1_1Engine.html#ad213d6b1a7c1e0d4d41275b9efe5f097">mxnet::Engine</a>
@@ -113,9 +175,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_r.html b/versions/master/doxygen/functions_r.html
index 3ef15b2..0b81014 100644
--- a/versions/master/doxygen/functions_r.html
+++ b/versions/master/doxygen/functions_r.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li class="current"><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,7 +122,7 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_r"></a>- r -</h3><ul>
+<h3><a class="anchor" id="index_r"></a>- r -</h3><ul>
<li>req
: <a class="el" href="structmxnet_1_1Resource.html#ae841e25cf5ee4aed895ace7fa080edc4">mxnet::Resource</a>
</li>
@@ -74,10 +136,10 @@ $(function() {
: <a class="el" href="classmxnet_1_1OpStatePtr.html#ae43213b3a65073a87bf489350a58d78e">mxnet::OpStatePtr</a>
</li>
<li>Reshape()
-: <a class="el" href="classmxnet_1_1NDArray.html#a1d01269333b9226fffddc89ce3b7fb63">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#ada618436206b59aa3ffcdb8474d5051c">mxnet::NDArray</a>
</li>
<li>reshape()
-: <a class="el" href="classmxnet_1_1TBlob.html#a3b89df7835e39c652889b5cdb39235fa">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a0c66b10f5a17528cc858762b60125789">mxnet::TBlob</a>
</li>
<li>resource
: <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html#a6b62df4767fd50d6a1fd9ebd44193bef">mxnet::op::EnvArguments</a>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<table cellspacing="0" cellpadding="0">
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<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li class="current"><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
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onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/functions_s.html b/versions/master/doxygen/functions_s.html
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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</table>
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li class="current"><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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@@ -60,10 +122,10 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
-<h3><a id="index_s"></a>- s -</h3><ul>
+<h3><a class="anchor" id="index_s"></a>- s -</h3><ul>
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+: <a class="el" href="structmxnet_1_1Context.html#a90542e0930244392f9a81ed0f206b612">mxnet::Context</a>
+, <a class="el" href="classmxnet_1_1NDArray.html#a524662078483f8f46c6c71714fbfde0b">mxnet::NDArray</a>
</li>
<li>scalar
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<li>shape()
-: <a class="el" href="classmxnet_1_1NDArray.html#a2fc268d12168d39d9095829b3b9a3836">mxnet::NDArray</a>
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</li>
-<li>shape_
-: <a class="el" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">mxnet::TBlob</a>
+<li>shape_()
+: <a class="el" href="classmxnet_1_1NDArray.html#a18e75568a3a4fc14f271793dde914b56">mxnet::NDArray</a>
+, <a class="el" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">mxnet::TBlob</a>
</li>
<li>SignalForKill()
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</li>
<li>size
: <a class="el" href="structmxnet_1_1Storage_1_1Handle.html#ab93b9db0c83e4ef3803fb587377a74a0">mxnet::Storage::Handle</a>
-, <a class="el" href="classmxnet_1_1TBlob.html#a44c93b8a96203a1a85fa9d4b85e123c7">mxnet::TBlob</a>
+, <a class="el" href="classmxnet_1_1TBlob.html#a523fd58883de4b9cd0b1a2852daa64ae">mxnet::TBlob</a>
</li>
<li>Size()
-: <a class="el" href="classmxnet_1_1TBlob.html#a7b45f281d954cc716267a7b3ec0f514c">mxnet::TBlob</a>
+: <a class="el" href="classmxnet_1_1TBlob.html#a3b04db1348834e1576cfb2ed54959703">mxnet::TBlob</a>
</li>
<li>Slice()
-: <a class="el" href="classmxnet_1_1NDArray.html#a0cfa7466a652feb2258bb990453a0a7d">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a7acb6462c11e071949db2b0e1fbfd66e">mxnet::NDArray</a>
</li>
<li>stream
: <a class="el" href="structmxnet_1_1RunContext.html#a5c4294a44199f4056915e32a7f70fe0e">mxnet::RunContext</a>
</li>
<li>SyncCopyFromCPU()
-: <a class="el" href="classmxnet_1_1NDArray.html#ac390edfdd8a7dd99f69ea373f4d18e1a">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a422c70b16646f8a273e9b4936fbe2e37">mxnet::NDArray</a>
</li>
<li>SyncCopyToCPU()
-: <a class="el" href="classmxnet_1_1NDArray.html#afb3dbc357c53e1e0b980257f6c5e2cac">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a4819d2e6431ccdf4b9f8efa8eb0bd1fd">mxnet::NDArray</a>
</li>
<li>SyncFn
: <a class="el" href="classmxnet_1_1Engine.html#a07f30ab85fca436e1bbcc72cd4d8bb35">mxnet::Engine</a>
@@ -170,9 +233,9 @@ $(function() {
</div><!-- contents -->
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
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+ <td style="padding-left: 0.5em;">
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
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+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
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+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
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+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
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+<li>true
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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+ <ul class="tablist">
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+ <li><a href="files.html"><span>Files</span></a></li>
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <div id="navrow3" class="tabs2">
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+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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-$(function() {
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- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
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+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <span class="left">
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+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
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+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
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+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
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+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
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+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
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+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
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+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li class="current"><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -60,12 +122,12 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
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+<h3><a class="anchor" id="index_v"></a>- v -</h3><ul>
<li>Value()
: <a class="el" href="classmxnet_1_1IIterator.html#aef2e0b74766cd0283849300c8a081929">mxnet::IIterator< DType ></a>
</li>
<li>var()
-: <a class="el" href="classmxnet_1_1NDArray.html#a168da55c01cc5c7a218d975af6b5b1f9">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">mxnet::NDArray</a>
, <a class="el" href="structmxnet_1_1Resource.html#a5a8048bdda73fefa3af322aaaba50eb8">mxnet::Resource</a>
</li>
<li>VarHandle
@@ -78,9 +140,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_vars.html b/versions/master/doxygen/functions_vars.html
index 256fa86..eb85efd 100644
--- a/versions/master/doxygen/functions_vars.html
+++ b/versions/master/doxygen/functions_vars.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Members - Variables</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,80 @@
</table>
</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li class="current"><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="#index_a"><span>a</span></a></li>
+ <li><a href="#index_b"><span>b</span></a></li>
+ <li><a href="#index_c"><span>c</span></a></li>
+ <li><a href="#index_d"><span>d</span></a></li>
+ <li><a href="#index_e"><span>e</span></a></li>
+ <li><a href="#index_f"><span>f</span></a></li>
+ <li><a href="#index_i"><span>i</span></a></li>
+ <li><a href="#index_k"><span>k</span></a></li>
+ <li><a href="#index_l"><span>l</span></a></li>
+ <li><a href="#index_n"><span>n</span></a></li>
+ <li><a href="#index_p"><span>p</span></a></li>
+ <li><a href="#index_r"><span>r</span></a></li>
+ <li><a href="#index_s"><span>s</span></a></li>
+ <li><a href="#index_t"><span>t</span></a></li>
+ <li><a href="#index_u"><span>u</span></a></li>
+ <li class="current"><a href="#index_v"><span>v</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,7 +115,7 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_a"></a>- a -</h3><ul>
+<h3><a class="anchor" id="index_a"></a>- a -</h3><ul>
<li>ag_node
: <a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ada6b942a22f14e3d61fccb645f9e9a27">mxnet::autograd::AGNodeEntry</a>
</li>
@@ -70,20 +125,17 @@ $(function() {
</ul>
-<h3><a id="index_b"></a>- b -</h3><ul>
+<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
<li>backward
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-, <a class="el" href="structNDArrayOpInfo.html#ac8a4bb0b4a71f372e5abb8ded36dd61c">NDArrayOpInfo</a>
-</li>
-<li>barrier_before_exit_
-: <a class="el" href="classmxnet_1_1KVStore.html#aa8ba7abf8334326cab19eb568cb31cfa">mxnet::KVStore</a>
+: <a class="el" href="structNativeOpInfo.html#a2d571a373ff0ca0da5d96295cc03f465">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#a6cafb44b639ed5256fe80a976e351cc1">NDArrayOpInfo</a>
</li>
</ul>
-<h3><a id="index_c"></a>- c -</h3><ul>
+<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
<li>callbacks
-: <a class="el" href="structMXCallbackList.html#a3957dba7b237dc6baff2ba578c652f76">MXCallbackList</a>
+: <a class="el" href="structMXCallbackList.html#ad56325e57f5fffc2b920eb3f8f378199">MXCallbackList</a>
</li>
<li>contexts
: <a class="el" href="structMXCallbackList.html#a332323a5f414bd3f5e423788804a0dcd">MXCallbackList</a>
@@ -95,7 +147,7 @@ $(function() {
</ul>
-<h3><a id="index_d"></a>- d -</h3><ul>
+<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
<li>data
: <a class="el" href="structmxnet_1_1DataBatch.html#acc98dd6f48816ca7462352a929e48959">mxnet::DataBatch</a>
, <a class="el" href="structmxnet_1_1DataInst.html#a3511bbb64add286c9cd1b03c70d4b111">mxnet::DataInst</a>
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: <a class="el" href="classmxnet_1_1IIterator.html#a65cd81ecf71d54a17a94ca65d27aa014">mxnet::IIterator< DType ></a>
</li>
<li>declare_backward_dependency
-: <a class="el" href="structNDArrayOpInfo.html#af73bb5dbfdc64e2442b98398f0b63ce0">NDArrayOpInfo</a>
+: <a class="el" href="structNDArrayOpInfo.html#a4327dd20a65eb3a7241f67ede18f9950">NDArrayOpInfo</a>
</li>
<li>dev_id
: <a class="el" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">mxnet::Context</a>
@@ -122,7 +174,7 @@ $(function() {
</ul>
-<h3><a id="index_e"></a>- e -</h3><ul>
+<h3><a class="anchor" id="index_e"></a>- e -</h3><ul>
<li>extra_data
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, <a class="el" href="structmxnet_1_1DataInst.html#a311b93ed629984fdcb29cb346d3062ee">mxnet::DataInst</a>
@@ -130,15 +182,15 @@ $(function() {
</ul>
-<h3><a id="index_f"></a>- f -</h3><ul>
+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
<li>forward
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-, <a class="el" href="structNDArrayOpInfo.html#ad39c62943775f93fd5cb1fdc39aa38a5">NDArrayOpInfo</a>
+: <a class="el" href="structNativeOpInfo.html#a65f675dec9ef798a96122e91d4cf3fa8">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#ac11f40fb25fa4abd275b6f1c7b3d3213">NDArrayOpInfo</a>
</li>
</ul>
-<h3><a id="index_i"></a>- i -</h3><ul>
+<h3><a class="anchor" id="index_i"></a>- i -</h3><ul>
<li>id
: <a class="el" href="structmxnet_1_1Resource.html#aa1fc8578c0fa849afe7da29c1cf1308b">mxnet::Resource</a>
</li>
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</li>
<li>infer_shape
-: <a class="el" href="structNativeOpInfo.html#a2d8f03e8586a1a1cb6b46ab8872a214a">NativeOpInfo</a>
-, <a class="el" href="structNDArrayOpInfo.html#a526a6092e581aea808b180758c2c4610">NDArrayOpInfo</a>
+: <a class="el" href="structNativeOpInfo.html#af4c12b25f9d708835619b768db5805ff">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#a139351ad186cde67a376d2f9d6682baf">NDArrayOpInfo</a>
</li>
<li>is_train
: <a class="el" href="structmxnet_1_1OpContext.html#abd7405c065167e4e15af032b1e0a5c42">mxnet::OpContext</a>
@@ -157,7 +209,7 @@ $(function() {
</ul>
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<li>key_var_num_args
: <a class="el" href="structmxnet_1_1OperatorPropertyReg.html#af3aee96c6a2289e7554d7503186a540c">mxnet::OperatorPropertyReg</a>
</li>
@@ -173,19 +225,19 @@ $(function() {
</ul>
-<h3><a id="index_l"></a>- l -</h3><ul>
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<li>list_arguments
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-, <a class="el" href="structNDArrayOpInfo.html#abc701d8c032a1c5b9358de8ab060d9c2">NDArrayOpInfo</a>
+: <a class="el" href="structNativeOpInfo.html#ab1f5d01bbb1d6507c23e640ac3eea834">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#ad6bb579606e6bd83569d035fa92a857c">NDArrayOpInfo</a>
</li>
<li>list_outputs
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-, <a class="el" href="structNDArrayOpInfo.html#ab99abece52b77824dde5df98837f4f7b">NDArrayOpInfo</a>
+: <a class="el" href="structNativeOpInfo.html#a1dfb6e3206e76dcf755ae0bca655081b">NativeOpInfo</a>
+, <a class="el" href="structNDArrayOpInfo.html#acd6f9dbac22ba35f0f9ac5062dc5c8ec">NDArrayOpInfo</a>
</li>
</ul>
-<h3><a id="index_n"></a>- n -</h3><ul>
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<li>name
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</li>
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</ul>
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+<h3><a class="anchor" id="index_p"></a>- p -</h3><ul>
<li>p_backward
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</ul>
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: <a class="el" href="structmxnet_1_1Resource.html#ae841e25cf5ee4aed895ace7fa080edc4">mxnet::Resource</a>
</li>
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</ul>
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</li>
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+<h3><a class="anchor" id="index_t"></a>- t -</h3><ul>
+<li>true
+: <a class="el" href="classmxnet_1_1KVStore.html#a265215cd3de11dd36d9e79330a535615">mxnet::KVStore</a>
+</li>
<li>type
: <a class="el" href="structmxnet_1_1ResourceRequest.html#ad67cb809ea1e0db24629ffbd3db00a63">mxnet::ResourceRequest</a>
</li>
@@ -285,14 +340,14 @@ $(function() {
</ul>
-<h3><a id="index_u"></a>- u -</h3><ul>
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<li>updater_
: <a class="el" href="classmxnet_1_1KVStore.html#a01befb768a81a8e7a366708240ee6d06">mxnet::KVStore</a>
</li>
</ul>
-<h3><a id="index_v"></a>- v -</h3><ul>
+<h3><a class="anchor" id="index_v"></a>- v -</h3><ul>
<li>var
: <a class="el" href="structmxnet_1_1Resource.html#a5a8048bdda73fefa3af322aaaba50eb8">mxnet::Resource</a>
</li>
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</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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</html>
diff --git a/versions/master/doxygen/functions_w.html b/versions/master/doxygen/functions_w.html
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<title>mxnet: Class Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,87 @@
</table>
</div>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="functions.html"><span>All</span></a></li>
+ <li><a href="functions_func.html"><span>Functions</span></a></li>
+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
+ <li><a href="functions_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="functions_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li class="current"><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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<li>WaitForAll()
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</li>
<li>WaitToRead()
-: <a class="el" href="classmxnet_1_1NDArray.html#a1cf4c49e6925d35586f75a1c9bdb51e4">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#aedd79a43d5fa4173f3ddd719fb939fbc">mxnet::NDArray</a>
</li>
<li>WaitToWrite()
-: <a class="el" href="classmxnet_1_1NDArray.html#ad8599450a4ba06b5890c5d06bd024d8e">mxnet::NDArray</a>
+: <a class="el" href="classmxnet_1_1NDArray.html#a4b88b5d86ec9ddf4ca96701107337461">mxnet::NDArray</a>
</li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/functions_~.html b/versions/master/doxygen/functions_~.html
new file mode 100644
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+++ b/versions/master/doxygen/functions_~.html
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+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
+<title>mxnet: Class Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+</head>
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+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+ <td style="padding-left: 0.5em;">
+ <div id="projectname">mxnet
+ </div>
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+</table>
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+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.6 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li class="current"><a href="functions.html"><span>Class Members</span></a></li>
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+ <ul class="tablist">
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+ <li><a href="functions_vars.html"><span>Variables</span></a></li>
+ <li><a href="functions_type.html"><span>Typedefs</span></a></li>
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+ <li><a href="functions_rela.html"><span>Related Functions</span></a></li>
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+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="functions.html#index_:"><span>:</span></a></li>
+ <li><a href="functions__.html#index__"><span>_</span></a></li>
+ <li><a href="functions_a.html#index_a"><span>a</span></a></li>
+ <li><a href="functions_b.html#index_b"><span>b</span></a></li>
+ <li><a href="functions_c.html#index_c"><span>c</span></a></li>
+ <li><a href="functions_d.html#index_d"><span>d</span></a></li>
+ <li><a href="functions_e.html#index_e"><span>e</span></a></li>
+ <li><a href="functions_f.html#index_f"><span>f</span></a></li>
+ <li><a href="functions_g.html#index_g"><span>g</span></a></li>
+ <li><a href="functions_i.html#index_i"><span>i</span></a></li>
+ <li><a href="functions_k.html#index_k"><span>k</span></a></li>
+ <li><a href="functions_l.html#index_l"><span>l</span></a></li>
+ <li><a href="functions_m.html#index_m"><span>m</span></a></li>
+ <li><a href="functions_n.html#index_n"><span>n</span></a></li>
+ <li><a href="functions_o.html#index_o"><span>o</span></a></li>
+ <li><a href="functions_p.html#index_p"><span>p</span></a></li>
+ <li><a href="functions_r.html#index_r"><span>r</span></a></li>
+ <li><a href="functions_s.html#index_s"><span>s</span></a></li>
+ <li><a href="functions_t.html#index_t"><span>t</span></a></li>
+ <li><a href="functions_u.html#index_u"><span>u</span></a></li>
+ <li><a href="functions_v.html#index_v"><span>v</span></a></li>
+ <li><a href="functions_w.html#index_w"><span>w</span></a></li>
+ <li class="current"><a href="functions_~.html#index_~"><span>~</span></a></li>
+ </ul>
+ </div>
+</div><!-- top -->
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0"
+ name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
+
+<h3><a class="anchor" id="index_~"></a>- ~ -</h3><ul>
+<li>~Engine()
+: <a class="el" href="classmxnet_1_1Engine.html#aff025321827e15096c02342225f2395b">mxnet::Engine</a>
+</li>
+<li>~Executor()
+: <a class="el" href="classmxnet_1_1Executor.html#adf445ff665c954061889d6bbee3c1c2a">mxnet::Executor</a>
+</li>
+<li>~IIterator()
+: <a class="el" href="classmxnet_1_1IIterator.html#a6e9049dca03b889bba0d18338e286070">mxnet::IIterator< DType ></a>
+</li>
+<li>~KVStore()
+: <a class="el" href="classmxnet_1_1KVStore.html#a02e23071eb46464b58f2e2bf5654d6d6">mxnet::KVStore</a>
+</li>
+<li>~ObjectPool()
+: <a class="el" href="classmxnet_1_1common_1_1ObjectPool.html#aa5ff3f223210e5f3d0881c272462aedb">mxnet::common::ObjectPool< T ></a>
+</li>
+<li>~Operator()
+: <a class="el" href="classmxnet_1_1Operator.html#a4d34cc792d7271bd8b5b60eaa0b55767">mxnet::Operator</a>
+</li>
+<li>~OperatorProperty()
+: <a class="el" href="classmxnet_1_1OperatorProperty.html#a610af8e9cb560104301ccd1efa2a96fd">mxnet::OperatorProperty</a>
+</li>
+<li>~ResourceManager()
+: <a class="el" href="classmxnet_1_1ResourceManager.html#a2b53c0843ddbaf7ccb9e06f448b20cac">mxnet::ResourceManager</a>
+</li>
+<li>~SimpleOpRegEntry()
+: <a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a94815080350681ad70e7815da1bc0eae">mxnet::op::SimpleOpRegEntry</a>
+</li>
+<li>~Storage()
+: <a class="el" href="classmxnet_1_1Storage.html#af1e1bfea9236ccfba64344226416b04a">mxnet::Storage</a>
+</li>
+</ul>
+</div><!-- contents -->
+<!-- start footer part -->
+<hr class="footer"/><address class="footer"><small>
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
+<img class="footer" src="doxygen.png" alt="doxygen"/>
+</a> 1.8.6
+</small></address>
+</body>
+</html>
diff --git a/versions/master/doxygen/globals.html b/versions/master/doxygen/globals.html
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,73 @@
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</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="globals.html"><span>All</span></a></li>
+ <li><a href="globals_func.html"><span>Functions</span></a></li>
+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="globals_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li class="current"><a href="globals.html#index_a"><span>a</span></a></li>
+ <li><a href="globals_c.html#index_c"><span>c</span></a></li>
+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li><a href="globals_e.html#index_e"><span>e</span></a></li>
+ <li><a href="globals_f.html#index_f"><span>f</span></a></li>
+ <li><a href="globals_k.html#index_k"><span>k</span></a></li>
+ <li><a href="globals_l.html#index_l"><span>l</span></a></li>
+ <li><a href="globals_m.html#index_m"><span>m</span></a></li>
+ <li><a href="globals_n.html#index_n"><span>n</span></a></li>
+ <li><a href="globals_p.html#index_p"><span>p</span></a></li>
+ <li><a href="globals_r.html#index_r"><span>r</span></a></li>
+ <li><a href="globals_s.html#index_s"><span>s</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,7 +108,7 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
-<h3><a id="index_a"></a>- a -</h3><ul>
+<h3><a class="anchor" id="index_a"></a>- a -</h3><ul>
<li>ASSIGN_DISPATCH
: <a class="el" href="operator__util_8h.html#a0e0ffc581a0c4a40815110163625b517">operator_util.h</a>
</li>
@@ -74,9 +122,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/globals_c.html b/versions/master/doxygen/globals_c.html
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<head>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: File Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
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<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: File Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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</div>
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
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+ <span class="left">
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="globals.html"><span>All</span></a></li>
+ <li><a href="globals_func.html"><span>Functions</span></a></li>
+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="globals_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="globals.html#index_a"><span>a</span></a></li>
+ <li><a href="globals_c.html#index_c"><span>c</span></a></li>
+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li class="current"><a href="globals_e.html#index_e"><span>e</span></a></li>
+ <li><a href="globals_f.html#index_f"><span>f</span></a></li>
+ <li><a href="globals_k.html#index_k"><span>k</span></a></li>
+ <li><a href="globals_l.html#index_l"><span>l</span></a></li>
+ <li><a href="globals_m.html#index_m"><span>m</span></a></li>
+ <li><a href="globals_n.html#index_n"><span>n</span></a></li>
+ <li><a href="globals_p.html#index_p"><span>p</span></a></li>
+ <li><a href="globals_r.html#index_r"><span>r</span></a></li>
+ <li><a href="globals_s.html#index_s"><span>s</span></a></li>
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onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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: <a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">c_api.h</a>
</li>
<li>ExecutorMonitorCallback
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: File Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
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</td>
@@ -29,26 +30,57 @@
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- $(document).ready(function() { init_search(); });
-});
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="globals.html"><span>All</span></a></li>
+ <li><a href="globals_func.html"><span>Functions</span></a></li>
+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li class="current"><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="globals_eval.html"><span>Enumerator</span></a></li>
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onmouseover="return searchBox.OnSearchSelectShow()"
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/globals_eval.html b/versions/master/doxygen/globals_eval.html
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: File Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,57 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
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+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="globals.html"><span>All</span></a></li>
+ <li><a href="globals_func.html"><span>Functions</span></a></li>
+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li class="current"><a href="globals_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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</div>
</td>
@@ -29,26 +30,73 @@
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</div>
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-$(function() {
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- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
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+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
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+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li><a href="globals_e.html#index_e"><span>e</span></a></li>
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+ <li><a href="globals_k.html#index_k"><span>k</span></a></li>
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
<li>flip()
: <a class="el" href="c__lapack__api_8h.html#ae6920bb50ceca504aefa6082a6158b98">c_lapack_api.h</a>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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<head>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
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<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
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+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
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</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
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+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="globals_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
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+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
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+ <li><a href="#index_l"><span>l</span></a></li>
+ <li><a href="#index_m"><span>m</span></a></li>
+ <li class="current"><a href="#index_s"><span>s</span></a></li>
+ </ul>
+ </div>
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onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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<li>dposv_()
: <a class="el" href="c__lapack__api_8h.html#ac9fe022dd825c3bd86d3b175d0a1c02b">c_lapack_api.h</a>
</li>
</ul>
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<li>loup()
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</li>
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<li>MXAutogradBackward()
: <a class="el" href="c__api_8h.html#a133d10a566764530bf3172e52afbe561">c_api.h</a>
</li>
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+: <a class="el" href="c__api_8h.html#a57a5d1ed585f9f431a31f921b4ca00ab">c_api.h</a>
+</li>
<li>MXAutogradComputeGradient()
: <a class="el" href="c__api_8h.html#a4ea5441aa16bff2121855a5afb7be84e">c_api.h</a>
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: <a class="el" href="c__api_8h.html#a53f3bbc41e275485d1d6cd21f40abb7f">c_api.h</a>
</li>
+<li>MXAutogradSetIsRecording()
+: <a class="el" href="c__api_8h.html#a75939e5be09a150d4d8905c9fd6e986e">c_api.h</a>
+</li>
<li>MXAutogradSetIsTraining()
: <a class="el" href="c__api_8h.html#a9cf2b0aa3ff66119fe793f6ed82b61b8">c_api.h</a>
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<li>MXExecutorBackward()
: <a class="el" href="c__api_8h.html#ad8f92bb15e11058abe12959cfc4dcbb3">c_api.h</a>
</li>
+<li>MXExecutorBackwardEx()
+: <a class="el" href="c__api_8h.html#a3a211f3e0c6270271ce4fe79228dd0c2">c_api.h</a>
+</li>
<li>MXExecutorBind()
: <a class="el" href="c__api_8h.html#a10a6deacde18b7d52b44eee0ddddf0e1">c_api.h</a>
</li>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
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+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="globals_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
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+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
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+ <li><a href="globals_c.html#index_c"><span>c</span></a></li>
+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li><a href="globals_e.html#index_e"><span>e</span></a></li>
+ <li><a href="globals_f.html#index_f"><span>f</span></a></li>
+ <li class="current"><a href="globals_k.html#index_k"><span>k</span></a></li>
+ <li><a href="globals_l.html#index_l"><span>l</span></a></li>
+ <li><a href="globals_m.html#index_m"><span>m</span></a></li>
+ <li><a href="globals_n.html#index_n"><span>n</span></a></li>
+ <li><a href="globals_p.html#index_p"><span>p</span></a></li>
+ <li><a href="globals_r.html#index_r"><span>r</span></a></li>
+ <li><a href="globals_s.html#index_s"><span>s</span></a></li>
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</div><!-- top -->
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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<li>kCustomOpBackward
: <a class="el" href="c__api_8h.html#aa2395b69772d66cb5c6c4cbe47749792afe6891ef7e38df52ac25db00a2ef73c6">c_api.h</a>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
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<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
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- $(document).ready(function() { init_search(); });
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
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+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li><a href="globals_e.html#index_e"><span>e</span></a></li>
+ <li><a href="globals_f.html#index_f"><span>f</span></a></li>
+ <li><a href="globals_k.html#index_k"><span>k</span></a></li>
+ <li class="current"><a href="globals_l.html#index_l"><span>l</span></a></li>
+ <li><a href="globals_m.html#index_m"><span>m</span></a></li>
+ <li><a href="globals_n.html#index_n"><span>n</span></a></li>
+ <li><a href="globals_p.html#index_p"><span>p</span></a></li>
+ <li><a href="globals_r.html#index_r"><span>r</span></a></li>
+ <li><a href="globals_s.html#index_s"><span>s</span></a></li>
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onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div class="contents">
<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
-<h3><a id="index_l"></a>- l -</h3><ul>
+<h3><a class="anchor" id="index_l"></a>- l -</h3><ul>
<li>loup()
: <a class="el" href="c__lapack__api_8h.html#af111a2f312a185185d0937bd091637e4">c_lapack_api.h</a>
</li>
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</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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diff --git a/versions/master/doxygen/globals_m.html b/versions/master/doxygen/globals_m.html
index 7d9c4ea..06749e1 100644
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@@ -3,15 +3,16 @@
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: File Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,73 @@
</table>
</div>
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+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="globals.html"><span>All</span></a></li>
+ <li><a href="globals_func.html"><span>Functions</span></a></li>
+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="globals_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="globals.html#index_a"><span>a</span></a></li>
+ <li><a href="globals_c.html#index_c"><span>c</span></a></li>
+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li><a href="globals_e.html#index_e"><span>e</span></a></li>
+ <li><a href="globals_f.html#index_f"><span>f</span></a></li>
+ <li><a href="globals_k.html#index_k"><span>k</span></a></li>
+ <li><a href="globals_l.html#index_l"><span>l</span></a></li>
+ <li class="current"><a href="globals_m.html#index_m"><span>m</span></a></li>
+ <li><a href="globals_n.html#index_n"><span>n</span></a></li>
+ <li><a href="globals_p.html#index_p"><span>p</span></a></li>
+ <li><a href="globals_r.html#index_r"><span>r</span></a></li>
+ <li><a href="globals_s.html#index_s"><span>s</span></a></li>
+ </ul>
+ </div>
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onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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<div class="contents">
<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
-<h3><a id="index_m"></a>- m -</h3><ul>
+<h3><a class="anchor" id="index_m"></a>- m -</h3><ul>
<li>mx_float
: <a class="el" href="c__api_8h.html#a4e709067707d9f90c534bd65494ae668">c_api.h</a>
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<li>MXAutogradBackward()
: <a class="el" href="c__api_8h.html#a133d10a566764530bf3172e52afbe561">c_api.h</a>
</li>
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+: <a class="el" href="c__api_8h.html#a57a5d1ed585f9f431a31f921b4ca00ab">c_api.h</a>
+</li>
<li>MXAutogradComputeGradient()
: <a class="el" href="c__api_8h.html#a4ea5441aa16bff2121855a5afb7be84e">c_api.h</a>
</li>
<li>MXAutogradMarkVariables()
: <a class="el" href="c__api_8h.html#a53f3bbc41e275485d1d6cd21f40abb7f">c_api.h</a>
</li>
+<li>MXAutogradSetIsRecording()
+: <a class="el" href="c__api_8h.html#a75939e5be09a150d4d8905c9fd6e986e">c_api.h</a>
+</li>
<li>MXAutogradSetIsTraining()
: <a class="el" href="c__api_8h.html#a9cf2b0aa3ff66119fe793f6ed82b61b8">c_api.h</a>
</li>
@@ -120,6 +174,9 @@ $(function() {
<li>MXExecutorBackward()
: <a class="el" href="c__api_8h.html#ad8f92bb15e11058abe12959cfc4dcbb3">c_api.h</a>
</li>
+<li>MXExecutorBackwardEx()
+: <a class="el" href="c__api_8h.html#a3a211f3e0c6270271ce4fe79228dd0c2">c_api.h</a>
+</li>
<li>MXExecutorBind()
: <a class="el" href="c__api_8h.html#a10a6deacde18b7d52b44eee0ddddf0e1">c_api.h</a>
</li>
@@ -163,7 +220,7 @@ $(function() {
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</li>
<li>MXGenericCallback
-: <a class="el" href="c__api_8h.html#a0ee6dd8389c0dacae0f7c48156ec2f34">c_api.h</a>
+: <a class="el" href="c__api_8h.html#a581410d3cb679f2d0fc2f65fca91ec17">c_api.h</a>
</li>
<li>MXGetFunction()
: <a class="el" href="c__api_8h.html#a9e2a1cb559d69daf541dac88c07409a8">c_api.h</a>
@@ -236,7 +293,7 @@ $(function() {
: <a class="el" href="c__api_8h.html#a5de45491037df7bf6b757b627100acd7">c_api.h</a>
</li>
<li>MXKVStoreServerController
-: <a class="el" href="c__api_8h.html#a6968ac8b790fb40a674a364a2a74cba6">c_api.h</a>
+: <a class="el" href="c__api_8h.html#a31f639b22167ac51b67381b16dfd5634">c_api.h</a>
</li>
<li>MXKVStoreSetBarrierBeforeExit()
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</li>
<li>MXKVStoreUpdater
-: <a class="el" href="c__api_8h.html#a80aad02a0ebd29de5ad0b65b2c8ccdb3">c_api.h</a>
+: <a class="el" href="c__api_8h.html#af59143cc01b560bb13f25a289b83f37b">c_api.h</a>
</li>
<li>MXListAllOpNames()
: <a class="el" href="c__api_8h.html#ac5a3fafe6616c4d246b24e4d11c65238">c_api.h</a>
@@ -573,9 +630,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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</html>
diff --git a/versions/master/doxygen/globals_n.html b/versions/master/doxygen/globals_n.html
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-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: File Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,73 @@
</table>
</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="globals.html"><span>All</span></a></li>
+ <li><a href="globals_func.html"><span>Functions</span></a></li>
+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="globals_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="globals.html#index_a"><span>a</span></a></li>
+ <li><a href="globals_c.html#index_c"><span>c</span></a></li>
+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li><a href="globals_e.html#index_e"><span>e</span></a></li>
+ <li><a href="globals_f.html#index_f"><span>f</span></a></li>
+ <li><a href="globals_k.html#index_k"><span>k</span></a></li>
+ <li><a href="globals_l.html#index_l"><span>l</span></a></li>
+ <li><a href="globals_m.html#index_m"><span>m</span></a></li>
+ <li class="current"><a href="globals_n.html#index_n"><span>n</span></a></li>
+ <li><a href="globals_p.html#index_p"><span>p</span></a></li>
+ <li><a href="globals_r.html#index_r"><span>r</span></a></li>
+ <li><a href="globals_s.html#index_s"><span>s</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,7 +108,7 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
-<h3><a id="index_n"></a>- n -</h3><ul>
+<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
<li>NDArrayHandle
: <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">c_api.h</a>
</li>
@@ -71,9 +119,9 @@ $(function() {
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
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<tbody>
<tr style="height: 56px;">
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+ <td style="padding-left: 0.5em;">
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</div>
</td>
@@ -29,26 +30,73 @@
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</div>
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="globals.html"><span>All</span></a></li>
+ <li><a href="globals_func.html"><span>Functions</span></a></li>
+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="globals_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="globals.html#index_a"><span>a</span></a></li>
+ <li><a href="globals_c.html#index_c"><span>c</span></a></li>
+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li><a href="globals_e.html#index_e"><span>e</span></a></li>
+ <li><a href="globals_f.html#index_f"><span>f</span></a></li>
+ <li><a href="globals_k.html#index_k"><span>k</span></a></li>
+ <li><a href="globals_l.html#index_l"><span>l</span></a></li>
+ <li><a href="globals_m.html#index_m"><span>m</span></a></li>
+ <li><a href="globals_n.html#index_n"><span>n</span></a></li>
+ <li class="current"><a href="globals_p.html#index_p"><span>p</span></a></li>
+ <li><a href="globals_r.html#index_r"><span>r</span></a></li>
+ <li><a href="globals_s.html#index_s"><span>s</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<h3><a class="anchor" id="index_p"></a>- p -</h3><ul>
<li>PredictorHandle
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/globals_r.html b/versions/master/doxygen/globals_r.html
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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<script type="text/javascript" src="search/search.js"></script>
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+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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-$(function() {
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- $(document).ready(function() { init_search(); });
-});
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
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+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="globals.html"><span>All</span></a></li>
+ <li><a href="globals_func.html"><span>Functions</span></a></li>
+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="globals_eval.html"><span>Enumerator</span></a></li>
+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="globals.html#index_a"><span>a</span></a></li>
+ <li><a href="globals_c.html#index_c"><span>c</span></a></li>
+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li><a href="globals_e.html#index_e"><span>e</span></a></li>
+ <li><a href="globals_f.html#index_f"><span>f</span></a></li>
+ <li><a href="globals_k.html#index_k"><span>k</span></a></li>
+ <li><a href="globals_l.html#index_l"><span>l</span></a></li>
+ <li><a href="globals_m.html#index_m"><span>m</span></a></li>
+ <li><a href="globals_n.html#index_n"><span>n</span></a></li>
+ <li><a href="globals_p.html#index_p"><span>p</span></a></li>
+ <li class="current"><a href="globals_r.html#index_r"><span>r</span></a></li>
+ <li><a href="globals_s.html#index_s"><span>s</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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<div class="contents">
<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
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+<h3><a class="anchor" id="index_r"></a>- r -</h3><ul>
<li>RecordIOHandle
: <a class="el" href="c__api_8h.html#a4cc6d9b5a408760fc893c0b568d6aeae">c_api.h</a>
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@@ -71,9 +119,9 @@ $(function() {
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<body>
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<tbody>
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+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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-$(function() {
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- $(document).ready(function() { init_search(); });
-});
-</script>
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="globals.html"><span>All</span></a></li>
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+ <li><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
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+ <li><a href="globals_defs.html"><span>Macros</span></a></li>
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+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="globals.html#index_a"><span>a</span></a></li>
+ <li><a href="globals_c.html#index_c"><span>c</span></a></li>
+ <li><a href="globals_d.html#index_d"><span>d</span></a></li>
+ <li><a href="globals_e.html#index_e"><span>e</span></a></li>
+ <li><a href="globals_f.html#index_f"><span>f</span></a></li>
+ <li><a href="globals_k.html#index_k"><span>k</span></a></li>
+ <li><a href="globals_l.html#index_l"><span>l</span></a></li>
+ <li><a href="globals_m.html#index_m"><span>m</span></a></li>
+ <li><a href="globals_n.html#index_n"><span>n</span></a></li>
+ <li><a href="globals_p.html#index_p"><span>p</span></a></li>
+ <li><a href="globals_r.html#index_r"><span>r</span></a></li>
+ <li class="current"><a href="globals_s.html#index_s"><span>s</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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<div class="contents">
<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
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<li>sposv_()
: <a class="el" href="c__lapack__api_8h.html#a960d4d5f204ab766924726b650c7105c">c_lapack_api.h</a>
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@@ -71,9 +119,9 @@ $(function() {
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<body>
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<table cellspacing="0" cellpadding="0">
<tbody>
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </div>
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+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li class="current"><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
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+ <li><a href="globals.html"><span>All</span></a></li>
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+ <li class="current"><a href="globals_type.html"><span>Typedefs</span></a></li>
+ <li><a href="globals_enum.html"><span>Enumerations</span></a></li>
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onmouseover="return searchBox.OnSearchSelectShow()"
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-: <a class="el" href="c__api_8h.html#a97d01e308e267e939b4d1eebaf2f11d9">c_api.h</a>
+: <a class="el" href="c__api_8h.html#ae8feb033b715209653ea91e089d060b0">c_api.h</a>
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<li>CustomOpCreateFunc
-: <a class="el" href="c__api_8h.html#a6c79906616c8c56a409a97c4111a6b88">c_api.h</a>
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-: <a class="el" href="c__api_8h.html#a0056a2f79906cce0f8aa78966c2d4a6c">c_api.h</a>
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</li>
<li>CustomOpFBFunc
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</li>
<li>CustomOpInferShapeFunc
-: <a class="el" href="c__api_8h.html#a004103d4a52000a4cdb377c577aa7a4d">c_api.h</a>
+: <a class="el" href="c__api_8h.html#a9a559987f509f9939030addd565d34a3">c_api.h</a>
</li>
<li>CustomOpInferTypeFunc
-: <a class="el" href="c__api_8h.html#a59855e0408519e73b05419cd0d9d976e">c_api.h</a>
+: <a class="el" href="c__api_8h.html#a09da22ebe9a09b084afc386fa7b0ef53">c_api.h</a>
</li>
<li>CustomOpListFunc
-: <a class="el" href="c__api_8h.html#a5bcf4b1e26b22c9363a016bd9d27085c">c_api.h</a>
+: <a class="el" href="c__api_8h.html#add8141d7f51e3a77544cc7b79433d16e">c_api.h</a>
</li>
<li>CustomOpPropCreator
-: <a class="el" href="c__api_8h.html#a470eba0beb11ed2fd858c50c9b2be33a">c_api.h</a>
+: <a class="el" href="c__api_8h.html#a3a7fd875ee6aad3f20981764626a6c7b">c_api.h</a>
</li>
<li>DataIterCreator
: <a class="el" href="c__api_8h.html#a0b9b13ccb06a6a4f05d211261fa883dd">c_api.h</a>
@@ -102,7 +134,7 @@ $(function() {
: <a class="el" href="c__api_8h.html#a6f224ee35892507405ced8429d874726">c_api.h</a>
</li>
<li>ExecutorMonitorCallback
-: <a class="el" href="c__api_8h.html#a2731805437dc3d76201fc21154b1337c">c_api.h</a>
+: <a class="el" href="c__api_8h.html#aa7071c027141653df82d2b72db566acd">c_api.h</a>
</li>
<li>FunctionHandle
: <a class="el" href="c__api_8h.html#a197f06e1d873d34572b679532c268d35">c_api.h</a>
@@ -119,13 +151,13 @@ $(function() {
, <a class="el" href="c__api_8h.html#a796e7ef8260ef40b24af7d1082aa2d84">c_api.h</a>
</li>
<li>MXGenericCallback
-: <a class="el" href="c__api_8h.html#a0ee6dd8389c0dacae0f7c48156ec2f34">c_api.h</a>
+: <a class="el" href="c__api_8h.html#a581410d3cb679f2d0fc2f65fca91ec17">c_api.h</a>
</li>
<li>MXKVStoreServerController
-: <a class="el" href="c__api_8h.html#a6968ac8b790fb40a674a364a2a74cba6">c_api.h</a>
+: <a class="el" href="c__api_8h.html#a31f639b22167ac51b67381b16dfd5634">c_api.h</a>
</li>
<li>MXKVStoreUpdater
-: <a class="el" href="c__api_8h.html#a80aad02a0ebd29de5ad0b65b2c8ccdb3">c_api.h</a>
+: <a class="el" href="c__api_8h.html#af59143cc01b560bb13f25a289b83f37b">c_api.h</a>
</li>
<li>NDArrayHandle
: <a class="el" href="c__api_8h.html#a99c2939783499d2a5ce0d76881dce9df">c_api.h</a>
@@ -149,9 +181,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/graph_legend.html b/versions/master/doxygen/graph_legend.html
index 2cfd53f..79020d4 100644
--- a/versions/master/doxygen/graph_legend.html
+++ b/versions/master/doxygen/graph_legend.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Graph Legend</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,41 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -63,9 +79,44 @@ $(function() {
</div><!--header-->
<div class="contents">
<p>This page explains how to interpret the graphs that are generated by doxygen.</p>
-<p>Consider the following example: </p><div class="fragment"><div class="line">/*! Invisible class because of truncation */</div><div class="line">class Invisible { };</div><div class="line"></div><div class="line">/*! Truncated class, inheritance relation is hidden */</div><div class="line">class Truncated : public Invisible { };</div><div class="line"></div><div class="line">/* Class not documented with doxygen comments */</div><div class="line">class Undocumented { };</div><div class= [...]
+<p>Consider the following example: </p>
+<div class="fragment"><div class="line"><span class="comment">/*! Invisible class because of truncation */</span></div>
+<div class="line"><span class="keyword">class </span>Invisible { };</div>
+<div class="line"><span class="comment"></span></div>
+<div class="line"><span class="comment">/*! Truncated class, inheritance relation is hidden */</span></div>
+<div class="line"><span class="keyword">class </span>Truncated : <span class="keyword">public</span> Invisible { };</div>
+<div class="line"></div>
+<div class="line"><span class="comment">/* Class not documented with doxygen comments */</span></div>
+<div class="line"><span class="keyword">class </span>Undocumented { };</div>
+<div class="line"><span class="comment"></span></div>
+<div class="line"><span class="comment">/*! Class that is inherited using public inheritance */</span></div>
+<div class="line"><span class="keyword">class </span>PublicBase : <span class="keyword">public</span> Truncated { };</div>
+<div class="line"><span class="comment"></span></div>
+<div class="line"><span class="comment">/*! A template class */</span></div>
+<div class="line"><span class="keyword">template</span><<span class="keyword">class</span> T> <span class="keyword">class </span>Templ { };</div>
+<div class="line"><span class="comment"></span></div>
+<div class="line"><span class="comment">/*! Class that is inherited using protected inheritance */</span></div>
+<div class="line"><span class="keyword">class </span>ProtectedBase { };</div>
+<div class="line"><span class="comment"></span></div>
+<div class="line"><span class="comment">/*! Class that is inherited using private inheritance */</span></div>
+<div class="line"><span class="keyword">class </span>PrivateBase { };</div>
+<div class="line"><span class="comment"></span></div>
+<div class="line"><span class="comment">/*! Class that is used by the Inherited class */</span></div>
+<div class="line"><span class="keyword">class </span>Used { };</div>
+<div class="line"><span class="comment"></span></div>
+<div class="line"><span class="comment">/*! Super class that inherits a number of other classes */</span></div>
+<div class="line"><span class="keyword">class </span>Inherited : <span class="keyword">public</span> PublicBase,</div>
+<div class="line"> <span class="keyword">protected</span> ProtectedBase,</div>
+<div class="line"> <span class="keyword">private</span> PrivateBase,</div>
+<div class="line"> <span class="keyword">public</span> Undocumented,</div>
+<div class="line"> <span class="keyword">public</span> Templ<int></div>
+<div class="line">{</div>
+<div class="line"> <span class="keyword">private</span>:</div>
+<div class="line"> Used *m_usedClass;</div>
+<div class="line">};</div>
+</div><!-- fragment --><p> This will result in the following graph:</p>
<center><div class="image">
-<img src="graph_legend.png"/>
+<img src="graph_legend.png" />
</div>
</center><p>The boxes in the above graph have the following meaning: </p>
<ul>
@@ -74,7 +125,7 @@ A filled gray box represents the struct or class for which the graph is generate
<li>
A box with a black border denotes a documented struct or class. </li>
<li>
-A box with a gray border denotes an undocumented struct or class. </li>
+A box with a grey border denotes an undocumented struct or class. </li>
<li>
A box with a red border denotes a documented struct or class forwhich not all inheritance/containment relations are shown. A graph is truncated if it does not fit within the specified boundaries. </li>
</ul>
@@ -87,16 +138,16 @@ A dark green arrow is used for protected inheritance. </li>
<li>
A dark red arrow is used for private inheritance. </li>
<li>
-A purple dashed arrow is used if a class is contained or used by another class. The arrow is labelled with the variable(s) through which the pointed class or struct is accessible. </li>
+A purple dashed arrow is used if a class is contained or used by another class. The arrow is labeled with the variable(s) through which the pointed class or struct is accessible. </li>
<li>
-A yellow dashed arrow denotes a relation between a template instance and the template class it was instantiated from. The arrow is labelled with the template parameters of the instance. </li>
+A yellow dashed arrow denotes a relation between a template instance and the template class it was instantiated from. The arrow is labeled with the template parameters of the instance. </li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/graph_legend.png b/versions/master/doxygen/graph_legend.png
index 473be05..9297281 100644
Binary files a/versions/master/doxygen/graph_legend.png and b/versions/master/doxygen/graph_legend.png differ
diff --git a/versions/master/doxygen/hierarchy.html b/versions/master/doxygen/hierarchy.html
index 00e3f73..c99a6ef 100644
--- a/versions/master/doxygen/hierarchy.html
+++ b/versions/master/doxygen/hierarchy.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Hierarchy</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,49 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li class="current"><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -66,59 +90,59 @@ $(function() {
<p><a href="inherits.html">Go to the graphical class hierarchy</a></p>
This inheritance list is sorted roughly, but not completely, alphabetically:</div><div class="directory">
<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span>]</div><table class="directory">
-<tr id="row_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html" target="_self">mxnet::autograd::AGNodeEntry</a></td><td class="desc"></td></tr>
-<tr id="row_1_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html" target="_self">mxnet::engine::CallbackOnComplete</a></td><td class="desc">OnComplete Callback to the engine, called by AsyncFn when action completes </td></tr>
-<tr id="row_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1Context.html" target="_self">mxnet::Context</a></td><td class="desc"><a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> information about the execution environment </td></tr>
-<tr id="row_3_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1DataBatch.html" target="_self">mxnet::DataBatch</a></td><td class="desc"><a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, returned by Iterator </td></tr>
-<tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1DataInst.html" target="_self">mxnet::DataInst</a></td><td class="desc">Single data instance </td></tr>
-<tr id="row_5_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_5_" class="arrow" onclick="toggleFolder('5_')">▼</span><span class="icona"><span class="icon">C</span></span><b>DataIter</b></td><td class="desc"></td></tr>
-<tr id="row_5_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1IIterator.html" target="_self">mxnet::IIterator< DType ></a></td><td class="desc">Iterator type </td></tr>
-<tr id="row_6_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1Engine.html" target="_self">mxnet::Engine</a></td><td class="desc">Dependency engine that schedules operations </td></tr>
-<tr id="row_7_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html" target="_self">mxnet::op::EnvArguments</a></td><td class="desc">Environment arguments that is used by the function. These can be things like scalar arguments when add a value with scalar </td></tr>
-<tr id="row_8_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1Executor.html" target="_self">mxnet::Executor</a></td><td class="desc"><a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> of a computation graph. <a class="el" href="classmxnet_1_1Executor.html" title="Executor o [...]
-<tr id="row_9_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_9_" class="arrow" onclick="toggleFolder('9_')">▼</span><span class="icona"><span class="icon">C</span></span><b>FieldEntryBase</b></td><td class="desc"></td></tr>
-<tr id="row_9_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html" target="_self">dmlc::parameter::FieldEntry< mxnet::TShape ></a></td><td class="desc"></td></tr>
-<tr id="row_10_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_10_" class="arrow" onclick="toggleFolder('10_')">▼</span><span class="icona"><span class="icon">C</span></span><b>FunctionRegEntryBase</b></td><td class="desc"></td></tr>
-<tr id="row_10_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1DataIteratorReg.html" target="_self">mxnet::DataIteratorReg</a></td><td class="desc">Registry entry for DataIterator factory functions </td></tr>
-<tr id="row_10_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html" target="_self">mxnet::NDArrayFunctionReg</a></td><td class="desc">Registry entry for NDArrayFunction </td></tr>
-<tr id="row_10_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1OperatorPropertyReg.html" target="_self">mxnet::OperatorPropertyReg</a></td><td class="desc">Registry entry for <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> fac [...]
-<tr id="row_11_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_11_" class="arrow" onclick="toggleFolder('11_')">▼</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html" target="_self">mxnet::op::GradFunctionArgument</a></td><td class="desc">Super class of all gradient function argument </td></tr>
-<tr id="row_11_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1Input0.html" target="_self">mxnet::op::Input0</a></td><td class="desc">First input to the function </td></tr>
-<tr id="row_11_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1Input1.html" target="_self">mxnet::op::Input1</a></td><td class="desc">Second input to the function </td></tr>
-<tr id="row_11_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1OutputGrad.html" target="_self">mxnet::op::OutputGrad</a></td><td class="desc">Gradient of output value </td></tr>
-<tr id="row_11_3_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1op_1_1OutputValue.html" target="_self">mxnet::op::OutputValue</a></td><td class="desc">Ouput value of the function to the function </td></tr>
-<tr id="row_12_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1Storage_1_1Handle.html" target="_self">mxnet::Storage::Handle</a></td><td class="desc"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> handle </td></tr>
-<tr id="row_13_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1KVStore.html" target="_self">mxnet::KVStore</a></td><td class="desc">Distributed key-value store </td></tr>
-<tr id="row_14_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html" target="_self">mxnet::common::LazyAllocArray< TElem ></a></td><td class="desc"></td></tr>
-<tr id="row_15_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structMXCallbackList.html" target="_self">MXCallbackList</a></td><td class="desc"></td></tr>
-<tr id="row_16_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structNativeOpInfo.html" target="_self">NativeOpInfo</a></td><td class="desc"></td></tr>
-<tr id="row_17_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1NDArray.html" target="_self">mxnet::NDArray</a></td><td class="desc">Ndarray interface </td></tr>
-<tr id="row_18_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structNDArrayOpInfo.html" target="_self">NDArrayOpInfo</a></td><td class="desc"></td></tr>
-<tr id="row_19_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html" target="_self">mxnet::common::ObjectPool< T ></a></td><td class="desc">Object pool for fast allocation and deallocation </td></tr>
-<tr id="row_20_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html" target="_self">mxnet::common::ObjectPoolAllocatable< T ></a></td><td class="desc">Helper trait class for easy allocation and deallocation </td></tr>
-<tr id="row_21_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1OpContext.html" target="_self">mxnet::OpContext</a></td><td class="desc">All the possible information needed by <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd" title="perform a forward operation of Operator, save the output to TBlob. ">Operator.Forward</a> and [...]
-<tr id="row_22_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1Operator.html" target="_self">mxnet::Operator</a></td><td class="desc"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> interface. <a class="el" href="classmxnet_1_1Operator.html" title=" [...]
-<tr id="row_23_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1OperatorProperty.html" target="_self">mxnet::OperatorProperty</a></td><td class="desc"><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> is a object that s [...]
-<tr id="row_24_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1OpStatePtr.html" target="_self">mxnet::OpStatePtr</a></td><td class="desc"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> state. This is a pointer type, its content is mutable even if < [...]
-<tr id="row_25_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1Resource.html" target="_self">mxnet::Resource</a></td><td class="desc">Resources used by mxnet operations. A resource is something special other than <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, but will still participate </td></tr>
-<tr id="row_26_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1ResourceManager.html" target="_self">mxnet::ResourceManager</a></td><td class="desc">Global resource manager </td></tr>
-<tr id="row_27_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1ResourceRequest.html" target="_self">mxnet::ResourceRequest</a></td><td class="desc">The resources that can be requested by <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> [...]
-<tr id="row_28_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1RunContext.html" target="_self">mxnet::RunContext</a></td><td class="desc">Execution time context. The information needed in runtime for actual execution </td></tr>
-<tr id="row_29_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html" target="_self">mxnet::op::SimpleOpRegEntry</a></td><td class="desc">Registry entry to register simple operators via functions </td></tr>
-<tr id="row_30_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html" target="_self">mxnet::op::SimpleOpRegistry</a></td><td class="desc">Registry for <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> functions </td></tr>
-<tr id="row_31_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1Storage.html" target="_self">mxnet::Storage</a></td><td class="desc"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> manager across multiple devices </td></tr>
-<tr id="row_32_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classmxnet_1_1TBlob.html" target="_self">mxnet::TBlob</a></td><td class="desc">Tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class tha [...]
-<tr id="row_33_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html" target="_self">mxnet::common::helper::UniqueIf< T ></a></td><td class="desc">Helper for non-array type <code>T</code> </td></tr>
-<tr id="row_34_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html" target="_self">mxnet::common::helper::UniqueIf< T[]></a></td><td class="desc">Helper for an array of unknown bound <code>T</code> </td></tr>
-<tr id="row_35_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html" target="_self">mxnet::common::helper::UniqueIf< T[kSize]></a></td><td class="desc">Helper for an array of known bound <code>T</code> </td></tr>
+<tr id="row_0_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html" target="_self">mxnet::autograd::AGNodeEntry</a></td><td class="desc"></td></tr>
+<tr id="row_1_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html" target="_self">mxnet::engine::CallbackOnComplete</a></td><td class="desc">OnComplete Callback to the engine, called by AsyncFn when action completes </td></tr>
+<tr id="row_2_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1Context.html" target="_self">mxnet::Context</a></td><td class="desc"><a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> information about the execution environment </td></tr>
+<tr id="row_3_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1DataBatch.html" target="_self">mxnet::DataBatch</a></td><td class="desc"><a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, returned by Iterator </td></tr>
+<tr id="row_4_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1DataInst.html" target="_self">mxnet::DataInst</a></td><td class="desc">Single data instance </td></tr>
+<tr id="row_5_"><td class="entry"><img id="arr_5_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('5_')"/><img src="ftv2cl.png" alt="C" width="24" height="22" /><b>DataIter</b></td><td class="desc"></td></tr>
+<tr id="row_5_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1IIterator.html" target="_self">mxnet::IIterator< DType ></a></td><td class="desc">Iterator type </td></tr>
+<tr id="row_6_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1Engine.html" target="_self">mxnet::Engine</a></td><td class="desc">Dependency engine that schedules operations </td></tr>
+<tr id="row_7_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html" target="_self">mxnet::op::EnvArguments</a></td><td class="desc">Environment arguments that is used by the function. These can be things like scalar arguments when add a value with scalar </td></tr>
+<tr id="row_8_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1Executor.html" target="_self">mxnet::Executor</a></td><td class="desc"><a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> of a computation graph. <a class="el" href="classmxnet_1_1Executor.html" title="Executor of [...]
+<tr id="row_9_" class="even"><td class="entry"><img id="arr_9_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('9_')"/><img src="ftv2cl.png" alt="C" width="24" height="22" /><b>FieldEntryBase</b></td><td class="desc"></td></tr>
+<tr id="row_9_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html" target="_self">dmlc::parameter::FieldEntry< mxnet::TShape ></a></td><td class="desc"></td></tr>
+<tr id="row_10_" class="even"><td class="entry"><img id="arr_10_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('10_')"/><img src="ftv2cl.png" alt="C" width="24" height="22" /><b>FunctionRegEntryBase</b></td><td class="desc"></td></tr>
+<tr id="row_10_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1DataIteratorReg.html" target="_self">mxnet::DataIteratorReg</a></td><td class="desc">Registry entry for DataIterator factory functions </td></tr>
+<tr id="row_10_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html" target="_self">mxnet::NDArrayFunctionReg</a></td><td class="desc">Registry entry for NDArrayFunction </td></tr>
+<tr id="row_10_2_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1OperatorPropertyReg.html" target="_self">mxnet::OperatorPropertyReg</a></td><td class="desc">Registry entry for <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Oper [...]
+<tr id="row_11_" class="even"><td class="entry"><img id="arr_11_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('11_')"/><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html" target="_self">mxnet::op::GradFunctionArgument</a></td><td class="desc">Super class of all gradient function argument </td></tr>
+<tr id="row_11_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1Input0.html" target="_self">mxnet::op::Input0</a></td><td class="desc">First input to the function </td></tr>
+<tr id="row_11_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1Input1.html" target="_self">mxnet::op::Input1</a></td><td class="desc">Second input to the function </td></tr>
+<tr id="row_11_2_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1OutputGrad.html" target="_self">mxnet::op::OutputGrad</a></td><td class="desc">Gradient of output value </td></tr>
+<tr id="row_11_3_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1op_1_1OutputValue.html" target="_self">mxnet::op::OutputValue</a></td><td class="desc">Ouput value of the function to the function </td></tr>
+<tr id="row_12_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1Storage_1_1Handle.html" target="_self">mxnet::Storage::Handle</a></td><td class="desc"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> handle </td></tr>
+<tr id="row_13_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1KVStore.html" target="_self">mxnet::KVStore</a></td><td class="desc">Distributed key-value store </td></tr>
+<tr id="row_14_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1common_1_1LazyAllocArray.html" target="_self">mxnet::common::LazyAllocArray< TElem ></a></td><td class="desc"></td></tr>
+<tr id="row_15_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structMXCallbackList.html" target="_self">MXCallbackList</a></td><td class="desc"></td></tr>
+<tr id="row_16_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structNativeOpInfo.html" target="_self">NativeOpInfo</a></td><td class="desc"></td></tr>
+<tr id="row_17_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1NDArray.html" target="_self">mxnet::NDArray</a></td><td class="desc">Ndarray interface </td></tr>
+<tr id="row_18_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structNDArrayOpInfo.html" target="_self">NDArrayOpInfo</a></td><td class="desc"></td></tr>
+<tr id="row_19_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html" target="_self">mxnet::common::ObjectPool< T ></a></td><td class="desc">Object pool for fast allocation and deallocation </td></tr>
+<tr id="row_20_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html" target="_self">mxnet::common::ObjectPoolAllocatable< T ></a></td><td class="desc">Helper trait class for easy allocation and deallocation </td></tr>
+<tr id="row_21_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1OpContext.html" target="_self">mxnet::OpContext</a></td><td class="desc">All the possible information needed by <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd" title="perform a forward operation of Operator, save the output to TBlob. ">Operator.Forward</a> and B [...]
+<tr id="row_22_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1Operator.html" target="_self">mxnet::Operator</a></td><td class="desc"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> interface. <a class="el" href="classmxnet_1_1Operator.html" title="Op [...]
+<tr id="row_23_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1OperatorProperty.html" target="_self">mxnet::OperatorProperty</a></td><td class="desc"><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> is a object that sto [...]
+<tr id="row_24_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1OpStatePtr.html" target="_self">mxnet::OpStatePtr</a></td><td class="desc"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> state. This is a pointer type, its content is mutable even if <a [...]
+<tr id="row_25_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1Resource.html" target="_self">mxnet::Resource</a></td><td class="desc">Resources used by mxnet operations. A resource is something special other than <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, but will still participate </td></tr>
+<tr id="row_26_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1ResourceManager.html" target="_self">mxnet::ResourceManager</a></td><td class="desc">Global resource manager </td></tr>
+<tr id="row_27_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1ResourceRequest.html" target="_self">mxnet::ResourceRequest</a></td><td class="desc">The resources that can be requested by <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> </ [...]
+<tr id="row_28_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1RunContext.html" target="_self">mxnet::RunContext</a></td><td class="desc">Execution time context. The information needed in runtime for actual execution </td></tr>
+<tr id="row_29_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html" target="_self">mxnet::op::SimpleOpRegEntry</a></td><td class="desc">Registry entry to register simple operators via functions </td></tr>
+<tr id="row_30_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html" target="_self">mxnet::op::SimpleOpRegistry</a></td><td class="desc">Registry for <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> functions </td></tr>
+<tr id="row_31_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1Storage.html" target="_self">mxnet::Storage</a></td><td class="desc"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> manager across multiple devices </td></tr>
+<tr id="row_32_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="classmxnet_1_1TBlob.html" target="_self">mxnet::TBlob</a></td><td class="desc">Tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that [...]
+<tr id="row_33_" class="even"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html" target="_self">mxnet::common::helper::UniqueIf< T ></a></td><td class="desc">Helper for non-array type <code>T</code> </td></tr>
+<tr id="row_34_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html" target="_self">mxnet::common::helper::UniqueIf< T[]></a></td><td class="desc">Helper for an array of unknown bound <code>T</code> </td></tr>
+<tr id="row_35_" class="even"><td class="entry"><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2cl.png" alt="C" width="24" height="22" /><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html" target="_self">mxnet::common::helper::UniqueIf< T[kSize]></a></td><td class="desc">Helper for an array of known bound <code>T</code> </td></tr>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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+ <ul class="tablist">
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+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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diff --git a/versions/master/doxygen/inherits.html b/versions/master/doxygen/inherits.html
index 65b710e..acf081c 100644
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Class Hierarchy</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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</div>
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
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-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
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+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li class="current"><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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<tr><td><img src="inherit_graph_2.png" border="0" alt="" usemap="#mxnet_1_1autograd_1_1AGNodeEntry"/>
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<tr><td><img src="inherit_graph_3.png" border="0" alt="" usemap="#mxnet_1_1common_1_1helper_1_1UniqueIf_3_01T_01_4"/>
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<tr><td><img src="inherit_graph_4.png" border="0" alt="" usemap="#mxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4"/>
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<tr><td><img src="inherit_graph_6.png" border="0" alt="" usemap="#mxnet_1_1common_1_1LazyAllocArray_3_01TElem_01_4"/>
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<tr><td><img src="inherit_graph_8.png" border="0" alt="" usemap="#mxnet_1_1common_1_1ObjectPoolAllocatable_3_01T_01_4"/>
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<tr><td><img src="inherit_graph_9.png" border="0" alt="" usemap="#mxnet_1_1Context"/>
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<tr><td><img src="inherit_graph_10.png" border="0" alt="" usemap="#mxnet_1_1DataBatch"/>
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<tr><td><img src="inherit_graph_11.png" border="0" alt="" usemap="#mxnet_1_1DataInst"/>
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<tr><td><img src="inherit_graph_12.png" border="0" alt="" usemap="#mxnet_1_1DataIteratorReg"/>
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<tr><td><img src="inherit_graph_13.png" border="0" alt="" usemap="#mxnet_1_1Engine"/>
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<tr><td><img src="inherit_graph_14.png" border="0" alt="" usemap="#mxnet_1_1engine_1_1CallbackOnComplete"/>
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<tr><td><img src="inherit_graph_15.png" border="0" alt="" usemap="#mxnet_1_1Executor"/>
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<tr><td><img src="inherit_graph_16.png" border="0" alt="" usemap="#mxnet_1_1IIterator_3_01DType_01_4"/>
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<tr><td><img src="inherit_graph_17.png" border="0" alt="" usemap="#mxnet_1_1KVStore"/>
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<tr><td><img src="inherit_graph_18.png" border="0" alt="" usemap="#mxnet_1_1NDArray"/>
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<tr><td><img src="inherit_graph_19.png" border="0" alt="" usemap="#mxnet_1_1op_1_1EnvArguments"/>
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<tr><td><img src="inherit_graph_20.png" border="0" alt="" usemap="#mxnet_1_1op_1_1GradFunctionArgument"/>
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<tr><td><img src="inherit_graph_21.png" border="0" alt="" usemap="#mxnet_1_1op_1_1SimpleOpRegEntry"/>
<map name="mxnet_1_1op_1_1SimpleOpRegEntry" id="mxnet_1_1op_1_1SimpleOpRegEntry">
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<tr><td><img src="inherit_graph_22.png" border="0" alt="" usemap="#mxnet_1_1op_1_1SimpleOpRegistry"/>
<map name="mxnet_1_1op_1_1SimpleOpRegistry" id="mxnet_1_1op_1_1SimpleOpRegistry">
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<tr><td><img src="inherit_graph_23.png" border="0" alt="" usemap="#mxnet_1_1OpContext"/>
<map name="mxnet_1_1OpContext" id="mxnet_1_1OpContext">
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<tr><td><img src="inherit_graph_24.png" border="0" alt="" usemap="#mxnet_1_1Operator"/>
<map name="mxnet_1_1Operator" id="mxnet_1_1Operator">
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<tr><td><img src="inherit_graph_25.png" border="0" alt="" usemap="#mxnet_1_1OperatorProperty"/>
<map name="mxnet_1_1OperatorProperty" id="mxnet_1_1OperatorProperty">
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<tr><td><img src="inherit_graph_26.png" border="0" alt="" usemap="#mxnet_1_1OpStatePtr"/>
<map name="mxnet_1_1OpStatePtr" id="mxnet_1_1OpStatePtr">
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<tr><td><img src="inherit_graph_27.png" border="0" alt="" usemap="#mxnet_1_1Resource"/>
<map name="mxnet_1_1Resource" id="mxnet_1_1Resource">
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<tr><td><img src="inherit_graph_28.png" border="0" alt="" usemap="#mxnet_1_1ResourceManager"/>
<map name="mxnet_1_1ResourceManager" id="mxnet_1_1ResourceManager">
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<tr><td><img src="inherit_graph_29.png" border="0" alt="" usemap="#mxnet_1_1ResourceRequest"/>
<map name="mxnet_1_1ResourceRequest" id="mxnet_1_1ResourceRequest">
-<area shape="rect" id="node1" href="structmxnet_1_1ResourceRequest.html" title="The resources that can be requested by Operator. " alt="" coords="5,5,177,31"/>
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<tr><td><img src="inherit_graph_30.png" border="0" alt="" usemap="#mxnet_1_1RunContext"/>
<map name="mxnet_1_1RunContext" id="mxnet_1_1RunContext">
-<area shape="rect" id="node1" href="structmxnet_1_1RunContext.html" title="execution time context. The information needed in runtime for actual execution. " alt="" coords="5,5,141,31"/>
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<tr><td><img src="inherit_graph_31.png" border="0" alt="" usemap="#mxnet_1_1Storage"/>
<map name="mxnet_1_1Storage" id="mxnet_1_1Storage">
-<area shape="rect" id="node1" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. " alt="" coords="5,5,117,31"/>
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<tr><td><img src="inherit_graph_32.png" border="0" alt="" usemap="#mxnet_1_1Storage_1_1Handle"/>
<map name="mxnet_1_1Storage_1_1Handle" id="mxnet_1_1Storage_1_1Handle">
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<tr><td><img src="inherit_graph_33.png" border="0" alt="" usemap="#mxnet_1_1TBlob"/>
<map name="mxnet_1_1TBlob" id="mxnet_1_1TBlob">
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<tr><td><img src="inherit_graph_34.png" border="0" alt="" usemap="#NativeOpInfo"/>
<map name="NativeOpInfo" id="NativeOpInfo">
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<tr><td><img src="inherit_graph_35.png" border="0" alt="" usemap="#NDArrayOpInfo"/>
<map name="NDArrayOpInfo" id="NDArrayOpInfo">
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</table>
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diff --git a/versions/master/doxygen/io_8h.html b/versions/master/doxygen/io_8h.html
index a8c5bed..43d9a42 100644
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@@ -3,15 +3,16 @@
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<title>mxnet: include/mxnet/io.h File Reference</title>
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<script type="text/javascript" src="jquery.js"></script>
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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@@ -29,25 +30,46 @@
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-$(function() {
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- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
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@@ -74,24 +96,20 @@ $(function() {
<p>mxnet io data structure and data iterator
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/data.h></code><br />
-<code>#include <dmlc/registry.h></code><br />
-<code>#include <vector></code><br />
-<code>#include <string></code><br />
-<code>#include <utility></code><br />
-<code>#include <queue></code><br />
-<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br />
-<code>#include "<a class="el" href="ndarray_8h_source.html">./ndarray.h</a>"</code><br />
+<div class="textblock"><code>#include <dmlc/data.h></code><br/>
+<code>#include <dmlc/registry.h></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <string></code><br/>
+<code>#include <utility></code><br/>
+<code>#include <queue></code><br/>
+<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br/>
+<code>#include "<a class="el" href="ndarray_8h_source.html">./ndarray.h</a>"</code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for io.h:</div>
<div class="dyncontent">
<div class="center"><img src="io_8h__incl.png" border="0" usemap="#include_2mxnet_2io_8h" alt=""/></div>
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</div>
</div>
<p><a href="io_8h_source.html">Go to the source code of this file.</a></p>
@@ -99,43 +117,42 @@ Include dependency graph for io.h:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1IIterator.html">mxnet::IIterator< DType ></a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">iterator type <a href="classmxnet_1_1IIterator.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">iterator type <a href="classmxnet_1_1IIterator.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataInst.html">mxnet::DataInst</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">a single data instance <a href="structmxnet_1_1DataInst.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">a single data instance <a href="structmxnet_1_1DataInst.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataBatch.html">mxnet::DataBatch</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, returned by Iterator. <a href="structmxnet_1_1DataBatch.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, returned by Iterator. <a href="structmxnet_1_1DataBatch.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataIteratorReg.html">mxnet::DataIteratorReg</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for DataIterator factory functions. <a href="structmxnet_1_1DataIteratorReg.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for DataIterator factory functions. <a href="structmxnet_1_1DataIteratorReg.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
+<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
+<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="define-members"></a>
Macros</h2></td></tr>
<tr class="memitem:ad3d0e409cb072cac4a1eb93ee390077b"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="io_8h.html#ad3d0e409cb072cac4a1eb93ee390077b">MXNET_REGISTER_IO_ITER</a>(name)   DMLC_REGISTRY_REGISTER(::<a class="el" href="structmxnet_1_1DataIteratorReg.html">mxnet::DataIteratorReg</a>, DataIteratorReg, name)</td></tr>
-<tr class="memdesc:ad3d0e409cb072cac4a1eb93ee390077b"><td class="mdescLeft"> </td><td class="mdescRight">Macro to register Iterators. <a href="#ad3d0e409cb072cac4a1eb93ee390077b">More...</a><br /></td></tr>
+<tr class="memdesc:ad3d0e409cb072cac4a1eb93ee390077b"><td class="mdescLeft"> </td><td class="mdescRight">Macro to register Iterators. <a href="#ad3d0e409cb072cac4a1eb93ee390077b">More...</a><br/></td></tr>
<tr class="separator:ad3d0e409cb072cac4a1eb93ee390077b"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
-<tr class="memitem:a6dca73c81706d54b0ddb0ef967d1e132"><td class="memItemLeft" align="right" valign="top">typedef std::function< IIterator< DataBatch > *()> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6dca73c81706d54b0ddb0ef967d1e132">mxnet::DataIteratorFactory</a></td></tr>
-<tr class="memdesc:a6dca73c81706d54b0ddb0ef967d1e132"><td class="mdescLeft"> </td><td class="mdescRight">typedef the factory function of data iterator <a href="namespacemxnet.html#a6dca73c81706d54b0ddb0ef967d1e132">More...</a><br /></td></tr>
+<tr class="memitem:a6dca73c81706d54b0ddb0ef967d1e132"><td class="memItemLeft" align="right" valign="top">typedef std::function<br class="typebreak"/>
+< IIterator< DataBatch > *()> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6dca73c81706d54b0ddb0ef967d1e132">mxnet::DataIteratorFactory</a></td></tr>
+<tr class="memdesc:a6dca73c81706d54b0ddb0ef967d1e132"><td class="mdescLeft"> </td><td class="mdescRight">typedef the factory function of data iterator <a href="#a6dca73c81706d54b0ddb0ef967d1e132">More...</a><br/></td></tr>
<tr class="separator:a6dca73c81706d54b0ddb0ef967d1e132"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>mxnet io data structure and data iterator </p>
<p>Copyright (c) 2015 by Contributors </p>
</div><h2 class="groupheader">Macro Definition Documentation</h2>
-<a id="ad3d0e409cb072cac4a1eb93ee390077b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad3d0e409cb072cac4a1eb93ee390077b">◆ </a></span>MXNET_REGISTER_IO_ITER</h2>
-
+<a class="anchor" id="ad3d0e409cb072cac4a1eb93ee390077b"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -150,15 +167,21 @@ Typedefs</h2></td></tr>
</div><div class="memdoc">
<p>Macro to register Iterators. </p>
-<div class="fragment"><div class="line"><span class="comment">// example of registering a mnist iterator</span></div><div class="line">REGISTER_IO_ITE(MNISTIter)</div><div class="line">.describe(<span class="stringliteral">"Mnist data iterator"</span>)</div><div class="line">.set_body([]() {</div><div class="line"> <span class="keywordflow">return</span> <span class="keyword">new</span> PrefetcherIter(<span class="keyword">new</span> MNISTIter());</div><div class="line"> }) [...]
+<div class="fragment"><div class="line"><span class="comment">// example of registering a mnist iterator</span></div>
+<div class="line">REGISTER_IO_ITE(MNISTIter)</div>
+<div class="line">.describe(<span class="stringliteral">"Mnist data iterator"</span>)</div>
+<div class="line">.set_body([]() {</div>
+<div class="line"> <span class="keywordflow">return</span> <span class="keyword">new</span> PrefetcherIter(<span class="keyword">new</span> MNISTIter());</div>
+<div class="line"> });</div>
+</div><!-- fragment -->
</div>
</div>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/io.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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</div>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -66,9 +88,61 @@ $(function() {
<div class="title">io.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="io_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_IO_H_</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#define MXNET_IO_H_</span></div><div class="line"><a name="l00008"></a><span class [...]
+<a href="io_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_IO_H_</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_IO_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <dmlc/data.h></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <dmlc/registry.h></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <string></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <utility></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <queue></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include "<a class="code" href="ndarray_8h.html">./ndarray.h</a>"</span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">template</span><<span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00024"></a><span class="lineno"><a class="line" href="classmxnet_1_1IIterator.html"> 24</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1IIterator.html">IIterator</a> : <span class="keyword">public</span> dmlc::DataIter<DType> {</div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1IIterator.html#a774e490e5f3eee767fdc11b779bbd1fe">Init</a>(<span class="keyword">const</span> std::vector<std::pair<std::string, std::string> >& kwargs) = 0;</div>
+<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1IIterator.html#a37fc8b163cd0158686183cc0f319529a">BeforeFirst</a>(<span class="keywordtype">void</span>) = 0;</div>
+<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keyword">virtual</span> <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1IIterator.html#ac02dda5d191a7bc629aaa93e5abab3d5">Next</a>(<span class="keywordtype">void</span>) = 0;</div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keyword">virtual</span> <span class="keyword">const</span> DType &<a class="code" href="classmxnet_1_1IIterator.html#aef2e0b74766cd0283849300c8a081929">Value</a>(<span class="keywordtype">void</span>) <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="classmxnet_1_1IIterator.html#a6e9049dca03b889bba0d18338e286070"> 38</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1IIterator.html#a6e9049dca03b889bba0d18338e286070">~IIterator</a>(<span class="keywordtype">void</span>) {}</div>
+<div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="classmxnet_1_1IIterator.html#a65cd81ecf71d54a17a94ca65d27aa014"> 40</a></span>  std::vector<std::string> <a class="code" href="classmxnet_1_1IIterator.html#a65cd81ecf71d54a17a94ca65d27aa014">data_names</a>;</div>
+<div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="classmxnet_1_1IIterator.html#a79ed9023cf4e525c8b4a8c6fa96eea05"> 42</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1IIterator.html#a79ed9023cf4e525c8b4a8c6fa96eea05">SetDataName</a>(<span class="keyword">const</span> std::string data_name) {</div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <a class="code" href="classmxnet_1_1IIterator.html#a65cd81ecf71d54a17a94ca65d27aa014">data_names</a>.push_back(data_name);</div>
+<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  }</div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span> }; <span class="comment">// class IIterator</span></div>
+<div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div>
+<div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataInst.html"> 48</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1DataInst.html">DataInst</a> {</div>
+<div class="line"><a name="l00050"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataInst.html#a548411c4a3dc77475c5ae6bfa6ee3564"> 50</a></span>  <span class="keywordtype">unsigned</span> <a class="code" href="structmxnet_1_1DataInst.html#a548411c4a3dc77475c5ae6bfa6ee3564">index</a>;</div>
+<div class="line"><a name="l00052"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataInst.html#a3511bbb64add286c9cd1b03c70d4b111"> 52</a></span>  std::vector<TBlob> <a class="code" href="structmxnet_1_1DataInst.html#a3511bbb64add286c9cd1b03c70d4b111">data</a>;</div>
+<div class="line"><a name="l00054"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataInst.html#a311b93ed629984fdcb29cb346d3062ee"> 54</a></span>  std::string <a class="code" href="structmxnet_1_1DataInst.html#a311b93ed629984fdcb29cb346d3062ee">extra_data</a>;</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> }; <span class="comment">// struct DataInst</span></div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div>
+<div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataBatch.html"> 60</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1DataBatch.html">DataBatch</a> {</div>
+<div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataBatch.html#acc98dd6f48816ca7462352a929e48959"> 62</a></span>  std::vector<NDArray> <a class="code" href="structmxnet_1_1DataBatch.html#acc98dd6f48816ca7462352a929e48959">data</a>;</div>
+<div class="line"><a name="l00064"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataBatch.html#a26a2ca473161c2c2a914cbc7ae4eb74e"> 64</a></span>  std::vector<uint64_t> <a class="code" href="structmxnet_1_1DataBatch.html#a26a2ca473161c2c2a914cbc7ae4eb74e">index</a>;</div>
+<div class="line"><a name="l00066"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataBatch.html#a2e9faeff00dd061f04a88c3ea5758aa2"> 66</a></span>  std::string <a class="code" href="structmxnet_1_1DataBatch.html#a2e9faeff00dd061f04a88c3ea5758aa2">extra_data</a>;</div>
+<div class="line"><a name="l00068"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataBatch.html#aad93df826690100bb16689952fc83558"> 68</a></span>  <span class="keywordtype">int</span> <a class="code" href="structmxnet_1_1DataBatch.html#aad93df826690100bb16689952fc83558">num_batch_padd</a>;</div>
+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span> }; <span class="comment">// struct DataBatch</span></div>
+<div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div>
+<div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a6dca73c81706d54b0ddb0ef967d1e132"> 72</a></span> <span class="keyword">typedef</span> std::function<IIterator<DataBatch> *()> <a class="code" href="namespacemxnet.html#a6dca73c81706d54b0ddb0ef967d1e132">DataIteratorFactory</a>;</div>
+<div class="line"><a name="l00076"></a><span class="lineno"><a class="line" href="structmxnet_1_1DataIteratorReg.html"> 76</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1DataIteratorReg.html">DataIteratorReg</a></div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  : <span class="keyword">public</span> dmlc::FunctionRegEntryBase<DataIteratorReg,</div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  DataIteratorFactory> {</div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span> };</div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="comment">//--------------------------------------------------------------</span></div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="comment">// The following part are API Registration of Iterators</span></div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="comment">//--------------------------------------------------------------</span></div>
+<div class="line"><a name="l00095"></a><span class="lineno"><a class="line" href="io_8h.html#ad3d0e409cb072cac4a1eb93ee390077b"> 95</a></span> <span class="comment"></span><span class="preprocessor">#define MXNET_REGISTER_IO_ITER(name) \</span></div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="preprocessor"> DMLC_REGISTRY_REGISTER(::mxnet::DataIteratorReg, DataIteratorReg, name)</span></div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="preprocessor"></span>} <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="preprocessor">#endif // MXNET_IO_H_</span></div>
+<div class="ttc" id="classmxnet_1_1IIterator_html_a65cd81ecf71d54a17a94ca65d27aa014"><div class="ttname"><a href="classmxnet_1_1IIterator.html#a65cd81ecf71d54a17a94ca65d27aa014">mxnet::IIterator::data_names</a></div><div class="ttdeci">std::vector< std::string > data_names</div><div class="ttdoc">store the name of each data, it could be used for making NDArrays </div><div class="ttdef"><b>Definition:</b> io.h:40</div></div>
<div class="ttc" id="classmxnet_1_1IIterator_html_a79ed9023cf4e525c8b4a8c6fa96eea05"><div class="ttname"><a href="classmxnet_1_1IIterator.html#a79ed9023cf4e525c8b4a8c6fa96eea05">mxnet::IIterator::SetDataName</a></div><div class="ttdeci">void SetDataName(const std::string data_name)</div><div class="ttdoc">set data name to each attribute of data </div><div class="ttdef"><b>Definition:</b> io.h:42</div></div>
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
<div class="ttc" id="structmxnet_1_1DataInst_html_a311b93ed629984fdcb29cb346d3062ee"><div class="ttname"><a href="structmxnet_1_1DataInst.html#a311b93ed629984fdcb29cb346d3062ee">mxnet::DataInst::extra_data</a></div><div class="ttdeci">std::string extra_data</div><div class="ttdoc">extra data to be fed to the network </div><div class="ttdef"><b>Definition:</b> io.h:54</div></div>
<div class="ttc" id="structmxnet_1_1DataBatch_html_aad93df826690100bb16689952fc83558"><div class="ttname"><a href="structmxnet_1_1DataBatch.html#aad93df826690100bb16689952fc83558">mxnet::DataBatch::num_batch_padd</a></div><div class="ttdeci">int num_batch_padd</div><div class="ttdoc">num of example padded to batch </div><div class="ttdef"><b>Definition:</b> io.h:68</div></div>
<div class="ttc" id="structmxnet_1_1DataBatch_html_a26a2ca473161c2c2a914cbc7ae4eb74e"><div class="ttname"><a href="structmxnet_1_1DataBatch.html#a26a2ca473161c2c2a914cbc7ae4eb74e">mxnet::DataBatch::index</a></div><div class="ttdeci">std::vector< uint64_t > index</div><div class="ttdoc">index of image data </div><div class="ttdef"><b>Definition:</b> io.h:64</div></div>
@@ -91,9 +165,9 @@ $(function() {
</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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<p>key-value store interface for mxnet
<a href="#details">More...</a></p>
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-<code>#include <vector></code><br />
-<code>#include <unordered_map></code><br />
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-<code>#include <atomic></code><br />
-<code>#include "<a class="el" href="ndarray_8h_source.html">./ndarray.h</a>"</code><br />
+<div class="textblock"><code>#include <dmlc/io.h></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <unordered_map></code><br/>
+<code>#include <string></code><br/>
+<code>#include <functional></code><br/>
+<code>#include <atomic></code><br/>
+<code>#include "<a class="el" href="ndarray_8h_source.html">./ndarray.h</a>"</code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for kvstore.h:</div>
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">distributed key-value store <a href="classmxnet_1_1KVStore.html#details">More...</a><br/></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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onmouseover="return searchBox.OnSearchSelectShow()"
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+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <atomic></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include "<a class="code" href="ndarray_8h.html">./ndarray.h</a>"</span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#if MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor"></span><span class="preprocessor">#include "ps/ps.h"</span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#endif // MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00026"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html"> 26</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1KVStore.html">KVStore</a> {</div>
+<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00029"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a02e23071eb46464b58f2e2bf5654d6d6"> 29</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1KVStore.html#a02e23071eb46464b58f2e2bf5654d6d6">~KVStore</a>() {}</div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div>
+<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1KVStore.html">KVStore</a> *<a class="code" href="classmxnet_1_1KVStore.html#a6d54b075d3b43577f8339b25000a0c82">Create</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> *<a class="code" href="classmxnet_1_1KVStore.html#a722b2150dbf50d528fce62307809f949">type</a> = <span class="stringliteral">"local"</sp [...]
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div>
+<div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a722b2150dbf50d528fce62307809f949"> 46</a></span>  <span class="keyword">inline</span> <span class="keyword">const</span> std::string& <a class="code" href="classmxnet_1_1KVStore.html#a722b2150dbf50d528fce62307809f949">type</a>() { <span class="keywordflow">return</span> <a class="code" href="classmxnet_1_1KVStore.html#ad1023ca677c6e87ccc7d92c4df48bd40">type_</a>; }</div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265">Init</a>(<span class="keyword">const</span> std::vector<int>& keys,</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keyword">const</span> std::vector<NDArray>& values) = 0;</div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265">Init</a>(<span class="keyword">const</span> std::vector<std::string>& str_keys,</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">const</span> std::vector<NDArray>& values) = 0;</div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">Push</a>(<span class="keyword">const</span> std::vector<int>& keys,</div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">const</span> std::vector<NDArray>& values,</div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordtype">int</span> priority = 0) = 0;</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">Push</a>(<span class="keyword">const</span> std::vector<std::string>& str_keys,</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">const</span> std::vector<NDArray>& values,</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordtype">int</span> priority = 0) = 0;</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#a98e041793c133337cecc441e441ed721">Pull</a>(<span class="keyword">const</span> std::vector<int>& keys,</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">const</span> std::vector<NDArray*>& values,</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordtype">int</span> priority = 0) = 0;</div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#a98e041793c133337cecc441e441ed721">Pull</a>(<span class="keyword">const</span> std::vector<std::string>& str_keys,</div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">const</span> std::vector<NDArray*>& values,</div>
+<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordtype">int</span> priority = 0) = 0;</div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div>
+<div class="line"><a name="l00162"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64"> 162</a></span>  <span class="keyword">typedef</span> std::function<void(int, const NDArray&, NDArray*)> <a class="code" href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64">Updater</a>;</div>
+<div class="line"><a name="l00172"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a352ef7eae04211eca1ed47401a6d15d5"> 172</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#a352ef7eae04211eca1ed47401a6d15d5">set_updater</a>(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64">Updater</a>& updater) {</div>
+<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  CHECK(updater) << <span class="stringliteral">"invalid updater"</span>;</div>
+<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="classmxnet_1_1KVStore.html#a01befb768a81a8e7a366708240ee6d06">updater_</a> = updater;</div>
+<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  }</div>
+<div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div>
+<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="comment">/******************************************************</span></div>
+<div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="comment"> * the following are used for multi-machines.</span></div>
+<div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="comment"> ******************************************************/</span></div>
+<div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div>
+<div class="line"><a name="l00185"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a5d919a7a4c9495e9a569c8d733d6325f"> 185</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#a5d919a7a4c9495e9a569c8d733d6325f">InitPSEnv</a>(<span class="keyword">const</span> std::unordered_map<std::string, std::string>& envs) {</div>
+<div class="line"><a name="l00186"></a><span class="lineno"> 186</span> <span class="preprocessor">#if MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="preprocessor"></span> ps::Environment::Init(envs);</div>
+<div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="preprocessor"></span> LOG(FATAL) << <span class="stringliteral">"compile with USE_DIST_KVSTORE=1 to init parameter server's environment"</span>;</div>
+<div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <span class="preprocessor">#endif // MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00192"></a><span class="lineno"> 192</span> </div>
+<div class="line"><a name="l00198"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#ab216fb292fa77e843e95f474fa671a20"> 198</a></span>  <span class="keyword">static</span> <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1KVStore.html#ab216fb292fa77e843e95f474fa671a20">IsWorkerNode</a>() {</div>
+<div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="preprocessor">#if MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00200"></a><span class="lineno"> 200</span> <span class="preprocessor"></span> <span class="keyword">const</span> <span class="keywordtype">char</span>* role_str = ps::Environment::Get()->find(<span class="stringliteral">"DMLC_ROLE"</span>);</div>
+<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordflow">return</span> (role_str == <span class="keyword">nullptr</span>) || (!strcmp(role_str, <span class="stringliteral">"worker"</span>));</div>
+<div class="line"><a name="l00202"></a><span class="lineno"> 202</span> <span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00203"></a><span class="lineno"> 203</span> <span class="preprocessor"></span> <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
+<div class="line"><a name="l00204"></a><span class="lineno"> 204</span> <span class="preprocessor">#endif // MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div>
+<div class="line"><a name="l00212"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a101178924ce1d635b0c8c2a5bce0ceaf"> 212</a></span>  <span class="keyword">static</span> <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1KVStore.html#a101178924ce1d635b0c8c2a5bce0ceaf">IsServerNode</a>() {</div>
+<div class="line"><a name="l00213"></a><span class="lineno"> 213</span> <span class="preprocessor">#if MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00214"></a><span class="lineno"> 214</span> <span class="preprocessor"></span> <span class="keyword">const</span> <span class="keywordtype">char</span>* role_str = ps::Environment::Get()->find(<span class="stringliteral">"DMLC_ROLE"</span>);</div>
+<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">return</span> (role_str != <span class="keyword">nullptr</span>) && (!strcmp(role_str, <span class="stringliteral">"server"</span>));</div>
+<div class="line"><a name="l00216"></a><span class="lineno"> 216</span> <span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00217"></a><span class="lineno"> 217</span> <span class="preprocessor"></span> <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
+<div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="preprocessor">#endif // MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div>
+<div class="line"><a name="l00221"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#afa87753d90fc7e75234229834ea23abb"> 221</a></span>  <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#afa87753d90fc7e75234229834ea23abb">set_barrier_before_exit</a>(<span class="keyword">const</span> <span class="keywordtype">bool</span> barrier_before_exit) {</div>
+<div class="line"><a name="l00222"></a><span class="lineno"> 222</span> <span class="preprocessor">#if MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <span class="preprocessor"></span> <span class="keywordflow">if</span> (!<a class="code" href="classmxnet_1_1KVStore.html#ab216fb292fa77e843e95f474fa671a20">IsWorkerNode</a>()) LOG(FATAL) << <span class="stringliteral">"barrier_before_exit takes effect only on worker nodes"</span>;</div>
+<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  barrier_before_exit_ = barrier_before_exit;</div>
+<div class="line"><a name="l00225"></a><span class="lineno"> 225</span> <span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <span class="preprocessor"></span> LOG(FATAL) << <span class="stringliteral">"compile with USE_DIST_KVSTORE=1 to enable barrier"</span>;</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div>
+<div class="line"><a name="l00235"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a41113b9dfeab6c705ba064cc3f010fb4"> 235</a></span>  <span class="keyword">static</span> <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1KVStore.html#a41113b9dfeab6c705ba064cc3f010fb4">IsSchedulerNode</a>() {</div>
+<div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="preprocessor">#if MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="preprocessor"></span> <span class="keyword">const</span> <span class="keywordtype">char</span>* role_str = ps::Environment::Get()->find(<span class="stringliteral">"DMLC_ROLE"</span>);</div>
+<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordflow">return</span> (role_str != <span class="keyword">nullptr</span>) && (!strcmp(role_str, <span class="stringliteral">"scheduler"</span>));</div>
+<div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00240"></a><span class="lineno"> 240</span> <span class="preprocessor"></span> <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
+<div class="line"><a name="l00241"></a><span class="lineno"> 241</span> <span class="preprocessor">#endif // MXNET_USE_DIST_KVSTORE</span></div>
+<div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div>
+<div class="line"><a name="l00250"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a262ef45340106fdb5c2a296c3cb711b1"> 250</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1KVStore.html#a262ef45340106fdb5c2a296c3cb711b1">get_rank</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">return</span> 0;</div>
+<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  }</div>
+<div class="line"><a name="l00253"></a><span class="lineno"> 253</span> </div>
+<div class="line"><a name="l00257"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a1fd19b2a123b6069b35e1e8b7fad9dc3"> 257</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1KVStore.html#a1fd19b2a123b6069b35e1e8b7fad9dc3">get_group_size</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">return</span> 1;</div>
+<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  }</div>
+<div class="line"><a name="l00260"></a><span class="lineno"> 260</span> </div>
+<div class="line"><a name="l00269"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#af5c48df3aade761a547a1dc6f7b8702d"> 269</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1KVStore.html#af5c48df3aade761a547a1dc6f7b8702d">get_num_dead_node</a>(<span class="keywordtype">int</span> node_id, <span class="keywordtype">int</span> timeout = 60)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordflow">return</span> 0;</div>
+<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  }</div>
+<div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div>
+<div class="line"><a name="l00280"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a4f9275fb7a11589fae1dcc5a8a3354ac"> 280</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#a4f9275fb7a11589fae1dcc5a8a3354ac">Barrier</a>() { }</div>
+<div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div>
+<div class="line"><a name="l00293"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a4ff4ca05f3c519ebbc2e46264e8b63dd"> 293</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#a4ff4ca05f3c519ebbc2e46264e8b63dd">SendCommandToServers</a>(<span class="keywordtype">int</span> cmd_id, <span class="keyword">const</span> std::string& cmd_body) { }</div>
+<div class="line"><a name="l00294"></a><span class="lineno"> 294</span> </div>
+<div class="line"><a name="l00298"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3"> 298</a></span>  <span class="keyword">typedef</span> std::function<void(int, const std::string&)> <a class="code" href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3">Controller</a>;</div>
+<div class="line"><a name="l00299"></a><span class="lineno"> 299</span> </div>
+<div class="line"><a name="l00313"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#ae137ab8b5407060529c18a8084946d0b"> 313</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1KVStore.html#ae137ab8b5407060529c18a8084946d0b">RunServer</a>(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3">Controller</a>& controller) [...]
+<div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div>
+<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keyword">protected</span>:</div>
+<div class="line"><a name="l00319"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a01befb768a81a8e7a366708240ee6d06"> 319</a></span>  <a class="code" href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64">Updater</a> <a class="code" href="classmxnet_1_1KVStore.html#a01befb768a81a8e7a366708240ee6d06">updater_</a>;</div>
+<div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div>
+<div class="line"><a name="l00324"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#ad1023ca677c6e87ccc7d92c4df48bd40"> 324</a></span>  std::string <a class="code" href="classmxnet_1_1KVStore.html#ad1023ca677c6e87ccc7d92c4df48bd40">type_</a>;</div>
+<div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div>
+<div class="line"><a name="l00329"></a><span class="lineno"><a class="line" href="classmxnet_1_1KVStore.html#a265215cd3de11dd36d9e79330a535615"> 329</a></span>  std::atomic<bool> barrier_before_exit_{<span class="keyword">true</span>};</div>
+<div class="line"><a name="l00330"></a><span class="lineno"> 330</span> };</div>
+<div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div>
+<div class="line"><a name="l00332"></a><span class="lineno"> 332</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00333"></a><span class="lineno"> 333</span> <span class="preprocessor">#endif // MXNET_KVSTORE_H_</span></div>
+<div class="ttc" id="classmxnet_1_1KVStore_html"><div class="ttname"><a href="classmxnet_1_1KVStore.html">mxnet::KVStore</a></div><div class="ttdoc">distributed key-value store </div><div class="ttdef"><b>Definition:</b> kvstore.h:26</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a6e4a76bd57f185349c58a5a658c32a64"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a6e4a76bd57f185349c58a5a658c32a64">mxnet::KVStore::Updater</a></div><div class="ttdeci">std::function< void(int, const NDArray &, NDArray *)> Updater</div><div class="ttdoc">the prototype of user-defined updater </div><div class="ttdef"><b>Definition:</b> kvstore.h:162</div></div>
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
+<div class="ttc" id="classmxnet_1_1KVStore_html_a262ef45340106fdb5c2a296c3cb711b1"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a262ef45340106fdb5c2a296c3cb711b1">mxnet::KVStore::get_rank</a></div><div class="ttdeci">virtual int get_rank() const </div><div class="ttdef"><b>Definition:</b> kvstore.h:250</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a6d54b075d3b43577f8339b25000a0c82"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a6d54b075d3b43577f8339b25000a0c82">mxnet::KVStore::Create</a></div><div class="ttdeci">static KVStore * Create(const char *type="local")</div><div class="ttdoc">Factory function to create a new KVStore. </div></div>
-<div class="ttc" id="classmxnet_1_1KVStore_html_a2314122405a7f3ed5f9df55cb6a66f2a"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a2314122405a7f3ed5f9df55cb6a66f2a">mxnet::KVStore::get_group_size</a></div><div class="ttdeci">virtual int get_group_size() const</div><div class="ttdef"><b>Definition:</b> kvstore.h:257</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a01befb768a81a8e7a366708240ee6d06"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a01befb768a81a8e7a366708240ee6d06">mxnet::KVStore::updater_</a></div><div class="ttdeci">Updater updater_</div><div class="ttdoc">the user-defined updater </div><div class="ttdef"><b>Definition:</b> kvstore.h:319</div></div>
<div class="ttc" id="ndarray_8h_html"><div class="ttname"><a href="ndarray_8h.html">ndarray.h</a></div><div class="ttdoc">NDArray interface that handles array arithematics. </div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a722b2150dbf50d528fce62307809f949"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a722b2150dbf50d528fce62307809f949">mxnet::KVStore::type</a></div><div class="ttdeci">const std::string & type()</div><div class="ttdoc">return the type </div><div class="ttdef"><b>Definition:</b> kvstore.h:46</div></div>
+<div class="ttc" id="classmxnet_1_1KVStore_html_a98e041793c133337cecc441e441ed721"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a98e041793c133337cecc441e441ed721">mxnet::KVStore::Pull</a></div><div class="ttdeci">virtual void Pull(const std::vector< int > &keys, const std::vector< NDArray * > &values, int priority=0)=0</div><div class="ttdoc">pull a list of key-value pairs from the store </div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a41113b9dfeab6c705ba064cc3f010fb4"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a41113b9dfeab6c705ba064cc3f010fb4">mxnet::KVStore::IsSchedulerNode</a></div><div class="ttdeci">static bool IsSchedulerNode()</div><div class="ttdef"><b>Definition:</b> kvstore.h:235</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a4f9275fb7a11589fae1dcc5a8a3354ac"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a4f9275fb7a11589fae1dcc5a8a3354ac">mxnet::KVStore::Barrier</a></div><div class="ttdeci">virtual void Barrier()</div><div class="ttdoc">global barrier among all worker machines </div><div class="ttdef"><b>Definition:</b> kvstore.h:280</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a5d919a7a4c9495e9a569c8d733d6325f"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a5d919a7a4c9495e9a569c8d733d6325f">mxnet::KVStore::InitPSEnv</a></div><div class="ttdeci">static void InitPSEnv(const std::unordered_map< std::string, std::string > &envs)</div><div class="ttdoc">initalize ps-lite environment variables </div><div class="ttdef"><b>Definition:</b> kvstore.h:185</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_af4e56c1f2b90bdd02e2c4e7d2abf6265"><div class="ttname"><a href="classmxnet_1_1KVStore.html#af4e56c1f2b90bdd02e2c4e7d2abf6265">mxnet::KVStore::Init</a></div><div class="ttdeci">virtual void Init(const std::vector< int > &keys, const std::vector< NDArray > &values)=0</div><div class="ttdoc">Initialize a list of key-value pair to the store. </div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_ab216fb292fa77e843e95f474fa671a20"><div class="ttname"><a href="classmxnet_1_1KVStore.html#ab216fb292fa77e843e95f474fa671a20">mxnet::KVStore::IsWorkerNode</a></div><div class="ttdeci">static bool IsWorkerNode()</div><div class="ttdef"><b>Definition:</b> kvstore.h:198</div></div>
-<div class="ttc" id="classmxnet_1_1KVStore_html_ab03a9d2faa1d9a1920c6fec2f74fbb72"><div class="ttname"><a href="classmxnet_1_1KVStore.html#ab03a9d2faa1d9a1920c6fec2f74fbb72">mxnet::KVStore::Pull</a></div><div class="ttdeci">virtual void Pull(const std::vector< int > &keys, const std::vector< NDArray *> &values, int priority=0)=0</div><div class="ttdoc">pull a list of key-value pairs from the store </div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a02e23071eb46464b58f2e2bf5654d6d6"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a02e23071eb46464b58f2e2bf5654d6d6">mxnet::KVStore::~KVStore</a></div><div class="ttdeci">virtual ~KVStore()</div><div class="ttdoc">virtual destructor </div><div class="ttdef"><b>Definition:</b> kvstore.h:29</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_afa87753d90fc7e75234229834ea23abb"><div class="ttname"><a href="classmxnet_1_1KVStore.html#afa87753d90fc7e75234229834ea23abb">mxnet::KVStore::set_barrier_before_exit</a></div><div class="ttdeci">void set_barrier_before_exit(const bool barrier_before_exit)</div><div class="ttdef"><b>Definition:</b> kvstore.h:221</div></div>
-<div class="ttc" id="classmxnet_1_1KVStore_html_a9bbe72794d75a2d8d804124ee686c01c"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a9bbe72794d75a2d8d804124ee686c01c">mxnet::KVStore::get_num_dead_node</a></div><div class="ttdeci">virtual int get_num_dead_node(int node_id, int timeout=60) const</div><div class="ttdef"><b>Definition:</b> kvstore.h:269</div></div>
+<div class="ttc" id="classmxnet_1_1KVStore_html_af5c48df3aade761a547a1dc6f7b8702d"><div class="ttname"><a href="classmxnet_1_1KVStore.html#af5c48df3aade761a547a1dc6f7b8702d">mxnet::KVStore::get_num_dead_node</a></div><div class="ttdeci">virtual int get_num_dead_node(int node_id, int timeout=60) const </div><div class="ttdef"><b>Definition:</b> kvstore.h:269</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_ae137ab8b5407060529c18a8084946d0b"><div class="ttname"><a href="classmxnet_1_1KVStore.html#ae137ab8b5407060529c18a8084946d0b">mxnet::KVStore::RunServer</a></div><div class="ttdeci">virtual void RunServer(const Controller &controller)</div><div class="ttdoc">Run as server (or scheduler) </div><div class="ttdef"><b>Definition:</b> kvstore.h:313</div></div>
-<div class="ttc" id="classmxnet_1_1KVStore_html_afa281ae77e3ca58beaee6f3a5feafbc1"><div class="ttname"><a href="classmxnet_1_1KVStore.html#afa281ae77e3ca58beaee6f3a5feafbc1">mxnet::KVStore::get_rank</a></div><div class="ttdeci">virtual int get_rank() const</div><div class="ttdef"><b>Definition:</b> kvstore.h:250</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a56a3b6d687700f7c71310cd47ab5a54c"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a56a3b6d687700f7c71310cd47ab5a54c">mxnet::KVStore::Push</a></div><div class="ttdeci">virtual void Push(const std::vector< int > &keys, const std::vector< NDArray > &values, int priority=0)=0</div><div class="ttdoc">push a list of key-value pairs into the store </div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a4ff4ca05f3c519ebbc2e46264e8b63dd"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a4ff4ca05f3c519ebbc2e46264e8b63dd">mxnet::KVStore::SendCommandToServers</a></div><div class="ttdeci">virtual void SendCommandToServers(int cmd_id, const std::string &cmd_body)</div><div class="ttdoc">Send a command to all server nodes. </div><div class="ttdef"><b>Definition:</b> kvstore.h:293</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_ad1023ca677c6e87ccc7d92c4df48bd40"><div class="ttname"><a href="classmxnet_1_1KVStore.html#ad1023ca677c6e87ccc7d92c4df48bd40">mxnet::KVStore::type_</a></div><div class="ttdeci">std::string type_</div><div class="ttdoc">the kvstore type </div><div class="ttdef"><b>Definition:</b> kvstore.h:324</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a50db5cb1a140fb84c8e31def54270ae3"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a50db5cb1a140fb84c8e31def54270ae3">mxnet::KVStore::Controller</a></div><div class="ttdeci">std::function< void(int, const std::string &)> Controller</div><div class="ttdoc">the prototype of a server controller </div><div class="ttdef"><b>Definition:</b> kvstore.h:298</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a352ef7eae04211eca1ed47401a6d15d5"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a352ef7eae04211eca1ed47401a6d15d5">mxnet::KVStore::set_updater</a></div><div class="ttdeci">virtual void set_updater(const Updater &updater)</div><div class="ttdoc">set an updater </div><div class="ttdef"><b>Definition:</b> kvstore.h:172</div></div>
-<div class="ttc" id="classmxnet_1_1KVStore_html_aa8ba7abf8334326cab19eb568cb31cfa"><div class="ttname"><a href="classmxnet_1_1KVStore.html#aa8ba7abf8334326cab19eb568cb31cfa">mxnet::KVStore::barrier_before_exit_</a></div><div class="ttdeci">std::atomic< bool > barrier_before_exit_</div><div class="ttdoc">whether to do barrier when finalize </div><div class="ttdef"><b>Definition:</b> kvstore.h:329</div></div>
+<div class="ttc" id="classmxnet_1_1KVStore_html_a1fd19b2a123b6069b35e1e8b7fad9dc3"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a1fd19b2a123b6069b35e1e8b7fad9dc3">mxnet::KVStore::get_group_size</a></div><div class="ttdeci">virtual int get_group_size() const </div><div class="ttdef"><b>Definition:</b> kvstore.h:257</div></div>
<div class="ttc" id="classmxnet_1_1KVStore_html_a101178924ce1d635b0c8c2a5bce0ceaf"><div class="ttname"><a href="classmxnet_1_1KVStore.html#a101178924ce1d635b0c8c2a5bce0ceaf">mxnet::KVStore::IsServerNode</a></div><div class="ttdeci">static bool IsServerNode()</div><div class="ttdef"><b>Definition:</b> kvstore.h:212</div></div>
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+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/lazy__alloc__array_8h.html b/versions/master/doxygen/lazy__alloc__array_8h.html
index 0e803a7..edecf8f 100644
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+++ b/versions/master/doxygen/lazy__alloc__array_8h.html
@@ -3,15 +3,16 @@
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<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: src/common/lazy_alloc_array.h File Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
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+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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onmouseover="return searchBox.OnSearchSelectShow()"
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
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@@ -72,16 +94,18 @@ $(function() {
<p>An array that lazily allocate elements as First time the cell get visited.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/logging.h></code><br />
-<code>#include <memory></code><br />
-<code>#include <mutex></code><br />
-<code>#include <array></code><br />
-<code>#include <vector></code><br />
-<code>#include <atomic></code><br />
+<div class="textblock"><code>#include <dmlc/logging.h></code><br/>
+<code>#include <memory></code><br/>
+<code>#include <mutex></code><br/>
+<code>#include <array></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <atomic></code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for lazy_alloc_array.h:</div>
<div class="dyncontent">
<div class="center"><img src="lazy__alloc__array_8h__incl.png" border="0" usemap="#src_2common_2lazy__alloc__array_8h" alt=""/></div>
+<map name="src_2common_2lazy__alloc__array_8h" id="src_2common_2lazy__alloc__array_8h">
+</map>
</div>
</div>
<p><a href="lazy__alloc__array_8h_source.html">Go to the source code of this file.</a></p>
@@ -93,10 +117,10 @@ Classes</h2></td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
+<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
+<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacemxnet_1_1common"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html">mxnet::common</a></td></tr>
+<tr class="memitem:namespacemxnet_1_1common"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html">mxnet::common</a></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
@@ -105,9 +129,9 @@ Namespaces</h2></td></tr>
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<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/lazy__alloc__array_8h__incl.md5 b/versions/master/doxygen/lazy__alloc__array_8h__incl.md5
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<title>mxnet: src/common/lazy_alloc_array.h Source File</title>
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@@ -66,8 +88,152 @@ $(function() {
<div class="title">lazy_alloc_array.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="lazy__alloc__array_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_COMMON_LAZY_ALLOC_ARRAY_H_</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#define MXNET_COMMON_LAZY_ALLOC_ARRAY_H_</spa [...]
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
+<a href="lazy__alloc__array_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_COMMON_LAZY_ALLOC_ARRAY_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_COMMON_LAZY_ALLOC_ARRAY_H_</span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <dmlc/logging.h></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <memory></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <mutex></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <array></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <atomic></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace </span>common {</div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="keyword">template</span><<span class="keyword">typename</span> TElem></div>
+<div class="line"><a name="l00021"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1LazyAllocArray.html"> 21</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html">LazyAllocArray</a> {</div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#a1dd703a9eabfe691793a8398706bcc09">LazyAllocArray</a>();</div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">template</span><<span class="keyword">typename</span> FCreate></div>
+<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keyword">inline</span> std::shared_ptr<TElem> <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#ac4528ab581eda5b0aba35faa3687c201">Get</a>(<span class="keywordtype">int</span> index, FCreate creator);</div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keyword">template</span><<span class="keyword">typename</span> FVisit></div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#a43a89c8e7b4e1a09f346694073a2d998">ForEach</a>(FVisit fvisit);</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#a3af1ab75feb0ba07db5af7e6ef5717b1">Clear</a>();</div>
+<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div>
+<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#a000e31ad914a20415519a2b39a1d08c3">SignalForKill</a>();</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">template</span><<span class="keyword">typename</span> SyncObject></div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">class </span>unique_unlock {</div>
+<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">explicit</span> unique_unlock(std::unique_lock<SyncObject> *lock)</div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  : lock_(lock) {</div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">if</span> (lock_) {</div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  lock_->unlock();</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  }</div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  }</div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  ~unique_unlock() {</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">if</span> (lock_) {</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  lock_->lock();</div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  }</div>
+<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  std::unique_lock<SyncObject> *lock_;</div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  };</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">static</span> constexpr std::size_t kInitSize = 16;</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  std::mutex create_mutex_;</div>
+<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  std::array<std::shared_ptr<TElem>, kInitSize> head_;</div>
+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  std::vector<std::shared_ptr<TElem> > more_;</div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  std::atomic<bool> exit_now_;</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span> };</div>
+<div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div>
+<div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="keyword">template</span><<span class="keyword">typename</span> TElem></div>
+<div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1LazyAllocArray.html#a1dd703a9eabfe691793a8398706bcc09"> 75</a></span> <span class="keyword">inline</span> <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#a1dd703a9eabfe691793a8398706bcc09">LazyAllocArray<TElem>::LazyAllocArray</a>()</div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  : exit_now_(false) {</div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span> }</div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="comment">// implementations</span></div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="keyword">template</span><<span class="keyword">typename</span> TElem></div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="keyword">template</span><<span class="keyword">typename</span> FCreate></div>
+<div class="line"><a name="l00082"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1LazyAllocArray.html#ac4528ab581eda5b0aba35faa3687c201"> 82</a></span> <span class="keyword">inline</span> std::shared_ptr<TElem> <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#ac4528ab581eda5b0aba35faa3687c201">LazyAllocArray<TElem>::Get</a>(<span class="keywordtype">int</span> index, FCreate creator) {</div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  CHECK_GE(index, 0);</div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordtype">size_t</span> idx = <span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(index);</div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">if</span> (idx < kInitSize) {</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  std::shared_ptr<TElem> ptr = head_[idx];</div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">if</span> (ptr) {</div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">return</span> ptr;</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  } <span class="keywordflow">else</span> {</div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  std::lock_guard<std::mutex> lock(create_mutex_);</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">if</span> (!exit_now_.load()) {</div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  std::shared_ptr<TElem> ptr = head_[idx];</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">if</span> (ptr) {</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordflow">return</span> ptr;</div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  }</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  ptr = head_[idx] = std::shared_ptr<TElem>(creator());</div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">return</span> ptr;</div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  }</div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  }</div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  } <span class="keywordflow">else</span> {</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  std::lock_guard<std::mutex> lock(create_mutex_);</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">if</span> (!exit_now_.load()) {</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  idx -= kInitSize;</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordflow">if</span> (more_.size() <= idx) {</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  more_.reserve(idx + 1);</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">while</span> (more_.size() <= idx) {</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  more_.push_back(std::shared_ptr<TElem>(<span class="keyword">nullptr</span>));</div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::shared_ptr<TElem> ptr = more_[idx];</div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">if</span> (ptr) {</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">return</span> ptr;</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  ptr = more_[idx] = std::shared_ptr<TElem>(creator());</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordflow">return</span> ptr;</div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  }</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span> }</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="keyword">template</span><<span class="keyword">typename</span> TElem></div>
+<div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1LazyAllocArray.html#a3af1ab75feb0ba07db5af7e6ef5717b1"> 122</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#a3af1ab75feb0ba07db5af7e6ef5717b1">LazyAllocArray<TElem>::Clear</a>() {</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  std::unique_lock<std::mutex> lock(create_mutex_);</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  exit_now_.store(<span class="keyword">true</span>);</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="comment">// Currently, head_ and more_ never get smaller, so it's safe to</span></div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// iterate them outside of the lock. The loops should catch</span></div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="comment">// any growth which might happen when create_mutex_ is unlocked</span></div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < head_.size(); ++i) {</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::shared_ptr<TElem> p = head_[i];</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  head_[i] = std::shared_ptr<TElem>(<span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  unique_unlock<std::mutex> unlocker(&lock);</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  p = std::shared_ptr<TElem>(<span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < more_.size(); ++i) {</div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  std::shared_ptr<TElem> p = more_[i];</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  more_[i] = std::shared_ptr<TElem>(<span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  unique_unlock<std::mutex> unlocker(&lock);</div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  p = std::shared_ptr<TElem>(<span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  }</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span> }</div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="keyword">template</span><<span class="keyword">typename</span> TElem></div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="keyword">template</span><<span class="keyword">typename</span> FVisit></div>
+<div class="line"><a name="l00144"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1LazyAllocArray.html#a43a89c8e7b4e1a09f346694073a2d998"> 144</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#a43a89c8e7b4e1a09f346694073a2d998">LazyAllocArray<TElem>::ForEach</a>(FVisit fvisit) {</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  std::lock_guard<std::mutex> lock(create_mutex_);</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < head_.size(); ++i) {</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">if</span> (head_[i].<span class="keyword">get</span>() != <span class="keyword">nullptr</span>) {</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  fvisit(i, head_[i].<span class="keyword">get</span>());</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  }</div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < more_.size(); ++i) {</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">if</span> (more_[i].<span class="keyword">get</span>() != <span class="keyword">nullptr</span>) {</div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  fvisit(i + kInitSize, more_[i].<span class="keyword">get</span>());</div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  }</div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div>
+<div class="line"><a name="l00156"></a><span class="lineno"> 156</span> }</div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <span class="keyword">template</span><<span class="keyword">typename</span> TElem></div>
+<div class="line"><a name="l00159"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1LazyAllocArray.html#a000e31ad914a20415519a2b39a1d08c3"> 159</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1common_1_1LazyAllocArray.html#a000e31ad914a20415519a2b39a1d08c3">LazyAllocArray<TElem>::SignalForKill</a>() {</div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  std::lock_guard<std::mutex> lock(create_mutex_);</div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  exit_now_.store(<span class="keyword">true</span>);</div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span> }</div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span> } <span class="comment">// namespace common</span></div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="preprocessor">#endif // MXNET_COMMON_LAZY_ALLOC_ARRAY_H_</span></div>
+<div class="ttc" id="classmxnet_1_1common_1_1LazyAllocArray_html_ac4528ab581eda5b0aba35faa3687c201"><div class="ttname"><a href="classmxnet_1_1common_1_1LazyAllocArray.html#ac4528ab581eda5b0aba35faa3687c201">mxnet::common::LazyAllocArray::Get</a></div><div class="ttdeci">std::shared_ptr< TElem > Get(int index, FCreate creator)</div><div class="ttdoc">Get element of corresponding index, if it is not created create by creator. </div><div class="ttdef"><b>Definition:</b> lazy_alloc_ar [...]
<div class="ttc" id="classmxnet_1_1common_1_1LazyAllocArray_html_a3af1ab75feb0ba07db5af7e6ef5717b1"><div class="ttname"><a href="classmxnet_1_1common_1_1LazyAllocArray.html#a3af1ab75feb0ba07db5af7e6ef5717b1">mxnet::common::LazyAllocArray::Clear</a></div><div class="ttdeci">void Clear()</div><div class="ttdoc">clear all the allocated elements in array </div><div class="ttdef"><b>Definition:</b> lazy_alloc_array.h:122</div></div>
<div class="ttc" id="classmxnet_1_1common_1_1LazyAllocArray_html_a43a89c8e7b4e1a09f346694073a2d998"><div class="ttname"><a href="classmxnet_1_1common_1_1LazyAllocArray.html#a43a89c8e7b4e1a09f346694073a2d998">mxnet::common::LazyAllocArray::ForEach</a></div><div class="ttdeci">void ForEach(FVisit fvisit)</div><div class="ttdoc">for each not null element of the array, call fvisit </div><div class="ttdef"><b>Definition:</b> lazy_alloc_array.h:144</div></div>
<div class="ttc" id="classmxnet_1_1common_1_1LazyAllocArray_html"><div class="ttname"><a href="classmxnet_1_1common_1_1LazyAllocArray.html">mxnet::common::LazyAllocArray</a></div><div class="ttdef"><b>Definition:</b> lazy_alloc_array.h:21</div></div>
@@ -76,9 +242,9 @@ $(function() {
</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/mxrtc_8h.html b/versions/master/doxygen/mxrtc_8h.html
index 84ec8bd..fc2a605 100644
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@@ -3,15 +3,16 @@
<head>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/mxrtc.h File Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
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</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
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-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -69,14 +91,13 @@ $(function() {
<p>Wrapper for NVRTC.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br />
+<div class="textblock"><code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for mxrtc.h:</div>
<div class="dyncontent">
<div class="center"><img src="mxrtc_8h__incl.png" border="0" usemap="#include_2mxnet_2mxrtc_8h" alt=""/></div>
<map name="include_2mxnet_2mxrtc_8h" id="include_2mxnet_2mxrtc_8h">
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+<area shape="rect" id="node2" href="base_8h.html" title="configuation of mxnet as well as basic data structure. " alt="" coords="587,80,660,107"/></map>
</div>
</div>
<p><a href="mxrtc_8h_source.html">Go to the source code of this file.</a></p>
@@ -87,9 +108,9 @@ Include dependency graph for mxrtc.h:</div>
</div></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
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index 44ccbfc..ff57dc2 100644
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<head>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/mxrtc.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
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+<!-- Generated by Doxygen 1.8.6 -->
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -66,16 +88,69 @@ $(function() {
<div class="title">mxrtc.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="mxrtc_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_MXRTC_H_</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#define MXNET_MXRTC_H_</span></div><div class="line"><a name="l00009"></a><s [...]
+<a href="mxrtc_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_MXRTC_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_MXRTC_H_</span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor"></span><span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#if ((MXNET_USE_CUDA) && (MXNET_USE_NVRTC))</span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor"></span><span class="preprocessor">#include <nvrtc.h></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <cuda.h></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <string></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <memory></span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <utility></span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <unordered_map></span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "<a class="code" href="ndarray_8h.html">./ndarray.h</a>"</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">class </span>MXRtc {</div>
+<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  MXRtc(<span class="keyword">const</span> std::string& name,</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  std::vector<std::pair<std::string, NDArray> > <span class="keyword">const</span>& input,</div>
+<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  std::vector<std::pair<std::string, NDArray> > <span class="keyword">const</span>& output,</div>
+<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">const</span> std::string& kernel);</div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordtype">void</span> push(std::vector<NDArray> <span class="keyword">const</span>& input,</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  std::vector<NDArray> <span class="keyword">const</span>& output,</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> grid_dim_X,</div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> grid_dim_Y,</div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> grid_dim_Z,</div>
+<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> block_dim_X,</div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> block_dim_Y,</div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> block_dim_Z);</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">char</span> str_type[];</div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">static</span> std::unordered_map<std::string, char*> kernel_registry;</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div>
+<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  std::string name_;</div>
+<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> num_input_, num_output_;</div>
+<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  std::string code_;</div>
+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordtype">char</span>* ptx_;</div>
+<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  std::unordered_map<int, CUmodule> module_;</div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  std::unordered_map<int, CUfunction> func_;</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  std::string decorate(<span class="keyword">const</span> std::string& name,</div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  std::vector<std::pair<std::string, NDArray> > <span class="keyword">const</span>& input,</div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  std::vector<std::pair<std::string, NDArray> > <span class="keyword">const</span>& output,</div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keyword">const</span> std::string kernel);</div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordtype">char</span>* compile(<span class="keyword">const</span> std::string& name, <span class="keyword">const</span> std::string& code);</div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span> };</div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span> <span class="preprocessor">#endif // MXNET_USE_CUDA && MXNET_USE_NVRTC</span></div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="preprocessor"></span><span class="preprocessor">#endif // MXNET_MXRTC_H_</span></div>
<div class="ttc" id="ndarray_8h_html"><div class="ttname"><a href="ndarray_8h.html">ndarray.h</a></div><div class="ttdoc">NDArray interface that handles array arithematics. </div></div>
<div class="ttc" id="base_8h_html"><div class="ttname"><a href="base_8h.html">base.h</a></div><div class="ttdoc">configuation of mxnet as well as basic data structure. </div></div>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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+ <ul class="tablist">
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespaces.html"><span>Namespace List</span></a></li>
+ <li><a href="namespacemembers.html"><span>Namespace Members</span></a></li>
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onmouseover="return searchBox.OnSearchSelectShow()"
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<table class="memberdecls">
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Namespaces</h2></td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
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-<tr class="memdesc:a7272c846fa03dee55916aadce0165a46"><td class="mdescLeft"> </td><td class="mdescRight">traits <a href="#a7272c846fa03dee55916aadce0165a46">More...</a><br /></td></tr>
+<tr class="memdesc:a7272c846fa03dee55916aadce0165a46"><td class="mdescLeft"> </td><td class="mdescRight">traits <a href="#a7272c846fa03dee55916aadce0165a46">More...</a><br/></td></tr>
<tr class="separator:a7272c846fa03dee55916aadce0165a46"><td class="memSeparator" colspan="2"> </td></tr>
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+<tr class="separator:a2bb53a09f94357722a2099ff7309f9f3"><td class="memSeparator" colspan="2"> </td></tr>
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+<tr class="separator:aa4d7485dc06589628bb4488ec9e8b1c9"><td class="memSeparator" colspan="2"> </td></tr>
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<h2 class="groupheader">Function Documentation</h2>
-<a id="a7272c846fa03dee55916aadce0165a46"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a7272c846fa03dee55916aadce0165a46">◆ </a></span>DMLC_DECLARE_TRAITS()</h2>
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<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -207,9 +221,9 @@ Functions</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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index d115a1b..41b4164 100644
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-<script type="text/javascript" src="search/searchdata.js"></script>
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+<script type="text/javascript">
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<table cellspacing="0" cellpadding="0">
<tbody>
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+ <td style="padding-left: 0.5em;">
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespaces.html"><span>Namespace List</span></a></li>
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<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
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@@ -77,9 +99,9 @@ Classes</h2></td></tr>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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diff --git a/versions/master/doxygen/namespacemembers.html b/versions/master/doxygen/namespacemembers.html
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<script type="text/javascript" src="jquery.js"></script>
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespaces.html"><span>Namespace List</span></a></li>
+ <li class="current"><a href="namespacemembers.html"><span>Namespace Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="namespacemembers.html"><span>All</span></a></li>
+ <li><a href="namespacemembers_func.html"><span>Functions</span></a></li>
+ <li><a href="namespacemembers_type.html"><span>Typedefs</span></a></li>
+ <li><a href="namespacemembers_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="namespacemembers_eval.html"><span>Enumerator</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="#index_a"><span>a</span></a></li>
+ <li><a href="#index_b"><span>b</span></a></li>
+ <li><a href="#index_c"><span>c</span></a></li>
+ <li><a href="#index_d"><span>d</span></a></li>
+ <li><a href="#index_e"><span>e</span></a></li>
+ <li><a href="#index_f"><span>f</span></a></li>
+ <li><a href="#index_g"><span>g</span></a></li>
+ <li><a href="#index_i"><span>i</span></a></li>
+ <li><a href="#index_k"><span>k</span></a></li>
+ <li><a href="#index_m"><span>m</span></a></li>
+ <li><a href="#index_n"><span>n</span></a></li>
+ <li><a href="#index_o"><span>o</span></a></li>
+ <li><a href="#index_r"><span>r</span></a></li>
+ <li><a href="#index_s"><span>s</span></a></li>
+ <li><a href="#index_t"><span>t</span></a></li>
+ <li><a href="#index_u"><span>u</span></a></li>
+ <li class="current"><a href="#index_v"><span>v</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -60,30 +112,30 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all namespace members with links to the namespace documentation for each member:</div>
-<h3><a id="index_a"></a>- a -</h3><ul>
+<h3><a class="anchor" id="index_a"></a>- a -</h3><ul>
<li>AGNodePtr
: <a class="el" href="namespacemxnet_1_1autograd.html#a8070fc188711b03b349a39d1b286d4a0">mxnet::autograd</a>
</li>
</ul>
-<h3><a id="index_b"></a>- b -</h3><ul>
+<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
<li>BinaryFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a880eff3ebc68810ef133a6d8b77facc9">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0">mxnet::op</a>
</li>
<li>BinaryGradFunctionT0
-: <a class="el" href="namespacemxnet_1_1op.html#abcef841f759a7874ef50f21ae0226c8a">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3">mxnet::op</a>
</li>
<li>BinaryGradFunctionT1
-: <a class="el" href="namespacemxnet_1_1op.html#a027d1f190d964f7494816733aff6c3f2">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849">mxnet::op</a>
</li>
<li>BinaryShapeFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a78c7bd2075894ac41351feca1b1e5c94">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5">mxnet::op</a>
</li>
</ul>
-<h3><a id="index_c"></a>- c -</h3><ul>
+<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
<li>CopyFromTo()
: <a class="el" href="namespacemxnet.html#aeee0e64a5e59bb0c222cbf44f4266f7a">mxnet</a>
</li>
@@ -99,7 +151,7 @@ $(function() {
</ul>
-<h3><a id="index_d"></a>- d -</h3><ul>
+<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
<li>DataIteratorFactory
: <a class="el" href="namespacemxnet.html#a6dca73c81706d54b0ddb0ef967d1e132">mxnet</a>
</li>
@@ -107,12 +159,12 @@ $(function() {
: <a class="el" href="namespacedmlc.html#a7272c846fa03dee55916aadce0165a46">dmlc</a>
</li>
<li>DMLC_DECLARE_TYPE_NAME()
-: <a class="el" href="namespacedmlc.html#a707f733f60ff1d7592676e9835af2277">dmlc</a>
+: <a class="el" href="namespacedmlc.html#ad2dedf41507bf76166d85fe447fa95de">dmlc</a>
</li>
</ul>
-<h3><a id="index_e"></a>- e -</h3><ul>
+<h3><a class="anchor" id="index_e"></a>- e -</h3><ul>
<li>ElementwiseSum()
: <a class="el" href="namespacemxnet.html#a15dfd6f3fa03dc2cf39dbab1225e1a51">mxnet</a>
</li>
@@ -122,7 +174,7 @@ $(function() {
</ul>
-<h3><a id="index_f"></a>- f -</h3><ul>
+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
<li>FCompute
: <a class="el" href="namespacemxnet.html#ad77e2048b7e8db91a12e05b477aac13e">mxnet</a>
</li>
@@ -150,7 +202,7 @@ $(function() {
</ul>
-<h3><a id="index_g"></a>- g -</h3><ul>
+<h3><a class="anchor" id="index_g"></a>- g -</h3><ul>
<li>GetExecNumMatchColor()
: <a class="el" href="namespacemxnet_1_1common.html#a56555edcc52c5b25bed90bd05e00b5fa">mxnet::common</a>
</li>
@@ -166,14 +218,14 @@ $(function() {
</ul>
-<h3><a id="index_i"></a>- i -</h3><ul>
+<h3><a class="anchor" id="index_i"></a>- i -</h3><ul>
<li>index_t
: <a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">mxnet</a>
</li>
</ul>
-<h3><a id="index_k"></a>- k -</h3><ul>
+<h3><a class="anchor" id="index_k"></a>- k -</h3><ul>
<li>kAcceptEmptyMutateTarget
: <a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd">mxnet</a>
</li>
@@ -183,6 +235,21 @@ $(function() {
<li>kArrayBeforeScalar
: <a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7">mxnet::op</a>
</li>
+<li>kAsync
+: <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada9f2b960005d2a3a5f35ac32809d84db7">mxnet</a>
+</li>
+<li>kCopyFromGPU
+: <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba739f2f416f05f4728c217f09e93958d1">mxnet</a>
+</li>
+<li>kCopyToGPU
+: <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba6cd75f41e0ec8d61b0a2f0e20ef6d1e8">mxnet</a>
+</li>
+<li>kCPUPrioritized
+: <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3bac41ceb98eeb9b2e208e3e242a7357142">mxnet</a>
+</li>
+<li>kCrossDeviceCopy
+: <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada3864860a073c64d74d3fc8e765fa6cad">mxnet</a>
+</li>
<li>kInplaceInOut
: <a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde">mxnet::op</a>
</li>
@@ -195,12 +262,18 @@ $(function() {
<li>kInplaceOutLhs
: <a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa01a199e9f1e7d97724be8aa618d5a3d4">mxnet::op</a>
</li>
+<li>kLocal
+: <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adafcf37e733f29d6aa14fb34fa737a809a">mxnet</a>
+</li>
<li>kNDArrayArgBeforeScalar
: <a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">mxnet</a>
</li>
<li>kNoInplace
: <a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2">mxnet::op</a>
</li>
+<li>kNormal
+: <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416">mxnet</a>
+</li>
<li>kNotRegisterSymbolic
: <a class="el" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b">mxnet::op</a>
</li>
@@ -216,6 +289,9 @@ $(function() {
<li>kScalarBeforeArray
: <a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13a44de4ba9d059034c023bc755cdc6026a">mxnet::op</a>
</li>
+<li>kSync
+: <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c">mxnet</a>
+</li>
<li>kWriteInplace
: <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51">mxnet</a>
</li>
@@ -225,14 +301,14 @@ $(function() {
</ul>
-<h3><a id="index_m"></a>- m -</h3><ul>
+<h3><a class="anchor" id="index_m"></a>- m -</h3><ul>
<li>MakeUnique()
-: <a class="el" href="namespacemxnet_1_1common.html#ad56ff4f856437bba25d38027aecfe766">mxnet::common</a>
+: <a class="el" href="namespacemxnet_1_1common.html#a9d9abe40c84b9924e51d5d57b3f0b7d6">mxnet::common</a>
</li>
</ul>
-<h3><a id="index_n"></a>- n -</h3><ul>
+<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
<li>NDArrayAPIFunction
: <a class="el" href="namespacemxnet.html#ab0d55b0edf98cf8f3caeb050662ae9ec">mxnet</a>
</li>
@@ -242,7 +318,7 @@ $(function() {
</ul>
-<h3><a id="index_o"></a>- o -</h3><ul>
+<h3><a class="anchor" id="index_o"></a>- o -</h3><ul>
<li>Op
: <a class="el" href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52">mxnet</a>
</li>
@@ -270,7 +346,7 @@ $(function() {
</ul>
-<h3><a id="index_r"></a>- r -</h3><ul>
+<h3><a class="anchor" id="index_r"></a>- r -</h3><ul>
<li>RANDOM_ENGINE
: <a class="el" href="namespacemxnet_1_1common.html#aceba8f2d6de65796966d7f1d341dcf33">mxnet::common</a>
</li>
@@ -283,7 +359,7 @@ $(function() {
</ul>
-<h3><a id="index_s"></a>- s -</h3><ul>
+<h3><a class="anchor" id="index_s"></a>- s -</h3><ul>
<li>SampleExponential()
: <a class="el" href="namespacemxnet.html#a51f9b094369a349c05463de2be9f0a31">mxnet</a>
</li>
@@ -315,41 +391,41 @@ $(function() {
: <a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">mxnet::op</a>
</li>
<li>SourceFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a65e8bade5ae0539689b705b921ed9a9d">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a8dc66fcca4ca52343570931503fca2c3">mxnet::op</a>
</li>
<li>SourceShapeFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a3419f1ab0d1a62068a5e6c59efdc0fc9">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#af71f289b0df41398bcbac75d37435a07">mxnet::op</a>
</li>
</ul>
-<h3><a id="index_t"></a>- t -</h3><ul>
+<h3><a class="anchor" id="index_t"></a>- t -</h3><ul>
<li>TShape
: <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">mxnet</a>
</li>
</ul>
-<h3><a id="index_u"></a>- u -</h3><ul>
+<h3><a class="anchor" id="index_u"></a>- u -</h3><ul>
<li>UnaryFunction
-: <a class="el" href="namespacemxnet_1_1op.html#aa37d5216112b72e8110fc8654860910b">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11">mxnet::op</a>
</li>
<li>UnaryGradFunctionT0
-: <a class="el" href="namespacemxnet_1_1op.html#a1e5767bffea568a1c38379060cbb68b4">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b">mxnet::op</a>
</li>
<li>UnaryGradFunctionT1
-: <a class="el" href="namespacemxnet_1_1op.html#a6ad11641816278f8fdb6fe87c7fd3740">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3">mxnet::op</a>
</li>
<li>UnaryGradFunctionT2
-: <a class="el" href="namespacemxnet_1_1op.html#ac29102128f7e7785493bbb5b7db80997">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11">mxnet::op</a>
</li>
<li>UnaryShapeFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a2d6fbc6ef1e22ea4433b3cf44ad3cef7">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311">mxnet::op</a>
</li>
</ul>
-<h3><a id="index_v"></a>- v -</h3><ul>
+<h3><a class="anchor" id="index_v"></a>- v -</h3><ul>
<li>VarHandle
: <a class="el" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">mxnet::engine</a>
</li>
@@ -357,9 +433,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/namespacemembers_enum.html b/versions/master/doxygen/namespacemembers_enum.html
index 68b49e1..2d52410 100644
--- a/versions/master/doxygen/namespacemembers_enum.html
+++ b/versions/master/doxygen/namespacemembers_enum.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Namespace Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,56 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespaces.html"><span>Namespace List</span></a></li>
+ <li class="current"><a href="namespacemembers.html"><span>Namespace Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespacemembers.html"><span>All</span></a></li>
+ <li><a href="namespacemembers_func.html"><span>Functions</span></a></li>
+ <li><a href="namespacemembers_type.html"><span>Typedefs</span></a></li>
+ <li class="current"><a href="namespacemembers_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="namespacemembers_eval.html"><span>Enumerator</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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</div><!-- contents -->
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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diff --git a/versions/master/doxygen/namespacemembers_eval.html b/versions/master/doxygen/namespacemembers_eval.html
index 521c346..71b1de7 100644
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+++ b/versions/master/doxygen/namespacemembers_eval.html
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Namespace Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,56 @@
</table>
</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespaces.html"><span>Namespace List</span></a></li>
+ <li class="current"><a href="namespacemembers.html"><span>Namespace Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespacemembers.html"><span>All</span></a></li>
+ <li><a href="namespacemembers_func.html"><span>Functions</span></a></li>
+ <li><a href="namespacemembers_type.html"><span>Typedefs</span></a></li>
+ <li><a href="namespacemembers_enum.html"><span>Enumerations</span></a></li>
+ <li class="current"><a href="namespacemembers_eval.html"><span>Enumerator</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -68,6 +99,21 @@ $(function() {
<li>kArrayBeforeScalar
: <a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7">mxnet::op</a>
</li>
+<li>kAsync
+: <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada9f2b960005d2a3a5f35ac32809d84db7">mxnet</a>
+</li>
+<li>kCopyFromGPU
+: <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba739f2f416f05f4728c217f09e93958d1">mxnet</a>
+</li>
+<li>kCopyToGPU
+: <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba6cd75f41e0ec8d61b0a2f0e20ef6d1e8">mxnet</a>
+</li>
+<li>kCPUPrioritized
+: <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3bac41ceb98eeb9b2e208e3e242a7357142">mxnet</a>
+</li>
+<li>kCrossDeviceCopy
+: <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada3864860a073c64d74d3fc8e765fa6cad">mxnet</a>
+</li>
<li>kInplaceInOut
: <a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde">mxnet::op</a>
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<li>kInplaceOutLhs
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</li>
+<li>kLocal
+: <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adafcf37e733f29d6aa14fb34fa737a809a">mxnet</a>
+</li>
<li>kNDArrayArgBeforeScalar
: <a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">mxnet</a>
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<li>kNoInplace
: <a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2">mxnet::op</a>
</li>
+<li>kNormal
+: <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416">mxnet</a>
+</li>
<li>kNotRegisterSymbolic
: <a class="el" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b">mxnet::op</a>
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<li>kScalarBeforeArray
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</li>
+<li>kSync
+: <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c">mxnet</a>
+</li>
<li>kWriteInplace
: <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51">mxnet</a>
</li>
@@ -111,9 +166,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/namespacemembers_func.html b/versions/master/doxygen/namespacemembers_func.html
index 06762cc..5c62bb5 100644
--- a/versions/master/doxygen/namespacemembers_func.html
+++ b/versions/master/doxygen/namespacemembers_func.html
@@ -3,15 +3,16 @@
<head>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Namespace Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,56 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespaces.html"><span>Namespace List</span></a></li>
+ <li class="current"><a href="namespacemembers.html"><span>Namespace Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespacemembers.html"><span>All</span></a></li>
+ <li class="current"><a href="namespacemembers_func.html"><span>Functions</span></a></li>
+ <li><a href="namespacemembers_type.html"><span>Typedefs</span></a></li>
+ <li><a href="namespacemembers_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="namespacemembers_eval.html"><span>Enumerator</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -72,7 +103,7 @@ $(function() {
: <a class="el" href="namespacedmlc.html#a7272c846fa03dee55916aadce0165a46">dmlc</a>
</li>
<li>DMLC_DECLARE_TYPE_NAME()
-: <a class="el" href="namespacedmlc.html#a89fdf041b602fe0f1c53e54b95fd307a">dmlc</a>
+: <a class="el" href="namespacedmlc.html#a2bb53a09f94357722a2099ff7309f9f3">dmlc</a>
</li>
<li>ElementwiseSum()
: <a class="el" href="namespacemxnet.html#a15dfd6f3fa03dc2cf39dbab1225e1a51">mxnet</a>
@@ -87,7 +118,7 @@ $(function() {
: <a class="el" href="namespacemxnet_1_1common.html#abc7580a95e3de5cf38d5bc2617c6d541">mxnet::common</a>
</li>
<li>MakeUnique()
-: <a class="el" href="namespacemxnet_1_1common.html#ad56ff4f856437bba25d38027aecfe766">mxnet::common</a>
+: <a class="el" href="namespacemxnet_1_1common.html#a9d9abe40c84b9924e51d5d57b3f0b7d6">mxnet::common</a>
</li>
<li>operator*()
: <a class="el" href="namespacemxnet.html#af26b09fbcda865d34ec4fdad482d266a">mxnet</a>
@@ -129,9 +160,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/namespacemembers_type.html b/versions/master/doxygen/namespacemembers_type.html
index dc2d359..c6c54c8 100644
--- a/versions/master/doxygen/namespacemembers_type.html
+++ b/versions/master/doxygen/namespacemembers_type.html
@@ -3,15 +3,16 @@
<head>
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Namespace Members</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,74 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespaces.html"><span>Namespace List</span></a></li>
+ <li class="current"><a href="namespacemembers.html"><span>Namespace Members</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow3" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespacemembers.html"><span>All</span></a></li>
+ <li><a href="namespacemembers_func.html"><span>Functions</span></a></li>
+ <li class="current"><a href="namespacemembers_type.html"><span>Typedefs</span></a></li>
+ <li><a href="namespacemembers_enum.html"><span>Enumerations</span></a></li>
+ <li><a href="namespacemembers_eval.html"><span>Enumerator</span></a></li>
+ </ul>
+ </div>
+ <div id="navrow4" class="tabs3">
+ <ul class="tablist">
+ <li><a href="#index_a"><span>a</span></a></li>
+ <li><a href="#index_b"><span>b</span></a></li>
+ <li><a href="#index_c"><span>c</span></a></li>
+ <li><a href="#index_d"><span>d</span></a></li>
+ <li><a href="#index_f"><span>f</span></a></li>
+ <li><a href="#index_g"><span>g</span></a></li>
+ <li><a href="#index_i"><span>i</span></a></li>
+ <li><a href="#index_n"><span>n</span></a></li>
+ <li><a href="#index_o"><span>o</span></a></li>
+ <li><a href="#index_r"><span>r</span></a></li>
+ <li><a href="#index_s"><span>s</span></a></li>
+ <li><a href="#index_t"><span>t</span></a></li>
+ <li><a href="#index_u"><span>u</span></a></li>
+ <li class="current"><a href="#index_v"><span>v</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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<div id="MSearchResultsWindow">
@@ -60,44 +109,44 @@ $(function() {
<div class="contents">
 
-<h3><a id="index_a"></a>- a -</h3><ul>
+<h3><a class="anchor" id="index_a"></a>- a -</h3><ul>
<li>AGNodePtr
: <a class="el" href="namespacemxnet_1_1autograd.html#a8070fc188711b03b349a39d1b286d4a0">mxnet::autograd</a>
</li>
</ul>
-<h3><a id="index_b"></a>- b -</h3><ul>
+<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
<li>BinaryFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a880eff3ebc68810ef133a6d8b77facc9">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0">mxnet::op</a>
</li>
<li>BinaryGradFunctionT0
-: <a class="el" href="namespacemxnet_1_1op.html#abcef841f759a7874ef50f21ae0226c8a">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3">mxnet::op</a>
</li>
<li>BinaryGradFunctionT1
-: <a class="el" href="namespacemxnet_1_1op.html#a027d1f190d964f7494816733aff6c3f2">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849">mxnet::op</a>
</li>
<li>BinaryShapeFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a78c7bd2075894ac41351feca1b1e5c94">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5">mxnet::op</a>
</li>
</ul>
-<h3><a id="index_c"></a>- c -</h3><ul>
+<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
<li>cpu
: <a class="el" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">mxnet</a>
</li>
</ul>
-<h3><a id="index_d"></a>- d -</h3><ul>
+<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
<li>DataIteratorFactory
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</li>
</ul>
-<h3><a id="index_f"></a>- f -</h3><ul>
+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
<li>FCompute
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</li>
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</ul>
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<li>gpu
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</li>
</ul>
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<li>index_t
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</li>
</ul>
-<h3><a id="index_n"></a>- n -</h3><ul>
+<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
<li>NDArrayAPIFunction
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</li>
</ul>
-<h3><a id="index_o"></a>- o -</h3><ul>
+<h3><a class="anchor" id="index_o"></a>- o -</h3><ul>
<li>Op
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</li>
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</ul>
-<h3><a id="index_r"></a>- r -</h3><ul>
+<h3><a class="anchor" id="index_r"></a>- r -</h3><ul>
<li>RANDOM_ENGINE
: <a class="el" href="namespacemxnet_1_1common.html#aceba8f2d6de65796966d7f1d341dcf33">mxnet::common</a>
</li>
@@ -166,43 +215,43 @@ $(function() {
</ul>
-<h3><a id="index_s"></a>- s -</h3><ul>
+<h3><a class="anchor" id="index_s"></a>- s -</h3><ul>
<li>SourceFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a65e8bade5ae0539689b705b921ed9a9d">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a8dc66fcca4ca52343570931503fca2c3">mxnet::op</a>
</li>
<li>SourceShapeFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a3419f1ab0d1a62068a5e6c59efdc0fc9">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#af71f289b0df41398bcbac75d37435a07">mxnet::op</a>
</li>
</ul>
-<h3><a id="index_t"></a>- t -</h3><ul>
+<h3><a class="anchor" id="index_t"></a>- t -</h3><ul>
<li>TShape
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</li>
</ul>
-<h3><a id="index_u"></a>- u -</h3><ul>
+<h3><a class="anchor" id="index_u"></a>- u -</h3><ul>
<li>UnaryFunction
-: <a class="el" href="namespacemxnet_1_1op.html#aa37d5216112b72e8110fc8654860910b">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11">mxnet::op</a>
</li>
<li>UnaryGradFunctionT0
-: <a class="el" href="namespacemxnet_1_1op.html#a1e5767bffea568a1c38379060cbb68b4">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b">mxnet::op</a>
</li>
<li>UnaryGradFunctionT1
-: <a class="el" href="namespacemxnet_1_1op.html#a6ad11641816278f8fdb6fe87c7fd3740">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3">mxnet::op</a>
</li>
<li>UnaryGradFunctionT2
-: <a class="el" href="namespacemxnet_1_1op.html#ac29102128f7e7785493bbb5b7db80997">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11">mxnet::op</a>
</li>
<li>UnaryShapeFunction
-: <a class="el" href="namespacemxnet_1_1op.html#a2d6fbc6ef1e22ea4433b3cf44ad3cef7">mxnet::op</a>
+: <a class="el" href="namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311">mxnet::op</a>
</li>
</ul>
-<h3><a id="index_v"></a>- v -</h3><ul>
+<h3><a class="anchor" id="index_v"></a>- v -</h3><ul>
<li>VarHandle
: <a class="el" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">mxnet::engine</a>
</li>
@@ -210,9 +259,9 @@ $(function() {
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/namespacemxnet.html b/versions/master/doxygen/namespacemxnet.html
index ea1db8c..b52a182 100644
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<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet Namespace Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <input type="text" id="MSearchField" value="Search" accesskey="S"
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+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespaces.html"><span>Namespace List</span></a></li>
+ <li><a href="namespacemembers.html"><span>Namespace Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -74,223 +96,229 @@ $(function() {
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacemxnet_1_1autograd"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1autograd.html">autograd</a></td></tr>
+<tr class="memitem:namespacemxnet_1_1autograd"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1autograd.html">autograd</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacemxnet_1_1common"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html">common</a></td></tr>
+<tr class="memitem:namespacemxnet_1_1common"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html">common</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacemxnet_1_1engine"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1engine.html">engine</a></td></tr>
-<tr class="memdesc:namespacemxnet_1_1engine"><td class="mdescLeft"> </td><td class="mdescRight">namespace of engine internal types. <br /></td></tr>
+<tr class="memitem:namespacemxnet_1_1engine"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1engine.html">engine</a></td></tr>
+<tr class="memdesc:namespacemxnet_1_1engine"><td class="mdescLeft"> </td><td class="mdescRight">namespace of engine internal types. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacemxnet_1_1op"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html">op</a></td></tr>
-<tr class="memdesc:namespacemxnet_1_1op"><td class="mdescLeft"> </td><td class="mdescRight">namespace of arguments <br /></td></tr>
+<tr class="memitem:namespacemxnet_1_1op"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html">op</a></td></tr>
+<tr class="memdesc:namespacemxnet_1_1op"><td class="mdescLeft"> </td><td class="mdescRight">namespace of arguments <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html">Context</a></td></tr>
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-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataBatch.html">DataBatch</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, returned by Iterator. <a href="structmxnet_1_1DataBatch.html#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataInst.html">DataInst</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">a single data instance <a href="structmxnet_1_1DataInst.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> information about the execution environment. <a href="structmxnet_1_1Context.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataIteratorReg.html">DataIteratorReg</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for DataIterator factory functions. <a href="structmxnet_1_1DataIteratorReg.html#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1RunContext.html">RunContext</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">execution time context. The information needed in runtime for actual execution. <a href="structmxnet_1_1RunContext.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Engine.html">Engine</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Dependency engine that schedules operations. <a href="classmxnet_1_1Engine.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Dependency engine that schedules operations. <a href="classmxnet_1_1Engine.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Executor.html">Executor</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> of a computation graph. <a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> can be created by Binding a symbol. <a href="classmxnet_1_1Executor.html#details">More...</a [...]
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> of a computation graph. <a class="el" href="classmxnet_1_1Executor.html" title="Executor of a computation graph. Executor can be created by Binding a symbol. ">Executor</a> can be created by Binding a symbol. <a href="classmxnet_1_1Executor.html#details">More...</a [...]
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1IIterator.html">IIterator</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">iterator type <a href="classmxnet_1_1IIterator.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">iterator type <a href="classmxnet_1_1IIterator.html#details">More...</a><br/></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataInst.html">DataInst</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">a single data instance <a href="structmxnet_1_1DataInst.html#details">More...</a><br/></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataBatch.html">DataBatch</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, returned by Iterator. <a href="structmxnet_1_1DataBatch.html#details">More...</a><br/></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataIteratorReg.html">DataIteratorReg</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for DataIterator factory functions. <a href="structmxnet_1_1DataIteratorReg.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1KVStore.html">KVStore</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">distributed key-value store <a href="classmxnet_1_1KVStore.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">distributed key-value store <a href="classmxnet_1_1KVStore.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">ndarray interface <a href="classmxnet_1_1NDArray.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">ndarray interface <a href="classmxnet_1_1NDArray.html#details">More...</a><br/></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for NDArrayFunction. <a href="structmxnet_1_1NDArrayFunctionReg.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for NDArrayFunction. <a href="structmxnet_1_1NDArrayFunctionReg.html#details">More...</a><br/></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OpContext.html">OpContext</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">All the possible information needed by <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd" title="perform a forward operation of Operator, save the output to TBlob. ">Operator.Forward</a> and Backward This is the superset of <a class="el" href="structmxnet_1_1RunContext.html" title="execution time context. The information needed in runtime for actual execution. ">RunContext</a>. We [...]
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">All the possible information needed by <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd" title="perform a forward operation of Operator, save the output to TBlob. ">Operator.Forward</a> and Backward This is the superset of <a class="el" href="structmxnet_1_1RunContext.html" title="execution time context. The information needed in runtime for actual execution. ">RunContext</a>. We [...]
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> state. This is a pointer type, its content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is const. <a [...]
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Operator.html">Operator</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> interface. <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> defines basic operation unit of optimized computation graph in [...]
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> interface. <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> defines basic operation unit of optimized computation graph in [...]
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> is a object that stores all information about <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. It als [...]
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> is a object that stores all information about <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. It als [...]
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OperatorPropertyReg.html">OperatorPropertyReg</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> factory functions. <a href="structmxnet_1_1OperatorPropertyReg.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> factory functions. <a href="structmxnet_1_1OperatorPropertyReg.html#details">More...</a><br/></td></tr>
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-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> state. This is a pointer type, its content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is const. <a [...]
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">The resources that can be requested by <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. <a href="structmxnet_1_1ResourceRequest.html#details">More...</a><br/></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html">Resource</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Resources used by mxnet operations. A resource is something special other than <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, but will still participate. <a href="structmxnet_1_1Resource.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Resources used by mxnet operations. A resource is something special other than <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, but will still participate. <a href="structmxnet_1_1Resource.html#details">More...</a><br/></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1ResourceManager.html">ResourceManager</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Global resource manager. <a href="classmxnet_1_1ResourceManager.html#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">The resources that can be requested by <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. <a href="structmxnet_1_1ResourceRequest.html#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1RunContext.html">RunContext</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">execution time context. The information needed in runtime for actual execution. <a href="structmxnet_1_1RunContext.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Global resource manager. <a href="classmxnet_1_1ResourceManager.html#details">More...</a><br/></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Storage.html">Storage</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> manager across multiple devices. <a href="classmxnet_1_1Storage.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> manager across multiple devices. <a href="classmxnet_1_1Storage.html#details">More...</a><br/></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html">TBlob</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> itself do not involve any arithmentic operations, but it can be converted to tens [...]
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> itself do not involve any arithmentic operations, but it can be converted to tens [...]
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
<tr class="memitem:a850af9d76d9d4beaeb5234c1434973e3"><td class="memItemLeft" align="right" valign="top">typedef mshadow::cpu </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">cpu</a></td></tr>
-<tr class="memdesc:a850af9d76d9d4beaeb5234c1434973e3"><td class="mdescLeft"> </td><td class="mdescRight">mxnet cpu <a href="#a850af9d76d9d4beaeb5234c1434973e3">More...</a><br /></td></tr>
+<tr class="memdesc:a850af9d76d9d4beaeb5234c1434973e3"><td class="mdescLeft"> </td><td class="mdescRight">mxnet cpu <a href="#a850af9d76d9d4beaeb5234c1434973e3">More...</a><br/></td></tr>
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<tr class="memitem:a31ef33571a4f6ba8847eeee1f4907141"><td class="memItemLeft" align="right" valign="top">typedef mshadow::gpu </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a31ef33571a4f6ba8847eeee1f4907141">gpu</a></td></tr>
-<tr class="memdesc:a31ef33571a4f6ba8847eeee1f4907141"><td class="mdescLeft"> </td><td class="mdescRight">mxnet gpu <a href="#a31ef33571a4f6ba8847eeee1f4907141">More...</a><br /></td></tr>
+<tr class="memdesc:a31ef33571a4f6ba8847eeee1f4907141"><td class="mdescLeft"> </td><td class="mdescRight">mxnet gpu <a href="#a31ef33571a4f6ba8847eeee1f4907141">More...</a><br/></td></tr>
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<tr class="memitem:abadedc1b42ee3e5b8852e63783b982ef"><td class="memItemLeft" align="right" valign="top">typedef mshadow::index_t </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a></td></tr>
-<tr class="memdesc:abadedc1b42ee3e5b8852e63783b982ef"><td class="mdescLeft"> </td><td class="mdescRight">index type usually use unsigned <a href="#abadedc1b42ee3e5b8852e63783b982ef">More...</a><br /></td></tr>
+<tr class="memdesc:abadedc1b42ee3e5b8852e63783b982ef"><td class="mdescLeft"> </td><td class="mdescRight">index type usually use unsigned <a href="#abadedc1b42ee3e5b8852e63783b982ef">More...</a><br/></td></tr>
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<tr class="memitem:a4421326e33f2a848cb2fa3e70ab24ff4"><td class="memItemLeft" align="right" valign="top">typedef mshadow::default_real_t </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a></td></tr>
-<tr class="memdesc:a4421326e33f2a848cb2fa3e70ab24ff4"><td class="mdescLeft"> </td><td class="mdescRight">data type that will be used to store ndarray <a href="#a4421326e33f2a848cb2fa3e70ab24ff4">More...</a><br /></td></tr>
+<tr class="memdesc:a4421326e33f2a848cb2fa3e70ab24ff4"><td class="mdescLeft"> </td><td class="mdescRight">data type that will be used to store ndarray <a href="#a4421326e33f2a848cb2fa3e70ab24ff4">More...</a><br/></td></tr>
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<tr class="memitem:a6e14bb5f6ffc81892feca8d1da658687"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> = nnvm::TShape</td></tr>
-<tr class="memdesc:a6e14bb5f6ffc81892feca8d1da658687"><td class="mdescLeft"> </td><td class="mdescRight">Shape data structure used to record shape information. <a href="#a6e14bb5f6ffc81892feca8d1da658687">More...</a><br /></td></tr>
+<tr class="memdesc:a6e14bb5f6ffc81892feca8d1da658687"><td class="mdescLeft"> </td><td class="mdescRight">Shape data structure used to record shape information. <a href="#a6e14bb5f6ffc81892feca8d1da658687">More...</a><br/></td></tr>
<tr class="separator:a6e14bb5f6ffc81892feca8d1da658687"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac2fb286fd7ddfbc7af1456f6c7a7ee52"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52">Op</a> = nnvm::Op</td></tr>
-<tr class="memdesc:ac2fb286fd7ddfbc7af1456f6c7a7ee52"><td class="mdescLeft"> </td><td class="mdescRight">operator structure from NNVM <a href="#ac2fb286fd7ddfbc7af1456f6c7a7ee52">More...</a><br /></td></tr>
+<tr class="memdesc:ac2fb286fd7ddfbc7af1456f6c7a7ee52"><td class="mdescLeft"> </td><td class="mdescRight">operator structure from NNVM <a href="#ac2fb286fd7ddfbc7af1456f6c7a7ee52">More...</a><br/></td></tr>
<tr class="separator:ac2fb286fd7ddfbc7af1456f6c7a7ee52"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6dca73c81706d54b0ddb0ef967d1e132"><td class="memItemLeft" align="right" valign="top">typedef std::function< <a class="el" href="classmxnet_1_1IIterator.html">IIterator</a>< <a class="el" href="structmxnet_1_1DataBatch.html">DataBatch</a> > *()> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6dca73c81706d54b0ddb0ef967d1e132">DataIteratorFactory</a></td></tr>
-<tr class="memdesc:a6dca73c81706d54b0ddb0ef967d1e132"><td class="mdescLeft"> </td><td class="mdescRight">typedef the factory function of data iterator <a href="#a6dca73c81706d54b0ddb0ef967d1e132">More...</a><br /></td></tr>
+<tr class="memitem:a6dca73c81706d54b0ddb0ef967d1e132"><td class="memItemLeft" align="right" valign="top">typedef std::function<br class="typebreak"/>
+< <a class="el" href="classmxnet_1_1IIterator.html">IIterator</a>< <a class="el" href="structmxnet_1_1DataBatch.html">DataBatch</a> > *()> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6dca73c81706d54b0ddb0ef967d1e132">DataIteratorFactory</a></td></tr>
+<tr class="memdesc:a6dca73c81706d54b0ddb0ef967d1e132"><td class="mdescLeft"> </td><td class="mdescRight">typedef the factory function of data iterator <a href="#a6dca73c81706d54b0ddb0ef967d1e132">More...</a><br/></td></tr>
<tr class="separator:a6dca73c81706d54b0ddb0ef967d1e132"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ab0d55b0edf98cf8f3caeb050662ae9ec"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(<a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> **used_vars, <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *scalars, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> **mutate_vars, int num_params, char **param_keys, char **param_vals)> </td><td class="memItemRight" valign="bottom"><a [...]
-<tr class="memdesc:ab0d55b0edf98cf8f3caeb050662ae9ec"><td class="mdescLeft"> </td><td class="mdescRight">definition of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function <a href="#ab0d55b0edf98cf8f3caeb050662ae9ec">More...</a><br /></td></tr>
+<tr class="memitem:ab0d55b0edf98cf8f3caeb050662ae9ec"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(<a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> <br class="typebreak"/>
+**used_vars, <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *scalars, <br class="typebreak"/>
+<a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> **mutate_vars, int <br class="typebreak"/>
+num_params, char **param_keys, <br class="typebreak"/>
+char **param_vals)> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ab0d55b0edf98cf8f3caeb050662ae9ec">NDArrayAPIFunction</a></td></tr>
+<tr class="memdesc:ab0d55b0edf98cf8f3caeb050662ae9ec"><td class="mdescLeft"> </td><td class="mdescRight">definition of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function <a href="#ab0d55b0edf98cf8f3caeb050662ae9ec">More...</a><br/></td></tr>
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<tr class="memitem:acb83431b596f805fabcc4ba400c62e85"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#acb83431b596f805fabcc4ba400c62e85">FCreateOpState</a> = std::function< <a class="el" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a>(const NodeAttrs &attrs, <a class="el" href="structmxnet_1_1Context.html">Context</a> ctx, const std::vector< <a class="el" href="namespacemxnet [...]
-<tr class="memdesc:acb83431b596f805fabcc4ba400c62e85"><td class="mdescLeft"> </td><td class="mdescRight">Create a Layer style, forward/backward operator. This is easy to write code that contains state. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator [...]
+<tr class="memdesc:acb83431b596f805fabcc4ba400c62e85"><td class="mdescLeft"> </td><td class="mdescRight">Create a Layer style, forward/backward operator. This is easy to write code that contains state. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator [...]
<tr class="separator:acb83431b596f805fabcc4ba400c62e85"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a666f1055e5401ecec0278e8ba5790732"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a666f1055e5401ecec0278e8ba5790732">FExecType</a> = std::function< <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">ExecType</a>(const NodeAttrs &attrs)></td></tr>
-<tr class="memdesc:a666f1055e5401ecec0278e8ba5790732"><td class="mdescLeft"> </td><td class="mdescRight">Execution mode of this operator. <a href="#a666f1055e5401ecec0278e8ba5790732">More...</a><br /></td></tr>
+<tr class="memdesc:a666f1055e5401ecec0278e8ba5790732"><td class="mdescLeft"> </td><td class="mdescRight">Execution mode of this operator. <a href="#a666f1055e5401ecec0278e8ba5790732">More...</a><br/></td></tr>
<tr class="separator:a666f1055e5401ecec0278e8ba5790732"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9097a269dd76d833c014149ff3eaab9b"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a9097a269dd76d833c014149ff3eaab9b">FStatefulCompute</a> = std::function< void(const <a class="el" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a> &state, const <a class="el" href="structmxnet_1_1OpContext.html">OpContext</a> &ctx, const std::vector< <a class="el" href="cl [...]
-<tr class="memdesc:a9097a269dd76d833c014149ff3eaab9b"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for stateful operator. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is [...]
+<tr class="memdesc:a9097a269dd76d833c014149ff3eaab9b"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for stateful operator. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is [...]
<tr class="separator:a9097a269dd76d833c014149ff3eaab9b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab12f1853179b1456152a563edff844a9"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ab12f1853179b1456152a563edff844a9">FStatefulComputeEx</a> = std::function< void(const <a class="el" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a> &state, const <a class="el" href="structmxnet_1_1OpContext.html">OpContext</a> &ctx, const std::vector< <a class="el" href=" [...]
-<tr class="memdesc:ab12f1853179b1456152a563edff844a9"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for stateful operator using <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> interface. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class=" [...]
+<tr class="memdesc:ab12f1853179b1456152a563edff844a9"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for stateful operator using <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> interface. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class=" [...]
<tr class="separator:ab12f1853179b1456152a563edff844a9"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:afaa687cbca9e992614be285711c1d72d"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#afaa687cbca9e992614be285711c1d72d">FResourceRequest</a> = std::function< std::vector< <a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> >(const NodeAttrs &n)></td></tr>
-<tr class="memdesc:afaa687cbca9e992614be285711c1d72d"><td class="mdescLeft"> </td><td class="mdescRight">The resource request from the operator. <a href="#afaa687cbca9e992614be285711c1d72d">More...</a><br /></td></tr>
+<tr class="memdesc:afaa687cbca9e992614be285711c1d72d"><td class="mdescLeft"> </td><td class="mdescRight">The resource request from the operator. <a href="#afaa687cbca9e992614be285711c1d72d">More...</a><br/></td></tr>
<tr class="separator:afaa687cbca9e992614be285711c1d72d"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0906e49a921265634672f555451075ed"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a0906e49a921265634672f555451075ed">FNDArrayFunction</a> = std::function< void(const nnvm::NodeAttrs &attrs, const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &inputs, std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> [...]
-<tr class="memdesc:a0906e49a921265634672f555451075ed"><td class="mdescLeft"> </td><td class="mdescRight">Register an operator called as a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function. <a href="#a0906e49a921265634672f555451075ed">More...</a><br /></td></tr>
+<tr class="memdesc:a0906e49a921265634672f555451075ed"><td class="mdescLeft"> </td><td class="mdescRight">Register an operator called as a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function. <a href="#a0906e49a921265634672f555451075ed">More...</a><br/></td></tr>
<tr class="separator:a0906e49a921265634672f555451075ed"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad77e2048b7e8db91a12e05b477aac13e"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ad77e2048b7e8db91a12e05b477aac13e">FCompute</a> = std::function< void(const nnvm::NodeAttrs &attrs, const <a class="el" href="structmxnet_1_1OpContext.html">OpContext</a> &ctx, const std::vector< <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> > &inputs, const [...]
-<tr class="memdesc:ad77e2048b7e8db91a12e05b477aac13e"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for simple stateless forward only operator. <a href="#ad77e2048b7e8db91a12e05b477aac13e">More...</a><br /></td></tr>
+<tr class="memdesc:ad77e2048b7e8db91a12e05b477aac13e"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for simple stateless forward only operator. <a href="#ad77e2048b7e8db91a12e05b477aac13e">More...</a><br/></td></tr>
<tr class="separator:ad77e2048b7e8db91a12e05b477aac13e"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6f5428fbf261a94eaac079ec24413815"><td class="memItemLeft" align="right" valign="top">typedef std::function< <a class="el" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a> *()> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6f5428fbf261a94eaac079ec24413815">OperatorPropertyFactory</a></td></tr>
-<tr class="memdesc:a6f5428fbf261a94eaac079ec24413815"><td class="mdescLeft"> </td><td class="mdescRight">typedef the factory function of operator property <a href="#a6f5428fbf261a94eaac079ec24413815">More...</a><br /></td></tr>
+<tr class="memitem:a6f5428fbf261a94eaac079ec24413815"><td class="memItemLeft" align="right" valign="top">typedef std::function<br class="typebreak"/>
+< <a class="el" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a> *()> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6f5428fbf261a94eaac079ec24413815">OperatorPropertyFactory</a></td></tr>
+<tr class="memdesc:a6f5428fbf261a94eaac079ec24413815"><td class="mdescLeft"> </td><td class="mdescRight">typedef the factory function of operator property <a href="#a6f5428fbf261a94eaac079ec24413815">More...</a><br/></td></tr>
<tr class="separator:a6f5428fbf261a94eaac079ec24413815"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="enum-members"></a>
Enumerations</h2></td></tr>
-<tr class="memitem:a998b74220fab2b012cf8a179650e1b3b"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">FnProperty</a> { <br />
+<tr class="memitem:a998b74220fab2b012cf8a179650e1b3b"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">FnProperty</a> { <br/>
  <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416">FnProperty::kNormal</a>,
<a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba739f2f416f05f4728c217f09e93958d1">FnProperty::kCopyFromGPU</a>,
<a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba6cd75f41e0ec8d61b0a2f0e20ef6d1e8">FnProperty::kCopyToGPU</a>,
<a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3bac41ceb98eeb9b2e208e3e242a7357142">FnProperty::kCPUPrioritized</a>,
-<br />
+<br/>
  <a class="el" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7">FnProperty::kAsync</a>
-<br />
- }<tr class="memdesc:a998b74220fab2b012cf8a179650e1b3b"><td class="mdescLeft"> </td><td class="mdescRight">Function property, used to hint what action is pushed to engine. <a href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">More...</a><br /></td></tr>
-</td></tr>
+<br/>
+ }</td></tr>
+<tr class="memdesc:a998b74220fab2b012cf8a179650e1b3b"><td class="mdescLeft"> </td><td class="mdescRight">Function property, used to hint what action is pushed to engine. <a href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3b">More...</a><br/></td></tr>
<tr class="separator:a998b74220fab2b012cf8a179650e1b3b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a89a5f0f5cfd9e1e94604a7b42dda818a"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818a">NDArrayFunctionTypeMask</a> { <a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">kNDArrayArgBeforeScalar</a> = 1,
<a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aaef9818c8aae9d0046b3d9033a62fcfbb">kScalarArgBeforeNDArray</a> = 1 << 1,
<a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd">kAcceptEmptyMutateTarget</a> = 1 << 2
- }<tr class="memdesc:a89a5f0f5cfd9e1e94604a7b42dda818a"><td class="mdescLeft"> </td><td class="mdescRight">mask information on how functions can be exposed <a href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818a">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:a89a5f0f5cfd9e1e94604a7b42dda818a"><td class="mdescLeft"> </td><td class="mdescRight">mask information on how functions can be exposed <a href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818a">More...</a><br/></td></tr>
<tr class="separator:a89a5f0f5cfd9e1e94604a7b42dda818a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7cd7094ef222e9ae1f520c3b6a214398"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> { <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26">kNullOp</a>,
<a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a">kWriteTo</a>,
<a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51">kWriteInplace</a>,
<a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383">kAddTo</a>
- }<tr class="memdesc:a7cd7094ef222e9ae1f520c3b6a214398"><td class="mdescLeft"> </td><td class="mdescRight">operation request type to Forward and Backward <a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:a7cd7094ef222e9ae1f520c3b6a214398"><td class="mdescLeft"> </td><td class="mdescRight">operation request type to Forward and Backward <a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">More...</a><br/></td></tr>
<tr class="separator:a7cd7094ef222e9ae1f520c3b6a214398"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad7801fb2dd68294d4c7a9e289bc071ad"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">ExecType</a> { <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c">ExecType::kSync</a>,
<a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada9f2b960005d2a3a5f35ac32809d84db7">ExecType::kAsync</a>,
<a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adafcf37e733f29d6aa14fb34fa737a809a">ExecType::kLocal</a>,
<a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada3864860a073c64d74d3fc8e765fa6cad">ExecType::kCrossDeviceCopy</a>
- }<tr class="memdesc:ad7801fb2dd68294d4c7a9e289bc071ad"><td class="mdescLeft"> </td><td class="mdescRight">the execution type of the operator <a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:ad7801fb2dd68294d4c7a9e289bc071ad"><td class="mdescLeft"> </td><td class="mdescRight">the execution type of the operator <a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">More...</a><br/></td></tr>
<tr class="separator:ad7801fb2dd68294d4c7a9e289bc071ad"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:aeee0e64a5e59bb0c222cbf44f4266f7a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#aeee0e64a5e59bb0c222cbf44f4266f7a">CopyFromTo</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &from, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *to, int priority=0)</td></tr>
-<tr class="memdesc:aeee0e64a5e59bb0c222cbf44f4266f7a"><td class="mdescLeft"> </td><td class="mdescRight">issue an copy operation from one <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> to another the two ndarray can sit on different devices this operation will be scheduled by the engine <a href="#aeee0e64a5e59bb0c222cbf44f4266f7a">More...</a><br /></td></tr>
+<tr class="memdesc:aeee0e64a5e59bb0c222cbf44f4266f7a"><td class="mdescLeft"> </td><td class="mdescRight">issue an copy operation from one <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> to another the two ndarray can sit on different devices this operation will be scheduled by the engine <a href="#aeee0e64a5e59bb0c222cbf44f4266f7a">More...</a><br/></td></tr>
<tr class="separator:aeee0e64a5e59bb0c222cbf44f4266f7a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a15dfd6f3fa03dc2cf39dbab1225e1a51"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a15dfd6f3fa03dc2cf39dbab1225e1a51">ElementwiseSum</a> (const std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > &source, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *out, int priority=0)</td></tr>
-<tr class="memdesc:a15dfd6f3fa03dc2cf39dbab1225e1a51"><td class="mdescLeft"> </td><td class="mdescRight">Perform elementwise sum over each data from source, store result into out. <a href="#a15dfd6f3fa03dc2cf39dbab1225e1a51">More...</a><br /></td></tr>
+<tr class="memdesc:a15dfd6f3fa03dc2cf39dbab1225e1a51"><td class="mdescLeft"> </td><td class="mdescRight">Perform elementwise sum over each data from source, store result into out. <a href="#a15dfd6f3fa03dc2cf39dbab1225e1a51">More...</a><br/></td></tr>
<tr class="separator:a15dfd6f3fa03dc2cf39dbab1225e1a51"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4dafe3aae43025fd21bc44b98c18ed1e"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a4dafe3aae43025fd21bc44b98c18ed1e">operator+</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &rhs)</td></tr>
-<tr class="memdesc:a4dafe3aae43025fd21bc44b98c18ed1e"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add <a href="#a4dafe3aae43025fd21bc44b98c18ed1e">More...</a><br /></td></tr>
+<tr class="memdesc:a4dafe3aae43025fd21bc44b98c18ed1e"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add <a href="#a4dafe3aae43025fd21bc44b98c18ed1e">More...</a><br/></td></tr>
<tr class="separator:a4dafe3aae43025fd21bc44b98c18ed1e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a30bfda0e3e1b347e3057097cafc628be"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a30bfda0e3e1b347e3057097cafc628be">operator+</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs)</td></tr>
-<tr class="memdesc:a30bfda0e3e1b347e3057097cafc628be"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add <a href="#a30bfda0e3e1b347e3057097cafc628be">More...</a><br /></td></tr>
+<tr class="memdesc:a30bfda0e3e1b347e3057097cafc628be"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add <a href="#a30bfda0e3e1b347e3057097cafc628be">More...</a><br/></td></tr>
<tr class="separator:a30bfda0e3e1b347e3057097cafc628be"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae3356844b657c9ae1b84d3ddd56bccf6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ae3356844b657c9ae1b84d3ddd56bccf6">operator-</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &rhs)</td></tr>
-<tr class="memdesc:ae3356844b657c9ae1b84d3ddd56bccf6"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtraction <a href="#ae3356844b657c9ae1b84d3ddd56bccf6">More...</a><br /></td></tr>
+<tr class="memdesc:ae3356844b657c9ae1b84d3ddd56bccf6"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtraction <a href="#ae3356844b657c9ae1b84d3ddd56bccf6">More...</a><br/></td></tr>
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<tr class="memitem:a0c8542953df5093060985db5518a339b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a0c8542953df5093060985db5518a339b">operator-</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs)</td></tr>
-<tr class="memdesc:a0c8542953df5093060985db5518a339b"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtraction <a href="#a0c8542953df5093060985db5518a339b">More...</a><br /></td></tr>
+<tr class="memdesc:a0c8542953df5093060985db5518a339b"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtraction <a href="#a0c8542953df5093060985db5518a339b">More...</a><br/></td></tr>
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<tr class="memitem:af26b09fbcda865d34ec4fdad482d266a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#af26b09fbcda865d34ec4fdad482d266a">operator*</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &rhs)</td></tr>
-<tr class="memdesc:af26b09fbcda865d34ec4fdad482d266a"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication <a href="#af26b09fbcda865d34ec4fdad482d266a">More...</a><br /></td></tr>
+<tr class="memdesc:af26b09fbcda865d34ec4fdad482d266a"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication <a href="#af26b09fbcda865d34ec4fdad482d266a">More...</a><br/></td></tr>
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<tr class="memitem:a66510f6a344ee38bb89cb552e974b838"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a66510f6a344ee38bb89cb552e974b838">operator*</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs)</td></tr>
-<tr class="memdesc:a66510f6a344ee38bb89cb552e974b838"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication <a href="#a66510f6a344ee38bb89cb552e974b838">More...</a><br /></td></tr>
+<tr class="memdesc:a66510f6a344ee38bb89cb552e974b838"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication <a href="#a66510f6a344ee38bb89cb552e974b838">More...</a><br/></td></tr>
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<tr class="memitem:a502e3e00dc61cedb9c6eef711f1176ac"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a502e3e00dc61cedb9c6eef711f1176ac">operator/</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &rhs)</td></tr>
-<tr class="memdesc:a502e3e00dc61cedb9c6eef711f1176ac"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division <a href="#a502e3e00dc61cedb9c6eef711f1176ac">More...</a><br /></td></tr>
+<tr class="memdesc:a502e3e00dc61cedb9c6eef711f1176ac"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division <a href="#a502e3e00dc61cedb9c6eef711f1176ac">More...</a><br/></td></tr>
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<tr class="memitem:a3455659b714488e8e53e4e4580a43ec7"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a3455659b714488e8e53e4e4580a43ec7">operator/</a> (const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs)</td></tr>
-<tr class="memdesc:a3455659b714488e8e53e4e4580a43ec7"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division <a href="#a3455659b714488e8e53e4e4580a43ec7">More...</a><br /></td></tr>
+<tr class="memdesc:a3455659b714488e8e53e4e4580a43ec7"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division <a href="#a3455659b714488e8e53e4e4580a43ec7">More...</a><br/></td></tr>
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<tr class="memitem:a5bf83454c8f41259eb9d471f59feda2b"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a5bf83454c8f41259eb9d471f59feda2b">RandomSeed</a> (uint32_t seed)</td></tr>
-<tr class="memdesc:a5bf83454c8f41259eb9d471f59feda2b"><td class="mdescLeft"> </td><td class="mdescRight">Seed the random number generator. <a href="#a5bf83454c8f41259eb9d471f59feda2b">More...</a><br /></td></tr>
+<tr class="memdesc:a5bf83454c8f41259eb9d471f59feda2b"><td class="mdescLeft"> </td><td class="mdescRight">Seed the random number generator. <a href="#a5bf83454c8f41259eb9d471f59feda2b">More...</a><br/></td></tr>
<tr class="separator:a5bf83454c8f41259eb9d471f59feda2b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2846f4556c9ca9bd0f567504ce60f274"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">SampleUniform</a> (<a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> begin, <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> end, <a class="el" href="classmxnet_1_1NDArray.html">NDArr [...]
-<tr class="memdesc:a2846f4556c9ca9bd0f567504ce60f274"><td class="mdescLeft"> </td><td class="mdescRight">Sample uniform distribution for each elements of out. <a href="#a2846f4556c9ca9bd0f567504ce60f274">More...</a><br /></td></tr>
+<tr class="memdesc:a2846f4556c9ca9bd0f567504ce60f274"><td class="mdescLeft"> </td><td class="mdescRight">Sample uniform distribution for each elements of out. <a href="#a2846f4556c9ca9bd0f567504ce60f274">More...</a><br/></td></tr>
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<tr class="memitem:a5a28062f52ca576a126599e7ad487077"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">SampleGaussian</a> (<a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> mu, <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> sigma, <a class="el" href="classmxnet_1_1NDArray.html">NDArr [...]
-<tr class="memdesc:a5a28062f52ca576a126599e7ad487077"><td class="mdescLeft"> </td><td class="mdescRight">Sample gaussian distribution for each elements of out. <a href="#a5a28062f52ca576a126599e7ad487077">More...</a><br /></td></tr>
+<tr class="memdesc:a5a28062f52ca576a126599e7ad487077"><td class="mdescLeft"> </td><td class="mdescRight">Sample gaussian distribution for each elements of out. <a href="#a5a28062f52ca576a126599e7ad487077">More...</a><br/></td></tr>
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<tr class="memitem:a050beaa505f11e0b844deb91efe0cac2"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a050beaa505f11e0b844deb91efe0cac2">SampleGamma</a> (<a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> alpha, <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> beta, <a class="el" href="classmxnet_1_1NDArray.html">NDArra [...]
-<tr class="memdesc:a050beaa505f11e0b844deb91efe0cac2"><td class="mdescLeft"> </td><td class="mdescRight">Sample gamma distribution for each elements of out. <a href="#a050beaa505f11e0b844deb91efe0cac2">More...</a><br /></td></tr>
+<tr class="memdesc:a050beaa505f11e0b844deb91efe0cac2"><td class="mdescLeft"> </td><td class="mdescRight">Sample gamma distribution for each elements of out. <a href="#a050beaa505f11e0b844deb91efe0cac2">More...</a><br/></td></tr>
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<tr class="memitem:a51f9b094369a349c05463de2be9f0a31"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a51f9b094369a349c05463de2be9f0a31">SampleExponential</a> (<a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> lambda, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *out)</td></tr>
-<tr class="memdesc:a51f9b094369a349c05463de2be9f0a31"><td class="mdescLeft"> </td><td class="mdescRight">Sample exponential distribution for each elements of out. <a href="#a51f9b094369a349c05463de2be9f0a31">More...</a><br /></td></tr>
+<tr class="memdesc:a51f9b094369a349c05463de2be9f0a31"><td class="mdescLeft"> </td><td class="mdescRight">Sample exponential distribution for each elements of out. <a href="#a51f9b094369a349c05463de2be9f0a31">More...</a><br/></td></tr>
<tr class="separator:a51f9b094369a349c05463de2be9f0a31"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aed55e8197182b7c66126902b2a43739a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#aed55e8197182b7c66126902b2a43739a">SamplePoisson</a> (<a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> lambda, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *out)</td></tr>
-<tr class="memdesc:aed55e8197182b7c66126902b2a43739a"><td class="mdescLeft"> </td><td class="mdescRight">Sample Poisson distribution for each elements of out. <a href="#aed55e8197182b7c66126902b2a43739a">More...</a><br /></td></tr>
+<tr class="memdesc:aed55e8197182b7c66126902b2a43739a"><td class="mdescLeft"> </td><td class="mdescRight">Sample Poisson distribution for each elements of out. <a href="#aed55e8197182b7c66126902b2a43739a">More...</a><br/></td></tr>
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<tr class="memitem:ae82f70918b2d946f7ad66a158b1733dc"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ae82f70918b2d946f7ad66a158b1733dc">SampleNegBinomial</a> (int32_t k, <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> p, <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> *out)</td></tr>
-<tr class="memdesc:ae82f70918b2d946f7ad66a158b1733dc"><td class="mdescLeft"> </td><td class="mdescRight">Sample negative binomial distribution for each elements of out. <a href="#ae82f70918b2d946f7ad66a158b1733dc">More...</a><br /></td></tr>
+<tr class="memdesc:ae82f70918b2d946f7ad66a158b1733dc"><td class="mdescLeft"> </td><td class="mdescRight">Sample negative binomial distribution for each elements of out. <a href="#ae82f70918b2d946f7ad66a158b1733dc">More...</a><br/></td></tr>
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<tr class="memitem:ad8e0dad88ce27133fd24811763b7afdb"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ad8e0dad88ce27133fd24811763b7afdb">SampleGenNegBinomial</a> (<a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> mu, <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> alpha, <a class="el" href="classmxnet_1_1NDArray.html" [...]
-<tr class="memdesc:ad8e0dad88ce27133fd24811763b7afdb"><td class="mdescLeft"> </td><td class="mdescRight">Sample generalized negative binomial distribution for each elements of out. <a href="#ad8e0dad88ce27133fd24811763b7afdb">More...</a><br /></td></tr>
+<tr class="memdesc:ad8e0dad88ce27133fd24811763b7afdb"><td class="mdescLeft"> </td><td class="mdescRight">Sample generalized negative binomial distribution for each elements of out. <a href="#ad8e0dad88ce27133fd24811763b7afdb">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
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<p>Macros/inlines to assist CLion to parse Cuda files (*.cu, *.cuh)</p>
<p>Copyright (c) 2015 by Contributors </p>
</div><h2 class="groupheader">Typedef Documentation</h2>
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<h2 class="groupheader">Enumeration Type Documentation</h2>
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<p>the execution type of the operator </p>
<table class="fieldtable">
-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c"></a>kSync </td><td class="fielddoc"><p>Forward/Backward are synchronize calls. </p>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c"></a>kSync</em> </td><td class="fielddoc">
+<p>Forward/Backward are synchronize calls. </p>
</td></tr>
-<tr><td class="fieldname"><a id="ad7801fb2dd68294d4c7a9e289bc071ada9f2b960005d2a3a5f35ac32809d84db7"></a>kAsync </td><td class="fielddoc"><p>Forward/Backward are asynchronize, will call <a class="el" href="structmxnet_1_1OpContext.html#a40ce0e3d4ac21412cfafafc5b1fd32ef" title="the callback when operation completes, used by asynchronize ops ">OpContext.async_on_complete</a> when operation finishes. </p>
+<tr><td class="fieldname"><em><a class="anchor" id="ad7801fb2dd68294d4c7a9e289bc071ada9f2b960005d2a3a5f35ac32809d84db7"></a>kAsync</em> </td><td class="fielddoc">
+<p>Forward/Backward are asynchronize, will call <a class="el" href="structmxnet_1_1OpContext.html#a40ce0e3d4ac21412cfafafc5b1fd32ef" title="the callback when operation completes, used by asynchronize ops ">OpContext.async_on_complete</a> when operation finishes. </p>
</td></tr>
-<tr><td class="fieldname"><a id="ad7801fb2dd68294d4c7a9e289bc071adafcf37e733f29d6aa14fb34fa737a809a"></a>kLocal </td><td class="fielddoc"><p>Run this operator on the scheduling thread without pushing to engine. </p>
+<tr><td class="fieldname"><em><a class="anchor" id="ad7801fb2dd68294d4c7a9e289bc071adafcf37e733f29d6aa14fb34fa737a809a"></a>kLocal</em> </td><td class="fielddoc">
+<p>Run this operator on the scheduling thread without pushing to engine. </p>
</td></tr>
-<tr><td class="fieldname"><a id="ad7801fb2dd68294d4c7a9e289bc071ada3864860a073c64d74d3fc8e765fa6cad"></a>kCrossDeviceCopy </td><td class="fielddoc"><p>Cross device copy operation, this is a special operator That indicates copy across devices, the input and output can sit on different device. In current implementation, copy operator is specially handled by executor. This flag is used for special case treatment and future extension of different copy ops. </p>
+<tr><td class="fieldname"><em><a class="anchor" id="ad7801fb2dd68294d4c7a9e289bc071ada3864860a073c64d74d3fc8e765fa6cad"></a>kCrossDeviceCopy</em> </td><td class="fielddoc">
+<p>Cross device copy operation, this is a special operator That indicates copy across devices, the input and output can sit on different device. In current implementation, copy operator is specially handled by executor. This flag is used for special case treatment and future extension of different copy ops. </p>
</td></tr>
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<p>Function property, used to hint what action is pushed to engine. </p>
<table class="fieldtable">
-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416"></a>kNormal </td><td class="fielddoc"><p>Normal operation. </p>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="a998b74220fab2b012cf8a179650e1b3ba07fa7a19aa722c635a15e94cb7f50416"></a>kNormal</em> </td><td class="fielddoc">
+<p>Normal operation. </p>
</td></tr>
-<tr><td class="fieldname"><a id="a998b74220fab2b012cf8a179650e1b3ba739f2f416f05f4728c217f09e93958d1"></a>kCopyFromGPU </td><td class="fielddoc"><p>Copy operation from GPU to other devices. </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a998b74220fab2b012cf8a179650e1b3ba739f2f416f05f4728c217f09e93958d1"></a>kCopyFromGPU</em> </td><td class="fielddoc">
+<p>Copy operation from GPU to other devices. </p>
</td></tr>
-<tr><td class="fieldname"><a id="a998b74220fab2b012cf8a179650e1b3ba6cd75f41e0ec8d61b0a2f0e20ef6d1e8"></a>kCopyToGPU </td><td class="fielddoc"><p>Copy operation from CPU to other devices. </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a998b74220fab2b012cf8a179650e1b3ba6cd75f41e0ec8d61b0a2f0e20ef6d1e8"></a>kCopyToGPU</em> </td><td class="fielddoc">
+<p>Copy operation from CPU to other devices. </p>
</td></tr>
-<tr><td class="fieldname"><a id="a998b74220fab2b012cf8a179650e1b3bac41ceb98eeb9b2e208e3e242a7357142"></a>kCPUPrioritized </td><td class="fielddoc"><p>Prioritized sync operation on CPU. </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a998b74220fab2b012cf8a179650e1b3bac41ceb98eeb9b2e208e3e242a7357142"></a>kCPUPrioritized</em> </td><td class="fielddoc">
+<p>Prioritized sync operation on CPU. </p>
</td></tr>
-<tr><td class="fieldname"><a id="a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7"></a>kAsync </td><td class="fielddoc"><p>Asynchronous function call. </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7"></a>kAsync</em> </td><td class="fielddoc">
+<p>Asynchronous function call. </p>
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<p>mask information on how functions can be exposed </p>
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-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51"></a>kNDArrayArgBeforeScalar </td><td class="fielddoc"><p>all the use_vars should go before scalar </p>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51"></a>kNDArrayArgBeforeScalar</em> </td><td class="fielddoc">
+<p>all the use_vars should go before scalar </p>
</td></tr>
-<tr><td class="fieldname"><a id="a89a5f0f5cfd9e1e94604a7b42dda818aaef9818c8aae9d0046b3d9033a62fcfbb"></a>kScalarArgBeforeNDArray </td><td class="fielddoc"><p>all the scalar should go before use_vars </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a89a5f0f5cfd9e1e94604a7b42dda818aaef9818c8aae9d0046b3d9033a62fcfbb"></a>kScalarArgBeforeNDArray</em> </td><td class="fielddoc">
+<p>all the scalar should go before use_vars </p>
</td></tr>
-<tr><td class="fieldname"><a id="a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd"></a>kAcceptEmptyMutateTarget </td><td class="fielddoc"><p>whether this function allows the handles in the target to be empty <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> that are not yet initialized, and will initialize them when the function is invoked. </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd"></a>kAcceptEmptyMutateTarget</em> </td><td class="fielddoc">
+<p>whether this function allows the handles in the target to be empty <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> that are not yet initialized, and will initialize them when the function is invoked. </p>
<p>most function should support this, except copy between different devices, which requires the <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> to be pre-initialized with context </p>
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<p>operation request type to Forward and Backward </p>
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-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26"></a>kNullOp </td><td class="fielddoc"><p>no operation, do not write anything </p>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26"></a>kNullOp</em> </td><td class="fielddoc">
+<p>no operation, do not write anything </p>
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-<tr><td class="fieldname"><a id="a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a"></a>kWriteTo </td><td class="fielddoc"><p>write gradient to provided space </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a"></a>kWriteTo</em> </td><td class="fielddoc">
+<p>write gradient to provided space </p>
</td></tr>
-<tr><td class="fieldname"><a id="a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51"></a>kWriteInplace </td><td class="fielddoc"><p>perform an inplace write, Target shares memory with one of input arguments. This option only happen when </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51"></a>kWriteInplace</em> </td><td class="fielddoc">
+<p>perform an inplace write, Target shares memory with one of input arguments. This option only happen when </p>
</td></tr>
-<tr><td class="fieldname"><a id="a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383"></a>kAddTo </td><td class="fielddoc"><p>add to the provided space </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383"></a>kAddTo</em> </td><td class="fielddoc">
+<p>add to the provided space </p>
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<h2 class="groupheader">Function Documentation</h2>
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+ <td class="paramtype">const NDArray & </td>
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+ <td class="paramtype">NDArray * </td>
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+ <td class="paramtype">const std::vector< NDArray > & </td>
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+ <td class="paramtype">NDArray * </td>
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+ <td class="paramtype">const NDArray & </td>
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+ <td class="paramtype">const NDArray & </td>
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+ <td class="paramtype">const real_t & </td>
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+ <td class="paramtype">const NDArray & </td>
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+ <td class="paramtype">const NDArray & </td>
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+ <td class="paramtype">const NDArray & </td>
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+ <td class="paramtype">const real_t & </td>
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- <td class="paramtype">const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td>
+ <td class="paramtype">const NDArray & </td>
<td class="paramname"><em>lhs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype">const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td>
+ <td class="paramtype">const NDArray & </td>
<td class="paramname"><em>rhs</em> </td>
</tr>
<tr>
@@ -963,22 +953,20 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a0c8542953df5093060985db5518a339b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a0c8542953df5093060985db5518a339b">◆ </a></span>operator-() <span class="overload">[2/2]</span></h2>
-
+<a class="anchor" id="a0c8542953df5093060985db5518a339b"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> mxnet::operator- </td>
<td>(</td>
- <td class="paramtype">const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td>
+ <td class="paramtype">const NDArray & </td>
<td class="paramname"><em>lhs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype">const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> & </td>
+ <td class="paramtype">const real_t & </td>
<td class="paramname"><em>rhs</em> </td>
</tr>
<tr>
@@ -1001,22 +989,20 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a502e3e00dc61cedb9c6eef711f1176ac"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a502e3e00dc61cedb9c6eef711f1176ac">◆ </a></span>operator/() <span class="overload">[1/2]</span></h2>
-
+<a class="anchor" id="a502e3e00dc61cedb9c6eef711f1176ac"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> mxnet::operator/ </td>
<td>(</td>
- <td class="paramtype">const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td>
+ <td class="paramtype">const NDArray & </td>
<td class="paramname"><em>lhs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype">const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td>
+ <td class="paramtype">const NDArray & </td>
<td class="paramname"><em>rhs</em> </td>
</tr>
<tr>
@@ -1039,22 +1025,20 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a3455659b714488e8e53e4e4580a43ec7"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a3455659b714488e8e53e4e4580a43ec7">◆ </a></span>operator/() <span class="overload">[2/2]</span></h2>
-
+<a class="anchor" id="a3455659b714488e8e53e4e4580a43ec7"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> mxnet::operator/ </td>
<td>(</td>
- <td class="paramtype">const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> & </td>
+ <td class="paramtype">const NDArray & </td>
<td class="paramname"><em>lhs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype">const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> & </td>
+ <td class="paramtype">const real_t & </td>
<td class="paramname"><em>rhs</em> </td>
</tr>
<tr>
@@ -1077,9 +1061,7 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a5bf83454c8f41259eb9d471f59feda2b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a5bf83454c8f41259eb9d471f59feda2b">◆ </a></span>RandomSeed()</h2>
-
+<a class="anchor" id="a5bf83454c8f41259eb9d471f59feda2b"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -1103,22 +1085,20 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a51f9b094369a349c05463de2be9f0a31"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a51f9b094369a349c05463de2be9f0a31">◆ </a></span>SampleExponential()</h2>
-
+<a class="anchor" id="a51f9b094369a349c05463de2be9f0a31"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname">void mxnet::SampleExponential </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>lambda</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * </td>
+ <td class="paramtype">NDArray * </td>
<td class="paramname"><em>out</em> </td>
</tr>
<tr>
@@ -1140,28 +1120,26 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a050beaa505f11e0b844deb91efe0cac2"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a050beaa505f11e0b844deb91efe0cac2">◆ </a></span>SampleGamma()</h2>
-
+<a class="anchor" id="a050beaa505f11e0b844deb91efe0cac2"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname">void mxnet::SampleGamma </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>alpha</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>beta</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * </td>
+ <td class="paramtype">NDArray * </td>
<td class="paramname"><em>out</em> </td>
</tr>
<tr>
@@ -1184,28 +1162,26 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a5a28062f52ca576a126599e7ad487077"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a5a28062f52ca576a126599e7ad487077">◆ </a></span>SampleGaussian()</h2>
-
+<a class="anchor" id="a5a28062f52ca576a126599e7ad487077"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname">void mxnet::SampleGaussian </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>mu</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>sigma</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * </td>
+ <td class="paramtype">NDArray * </td>
<td class="paramname"><em>out</em> </td>
</tr>
<tr>
@@ -1228,28 +1204,26 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="ad8e0dad88ce27133fd24811763b7afdb"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad8e0dad88ce27133fd24811763b7afdb">◆ </a></span>SampleGenNegBinomial()</h2>
-
+<a class="anchor" id="ad8e0dad88ce27133fd24811763b7afdb"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname">void mxnet::SampleGenNegBinomial </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>mu</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>alpha</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * </td>
+ <td class="paramtype">NDArray * </td>
<td class="paramname"><em>out</em> </td>
</tr>
<tr>
@@ -1272,9 +1246,7 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="ae82f70918b2d946f7ad66a158b1733dc"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ae82f70918b2d946f7ad66a158b1733dc">◆ </a></span>SampleNegBinomial()</h2>
-
+<a class="anchor" id="ae82f70918b2d946f7ad66a158b1733dc"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -1287,13 +1259,13 @@ Functions</h2></td></tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>p</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * </td>
+ <td class="paramtype">NDArray * </td>
<td class="paramname"><em>out</em> </td>
</tr>
<tr>
@@ -1316,22 +1288,20 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="aed55e8197182b7c66126902b2a43739a"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aed55e8197182b7c66126902b2a43739a">◆ </a></span>SamplePoisson()</h2>
-
+<a class="anchor" id="aed55e8197182b7c66126902b2a43739a"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname">void mxnet::SamplePoisson </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>lambda</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * </td>
+ <td class="paramtype">NDArray * </td>
<td class="paramname"><em>out</em> </td>
</tr>
<tr>
@@ -1353,28 +1323,26 @@ Functions</h2></td></tr>
</div>
</div>
-<a id="a2846f4556c9ca9bd0f567504ce60f274"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a2846f4556c9ca9bd0f567504ce60f274">◆ </a></span>SampleUniform()</h2>
-
+<a class="anchor" id="a2846f4556c9ca9bd0f567504ce60f274"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname">void mxnet::SampleUniform </td>
<td>(</td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>begin</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td>
+ <td class="paramtype">real_t </td>
<td class="paramname"><em>end</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
- <td class="paramtype"><a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> * </td>
+ <td class="paramtype">NDArray * </td>
<td class="paramname"><em>out</em> </td>
</tr>
<tr>
@@ -1400,9 +1368,9 @@ Functions</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/namespacemxnet_1_1autograd.html b/versions/master/doxygen/namespacemxnet_1_1autograd.html
index e1312b1..8da2dd0 100644
--- a/versions/master/doxygen/namespacemxnet_1_1autograd.html
+++ b/versions/master/doxygen/namespacemxnet_1_1autograd.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::autograd Namespace Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="namespaces.html"><span>Namespace List</span></a></li>
+ <li><a href="namespacemembers.html"><span>Namespace Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -81,9 +103,7 @@ Typedefs</h2></td></tr>
<tr class="separator:a8070fc188711b03b349a39d1b286d4a0"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<h2 class="groupheader">Typedef Documentation</h2>
-<a id="a8070fc188711b03b349a39d1b286d4a0"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a8070fc188711b03b349a39d1b286d4a0">◆ </a></span>AGNodePtr</h2>
-
+<a class="anchor" id="a8070fc188711b03b349a39d1b286d4a0"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -98,9 +118,9 @@ Typedefs</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/namespacemxnet_1_1common.html b/versions/master/doxygen/namespacemxnet_1_1common.html
index 16c5fd5..9cfe08b 100644
--- a/versions/master/doxygen/namespacemxnet_1_1common.html
+++ b/versions/master/doxygen/namespacemxnet_1_1common.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::common Namespace Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
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Namespaces</h2></td></tr>
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper trait class for easy allocation and deallocation. <a href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#details">More...</a><br/></td></tr>
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<tr class="memitem:a39b3453302aa3aa56230fbc88005861d"><td class="memTemplParams" colspan="2">template<class T > </td></tr>
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+<tr class="memdesc:a39b3453302aa3aa56230fbc88005861d"><td class="mdescLeft"> </td><td class="mdescRight">Constructs an object of type <code>T</code> and wraps it in a <code>std</code><code>::</code><code>unique_ptr</code>. <a href="#a39b3453302aa3aa56230fbc88005861d">More...</a><br/></td></tr>
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-<tr class="memdesc:adb63438b723f69cdf56f7919275f4596"><td class="mdescLeft"> </td><td class="mdescRight">Constructs an object of type <code>T</code> and wraps it in a <code>std</code><code>::</code><code>unique_ptr</code>. <a href="#adb63438b723f69cdf56f7919275f4596">More...</a><br /></td></tr>
-<tr class="separator:adb63438b723f69cdf56f7919275f4596"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6ca270747bb7ab942c61d364f14f3042"><td class="memTemplParams" colspan="2">template<class T , class... Args> </td></tr>
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+<tr class="memdesc:a6ca270747bb7ab942c61d364f14f3042"><td class="mdescLeft"> </td><td class="mdescRight">Constructs an object of type <code>T</code> and wraps it in a <code>std</code><code>::</code><code>unique_ptr</code>. <a href="#a6ca270747bb7ab942c61d364f14f3042">More...</a><br/></td></tr>
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<tr class="memitem:a1abcb76ccd9022ebacdacd69c821cdc2"><td class="memTemplParams" colspan="2">template<typename FCompType > </td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#aceba8f2d6de65796966d7f1d341dcf33">◆ </a></span>RANDOM_ENGINE</h2>
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<div class="memproto">
<table class="memname">
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-<h2 class="memtitle"><span class="permalink"><a href="#a56555edcc52c5b25bed90bd05e00b5fa">◆ </a></span>GetExecNumMatchColor()</h2>
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<td class="paramkey"></td>
<td></td>
- <td class="paramtype">const <a class="el" href="structmxnet_1_1Context.html">Context</a> & </td>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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Functions</h2></td></tr>
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+<tr class="memdesc:a9feee613a4f16a954dd68e55345a72ac"><td class="mdescLeft"> </td><td class="mdescRight">Get string representation of cuBLAS errors. <a href="#a9feee613a4f16a954dd68e55345a72ac">More...</a><br/></td></tr>
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-<tr class="memdesc:a97c06b2f4d26445a7386b0f54fae1feb"><td class="mdescLeft"> </td><td class="mdescRight">Get string representation of cuRAND errors. <a href="#a97c06b2f4d26445a7386b0f54fae1feb">More...</a><br /></td></tr>
+<tr class="memdesc:a97c06b2f4d26445a7386b0f54fae1feb"><td class="mdescLeft"> </td><td class="mdescRight">Get string representation of cuRAND errors. <a href="#a97c06b2f4d26445a7386b0f54fae1feb">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>common utils for cuda </p>
</div><h2 class="groupheader">Function Documentation</h2>
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<div class="memproto">
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@@ -157,9 +175,9 @@ Functions</h2></td></tr>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/namespacemxnet_1_1common_1_1helper.html b/versions/master/doxygen/namespacemxnet_1_1common_1_1helper.html
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<title>mxnet: mxnet::common::helper Namespace Reference</title>
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<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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<script type="text/javascript" src="search/search.js"></script>
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<table cellspacing="0" cellpadding="0">
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+ <ul class="tablist">
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+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
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+ <li><a href="files.html"><span>Files</span></a></li>
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-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for non-array type <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for non-array type <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html">UniqueIf< T[]></a></td></tr>
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for an array of unknown bound <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html">UniqueIf< T[kSize]></a></td></tr>
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for an array of known bound <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
@@ -89,9 +111,9 @@ Classes</h2></td></tr>
</div></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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</small></address>
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diff --git a/versions/master/doxygen/namespacemxnet_1_1engine.html b/versions/master/doxygen/namespacemxnet_1_1engine.html
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<script type="text/javascript" src="jquery.js"></script>
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+ <ul class="tablist">
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+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
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+ <li><a href="files.html"><span>Files</span></a></li>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">OnComplete Callback to the engine, called by AsyncFn when action completes. <a href="classmxnet_1_1engine_1_1CallbackOnComplete.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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+<tr class="memdesc:a9d36c4f33eae8531586dc2edf83ae7cf"><td class="mdescLeft"> </td><td class="mdescRight">Variable pointer type, usually hold by user used to specify dependencies. <a href="#a9d36c4f33eae8531586dc2edf83ae7cf">More...</a><br/></td></tr>
<tr class="separator:a9d36c4f33eae8531586dc2edf83ae7cf"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2d9b14b658e3f3c4e03ca49cd38ace94"><td class="memItemLeft" align="right" valign="top">typedef Opr * </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1engine.html#a2d9b14b658e3f3c4e03ca49cd38ace94">OprHandle</a></td></tr>
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+<tr class="memdesc:a2d9b14b658e3f3c4e03ca49cd38ace94"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> pointer type, usually hold by user. <a href="#a2d9b14b658e3f3c4e03ca49cd38ace94">More...</a><br/></td></tr>
<tr class="separator:a2d9b14b658e3f3c4e03ca49cd38ace94"><td class="memSeparator" colspan="2"> </td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>namespace of engine internal types. </p>
</div><h2 class="groupheader">Typedef Documentation</h2>
-<a id="a2d9b14b658e3f3c4e03ca49cd38ace94"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a2d9b14b658e3f3c4e03ca49cd38ace94">◆ </a></span>OprHandle</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a9d36c4f33eae8531586dc2edf83ae7cf">◆ </a></span>VarHandle</h2>
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<div class="memproto">
<table class="memname">
@@ -126,9 +144,9 @@ Typedefs</h2></td></tr>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/namespacemxnet_1_1op.html b/versions/master/doxygen/namespacemxnet_1_1op.html
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<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
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+<script type="text/javascript">
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<table cellspacing="0" cellpadding="0">
<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
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@@ -76,102 +98,100 @@ $(function() {
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Environment arguments that is used by the function. These can be things like scalar arguments when add a value with scalar. <a href="structmxnet_1_1op_1_1EnvArguments.html#details">More...</a><br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html">GradFunctionArgument</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">super class of all gradient function argument <a href="structmxnet_1_1op_1_1GradFunctionArgument.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">super class of all gradient function argument <a href="structmxnet_1_1op_1_1GradFunctionArgument.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">First input to the function. <a href="structmxnet_1_1op_1_1Input0.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">First input to the function. <a href="structmxnet_1_1op_1_1Input0.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1Input1.html">Input1</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Second input to the function. <a href="structmxnet_1_1op_1_1Input1.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Second input to the function. <a href="structmxnet_1_1op_1_1Input1.html#details">More...</a><br/></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1OutputValue.html">OutputValue</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Ouput value of the function to the function. <a href="structmxnet_1_1op_1_1OutputValue.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Gradient of output value. <a href="structmxnet_1_1op_1_1OutputGrad.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Gradient of output value. <a href="structmxnet_1_1op_1_1OutputGrad.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1OutputValue.html">OutputValue</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Ouput value of the function to the function. <a href="structmxnet_1_1op_1_1OutputValue.html#details">More...</a><br /></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Environment arguments that is used by the function. These can be things like scalar arguments when add a value with scalar. <a href="structmxnet_1_1op_1_1EnvArguments.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">SimpleOpRegEntry</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">registry entry to register simple operators via functions. <a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">registry entry to register simple operators via functions. <a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html">SimpleOpRegistry</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">registry for <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> functions <a href="classmxnet_1_1op_1_1SimpleOpRegistry.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">registry for <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> functions <a href="classmxnet_1_1op_1_1SimpleOpRegistry.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
-<tr class="memitem:a65e8bade5ae0539689b705b921ed9a9d"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a65e8bade5ae0539689b705b921ed9a9d">SourceFunction</a>) (const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *ret, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3 [...]
-<tr class="memdesc:a65e8bade5ae0539689b705b921ed9a9d"><td class="mdescLeft"> </td><td class="mdescRight">source function that generate output based on env The result container is pre-allocated with the correct shape. <a href="#a65e8bade5ae0539689b705b921ed9a9d">More...</a><br /></td></tr>
-<tr class="separator:a65e8bade5ae0539689b705b921ed9a9d"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a3419f1ab0d1a62068a5e6c59efdc0fc9"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a3419f1ab0d1a62068a5e6c59efdc0fc9">SourceShapeFunction</a>) (const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env)</td></tr>
-<tr class="memdesc:a3419f1ab0d1a62068a5e6c59efdc0fc9"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape. <a href="#a3419f1ab0d1a62068a5e6c59efdc0fc9">More...</a><br /></td></tr>
-<tr class="separator:a3419f1ab0d1a62068a5e6c59efdc0fc9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aa37d5216112b72e8110fc8654860910b"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#aa37d5216112b72e8110fc8654860910b">UnaryFunction</a>) (const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &src, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> [...]
-<tr class="memdesc:aa37d5216112b72e8110fc8654860910b"><td class="mdescLeft"> </td><td class="mdescRight">Unary function that takes a src and save result to ret. The result container is pre-allocated with the correct shape. <a href="#aa37d5216112b72e8110fc8654860910b">More...</a><br /></td></tr>
-<tr class="separator:aa37d5216112b72e8110fc8654860910b"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2d6fbc6ef1e22ea4433b3cf44ad3cef7"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a2d6fbc6ef1e22ea4433b3cf44ad3cef7">UnaryShapeFunction</a>) (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &src, const <a class="el" href="struct [...]
-<tr class="memdesc:a2d6fbc6ef1e22ea4433b3cf44ad3cef7"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape given source. <a href="#a2d6fbc6ef1e22ea4433b3cf44ad3cef7">More...</a><br /></td></tr>
-<tr class="separator:a2d6fbc6ef1e22ea4433b3cf44ad3cef7"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a1e5767bffea568a1c38379060cbb68b4"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a1e5767bffea568a1c38379060cbb68b4">UnaryGradFunctionT0</a>) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmx [...]
-<tr class="memdesc:a1e5767bffea568a1c38379060cbb68b4"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes output value of function and computes gradient wrt to input. <a href="#a1e5767bffea568a1c38379060cbb68b4">More...</a><br /></td></tr>
-<tr class="separator:a1e5767bffea568a1c38379060cbb68b4"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6ad11641816278f8fdb6fe87c7fd3740"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a6ad11641816278f8fdb6fe87c7fd3740">UnaryGradFunctionT1</a>) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1OutputValue.html">OutputValue</a> &out_value, const <a class="el" hre [...]
-<tr class="memdesc:a6ad11641816278f8fdb6fe87c7fd3740"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes output value of function and computes gradient wrt to input. <a href="#a6ad11641816278f8fdb6fe87c7fd3740">More...</a><br /></td></tr>
-<tr class="separator:a6ad11641816278f8fdb6fe87c7fd3740"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ac29102128f7e7785493bbb5b7db80997"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#ac29102128f7e7785493bbb5b7db80997">UnaryGradFunctionT2</a>) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> &in_data0, const <a class="el" href="structmx [...]
-<tr class="memdesc:ac29102128f7e7785493bbb5b7db80997"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes input value of function and computes gradient wrt to input. <a href="#ac29102128f7e7785493bbb5b7db80997">More...</a><br /></td></tr>
-<tr class="separator:ac29102128f7e7785493bbb5b7db80997"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a880eff3ebc68810ef133a6d8b77facc9"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a880eff3ebc68810ef133a6d8b77facc9">BinaryFunction</a>) (const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &lhs, const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &rhs, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArgume [...]
-<tr class="memdesc:a880eff3ebc68810ef133a6d8b77facc9"><td class="mdescLeft"> </td><td class="mdescRight">Binary function that takes lhs, rhs and save result to ret. The result container is pre-allocated with the correct shape. <a href="#a880eff3ebc68810ef133a6d8b77facc9">More...</a><br /></td></tr>
-<tr class="separator:a880eff3ebc68810ef133a6d8b77facc9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a78c7bd2075894ac41351feca1b1e5c94"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a78c7bd2075894ac41351feca1b1e5c94">BinaryShapeFunction</a>) (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &lhs, const <a class="el" href="names [...]
-<tr class="memdesc:a78c7bd2075894ac41351feca1b1e5c94"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape given source shapes. <a href="#a78c7bd2075894ac41351feca1b1e5c94">More...</a><br /></td></tr>
-<tr class="separator:a78c7bd2075894ac41351feca1b1e5c94"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:abcef841f759a7874ef50f21ae0226c8a"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#abcef841f759a7874ef50f21ae0226c8a">BinaryGradFunctionT0</a>) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classm [...]
-<tr class="memdesc:abcef841f759a7874ef50f21ae0226c8a"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes only output gradient and computes gradient wrt to input. We support total gradient as a whole to make it easy to combine a few ops. <a href="#abcef841f759a7874ef50f21ae0226c8a">More...</a><br /></td></tr>
-<tr class="separator:abcef841f759a7874ef50f21ae0226c8a"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a027d1f190d964f7494816733aff6c3f2"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a027d1f190d964f7494816733aff6c3f2">BinaryGradFunctionT1</a>) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> &lhs, const <a class="el" href="structmxnet_ [...]
-<tr class="memdesc:a027d1f190d964f7494816733aff6c3f2"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes inputs of function anod computes gradient wrt to input. <a href="#a027d1f190d964f7494816733aff6c3f2">More...</a><br /></td></tr>
-<tr class="separator:a027d1f190d964f7494816733aff6c3f2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8dc66fcca4ca52343570931503fca2c3"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a8dc66fcca4ca52343570931503fca2c3">SourceFunction</a> )(const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *ret, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3 [...]
+<tr class="memdesc:a8dc66fcca4ca52343570931503fca2c3"><td class="mdescLeft"> </td><td class="mdescRight">source function that generate output based on env The result container is pre-allocated with the correct shape. <a href="#a8dc66fcca4ca52343570931503fca2c3">More...</a><br/></td></tr>
+<tr class="separator:a8dc66fcca4ca52343570931503fca2c3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af71f289b0df41398bcbac75d37435a07"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#af71f289b0df41398bcbac75d37435a07">SourceShapeFunction</a> )(const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env)</td></tr>
+<tr class="memdesc:af71f289b0df41398bcbac75d37435a07"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape. <a href="#af71f289b0df41398bcbac75d37435a07">More...</a><br/></td></tr>
+<tr class="separator:af71f289b0df41398bcbac75d37435a07"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af5075d1f55b857402d9b9da07c164f11"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11">UnaryFunction</a> )(const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &src, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> [...]
+<tr class="memdesc:af5075d1f55b857402d9b9da07c164f11"><td class="mdescLeft"> </td><td class="mdescRight">Unary function that takes a src and save result to ret. The result container is pre-allocated with the correct shape. <a href="#af5075d1f55b857402d9b9da07c164f11">More...</a><br/></td></tr>
+<tr class="separator:af5075d1f55b857402d9b9da07c164f11"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab004eaba5bbfd4a05d7ddeb54ce1c311"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311">UnaryShapeFunction</a> )(const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &src, const <a class="el" href="struct [...]
+<tr class="memdesc:ab004eaba5bbfd4a05d7ddeb54ce1c311"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape given source. <a href="#ab004eaba5bbfd4a05d7ddeb54ce1c311">More...</a><br/></td></tr>
+<tr class="separator:ab004eaba5bbfd4a05d7ddeb54ce1c311"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5e30ef3bfc75cf4b111af7b1c74dab1b"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b">UnaryGradFunctionT0</a> )(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmx [...]
+<tr class="memdesc:a5e30ef3bfc75cf4b111af7b1c74dab1b"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes output value of function and computes gradient wrt to input. <a href="#a5e30ef3bfc75cf4b111af7b1c74dab1b">More...</a><br/></td></tr>
+<tr class="separator:a5e30ef3bfc75cf4b111af7b1c74dab1b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a362db0d0023a59204d018c32458ae4e3"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3">UnaryGradFunctionT1</a> )(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1OutputValue.html">OutputValue</a> &out_value, const <a class="el" hre [...]
+<tr class="memdesc:a362db0d0023a59204d018c32458ae4e3"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes output value of function and computes gradient wrt to input. <a href="#a362db0d0023a59204d018c32458ae4e3">More...</a><br/></td></tr>
+<tr class="separator:a362db0d0023a59204d018c32458ae4e3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6b53969567339c4b97a8e0da51580c11"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11">UnaryGradFunctionT2</a> )(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> &in_data0, const <a class="el" href="structmx [...]
+<tr class="memdesc:a6b53969567339c4b97a8e0da51580c11"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes input value of function and computes gradient wrt to input. <a href="#a6b53969567339c4b97a8e0da51580c11">More...</a><br/></td></tr>
+<tr class="separator:a6b53969567339c4b97a8e0da51580c11"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a97e7c6f5bbc6cdf02d443df6a36167e0"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0">BinaryFunction</a> )(const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &lhs, const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &rhs, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArgume [...]
+<tr class="memdesc:a97e7c6f5bbc6cdf02d443df6a36167e0"><td class="mdescLeft"> </td><td class="mdescRight">Binary function that takes lhs, rhs and save result to ret. The result container is pre-allocated with the correct shape. <a href="#a97e7c6f5bbc6cdf02d443df6a36167e0">More...</a><br/></td></tr>
+<tr class="separator:a97e7c6f5bbc6cdf02d443df6a36167e0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a05df0e7fd54e37144430654f8c200db5"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5">BinaryShapeFunction</a> )(const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &lhs, const <a class="el" href="names [...]
+<tr class="memdesc:a05df0e7fd54e37144430654f8c200db5"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape given source shapes. <a href="#a05df0e7fd54e37144430654f8c200db5">More...</a><br/></td></tr>
+<tr class="separator:a05df0e7fd54e37144430654f8c200db5"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad1ec651ba6eee751c85b957df8972fc3"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3">BinaryGradFunctionT0</a> )(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classm [...]
+<tr class="memdesc:ad1ec651ba6eee751c85b957df8972fc3"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes only output gradient and computes gradient wrt to input. We support total gradient as a whole to make it easy to combine a few ops. <a href="#ad1ec651ba6eee751c85b957df8972fc3">More...</a><br/></td></tr>
+<tr class="separator:ad1ec651ba6eee751c85b957df8972fc3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2ed3ee83974ac57efd4b251a85dca849"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849">BinaryGradFunctionT1</a> )(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> &lhs, const <a class="el" href="structmxnet_ [...]
+<tr class="memdesc:a2ed3ee83974ac57efd4b251a85dca849"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes inputs of function anod computes gradient wrt to input. <a href="#a2ed3ee83974ac57efd4b251a85dca849">More...</a><br/></td></tr>
+<tr class="separator:a2ed3ee83974ac57efd4b251a85dca849"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="enum-members"></a>
Enumerations</h2></td></tr>
-<tr class="memitem:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> { <br />
+<tr class="memitem:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> { <br/>
  <a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2">kNoInplace</a>,
<a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde">kInplaceInOut</a>,
<a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daab7974a6a3982dae42ba00af90dcabd80">kInplaceOutIn</a>,
<a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa31a764e0ff5966a10e26f7fbff308c18">kInplaceLhsOut</a>,
-<br />
+<br/>
  <a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa01a199e9f1e7d97724be8aa618d5a3d4">kInplaceOutLhs</a>
-<br />
- }<tr class="memdesc:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set inplace of operator <a href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">More...</a><br /></td></tr>
-</td></tr>
+<br/>
+ }</td></tr>
+<tr class="memdesc:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set inplace of operator <a href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">More...</a><br/></td></tr>
<tr class="separator:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae7310844fa858f1ec6b6428dd6977a13"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">SimpleOpScalarOption</a> { <a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13a44de4ba9d059034c023bc755cdc6026a">kScalarBeforeArray</a>,
<a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7">kArrayBeforeScalar</a>
- }<tr class="memdesc:ae7310844fa858f1ec6b6428dd6977a13"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set symbolic registration <a href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:ae7310844fa858f1ec6b6428dd6977a13"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set symbolic registration <a href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">More...</a><br/></td></tr>
<tr class="separator:ae7310844fa858f1ec6b6428dd6977a13"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a01bb0bb658bedb4d58bf7d11e4b17268"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">SimpleOpRegOption</a> { <a class="el" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b">kNotRegisterSymbolic</a>,
<a class="el" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1">kRegisterSymbolic</a>
- }<tr class="memdesc:a01bb0bb658bedb4d58bf7d11e4b17268"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set symbolic registration <a href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:a01bb0bb658bedb4d58bf7d11e4b17268"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set symbolic registration <a href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">More...</a><br/></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>namespace of arguments </p>
</div><h2 class="groupheader">Typedef Documentation</h2>
-<a id="a880eff3ebc68810ef133a6d8b77facc9"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a880eff3ebc68810ef133a6d8b77facc9">◆ </a></span>BinaryFunction</h2>
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+<a class="anchor" id="a97e7c6f5bbc6cdf02d443df6a36167e0"></a>
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- <td class="memname">typedef void(* mxnet::op::BinaryFunction) (const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &lhs, const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &rhs, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *ret, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req, <a class="el" href="struct [...]
+ <td class="memname">typedef void(* mxnet::op::BinaryFunction)(const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &lhs, const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &rhs, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *ret, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req, <a class="el" href="structm [...]
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@@ -191,14 +211,12 @@ Enumerations</h2></td></tr>
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</div>
-<a id="abcef841f759a7874ef50f21ae0226c8a"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#abcef841f759a7874ef50f21ae0226c8a">◆ </a></span>BinaryGradFunctionT0</h2>
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+<a class="anchor" id="ad1ec651ba6eee751c85b957df8972fc3"></a>
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- <td class="memname">typedef void(* mxnet::op::BinaryGradFunctionT0) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *lhs_grad, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *rhs_grad, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req [...]
+ <td class="memname">typedef void(* mxnet::op::BinaryGradFunctionT0)(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *lhs_grad, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *rhs_grad, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req_ [...]
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@@ -219,14 +237,12 @@ Enumerations</h2></td></tr>
</div>
</div>
-<a id="a027d1f190d964f7494816733aff6c3f2"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a027d1f190d964f7494816733aff6c3f2">◆ </a></span>BinaryGradFunctionT1</h2>
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+<a class="anchor" id="a2ed3ee83974ac57efd4b251a85dca849"></a>
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- <td class="memname">typedef void(* mxnet::op::BinaryGradFunctionT1) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> &lhs, const <a class="el" href="structmxnet_1_1op_1_1Input1.html">Input1</a> &rhs, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *lhs_gra [...]
+ <td class="memname">typedef void(* mxnet::op::BinaryGradFunctionT1)(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> &lhs, const <a class="el" href="structmxnet_1_1op_1_1Input1.html">Input1</a> &rhs, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *lhs_grad [...]
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@@ -249,14 +265,12 @@ Enumerations</h2></td></tr>
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-<a id="a78c7bd2075894ac41351feca1b1e5c94"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a78c7bd2075894ac41351feca1b1e5c94">◆ </a></span>BinaryShapeFunction</h2>
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- <td class="memname">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* mxnet::op::BinaryShapeFunction) (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &lhs, const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &rhs, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env)</td>
+ <td class="memname">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* mxnet::op::BinaryShapeFunction)(const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &lhs, const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &rhs, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env)</td>
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@@ -274,14 +288,12 @@ Enumerations</h2></td></tr>
</div>
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-<a id="a65e8bade5ae0539689b705b921ed9a9d"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a65e8bade5ae0539689b705b921ed9a9d">◆ </a></span>SourceFunction</h2>
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+<a class="anchor" id="a8dc66fcca4ca52343570931503fca2c3"></a>
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- <td class="memname">typedef void(* mxnet::op::SourceFunction) (const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *ret, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req, <a class="el" href="structmxnet_1_1RunContext.html">RunContext</a> ctx)</td>
+ <td class="memname">typedef void(* mxnet::op::SourceFunction)(const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *ret, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req, <a class="el" href="structmxnet_1_1RunContext.html">RunContext</a> ctx)</td>
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@@ -299,14 +311,12 @@ Enumerations</h2></td></tr>
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-<a id="a3419f1ab0d1a62068a5e6c59efdc0fc9"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a3419f1ab0d1a62068a5e6c59efdc0fc9">◆ </a></span>SourceShapeFunction</h2>
-
+<a class="anchor" id="af71f289b0df41398bcbac75d37435a07"></a>
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- <td class="memname">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* mxnet::op::SourceShapeFunction) (const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env)</td>
+ <td class="memname">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* mxnet::op::SourceShapeFunction)(const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env)</td>
</tr>
</table>
</div><div class="memdoc">
@@ -322,14 +332,12 @@ Enumerations</h2></td></tr>
</div>
</div>
-<a id="aa37d5216112b72e8110fc8654860910b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aa37d5216112b72e8110fc8654860910b">◆ </a></span>UnaryFunction</h2>
-
+<a class="anchor" id="af5075d1f55b857402d9b9da07c164f11"></a>
<div class="memitem">
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- <td class="memname">typedef void(* mxnet::op::UnaryFunction) (const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &src, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *ret, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req, <a class="el" href="structmxnet_1_1RunContext.html">RunContext</a> ctx)</td>
+ <td class="memname">typedef void(* mxnet::op::UnaryFunction)(const <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &src, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *ret, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req, <a class="el" href="structmxnet_1_1RunContext.html">RunContext</a> ctx)</td>
</tr>
</table>
</div><div class="memdoc">
@@ -348,14 +356,12 @@ Enumerations</h2></td></tr>
</div>
</div>
-<a id="a1e5767bffea568a1c38379060cbb68b4"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a1e5767bffea568a1c38379060cbb68b4">◆ </a></span>UnaryGradFunctionT0</h2>
-
+<a class="anchor" id="a5e30ef3bfc75cf4b111af7b1c74dab1b"></a>
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- <td class="memname">typedef void(* mxnet::op::UnaryGradFunctionT0) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *in_grad, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req, <a class="el" href="structmxnet_1_1RunContext.html">RunContext</a> [...]
+ <td class="memname">typedef void(* mxnet::op::UnaryGradFunctionT0)(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *in_grad, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req, <a class="el" href="structmxnet_1_1RunContext.html">RunContext</a> c [...]
</tr>
</table>
</div><div class="memdoc">
@@ -374,14 +380,12 @@ Enumerations</h2></td></tr>
</div>
</div>
-<a id="a6ad11641816278f8fdb6fe87c7fd3740"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a6ad11641816278f8fdb6fe87c7fd3740">◆ </a></span>UnaryGradFunctionT1</h2>
-
+<a class="anchor" id="a362db0d0023a59204d018c32458ae4e3"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">typedef void(* mxnet::op::UnaryGradFunctionT1) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1OutputValue.html">OutputValue</a> &out_value, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *in_grad, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3 [...]
+ <td class="memname">typedef void(* mxnet::op::UnaryGradFunctionT1)(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1OutputValue.html">OutputValue</a> &out_value, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *in_grad, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b [...]
</tr>
</table>
</div><div class="memdoc">
@@ -401,14 +405,12 @@ Enumerations</h2></td></tr>
</div>
</div>
-<a id="ac29102128f7e7785493bbb5b7db80997"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ac29102128f7e7785493bbb5b7db80997">◆ </a></span>UnaryGradFunctionT2</h2>
-
+<a class="anchor" id="a6b53969567339c4b97a8e0da51580c11"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">typedef void(* mxnet::op::UnaryGradFunctionT2) (const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> &in_data0, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *in_grad, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398"> [...]
+ <td class="memname">typedef void(* mxnet::op::UnaryGradFunctionT2)(const <a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> &out_grad, const <a class="el" href="structmxnet_1_1op_1_1Input0.html">Input0</a> &in_data0, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env, <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> *in_grad, <a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">O [...]
</tr>
</table>
</div><div class="memdoc">
@@ -428,14 +430,12 @@ Enumerations</h2></td></tr>
</div>
</div>
-<a id="a2d6fbc6ef1e22ea4433b3cf44ad3cef7"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a2d6fbc6ef1e22ea4433b3cf44ad3cef7">◆ </a></span>UnaryShapeFunction</h2>
-
+<a class="anchor" id="ab004eaba5bbfd4a05d7ddeb54ce1c311"></a>
<div class="memitem">
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<tr>
- <td class="memname">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* mxnet::op::UnaryShapeFunction) (const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &src, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env)</td>
+ <td class="memname">typedef <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>(* mxnet::op::UnaryShapeFunction)(const <a class="el" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &src, const <a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> &env)</td>
</tr>
</table>
</div><div class="memdoc">
@@ -453,9 +453,7 @@ Enumerations</h2></td></tr>
</div>
</div>
<h2 class="groupheader">Enumeration Type Documentation</h2>
-<a id="a3c3b8c77865c7c715cfe05ee2b2fa8da"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a3c3b8c77865c7c715cfe05ee2b2fa8da">◆ </a></span>SimpleOpInplaceOption</h2>
-
+<a class="anchor" id="a3c3b8c77865c7c715cfe05ee2b2fa8da"></a>
<div class="memitem">
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@@ -467,23 +465,26 @@ Enumerations</h2></td></tr>
<p>options in the registry to set inplace of operator </p>
<table class="fieldtable">
-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2"></a>kNoInplace </td><td class="fielddoc"><p>do not allow inplace in arguments </p>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2"></a>kNoInplace</em> </td><td class="fielddoc">
+<p>do not allow inplace in arguments </p>
</td></tr>
-<tr><td class="fieldname"><a id="a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde"></a>kInplaceInOut </td><td class="fielddoc"><p>in unary forward, allow inplace in with out </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde"></a>kInplaceInOut</em> </td><td class="fielddoc">
+<p>in unary forward, allow inplace in with out </p>
</td></tr>
-<tr><td class="fieldname"><a id="a3c3b8c77865c7c715cfe05ee2b2fa8daab7974a6a3982dae42ba00af90dcabd80"></a>kInplaceOutIn </td><td class="fielddoc"><p>in unary backward, allow inplace out_grad with in_grad </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a3c3b8c77865c7c715cfe05ee2b2fa8daab7974a6a3982dae42ba00af90dcabd80"></a>kInplaceOutIn</em> </td><td class="fielddoc">
+<p>in unary backward, allow inplace out_grad with in_grad </p>
</td></tr>
-<tr><td class="fieldname"><a id="a3c3b8c77865c7c715cfe05ee2b2fa8daa31a764e0ff5966a10e26f7fbff308c18"></a>kInplaceLhsOut </td><td class="fielddoc"><p>in binary forward, allow inplace left operand with out </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a3c3b8c77865c7c715cfe05ee2b2fa8daa31a764e0ff5966a10e26f7fbff308c18"></a>kInplaceLhsOut</em> </td><td class="fielddoc">
+<p>in binary forward, allow inplace left operand with out </p>
</td></tr>
-<tr><td class="fieldname"><a id="a3c3b8c77865c7c715cfe05ee2b2fa8daa01a199e9f1e7d97724be8aa618d5a3d4"></a>kInplaceOutLhs </td><td class="fielddoc"><p>in binary backward, allow inplace out_grad with lhs_grad </p>
+<tr><td class="fieldname"><em><a class="anchor" id="a3c3b8c77865c7c715cfe05ee2b2fa8daa01a199e9f1e7d97724be8aa618d5a3d4"></a>kInplaceOutLhs</em> </td><td class="fielddoc">
+<p>in binary backward, allow inplace out_grad with lhs_grad </p>
</td></tr>
</table>
</div>
</div>
-<a id="a01bb0bb658bedb4d58bf7d11e4b17268"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a01bb0bb658bedb4d58bf7d11e4b17268">◆ </a></span>SimpleOpRegOption</h2>
-
+<a class="anchor" id="a01bb0bb658bedb4d58bf7d11e4b17268"></a>
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@@ -495,15 +496,15 @@ Enumerations</h2></td></tr>
<p>options in the registry to set symbolic registration </p>
<table class="fieldtable">
-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b"></a>kNotRegisterSymbolic </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1"></a>kRegisterSymbolic </td><td class="fielddoc"></td></tr>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b"></a>kNotRegisterSymbolic</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1"></a>kRegisterSymbolic</em> </td><td class="fielddoc">
+</td></tr>
</table>
</div>
</div>
-<a id="ae7310844fa858f1ec6b6428dd6977a13"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ae7310844fa858f1ec6b6428dd6977a13">◆ </a></span>SimpleOpScalarOption</h2>
-
+<a class="anchor" id="ae7310844fa858f1ec6b6428dd6977a13"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -515,8 +516,10 @@ Enumerations</h2></td></tr>
<p>options in the registry to set symbolic registration </p>
<table class="fieldtable">
-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="ae7310844fa858f1ec6b6428dd6977a13a44de4ba9d059034c023bc755cdc6026a"></a>kScalarBeforeArray </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7"></a>kArrayBeforeScalar </td><td class="fielddoc"></td></tr>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="ae7310844fa858f1ec6b6428dd6977a13a44de4ba9d059034c023bc755cdc6026a"></a>kScalarBeforeArray</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7"></a>kArrayBeforeScalar</em> </td><td class="fielddoc">
+</td></tr>
</table>
</div>
@@ -524,9 +527,9 @@ Enumerations</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/namespaces.html b/versions/master/doxygen/namespaces.html
index 36dc781..fedd373 100644
--- a/versions/master/doxygen/namespaces.html
+++ b/versions/master/doxygen/namespaces.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Namespace List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,26 +30,47 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li class="current"><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li class="current"><a href="namespaces.html"><span>Namespace List</span></a></li>
+ <li><a href="namespacemembers.html"><span>Namespace Members</span></a></li>
+ </ul>
+ </div>
</div><!-- top -->
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -64,23 +86,23 @@ $(function() {
<div class="contents">
<div class="textblock">Here is a list of all namespaces with brief descriptions:</div><div class="directory">
<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span>]</div><table class="directory">
-<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacedmlc.html" target="_self">dmlc</a></td><td class="desc"></td></tr>
-<tr id="row_0_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacedmlc_1_1parameter.html" target="_self">parameter</a></td><td class="desc"></td></tr>
-<tr id="row_1_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_1_" class="arrow" onclick="toggleFolder('1_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet.html" target="_self">mxnet</a></td><td class="desc">Namespace of mxnet </td></tr>
-<tr id="row_1_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet_1_1autograd.html" target="_self">autograd</a></td><td class="desc"></td></tr>
-<tr id="row_1_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_1_" class="arrow" onclick="toggleFolder('1_1_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet_1_1common.html" target="_self">common</a></td><td class="desc"></td></tr>
-<tr id="row_1_1_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet_1_1common_1_1cuda.html" target="_self">cuda</a></td><td class="desc">Common utils for cuda </td></tr>
-<tr id="row_1_1_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacemxnet_1_1common_1_1helper.html" target="_self">helper</a></td><td class="desc">Helper functions </td></tr>
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+<tr id="row_0_" class="even"><td class="entry"><img id="arr_0_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('0_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacedmlc.html" target="_self">dmlc</a></td><td class="desc"></td></tr>
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+<tr id="row_1_0_"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1autograd.html" target="_self">autograd</a></td><td class="desc"></td></tr>
+<tr id="row_1_1_" class="even"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img id="arr_1_1_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('1_1_')"/><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1common.html" target="_self">common</a></td><td class="desc"></td></tr>
+<tr id="row_1_1_0_"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1common_1_1cuda.html" target="_self">cuda</a></td><td class="desc">Common utils for cuda </td></tr>
+<tr id="row_1_1_1_" class="even"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1common_1_1helper.html" target="_self">helper</a></td><td class="desc">Helper functions </td></tr>
+<tr id="row_1_2_"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1engine.html" target="_self">engine</a></td><td class="desc">Namespace of engine internal types </td></tr>
+<tr id="row_1_3_" class="even"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><img src="ftv2ns.png" alt="N" width="24" height="22" /><a class="el" href="namespacemxnet_1_1op.html" target="_self">op</a></td><td class="desc">Namespace of arguments </td></tr>
</table>
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<!-- start footer part -->
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-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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diff --git a/versions/master/doxygen/ndarray_8h.html b/versions/master/doxygen/ndarray_8h.html
index 4a7080a..e18585a 100644
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<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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</div>
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@@ -29,25 +30,46 @@
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
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@@ -76,41 +98,32 @@ $(function() {
<p>NDArray interface that handles array arithematics.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/base.h></code><br />
-<code>#include <dmlc/logging.h></code><br />
-<code>#include <dmlc/io.h></code><br />
-<code>#include <dmlc/type_traits.h></code><br />
-<code>#include <dmlc/registry.h></code><br />
-<code>#include <nnvm/node.h></code><br />
-<code>#include <vector></code><br />
-<code>#include <map></code><br />
-<code>#include <string></code><br />
-<code>#include <memory></code><br />
-<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br />
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-<code>#include "<a class="el" href="engine_8h_source.html">./engine.h</a>"</code><br />
+<div class="textblock"><code>#include <dmlc/base.h></code><br/>
+<code>#include <dmlc/logging.h></code><br/>
+<code>#include <dmlc/io.h></code><br/>
+<code>#include <dmlc/type_traits.h></code><br/>
+<code>#include <dmlc/registry.h></code><br/>
+<code>#include <nnvm/node.h></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <map></code><br/>
+<code>#include <string></code><br/>
+<code>#include <memory></code><br/>
+<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br/>
+<code>#include "<a class="el" href="storage_8h_source.html">./storage.h</a>"</code><br/>
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</div><div class="textblock"><div class="dynheader">
Include dependency graph for ndarray.h:</div>
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This graph shows which files directly or indirectly include this file:</div>
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<p><a href="ndarray_8h_source.html">Go to the source code of this file.</a></p>
@@ -120,34 +133,38 @@ Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">ndarray interface <a href="classmxnet_1_1NDArray.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">ndarray interface <a href="classmxnet_1_1NDArray.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">mxnet::NDArrayFunctionReg</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for NDArrayFunction. <a href="structmxnet_1_1NDArrayFunctionReg.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for NDArrayFunction. <a href="structmxnet_1_1NDArrayFunctionReg.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
+<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
+<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacemxnet_1_1autograd"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1autograd.html">mxnet::autograd</a></td></tr>
+<tr class="memitem:namespacemxnet_1_1autograd"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1autograd.html">mxnet::autograd</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacedmlc"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacedmlc.html">dmlc</a></td></tr>
+<tr class="memitem:namespacedmlc"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacedmlc.html">dmlc</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="define-members"></a>
Macros</h2></td></tr>
<tr class="memitem:a21d0a394bdf34194b9954252d9476d56"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="ndarray_8h.html#a21d0a394bdf34194b9954252d9476d56">MXNET_REGISTER_NDARRAY_FUN</a>(name)   DMLC_REGISTRY_REGISTER(::<a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">mxnet::NDArrayFunctionReg</a>, NDArrayFunctionReg, name)</td></tr>
-<tr class="memdesc:a21d0a394bdf34194b9954252d9476d56"><td class="mdescLeft"> </td><td class="mdescRight">Macro to register NDArray function. <a href="#a21d0a394bdf34194b9954252d9476d56">More...</a><br /></td></tr>
+<tr class="memdesc:a21d0a394bdf34194b9954252d9476d56"><td class="mdescLeft"> </td><td class="mdescRight">Macro to register NDArray function. <a href="#a21d0a394bdf34194b9954252d9476d56">More...</a><br/></td></tr>
<tr class="separator:a21d0a394bdf34194b9954252d9476d56"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
<tr class="memitem:a8070fc188711b03b349a39d1b286d4a0"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1autograd.html#a8070fc188711b03b349a39d1b286d4a0">mxnet::autograd::AGNodePtr</a> = std::shared_ptr< AGNode ></td></tr>
<tr class="separator:a8070fc188711b03b349a39d1b286d4a0"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ab0d55b0edf98cf8f3caeb050662ae9ec"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(NDArray **used_vars, real_t *scalars, NDArray **mutate_vars, int num_params, char **param_keys, char **param_vals)> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ab0d55b0edf98cf8f3caeb050662ae9ec">mxnet::NDArrayAPIFunction</a></td></tr>
-<tr class="memdesc:ab0d55b0edf98cf8f3caeb050662ae9ec"><td class="mdescLeft"> </td><td class="mdescRight">definition of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function <a href="namespacemxnet.html#ab0d55b0edf98cf8f3caeb050662ae9ec">More...</a><br /></td></tr>
+<tr class="memitem:ab0d55b0edf98cf8f3caeb050662ae9ec"><td class="memItemLeft" align="right" valign="top">typedef std::function< void(NDArray <br class="typebreak"/>
+**used_vars, real_t *scalars, <br class="typebreak"/>
+NDArray **mutate_vars, int <br class="typebreak"/>
+num_params, char **param_keys, <br class="typebreak"/>
+char **param_vals)> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ab0d55b0edf98cf8f3caeb050662ae9ec">mxnet::NDArrayAPIFunction</a></td></tr>
+<tr class="memdesc:ab0d55b0edf98cf8f3caeb050662ae9ec"><td class="mdescLeft"> </td><td class="mdescRight">definition of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function <a href="#ab0d55b0edf98cf8f3caeb050662ae9ec">More...</a><br/></td></tr>
<tr class="separator:ab0d55b0edf98cf8f3caeb050662ae9ec"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="enum-members"></a>
@@ -155,77 +172,75 @@ Enumerations</h2></td></tr>
<tr class="memitem:a89a5f0f5cfd9e1e94604a7b42dda818a"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818a">mxnet::NDArrayFunctionTypeMask</a> { <a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">mxnet::kNDArrayArgBeforeScalar</a> = 1,
<a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aaef9818c8aae9d0046b3d9033a62fcfbb">mxnet::kScalarArgBeforeNDArray</a> = 1 << 1,
<a class="el" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd">mxnet::kAcceptEmptyMutateTarget</a> = 1 << 2
- }<tr class="memdesc:a89a5f0f5cfd9e1e94604a7b42dda818a"><td class="mdescLeft"> </td><td class="mdescRight">mask information on how functions can be exposed <a href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818a">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:a89a5f0f5cfd9e1e94604a7b42dda818a"><td class="mdescLeft"> </td><td class="mdescRight">mask information on how functions can be exposed <a href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818a">More...</a><br/></td></tr>
<tr class="separator:a89a5f0f5cfd9e1e94604a7b42dda818a"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:aeee0e64a5e59bb0c222cbf44f4266f7a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#aeee0e64a5e59bb0c222cbf44f4266f7a">mxnet::CopyFromTo</a> (const NDArray &from, NDArray *to, int priority=0)</td></tr>
-<tr class="memdesc:aeee0e64a5e59bb0c222cbf44f4266f7a"><td class="mdescLeft"> </td><td class="mdescRight">issue an copy operation from one <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> to another the two ndarray can sit on different devices this operation will be scheduled by the engine <a href="namespacemxnet.html#aeee0e64a5e59bb0c222cbf44f4266f7a">More...</a><br /></td></tr>
+<tr class="memdesc:aeee0e64a5e59bb0c222cbf44f4266f7a"><td class="mdescLeft"> </td><td class="mdescRight">issue an copy operation from one <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> to another the two ndarray can sit on different devices this operation will be scheduled by the engine <a href="#aeee0e64a5e59bb0c222cbf44f4266f7a">More...</a><br/></td></tr>
<tr class="separator:aeee0e64a5e59bb0c222cbf44f4266f7a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a15dfd6f3fa03dc2cf39dbab1225e1a51"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a15dfd6f3fa03dc2cf39dbab1225e1a51">mxnet::ElementwiseSum</a> (const std::vector< NDArray > &source, NDArray *out, int priority=0)</td></tr>
-<tr class="memdesc:a15dfd6f3fa03dc2cf39dbab1225e1a51"><td class="mdescLeft"> </td><td class="mdescRight">Perform elementwise sum over each data from source, store result into out. <a href="namespacemxnet.html#a15dfd6f3fa03dc2cf39dbab1225e1a51">More...</a><br /></td></tr>
+<tr class="memdesc:a15dfd6f3fa03dc2cf39dbab1225e1a51"><td class="mdescLeft"> </td><td class="mdescRight">Perform elementwise sum over each data from source, store result into out. <a href="#a15dfd6f3fa03dc2cf39dbab1225e1a51">More...</a><br/></td></tr>
<tr class="separator:a15dfd6f3fa03dc2cf39dbab1225e1a51"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4dafe3aae43025fd21bc44b98c18ed1e"><td class="memItemLeft" align="right" valign="top">NDArray </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a4dafe3aae43025fd21bc44b98c18ed1e">mxnet::operator+</a> (const NDArray &lhs, const NDArray &rhs)</td></tr>
-<tr class="memdesc:a4dafe3aae43025fd21bc44b98c18ed1e"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add <a href="namespacemxnet.html#a4dafe3aae43025fd21bc44b98c18ed1e">More...</a><br /></td></tr>
+<tr class="memdesc:a4dafe3aae43025fd21bc44b98c18ed1e"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add <a href="#a4dafe3aae43025fd21bc44b98c18ed1e">More...</a><br/></td></tr>
<tr class="separator:a4dafe3aae43025fd21bc44b98c18ed1e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a30bfda0e3e1b347e3057097cafc628be"><td class="memItemLeft" align="right" valign="top">NDArray </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a30bfda0e3e1b347e3057097cafc628be">mxnet::operator+</a> (const NDArray &lhs, const real_t &rhs)</td></tr>
-<tr class="memdesc:a30bfda0e3e1b347e3057097cafc628be"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add <a href="namespacemxnet.html#a30bfda0e3e1b347e3057097cafc628be">More...</a><br /></td></tr>
+<tr class="memdesc:a30bfda0e3e1b347e3057097cafc628be"><td class="mdescLeft"> </td><td class="mdescRight">elementwise add <a href="#a30bfda0e3e1b347e3057097cafc628be">More...</a><br/></td></tr>
<tr class="separator:a30bfda0e3e1b347e3057097cafc628be"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae3356844b657c9ae1b84d3ddd56bccf6"><td class="memItemLeft" align="right" valign="top">NDArray </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ae3356844b657c9ae1b84d3ddd56bccf6">mxnet::operator-</a> (const NDArray &lhs, const NDArray &rhs)</td></tr>
-<tr class="memdesc:ae3356844b657c9ae1b84d3ddd56bccf6"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtraction <a href="namespacemxnet.html#ae3356844b657c9ae1b84d3ddd56bccf6">More...</a><br /></td></tr>
+<tr class="memdesc:ae3356844b657c9ae1b84d3ddd56bccf6"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtraction <a href="#ae3356844b657c9ae1b84d3ddd56bccf6">More...</a><br/></td></tr>
<tr class="separator:ae3356844b657c9ae1b84d3ddd56bccf6"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0c8542953df5093060985db5518a339b"><td class="memItemLeft" align="right" valign="top">NDArray </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a0c8542953df5093060985db5518a339b">mxnet::operator-</a> (const NDArray &lhs, const real_t &rhs)</td></tr>
-<tr class="memdesc:a0c8542953df5093060985db5518a339b"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtraction <a href="namespacemxnet.html#a0c8542953df5093060985db5518a339b">More...</a><br /></td></tr>
+<tr class="memdesc:a0c8542953df5093060985db5518a339b"><td class="mdescLeft"> </td><td class="mdescRight">elementwise subtraction <a href="#a0c8542953df5093060985db5518a339b">More...</a><br/></td></tr>
<tr class="separator:a0c8542953df5093060985db5518a339b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af26b09fbcda865d34ec4fdad482d266a"><td class="memItemLeft" align="right" valign="top">NDArray </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#af26b09fbcda865d34ec4fdad482d266a">mxnet::operator*</a> (const NDArray &lhs, const NDArray &rhs)</td></tr>
-<tr class="memdesc:af26b09fbcda865d34ec4fdad482d266a"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication <a href="namespacemxnet.html#af26b09fbcda865d34ec4fdad482d266a">More...</a><br /></td></tr>
+<tr class="memdesc:af26b09fbcda865d34ec4fdad482d266a"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication <a href="#af26b09fbcda865d34ec4fdad482d266a">More...</a><br/></td></tr>
<tr class="separator:af26b09fbcda865d34ec4fdad482d266a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a66510f6a344ee38bb89cb552e974b838"><td class="memItemLeft" align="right" valign="top">NDArray </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a66510f6a344ee38bb89cb552e974b838">mxnet::operator*</a> (const NDArray &lhs, const real_t &rhs)</td></tr>
-<tr class="memdesc:a66510f6a344ee38bb89cb552e974b838"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication <a href="namespacemxnet.html#a66510f6a344ee38bb89cb552e974b838">More...</a><br /></td></tr>
+<tr class="memdesc:a66510f6a344ee38bb89cb552e974b838"><td class="mdescLeft"> </td><td class="mdescRight">elementwise multiplication <a href="#a66510f6a344ee38bb89cb552e974b838">More...</a><br/></td></tr>
<tr class="separator:a66510f6a344ee38bb89cb552e974b838"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a502e3e00dc61cedb9c6eef711f1176ac"><td class="memItemLeft" align="right" valign="top">NDArray </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a502e3e00dc61cedb9c6eef711f1176ac">mxnet::operator/</a> (const NDArray &lhs, const NDArray &rhs)</td></tr>
-<tr class="memdesc:a502e3e00dc61cedb9c6eef711f1176ac"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division <a href="namespacemxnet.html#a502e3e00dc61cedb9c6eef711f1176ac">More...</a><br /></td></tr>
+<tr class="memdesc:a502e3e00dc61cedb9c6eef711f1176ac"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division <a href="#a502e3e00dc61cedb9c6eef711f1176ac">More...</a><br/></td></tr>
<tr class="separator:a502e3e00dc61cedb9c6eef711f1176ac"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3455659b714488e8e53e4e4580a43ec7"><td class="memItemLeft" align="right" valign="top">NDArray </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a3455659b714488e8e53e4e4580a43ec7">mxnet::operator/</a> (const NDArray &lhs, const real_t &rhs)</td></tr>
-<tr class="memdesc:a3455659b714488e8e53e4e4580a43ec7"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division <a href="namespacemxnet.html#a3455659b714488e8e53e4e4580a43ec7">More...</a><br /></td></tr>
+<tr class="memdesc:a3455659b714488e8e53e4e4580a43ec7"><td class="mdescLeft"> </td><td class="mdescRight">elementwise division <a href="#a3455659b714488e8e53e4e4580a43ec7">More...</a><br/></td></tr>
<tr class="separator:a3455659b714488e8e53e4e4580a43ec7"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5bf83454c8f41259eb9d471f59feda2b"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a5bf83454c8f41259eb9d471f59feda2b">mxnet::RandomSeed</a> (uint32_t seed)</td></tr>
-<tr class="memdesc:a5bf83454c8f41259eb9d471f59feda2b"><td class="mdescLeft"> </td><td class="mdescRight">Seed the random number generator. <a href="namespacemxnet.html#a5bf83454c8f41259eb9d471f59feda2b">More...</a><br /></td></tr>
+<tr class="memdesc:a5bf83454c8f41259eb9d471f59feda2b"><td class="mdescLeft"> </td><td class="mdescRight">Seed the random number generator. <a href="#a5bf83454c8f41259eb9d471f59feda2b">More...</a><br/></td></tr>
<tr class="separator:a5bf83454c8f41259eb9d471f59feda2b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2846f4556c9ca9bd0f567504ce60f274"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">mxnet::SampleUniform</a> (real_t begin, real_t end, NDArray *out)</td></tr>
-<tr class="memdesc:a2846f4556c9ca9bd0f567504ce60f274"><td class="mdescLeft"> </td><td class="mdescRight">Sample uniform distribution for each elements of out. <a href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">More...</a><br /></td></tr>
+<tr class="memdesc:a2846f4556c9ca9bd0f567504ce60f274"><td class="mdescLeft"> </td><td class="mdescRight">Sample uniform distribution for each elements of out. <a href="#a2846f4556c9ca9bd0f567504ce60f274">More...</a><br/></td></tr>
<tr class="separator:a2846f4556c9ca9bd0f567504ce60f274"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5a28062f52ca576a126599e7ad487077"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">mxnet::SampleGaussian</a> (real_t mu, real_t sigma, NDArray *out)</td></tr>
-<tr class="memdesc:a5a28062f52ca576a126599e7ad487077"><td class="mdescLeft"> </td><td class="mdescRight">Sample gaussian distribution for each elements of out. <a href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">More...</a><br /></td></tr>
+<tr class="memdesc:a5a28062f52ca576a126599e7ad487077"><td class="mdescLeft"> </td><td class="mdescRight">Sample gaussian distribution for each elements of out. <a href="#a5a28062f52ca576a126599e7ad487077">More...</a><br/></td></tr>
<tr class="separator:a5a28062f52ca576a126599e7ad487077"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a050beaa505f11e0b844deb91efe0cac2"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a050beaa505f11e0b844deb91efe0cac2">mxnet::SampleGamma</a> (real_t alpha, real_t beta, NDArray *out)</td></tr>
-<tr class="memdesc:a050beaa505f11e0b844deb91efe0cac2"><td class="mdescLeft"> </td><td class="mdescRight">Sample gamma distribution for each elements of out. <a href="namespacemxnet.html#a050beaa505f11e0b844deb91efe0cac2">More...</a><br /></td></tr>
+<tr class="memdesc:a050beaa505f11e0b844deb91efe0cac2"><td class="mdescLeft"> </td><td class="mdescRight">Sample gamma distribution for each elements of out. <a href="#a050beaa505f11e0b844deb91efe0cac2">More...</a><br/></td></tr>
<tr class="separator:a050beaa505f11e0b844deb91efe0cac2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a51f9b094369a349c05463de2be9f0a31"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a51f9b094369a349c05463de2be9f0a31">mxnet::SampleExponential</a> (real_t lambda, NDArray *out)</td></tr>
-<tr class="memdesc:a51f9b094369a349c05463de2be9f0a31"><td class="mdescLeft"> </td><td class="mdescRight">Sample exponential distribution for each elements of out. <a href="namespacemxnet.html#a51f9b094369a349c05463de2be9f0a31">More...</a><br /></td></tr>
+<tr class="memdesc:a51f9b094369a349c05463de2be9f0a31"><td class="mdescLeft"> </td><td class="mdescRight">Sample exponential distribution for each elements of out. <a href="#a51f9b094369a349c05463de2be9f0a31">More...</a><br/></td></tr>
<tr class="separator:a51f9b094369a349c05463de2be9f0a31"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aed55e8197182b7c66126902b2a43739a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#aed55e8197182b7c66126902b2a43739a">mxnet::SamplePoisson</a> (real_t lambda, NDArray *out)</td></tr>
-<tr class="memdesc:aed55e8197182b7c66126902b2a43739a"><td class="mdescLeft"> </td><td class="mdescRight">Sample Poisson distribution for each elements of out. <a href="namespacemxnet.html#aed55e8197182b7c66126902b2a43739a">More...</a><br /></td></tr>
+<tr class="memdesc:aed55e8197182b7c66126902b2a43739a"><td class="mdescLeft"> </td><td class="mdescRight">Sample Poisson distribution for each elements of out. <a href="#aed55e8197182b7c66126902b2a43739a">More...</a><br/></td></tr>
<tr class="separator:aed55e8197182b7c66126902b2a43739a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae82f70918b2d946f7ad66a158b1733dc"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ae82f70918b2d946f7ad66a158b1733dc">mxnet::SampleNegBinomial</a> (int32_t k, real_t p, NDArray *out)</td></tr>
-<tr class="memdesc:ae82f70918b2d946f7ad66a158b1733dc"><td class="mdescLeft"> </td><td class="mdescRight">Sample negative binomial distribution for each elements of out. <a href="namespacemxnet.html#ae82f70918b2d946f7ad66a158b1733dc">More...</a><br /></td></tr>
+<tr class="memdesc:ae82f70918b2d946f7ad66a158b1733dc"><td class="mdescLeft"> </td><td class="mdescRight">Sample negative binomial distribution for each elements of out. <a href="#ae82f70918b2d946f7ad66a158b1733dc">More...</a><br/></td></tr>
<tr class="separator:ae82f70918b2d946f7ad66a158b1733dc"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad8e0dad88ce27133fd24811763b7afdb"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ad8e0dad88ce27133fd24811763b7afdb">mxnet::SampleGenNegBinomial</a> (real_t mu, real_t alpha, NDArray *out)</td></tr>
-<tr class="memdesc:ad8e0dad88ce27133fd24811763b7afdb"><td class="mdescLeft"> </td><td class="mdescRight">Sample generalized negative binomial distribution for each elements of out. <a href="namespacemxnet.html#ad8e0dad88ce27133fd24811763b7afdb">More...</a><br /></td></tr>
+<tr class="memdesc:ad8e0dad88ce27133fd24811763b7afdb"><td class="mdescLeft"> </td><td class="mdescRight">Sample generalized negative binomial distribution for each elements of out. <a href="#ad8e0dad88ce27133fd24811763b7afdb">More...</a><br/></td></tr>
<tr class="separator:ad8e0dad88ce27133fd24811763b7afdb"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7272c846fa03dee55916aadce0165a46"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacedmlc.html#a7272c846fa03dee55916aadce0165a46">dmlc::DMLC_DECLARE_TRAITS</a> (has_saveload, <a class="el" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a>, true)</td></tr>
-<tr class="memdesc:a7272c846fa03dee55916aadce0165a46"><td class="mdescLeft"> </td><td class="mdescRight">traits <a href="namespacedmlc.html#a7272c846fa03dee55916aadce0165a46">More...</a><br /></td></tr>
+<tr class="memdesc:a7272c846fa03dee55916aadce0165a46"><td class="mdescLeft"> </td><td class="mdescRight">traits <a href="#a7272c846fa03dee55916aadce0165a46">More...</a><br/></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>NDArray interface that handles array arithematics. </p>
<p>Copyright (c) 2015 by Contributors </p>
</div><h2 class="groupheader">Macro Definition Documentation</h2>
-<a id="a21d0a394bdf34194b9954252d9476d56"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a21d0a394bdf34194b9954252d9476d56">◆ </a></span>MXNET_REGISTER_NDARRAY_FUN</h2>
-
+<a class="anchor" id="a21d0a394bdf34194b9954252d9476d56"></a>
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@@ -240,15 +255,18 @@ Functions</h2></td></tr>
</div><div class="memdoc">
<p>Macro to register NDArray function. </p>
-<p>Example: the following code is example to register a plus </p><div class="fragment"><div class="line">REGISTER_NDARRAY_FUN(Plus)</div><div class="line">.set_function(Plus);</div></div><!-- fragment -->
+<p>Example: the following code is example to register a plus </p>
+<div class="fragment"><div class="line">REGISTER_NDARRAY_FUN(Plus)</div>
+<div class="line">.set_function(Plus);</div>
+</div><!-- fragment -->
</div>
</div>
</div><!-- contents -->
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<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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--- a/versions/master/doxygen/ndarray_8h__dep__incl.map
+++ b/versions/master/doxygen/ndarray_8h__dep__incl.map
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
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+ <li class="current"><a href="files.html"><span>Files</span></a></li>
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+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
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<div id="MSearchSelectWindow"
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@@ -66,89 +88,476 @@ $(function() {
<div class="title">ndarray.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="ndarray_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_NDARRAY_H_</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#define MXNET_NDARRAY_H_</span></div><div class="line"><a name="l00008"> [...]
-<div class="ttc" id="classmxnet_1_1NDArray_html_a29673998ea5bec1cab7337595d907c67"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a29673998ea5bec1cab7337595d907c67">mxnet::NDArray::is_none</a></div><div class="ttdeci">bool is_none() const</div><div class="ttdef"><b>Definition:</b> ndarray.h:127</div></div>
+<a href="ndarray_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_NDARRAY_H_</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_NDARRAY_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <dmlc/base.h></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <dmlc/logging.h></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <dmlc/io.h></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <dmlc/type_traits.h></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <dmlc/registry.h></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <nnvm/node.h></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <map></span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <string></span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <memory></span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include "<a class="code" href="storage_8h.html">./storage.h</a>"</span></div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include "<a class="code" href="engine_8h.html">./engine.h</a>"</span></div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor"></span><span class="preprocessor">#include <mkl_memory.h></span></div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor"></span><span class="comment">// check c++11</span></div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#if DMLC_USE_CXX11 == 0</span></div>
+<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor"></span><span class="preprocessor">#error "cxx11 was required for ndarray module"</span></div>
+<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div>
+<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="comment">// forward declaration</span></div>
+<div class="line"><a name="l00033"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1autograd.html"> 33</a></span> <span class="keyword">namespace </span>autograd {</div>
+<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">class </span>AGNode;</div>
+<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div>
+<div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1autograd.html#a8070fc188711b03b349a39d1b286d4a0"> 36</a></span> <span class="keyword">using</span> <a class="code" href="namespacemxnet_1_1autograd.html#a8070fc188711b03b349a39d1b286d4a0">AGNodePtr</a> = std::shared_ptr<AGNode>;</div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div>
+<div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="classmxnet_1_1autograd_1_1AGNodeEntry.html"> 38</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">AGNodeEntry</a> {</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ada6b942a22f14e3d61fccb645f9e9a27"> 40</a></span>  <a class="code" href="namespacemxnet_1_1autograd.html#a8070fc188711b03b349a39d1b286d4a0">AGNodePtr</a> <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ada6b942a22f14e3d61fccb645f9e9a27">ag_node</a>;</div>
+<div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ad0fccdc6fc5d492306c0eaf6df11880a"> 41</a></span>  uint32_t <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ad0fccdc6fc5d492306c0eaf6df11880a">index</a>;</div>
+<div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ae5aa3f0c4136f8e2ca4d5e1e47f1877f"> 42</a></span>  uint32_t <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ae5aa3f0c4136f8e2ca4d5e1e47f1877f">version</a>;</div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div>
+<div class="line"><a name="l00044"></a><span class="lineno"><a class="line" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a8edff5e8fa169924abe520edcae76c45"> 44</a></span>  <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a8edff5e8fa169924abe520edcae76c45">clear</a>() {</div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ada6b942a22f14e3d61fccb645f9e9a27">ag_node</a>.reset();</div>
+<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ad0fccdc6fc5d492306c0eaf6df11880a">index</a> = <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ae5aa3f0c4136f8e2ca4d5e1e47f1877f">version</a> = 0;</div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  }</div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  nnvm::NodeEntry <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a7dbd6c573b3bc3eda29b9ce9a2d1283a">nn_entry</a>() <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html#adf925c9b87cbd5969e142123da6c7e30">is_none</a>() <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span> };</div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="keyword">class </span>AutogradRuntime;</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> } <span class="comment">// namespace autograd</span></div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div>
+<div class="line"><a name="l00059"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html"> 59</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> {</div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a12ec978e5dbe9092d21721a657c39756"> 62</a></span>  <a class="code" href="classmxnet_1_1NDArray.html#a12ec978e5dbe9092d21721a657c39756">NDArray</a>() {</div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="preprocessor"></span> Mkl_mem_ = MKLMemHolder::create();</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a8fb6d94c737c452e180650fdfe5228ad"> 74</a></span>  <a class="code" href="classmxnet_1_1NDArray.html#a8fb6d94c737c452e180650fdfe5228ad">NDArray</a>(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &<a class="code" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">shape</a>, <a cl [...]
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordtype">bool</span> delay_alloc = <span class="keyword">false</span>, <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">dtype</a> = mshadow::default_type_flag)</div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  : ptr_(std::make_shared<Chunk>(shape.Size(), ctx, delay_alloc, <a class="code" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">dtype</a>)),</div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  shape_(shape), dtype_(<a class="code" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">dtype</a>), entry_({<span class="keyword">nullptr</span>, 0, 0}) {</div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="preprocessor"></span> Mkl_mem_ = std::make_shared<MKLMemHolder>();</div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="classmxnet_1_1NDArray.html#a12ec978e5dbe9092d21721a657c39756">NDArray</a>(<span class="keyword">const</span> TBlob &<a class="code" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>, <span class="keywordtype">int</span> dev_id)</div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  : ptr_(std::make_shared<Chunk>(data, dev_id)), shape_(data.shape_),</div>
+<div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a09ff97852143fdd75dd1e071d0d53266"> 91</a></span>  dtype_(data.type_flag_), entry_({<span class="keyword">nullptr</span>, 0, 0}) {</div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="preprocessor"></span> Mkl_mem_ = std::make_shared<MKLMemHolder>();</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00099"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee"> 99</a></span>  <span class="keyword">inline</span> <span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>& <a class="code" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">shape</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">return</span> shape_;</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  }</div>
+<div class="line"><a name="l00105"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6"> 105</a></span>  <span class="keyword">inline</span> <span class="keyword">const</span> <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>& <a class="code" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <a class="code" href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a">CheckAndAlloc</a>();</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  SetTBlob();</div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keywordflow">return</span> tblob_;</div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> <a class="code" href="classmxnet_1_1NDArray.html#aed729c3bc9db04ab346fd3969b4fc982">grad</a>() <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00117"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1"> 117</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1Context.html">Context</a> <a class="code" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">ctx</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">return</span> ptr_->shandle.ctx;</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  }</div>
+<div class="line"><a name="l00123"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2"> 123</a></span>  <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">dtype</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">return</span> dtype_;</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div>
+<div class="line"><a name="l00127"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a2a15ffdbf29d4d5455a4fbb4b607880c"> 127</a></span>  <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1NDArray.html#a2a15ffdbf29d4d5455a4fbb4b607880c">is_none</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">return</span> ptr_.get() == <span class="keyword">nullptr</span>;</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  }</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1NDArray.html#aee74980eb87bf7c482d15458f5194cbe">fresh_out_grad</a>() <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#a32ed2be4ecf94c58b19bd43b48f2b1fa">set_fresh_out_grad</a>(<span class="keywordtype">bool</span> state) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00138"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#aedd79a43d5fa4173f3ddd719fb939fbc"> 138</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#aedd79a43d5fa4173f3ddd719fb939fbc">WaitToRead</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">if</span> (<a class="code" href="classmxnet_1_1NDArray.html#a2a15ffdbf29d4d5455a4fbb4b607880c">is_none</a>()) <span class="keywordflow">return</span>;</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#aed51bd7f294d9f2b569764a0c151d883">WaitForVar</a>(ptr_->var);</div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  }</div>
+<div class="line"><a name="l00146"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a4b88b5d86ec9ddf4ca96701107337461"> 146</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#a4b88b5d86ec9ddf4ca96701107337461">WaitToWrite</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">if</span> (<a class="code" href="classmxnet_1_1NDArray.html#a2a15ffdbf29d4d5455a4fbb4b607880c">is_none</a>()) <span class="keywordflow">return</span>;</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#a1c2f38927e4bf7a62e23353b0bd3d619">PushSync</a>([](<a class="code" href="structmxnet_1_1RunContext.html">RunContext</a>) {}, <a class="code" href="structmxnet_1_1Context.html">Context</a>{}, {}, {ptr_->var});</div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#aed51bd7f294d9f2b569764a0c151d883">WaitForVar</a>(ptr_->var);</div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  }</div>
+<div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc"> 156</a></span>  <span class="keyword">inline</span> <a class="code" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">Engine::VarHandle</a> <a class="code" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">var</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">return</span> ptr_->var;</div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  }</div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#a524662078483f8f46c6c71714fbfde0b">Save</a>(dmlc::Stream *strm) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1NDArray.html#a5203aa033a53942deaecb7bb3bf46480">Load</a>(dmlc::Stream *strm);</div>
+<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &<a class="code" href="classmxnet_1_1NDArray.html#aa7a198e3e4c80a48cf92a913009aab8f">operator=</a>(<a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> scalar);</div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &<a class="code" href="classmxnet_1_1NDArray.html#a7df5a5d9956a481635fc43ca4d9a196c">operator+=</a>(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &src);</div>
+<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &<a class="code" href="classmxnet_1_1NDArray.html#a7df5a5d9956a481635fc43ca4d9a196c">operator+=</a>(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &src);</div>
+<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &<a class="code" href="classmxnet_1_1NDArray.html#ad35ba01da6949494afe895b7429e4fc3">operator-=</a>(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &src);</div>
+<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &<a class="code" href="classmxnet_1_1NDArray.html#ad35ba01da6949494afe895b7429e4fc3">operator-=</a>(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &src);</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &<a class="code" href="classmxnet_1_1NDArray.html#a1b47078a60bf0369ff0a7df893caf65b">operator*=</a>(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &src);</div>
+<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &<a class="code" href="classmxnet_1_1NDArray.html#a1b47078a60bf0369ff0a7df893caf65b">operator*=</a>(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &src);</div>
+<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &<a class="code" href="classmxnet_1_1NDArray.html#aebc61006705874666423c45f161df546">operator/=</a>(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &src);</div>
+<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &<a class="code" href="classmxnet_1_1NDArray.html#aebc61006705874666423c45f161df546">operator/=</a>(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &src);</div>
+<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> <a class="code" href="classmxnet_1_1NDArray.html#a3fe1974c7c2d522b09b9bf82fd2865ba">T</a>() <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> <a class="code" href="classmxnet_1_1NDArray.html#a4a00e2868ad8b9ccb0a48079635b110f">Copy</a>(<a class="code" href="structmxnet_1_1Context.html">Context</a> <a class="code" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">ctx</a>) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#a422c70b16646f8a273e9b4936fbe2e37">SyncCopyFromCPU</a>(<span class="keyword">const</span> <span class="keywordtype">void</span> *<a class="code" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>, <span class="keywordtype">size_t</span> size) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#a4819d2e6431ccdf4b9f8efa8eb0bd1fd">SyncCopyToCPU</a>(<span class="keywordtype">void</span> *<a class="code" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>, <span class="keywordtype">size_t</span> size) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> <a class="code" href="classmxnet_1_1NDArray.html#a7acb6462c11e071949db2b0e1fbfd66e">Slice</a>(<a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> begin, <a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> end) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> <a class="code" href="classmxnet_1_1NDArray.html#a3b615512a8fb89b9edc99b35064ead95">At</a>(<a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> idx) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00285"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a3896fcce89afce4c1d2f825f7853ca0c"> 285</a></span>  <span class="keyword">inline</span> <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> <a class="code" href="classmxnet_1_1NDArray.html#a3896fcce89afce4c1d2f825f7853ca0c">AsArray</a>(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &<a [...]
+<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  CHECK_GE(shape_.Size() * mshadow::mshadow_sizeof(dtype_),</div>
+<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  shape.Size() * mshadow::mshadow_sizeof(dtype))</div>
+<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  << <span class="stringliteral">"NDArray.AsArray: target memory size is bigger"</span>;</div>
+<div class="line"><a name="l00289"></a><span class="lineno"> 289</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00290"></a><span class="lineno"> 290</span> <span class="preprocessor"></span> <span class="keywordflow">if</span> (Mkl_mem_ != <span class="keyword">nullptr</span>) {</div>
+<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// convert prv to cpu</span></div>
+<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  Mkl_mem_->check_and_prv_to_cpu(ptr_->shandle.dptr);</div>
+<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  }</div>
+<div class="line"><a name="l00294"></a><span class="lineno"> 294</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00295"></a><span class="lineno"> 295</span> <span class="preprocessor"></span> <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> ret = *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  ret.shape_ = <a class="code" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">shape</a>;</div>
+<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  ret.dtype_ = <a class="code" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">dtype</a>;</div>
+<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keywordflow">return</span> ret;</div>
+<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  }</div>
+<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> <a class="code" href="classmxnet_1_1NDArray.html#ada618436206b59aa3ffcdb8474d5051c">Reshape</a>(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &<a class="code" href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">shape</a>) <span class="keyword">const</sp [...]
+<div class="line"><a name="l00309"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a34a3694141050a257dc6b40a6ad2c4ec"> 309</a></span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> <a class="code" href="classmxnet_1_1NDArray.html#a34a3694141050a257dc6b40a6ad2c4ec">Detach</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> ret(*<span class="keyword">this</span>);</div>
+<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  ret.entry_ = <a class="code" href="classmxnet_1_1autograd_1_1AGNodeEntry.html">autograd::AGNodeEntry</a>{<span class="keyword">nullptr</span>, 0, 0};</div>
+<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordflow">return</span> ret;</div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  }</div>
+<div class="line"><a name="l00318"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a"> 318</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a">CheckAndAlloc</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  ptr_->CheckAndAlloc();</div>
+<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  }</div>
+<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#a524662078483f8f46c6c71714fbfde0b">Save</a>(dmlc::Stream* fo,</div>
+<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keyword">const</span> std::vector<NDArray>& <a class="code" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>,</div>
+<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keyword">const</span> std::vector<std::string>& names);</div>
+<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#a5203aa033a53942deaecb7bb3bf46480">Load</a>(dmlc::Stream* fi,</div>
+<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  std::vector<NDArray>* <a class="code" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>,</div>
+<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  std::vector<std::string>* keys);</div>
+<div class="line"><a name="l00339"></a><span class="lineno"> 339</span> </div>
+<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00341"></a><span class="lineno"><a class="line" href="classmxnet_1_1NDArray.html#a423cc97aa03ee0dd8bc13781b77b8e50"> 341</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1NDArray.html#a423cc97aa03ee0dd8bc13781b77b8e50">autograd::AutogradRuntime</a>;</div>
+<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keyword">struct </span>Chunk {</div>
+<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <a class="code" href="structmxnet_1_1Storage_1_1Handle.html">Storage::Handle</a> shandle;</div>
+<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">Engine::VarHandle</a> <a class="code" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">var</a>;</div>
+<div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keywordtype">bool</span> static_data;</div>
+<div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordtype">bool</span> delay_alloc;</div>
+<div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  Chunk() : static_data(true), delay_alloc(false) {</div>
+<div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <a class="code" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">var</a> = <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#a6e141b188f018d5d933ab99868631d5e">NewVariable</a>();</div>
+<div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  }</div>
+<div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  Chunk(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a> &<a class="code" href="classmxnet_1_1NDArray.html#aebd03e900ec7664a99b780a0cb1efea6">data</a>, <span class="keywordtype">int</span> dev_id)</div>
+<div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  : static_data(true),</div>
+<div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  delay_alloc(false) {</div>
+<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">var</a> = <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#a6e141b188f018d5d933ab99868631d5e">NewVariable</a>();</div>
+<div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keywordflow">if</span> (data.<a class="code" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>() == cpu::kDevMask) {</div>
+<div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  shandle.ctx = <a class="code" href="structmxnet_1_1Context.html#a4bb231d70623fde216de51589347e513">Context::CPU</a>();</div>
+<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  } <span class="keywordflow">else</span> {</div>
+<div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  CHECK_EQ(data.<a class="code" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>(), gpu::kDevMask);</div>
+<div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  shandle.ctx = <a class="code" href="structmxnet_1_1Context.html#a39d1a2b59bce135da127475995a58671">Context::GPU</a>(dev_id);</div>
+<div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  }</div>
+<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  shandle.dptr = data.<a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>;</div>
+<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  shandle.size = data.<a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.Size() * mshadow::mshadow_sizeof(data.<a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a>);</div>
+<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  }</div>
+<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  Chunk(uint64_t size, Context <a class="code" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">ctx</a>, <span class="keywordtype">bool</span> delay_alloc_, <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">dtype</a>)</div>
+<div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  : static_data(false), delay_alloc(true) {</div>
+<div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <a class="code" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">var</a> = <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#a6e141b188f018d5d933ab99868631d5e">NewVariable</a>();</div>
+<div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  shandle.size = size * mshadow::mshadow_sizeof(dtype);</div>
+<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  shandle.ctx = <a class="code" href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">ctx</a>;</div>
+<div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keywordflow">if</span> (!delay_alloc_) this-><a class="code" href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a">CheckAndAlloc</a>();</div>
+<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  }</div>
+<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a">CheckAndAlloc</a>(<span class="keywordtype">void</span>) {</div>
+<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keywordflow">if</span> (delay_alloc) {</div>
+<div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  shandle = <a class="code" href="classmxnet_1_1Storage.html#a96abc072908d72d3271360a7e3e4bbc2">Storage::Get</a>()-><a class="code" href="classmxnet_1_1Storage.html#aaa387e9ae8d66a63438543d640f6cfff">Alloc</a>(shandle.size, shandle.ctx);</div>
+<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  delay_alloc = <span class="keyword">false</span>;</div>
+<div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  }</div>
+<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  }</div>
+<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  ~Chunk() {</div>
+<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">if</span> (static_data || delay_alloc) {</div>
+<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#a738e5192dab345ab0ec9888b095903cf">DeleteVariable</a>([](RunContext s) {}, shandle.ctx, <a class="code" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">var</a>);</div>
+<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  } <span class="keywordflow">else</span> {</div>
+<div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  Storage::Handle h = this->shandle;</div>
+<div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#a738e5192dab345ab0ec9888b095903cf">DeleteVariable</a>([h](RunContext s) {</div>
+<div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="classmxnet_1_1Storage.html#a96abc072908d72d3271360a7e3e4bbc2">Storage::Get</a>()-><a class="code" href="classmxnet_1_1Storage.html#a5bdbd7b3ab712cd9e087ab007bdc64dd">Free</a>(h);</div>
+<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  }, shandle.ctx, <a class="code" href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">var</a>);</div>
+<div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  }</div>
+<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  }</div>
+<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  };</div>
+<div class="line"><a name="l00400"></a><span class="lineno"> 400</span> </div>
+<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordtype">void</span> SetTBlob()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  tblob_.<a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a> = <span class="keyword">static_cast<</span><span class="keywordtype">char</span>*<span class="keyword">></span>(ptr_->shandle.dptr) + byte_offset_;</div>
+<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  tblob_.<a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a> = shape_;</div>
+<div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  tblob_.<a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a> = dtype_;</div>
+<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  tblob_.SetDLTensor(ptr_->shandle.ctx.dev_mask(), ptr_->shandle.ctx.dev_id);</div>
+<div class="line"><a name="l00406"></a><span class="lineno"> 406</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00407"></a><span class="lineno"> 407</span> <span class="preprocessor"></span> tblob_.Mkl_mem_ = Mkl_mem_;</div>
+<div class="line"><a name="l00408"></a><span class="lineno"> 408</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00409"></a><span class="lineno"> 409</span> <span class="preprocessor"></span> }</div>
+<div class="line"><a name="l00410"></a><span class="lineno"> 410</span> </div>
+<div class="line"><a name="l00411"></a><span class="lineno"> 411</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00412"></a><span class="lineno"> 412</span> <span class="preprocessor"></span> std::shared_ptr<MKLMemHolder> Mkl_mem_;</div>
+<div class="line"><a name="l00413"></a><span class="lineno"> 413</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00414"></a><span class="lineno"> 414</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  std::shared_ptr<Chunk> ptr_;</div>
+<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> shape_;</div>
+<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keywordtype">size_t</span> byte_offset_ = 0;</div>
+<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordtype">int</span> dtype_ = -1;</div>
+<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  autograd::AGNodeEntry entry_;</div>
+<div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">mutable</span> TBlob tblob_;</div>
+<div class="line"><a name="l00432"></a><span class="lineno"> 432</span> };</div>
+<div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div>
+<div class="line"><a name="l00445"></a><span class="lineno"> 445</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#aeee0e64a5e59bb0c222cbf44f4266f7a">CopyFromTo</a>(<span class="keyword">const</span> NDArray &from, NDArray *to, <span class="keywordtype">int</span> priority = 0);</div>
+<div class="line"><a name="l00446"></a><span class="lineno"> 446</span> </div>
+<div class="line"><a name="l00447"></a><span class="lineno"> 447</span> </div>
+<div class="line"><a name="l00454"></a><span class="lineno"> 454</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a15dfd6f3fa03dc2cf39dbab1225e1a51">ElementwiseSum</a>(<span class="keyword">const</span> std::vector<NDArray> &source, NDArray *out, <span class="keywordtype">int</span> priority = 0);</div>
+<div class="line"><a name="l00455"></a><span class="lineno"> 455</span> </div>
+<div class="line"><a name="l00462"></a><span class="lineno"> 462</span> NDArray <a class="code" href="namespacemxnet.html#a4dafe3aae43025fd21bc44b98c18ed1e">operator+</a>(<span class="keyword">const</span> NDArray &lhs, <span class="keyword">const</span> NDArray &rhs);</div>
+<div class="line"><a name="l00469"></a><span class="lineno"> 469</span> NDArray <a class="code" href="namespacemxnet.html#a4dafe3aae43025fd21bc44b98c18ed1e">operator+</a>(<span class="keyword">const</span> NDArray &lhs, <span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs);</div>
+<div class="line"><a name="l00476"></a><span class="lineno"> 476</span> NDArray <a class="code" href="namespacemxnet.html#ae3356844b657c9ae1b84d3ddd56bccf6">operator-</a>(<span class="keyword">const</span> NDArray &lhs, <span class="keyword">const</span> NDArray &rhs);</div>
+<div class="line"><a name="l00483"></a><span class="lineno"> 483</span> NDArray <a class="code" href="namespacemxnet.html#ae3356844b657c9ae1b84d3ddd56bccf6">operator-</a>(<span class="keyword">const</span> NDArray &lhs, <span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs);</div>
+<div class="line"><a name="l00490"></a><span class="lineno"> 490</span> NDArray <a class="code" href="namespacemxnet.html#af26b09fbcda865d34ec4fdad482d266a">operator*</a>(<span class="keyword">const</span> NDArray &lhs, <span class="keyword">const</span> NDArray &rhs); \</div>
+<div class="line"><a name="l00497"></a><span class="lineno"> 497</span> NDArray <a class="code" href="namespacemxnet.html#af26b09fbcda865d34ec4fdad482d266a">operator*</a>(<span class="keyword">const</span> NDArray &lhs, <span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs);</div>
+<div class="line"><a name="l00504"></a><span class="lineno"> 504</span> NDArray <a class="code" href="namespacemxnet.html#a502e3e00dc61cedb9c6eef711f1176ac">operator/</a>(<span class="keyword">const</span> NDArray &lhs, <span class="keyword">const</span> NDArray &rhs);</div>
+<div class="line"><a name="l00511"></a><span class="lineno"> 511</span> NDArray <a class="code" href="namespacemxnet.html#a502e3e00dc61cedb9c6eef711f1176ac">operator/</a>(<span class="keyword">const</span> NDArray &lhs, <span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs);</div>
+<div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div>
+<div class="line"><a name="l00517"></a><span class="lineno"> 517</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a5bf83454c8f41259eb9d471f59feda2b">RandomSeed</a>(uint32_t seed);</div>
+<div class="line"><a name="l00524"></a><span class="lineno"> 524</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">SampleUniform</a>(<a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> begin, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> end, NDArray *out);</div>
+<div class="line"><a name="l00531"></a><span class="lineno"> 531</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">SampleGaussian</a>(<a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> mu, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> sigma, NDArray *out);</div>
+<div class="line"><a name="l00538"></a><span class="lineno"> 538</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a050beaa505f11e0b844deb91efe0cac2">SampleGamma</a>(<a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> alpha, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> beta, NDArray *out);</div>
+<div class="line"><a name="l00544"></a><span class="lineno"> 544</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a51f9b094369a349c05463de2be9f0a31">SampleExponential</a>(<a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> lambda, NDArray *out);</div>
+<div class="line"><a name="l00550"></a><span class="lineno"> 550</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#aed55e8197182b7c66126902b2a43739a">SamplePoisson</a>(<a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> lambda, NDArray *out);</div>
+<div class="line"><a name="l00557"></a><span class="lineno"> 557</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#ae82f70918b2d946f7ad66a158b1733dc">SampleNegBinomial</a>(int32_t k, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> p, NDArray *out);</div>
+<div class="line"><a name="l00564"></a><span class="lineno"> 564</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#ad8e0dad88ce27133fd24811763b7afdb">SampleGenNegBinomial</a>(<a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> mu, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> alpha, NDArray *out);</div>
+<div class="line"><a name="l00565"></a><span class="lineno"> 565</span> </div>
+<div class="line"><a name="l00566"></a><span class="lineno"> 566</span> </div>
+<div class="line"><a name="l00567"></a><span class="lineno"> 567</span> <span class="comment">//--------------------------------------------------------------</span></div>
+<div class="line"><a name="l00568"></a><span class="lineno"> 568</span> <span class="comment">// The following part are API Registration of NDArray functions.</span></div>
+<div class="line"><a name="l00569"></a><span class="lineno"> 569</span> <span class="comment">//--------------------------------------------------------------</span></div>
+<div class="line"><a name="l00570"></a><span class="lineno"> 570</span> </div>
+<div class="line"><a name="l00572"></a><span class="lineno"> 572</span> <span class="keyword">typedef</span> std::function<void (NDArray **used_vars,</div>
+<div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *scalars,</div>
+<div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  NDArray **mutate_vars,</div>
+<div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keywordtype">int</span> num_params,</div>
+<div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="keywordtype">char</span> **param_keys,</div>
+<div class="line"><a name="l00577"></a><span class="lineno"><a class="line" href="namespacemxnet.html#ab0d55b0edf98cf8f3caeb050662ae9ec"> 577</a></span>  <span class="keywordtype">char</span> **param_vals)> <a class="code" href="namespacemxnet.html#ab0d55b0edf98cf8f3caeb050662ae9ec">NDArrayAPIFunction</a>;</div>
+<div class="line"><a name="l00579"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818a"> 579</a></span> <span class="keyword">enum</span> <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818a">NDArrayFunctionTypeMask</a> {</div>
+<div class="line"><a name="l00581"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51"> 581</a></span>  <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">kNDArrayArgBeforeScalar</a> = 1,</div>
+<div class="line"><a name="l00583"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aaef9818c8aae9d0046b3d9033a62fcfbb"> 583</a></span>  <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aaef9818c8aae9d0046b3d9033a62fcfbb">kScalarArgBeforeNDArray</a> = 1 << 1,</div>
+<div class="line"><a name="l00592"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd"> 592</a></span>  <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd">kAcceptEmptyMutateTarget</a> = 1 << 2</div>
+<div class="line"><a name="l00593"></a><span class="lineno"> 593</span> };</div>
+<div class="line"><a name="l00595"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html"> 595</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a></div>
+<div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  : <span class="keyword">public</span> dmlc::FunctionRegEntryBase<NDArrayFunctionReg,</div>
+<div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  NDArrayAPIFunction> {</div>
+<div class="line"><a name="l00599"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29"> 599</a></span>  <span class="keywordtype">unsigned</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29">num_use_vars</a>;</div>
+<div class="line"><a name="l00601"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2"> 601</a></span>  <span class="keywordtype">unsigned</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">num_mutate_vars</a>;</div>
+<div class="line"><a name="l00603"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#a9591e2abf6cbca0903aec582b96b4d95"> 603</a></span>  <span class="keywordtype">unsigned</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a9591e2abf6cbca0903aec582b96b4d95">num_scalars</a>;</div>
+<div class="line"><a name="l00605"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf"> 605</a></span>  <span class="keywordtype">int</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf">type_mask</a>;</div>
+<div class="line"><a name="l00609"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#abd540887b52ba89572ad6a18b4ac739c"> 609</a></span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#abd540887b52ba89572ad6a18b4ac739c">NDArrayFunctionReg</a>()</div>
+<div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  : <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29">num_use_vars</a>(0),</div>
+<div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">num_mutate_vars</a>(0),</div>
+<div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a9591e2abf6cbca0903aec582b96b4d95">num_scalars</a>(0),</div>
+<div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf">type_mask</a>(0) {}</div>
+<div class="line"><a name="l00620"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#aa606f0cec91a2e069553f98d7e7addcd"> 620</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa606f0cec91a2e069553f98d7e7addcd">set_function</a>(<span class="keywordtype">void</span> (*fsetvalue)(<span class="keyword"> [...]
+<div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> *out)) {</div>
+<div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  body = [fsetvalue] (<a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **used_vars, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *s, <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **mutate_vars,</div>
+<div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="keywordtype">int</span> num_params, <span class="keywordtype">char</span> **param_keys, <span class="keywordtype">char</span> **param_vals) {</div>
+<div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  (*fsetvalue)(s[0], mutate_vars[0]);</div>
+<div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  };</div>
+<div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">num_mutate_vars</a> = 1; <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a9591e2abf6cbca0903aec582b96b4d95">num_scalars</a> = 1;</div>
+<div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  this->add_argument(<span class="stringliteral">"src"</span>, <span class="stringliteral">"real_t"</span>, <span class="stringliteral">"Source input to the function."</span>);</div>
+<div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  }</div>
+<div class="line"><a name="l00636"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#aa7f4633d042179d90c067aeb8da62060"> 636</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa7f4633d042179d90c067aeb8da62060">set_function</a>(<span class="keywordtype">void</span>(*fternary)(<span class="keyword">co [...]
+<div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keyword">const</span> <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &mhs,</div>
+<div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keyword">const</span> <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &rhs,</div>
+<div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> *out)) {</div>
+<div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  body = [fternary](<a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **used_vars,</div>
+<div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *s, <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **mutate_vars,</div>
+<div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="keywordtype">int</span> num_params, <span class="keywordtype">char</span> **param_keys, <span class="keywordtype">char</span> **param_vals) {</div>
+<div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  (*fternary)(*used_vars[0], *used_vars[1], *used_vars[2], mutate_vars[0]);</div>
+<div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  };</div>
+<div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29">num_use_vars</a> = 3; <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">num_mutate_vars</a> = 1;</div>
+<div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf">type_mask</a> = <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">kNDArrayArgBeforeScalar</a> | <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd">kAcceptEmptyMutateTarget</a>;</div>
+<div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  this->add_argument(<span class="stringliteral">"lhs"</span>, <span class="stringliteral">"NDArray"</span>, <span class="stringliteral">"Left operand to the function."</span>);</div>
+<div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  this->add_argument(<span class="stringliteral">"mhs"</span>, <span class="stringliteral">"NDArray"</span>, <span class="stringliteral">"Middle operand to the function."</span>);</div>
+<div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  this->add_argument(<span class="stringliteral">"rhs"</span>, <span class="stringliteral">"NDArray"</span>, <span class="stringliteral">"Right operand to the function."</span>);</div>
+<div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  }</div>
+<div class="line"><a name="l00658"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#aa57c24a22d49470496b221c72bf7a1a0"> 658</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa57c24a22d49470496b221c72bf7a1a0">set_function</a>(<span class="keywordtype">void</span> (*fbinary)(<span class="keyword">co [...]
+<div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keyword">const</span> <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> &rhs,</div>
+<div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> *out)) {</div>
+<div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  body = [fbinary] (<a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **used_vars, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *s, <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **mutate_vars,</div>
+<div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="keywordtype">int</span> num_params, <span class="keywordtype">char</span> **param_keys, <span class="keywordtype">char</span> **param_vals) {</div>
+<div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  (*fbinary)(*used_vars[0], *used_vars[1], mutate_vars[0]);</div>
+<div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  };</div>
+<div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29">num_use_vars</a> = 2; <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">num_mutate_vars</a> = 1;</div>
+<div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf">type_mask</a> = <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">kNDArrayArgBeforeScalar</a> | <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd">kAcceptEmptyMutateTarget</a>;</div>
+<div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  this->add_argument(<span class="stringliteral">"lhs"</span>, <span class="stringliteral">"NDArray"</span>, <span class="stringliteral">"Left operand to the function."</span>);</div>
+<div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  this->add_argument(<span class="stringliteral">"rhs"</span>, <span class="stringliteral">"NDArray"</span>, <span class="stringliteral">"Right operand to the function."</span>);</div>
+<div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  }</div>
+<div class="line"><a name="l00677"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#af6fc4419b30c74139eceb04e7d5758f5"> 677</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#af6fc4419b30c74139eceb04e7d5758f5">set_function</a>(<span class="keywordtype">void</span> (*fscalar)(<span class="keyword">co [...]
+<div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs,</div>
+<div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> *out)) {</div>
+<div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  body = [fscalar] (<a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **used_vars, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *s, <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **mutate_vars,</div>
+<div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <span class="keywordtype">int</span> num_params, <span class="keywordtype">char</span> **param_keys, <span class="keywordtype">char</span> **param_vals) {</div>
+<div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  (*fscalar)(*used_vars[0], s[0], mutate_vars[0]);</div>
+<div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  };</div>
+<div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29">num_use_vars</a> = 1; <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">num_mutate_vars</a> = 1; <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a9591e2abf6cbca0903aec582b96b4d95">num_scalars</a> = 1;</div>
+<div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf">type_mask</a> = <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">kNDArrayArgBeforeScalar</a> | <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd">kAcceptEmptyMutateTarget</a>;</div>
+<div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  this->add_argument(<span class="stringliteral">"lhs"</span>, <span class="stringliteral">"NDArray"</span>, <span class="stringliteral">"Left operand to the function."</span>);</div>
+<div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  this->add_argument(<span class="stringliteral">"rhs"</span>, <span class="stringliteral">"real_t"</span>, <span class="stringliteral">"Right operand to the function."</span>);</div>
+<div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  }</div>
+<div class="line"><a name="l00696"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#a5f4c0751bc3111a8707b160d0e99e1af"> 696</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a5f4c0751bc3111a8707b160d0e99e1af">set_function</a>(<span class="keywordtype">void</span> (*funary)(<span class="keyword">con [...]
+<div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> *out)) {</div>
+<div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  body = [funary] (<a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **used_vars, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *s, <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **mutate_vars,</div>
+<div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="keywordtype">int</span> num_params, <span class="keywordtype">char</span> **param_keys, <span class="keywordtype">char</span> **param_vals) {</div>
+<div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  (*funary)(*used_vars[0], mutate_vars[0]);</div>
+<div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  };</div>
+<div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29">num_use_vars</a> = 1; <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">num_mutate_vars</a> = 1;</div>
+<div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf">type_mask</a> = <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">kNDArrayArgBeforeScalar</a> | <a class="code" href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd">kAcceptEmptyMutateTarget</a>;</div>
+<div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  this->add_argument(<span class="stringliteral">"src"</span>, <span class="stringliteral">"NDArray"</span>, <span class="stringliteral">"Source input to the function."</span>);</div>
+<div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  }</div>
+<div class="line"><a name="l00713"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#a220e1866bea35e103adbe23ff92855d9"> 713</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a220e1866bea35e103adbe23ff92855d9">set_function</a>(</div>
+<div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keywordtype">void</span> (*fgeneric)(<a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **used_vars,</div>
+<div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *s,</div>
+<div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **mutate_vars,</div>
+<div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="keyword">const</span> std::map<std::string, std::string>& param)) {</div>
+<div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  body = [fgeneric] (<a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **used_vars, <a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> *s, <a class="code" href="classmxnet_1_1NDArray.html">NDArray</a> **mutate_vars,</div>
+<div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keywordtype">int</span> num_params, <span class="keywordtype">char</span> **param_keys, <span class="keywordtype">char</span> **param_vals) {</div>
+<div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  std::map<std::string, std::string> param;</div>
+<div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < num_params; ++i) {</div>
+<div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  param[param_keys[i]] = param_vals[i];</div>
+<div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  }</div>
+<div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  fgeneric(used_vars, s, mutate_vars, param);</div>
+<div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  };</div>
+<div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  }</div>
+<div class="line"><a name="l00733"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#a970756506340ff688dbb447858b1963c"> 733</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a970756506340ff688dbb447858b1963c">set_num_use_vars</a>(<span class="keywordtype">unsigned</span> n) {</div>
+<div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29">num_use_vars</a> = n; <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  }</div>
+<div class="line"><a name="l00741"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#af0a5efd67efe9b7c14abd49652e79a14"> 741</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#af0a5efd67efe9b7c14abd49652e79a14">set_num_mutate_vars</a>(<span class="keywordtype">unsigned</span> n) {</div>
+<div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">num_mutate_vars</a> = n; <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  }</div>
+<div class="line"><a name="l00749"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#a4f6a3ba75e30389c827b3b26a12415d1"> 749</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a4f6a3ba75e30389c827b3b26a12415d1">set_num_scalars</a>(<span class="keywordtype">unsigned</span> n) {</div>
+<div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a9591e2abf6cbca0903aec582b96b4d95">num_scalars</a> = n; <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  }</div>
+<div class="line"><a name="l00757"></a><span class="lineno"><a class="line" href="structmxnet_1_1NDArrayFunctionReg.html#acc3656ba948c28d3d155736ce064a3f4"> 757</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> &<a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#acc3656ba948c28d3d155736ce064a3f4">set_type_mask</a>(<span class="keywordtype">int</span> tmask) {</div>
+<div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <a class="code" href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf">type_mask</a> = tmask; <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  }</div>
+<div class="line"><a name="l00760"></a><span class="lineno"> 760</span> }; <span class="comment">// NDArrayFunctionReg</span></div>
+<div class="line"><a name="l00761"></a><span class="lineno"> 761</span> </div>
+<div class="line"><a name="l00773"></a><span class="lineno"><a class="line" href="ndarray_8h.html#a21d0a394bdf34194b9954252d9476d56"> 773</a></span> <span class="preprocessor">#define MXNET_REGISTER_NDARRAY_FUN(name) \</span></div>
+<div class="line"><a name="l00774"></a><span class="lineno"> 774</span> <span class="preprocessor"> DMLC_REGISTRY_REGISTER(::mxnet::NDArrayFunctionReg, NDArrayFunctionReg, name)</span></div>
+<div class="line"><a name="l00775"></a><span class="lineno"> 775</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00776"></a><span class="lineno"> 776</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00777"></a><span class="lineno"> 777</span> </div>
+<div class="line"><a name="l00778"></a><span class="lineno"><a class="line" href="namespacedmlc.html"> 778</a></span> <span class="keyword">namespace </span>dmlc {</div>
+<div class="line"><a name="l00780"></a><span class="lineno"> 780</span> <a class="code" href="namespacedmlc.html#a7272c846fa03dee55916aadce0165a46">DMLC_DECLARE_TRAITS</a>(has_saveload, <a class="code" href="classmxnet_1_1NDArray.html">mxnet::NDArray</a>, <span class="keyword">true</span>);</div>
+<div class="line"><a name="l00781"></a><span class="lineno"> 781</span> } <span class="comment">// namespace dmlc</span></div>
+<div class="line"><a name="l00782"></a><span class="lineno"> 782</span> <span class="preprocessor">#endif // MXNET_NDARRAY_H_</span></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a5203aa033a53942deaecb7bb3bf46480"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a5203aa033a53942deaecb7bb3bf46480">mxnet::NDArray::Load</a></div><div class="ttdeci">bool Load(dmlc::Stream *strm)</div><div class="ttdoc">load the content from binary stream </div></div>
+<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_af0a5efd67efe9b7c14abd49652e79a14"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#af0a5efd67efe9b7c14abd49652e79a14">mxnet::NDArrayFunctionReg::set_num_mutate_vars</a></div><div class="ttdeci">NDArrayFunctionReg & set_num_mutate_vars(unsigned n)</div><div class="ttdoc">set the number of mutate variables </div><div class="ttdef"><b>Definition:</b> ndarray.h:741</div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a35c9b5da0852b480e0114f0865338cbc"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a35c9b5da0852b480e0114f0865338cbc">mxnet::NDArray::var</a></div><div class="ttdeci">Engine::VarHandle var() const </div><div class="ttdef"><b>Definition:</b> ndarray.h:156</div></div>
<div class="ttc" id="namespacemxnet_html_a5bf83454c8f41259eb9d471f59feda2b"><div class="ttname"><a href="namespacemxnet.html#a5bf83454c8f41259eb9d471f59feda2b">mxnet::RandomSeed</a></div><div class="ttdeci">void RandomSeed(uint32_t seed)</div><div class="ttdoc">Seed the random number generator. </div></div>
<div class="ttc" id="engine_8h_html"><div class="ttname"><a href="engine_8h.html">engine.h</a></div><div class="ttdoc">Engine that schedules all the operations according to dependency. </div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a423cc97aa03ee0dd8bc13781b77b8e50"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a423cc97aa03ee0dd8bc13781b77b8e50">mxnet::NDArray::AutogradRuntime</a></div><div class="ttdeci">friend class autograd::AutogradRuntime</div><div class="ttdef"><b>Definition:</b> ndarray.h:341</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_a6cc7a57d9c33fefc08630e81951a004f"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">mxnet::TBlob::shape_</a></div><div class="ttdeci">TShape shape_</div><div class="ttdoc">shape of the tensor </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:46</div></div>
-<div class="ttc" id="classmxnet_1_1NDArray_html_a26d1ddb2bb0ed1e671ce7606498b942d"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a26d1ddb2bb0ed1e671ce7606498b942d">mxnet::NDArray::ctx</a></div><div class="ttdeci">Context ctx() const</div><div class="ttdef"><b>Definition:</b> ndarray.h:117</div></div>
<div class="ttc" id="classmxnet_1_1autograd_1_1AGNodeEntry_html_a8edff5e8fa169924abe520edcae76c45"><div class="ttname"><a href="classmxnet_1_1autograd_1_1AGNodeEntry.html#a8edff5e8fa169924abe520edcae76c45">mxnet::autograd::AGNodeEntry::clear</a></div><div class="ttdeci">void clear()</div><div class="ttdef"><b>Definition:</b> ndarray.h:44</div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_aebc61006705874666423c45f161df546"><div class="ttname"><a href="classmxnet_1_1NDArray.html#aebc61006705874666423c45f161df546">mxnet::NDArray::operator/=</a></div><div class="ttdeci">NDArray & operator/=(const NDArray &src)</div><div class="ttdoc">elementwise division from current ndarray this mutate the current NDArray </div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_aed729c3bc9db04ab346fd3969b4fc982"><div class="ttname"><a href="classmxnet_1_1NDArray.html#aed729c3bc9db04ab346fd3969b4fc982">mxnet::NDArray::grad</a></div><div class="ttdeci">NDArray grad() const </div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a422c70b16646f8a273e9b4936fbe2e37"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a422c70b16646f8a273e9b4936fbe2e37">mxnet::NDArray::SyncCopyFromCPU</a></div><div class="ttdeci">void SyncCopyFromCPU(const void *data, size_t size) const </div><div class="ttdoc">Do a synchronize copy from a continugous CPU memory region. </div></div>
<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_abd540887b52ba89572ad6a18b4ac739c"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#abd540887b52ba89572ad6a18b4ac739c">mxnet::NDArrayFunctionReg::NDArrayFunctionReg</a></div><div class="ttdeci">NDArrayFunctionReg()</div><div class="ttdoc">constructor </div><div class="ttdef"><b>Definition:</b> ndarray.h:609</div></div>
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
<div class="ttc" id="namespacemxnet_html_af26b09fbcda865d34ec4fdad482d266a"><div class="ttname"><a href="namespacemxnet.html#af26b09fbcda865d34ec4fdad482d266a">mxnet::operator*</a></div><div class="ttdeci">NDArray operator*(const NDArray &lhs, const NDArray &rhs)</div><div class="ttdoc">elementwise multiplication </div></div>
<div class="ttc" id="classmxnet_1_1Storage_html_a5bdbd7b3ab712cd9e087ab007bdc64dd"><div class="ttname"><a href="classmxnet_1_1Storage.html#a5bdbd7b3ab712cd9e087ab007bdc64dd">mxnet::Storage::Free</a></div><div class="ttdeci">virtual void Free(Handle handle)=0</div><div class="ttdoc">Free storage. </div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a7acb6462c11e071949db2b0e1fbfd66e"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a7acb6462c11e071949db2b0e1fbfd66e">mxnet::NDArray::Slice</a></div><div class="ttdeci">NDArray Slice(index_t begin, index_t end) const </div><div class="ttdoc">Slice a NDArray. </div></div>
<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_a970756506340ff688dbb447858b1963c"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#a970756506340ff688dbb447858b1963c">mxnet::NDArrayFunctionReg::set_num_use_vars</a></div><div class="ttdeci">NDArrayFunctionReg & set_num_use_vars(unsigned n)</div><div class="ttdoc">set the number of mutate variables </div><div class="ttdef"><b>Definition:</b> ndarray.h:733</div></div>
<div class="ttc" id="namespacedmlc_html_a7272c846fa03dee55916aadce0165a46"><div class="ttname"><a href="namespacedmlc.html#a7272c846fa03dee55916aadce0165a46">dmlc::DMLC_DECLARE_TRAITS</a></div><div class="ttdeci">DMLC_DECLARE_TRAITS(has_saveload, mxnet::NDArray, true)</div><div class="ttdoc">traits </div></div>
<div class="ttc" id="namespacemxnet_html_a4421326e33f2a848cb2fa3e70ab24ff4"><div class="ttname"><a href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">mxnet::real_t</a></div><div class="ttdeci">mshadow::default_real_t real_t</div><div class="ttdoc">data type that will be used to store ndarray </div><div class="ttdef"><b>Definition:</b> base.h:109</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a39d1a2b59bce135da127475995a58671"><div class="ttname"><a href="structmxnet_1_1Context.html#a39d1a2b59bce135da127475995a58671">mxnet::Context::GPU</a></div><div class="ttdeci">static Context GPU(int32_t dev_id=-1)</div></div>
<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_a0b24615f30e4fbe69ee75c7b319735bf"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf">mxnet::NDArrayFunctionReg::type_mask</a></div><div class="ttdeci">int type_mask</div><div class="ttdoc">information on how function should be called from API </div><div class="ttdef"><b>Definition:</b> ndarray.h:605</div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a3fe1974c7c2d522b09b9bf82fd2865ba"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a3fe1974c7c2d522b09b9bf82fd2865ba">mxnet::NDArray::T</a></div><div class="ttdeci">NDArray T() const </div><div class="ttdoc">return transpose of current NDArray </div></div>
<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_a5f4c0751bc3111a8707b160d0e99e1af"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#a5f4c0751bc3111a8707b160d0e99e1af">mxnet::NDArrayFunctionReg::set_function</a></div><div class="ttdeci">NDArrayFunctionReg & set_function(void(*funary)(const NDArray &src, NDArray *out))</div><div class="ttdoc">set the function body to a unary NDArray function this will also auto set the parameters correctly ...</div><d [...]
+<div class="ttc" id="classmxnet_1_1NDArray_html_a34a3694141050a257dc6b40a6ad2c4ec"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a34a3694141050a257dc6b40a6ad2c4ec">mxnet::NDArray::Detach</a></div><div class="ttdeci">NDArray Detach() const </div><div class="ttdoc">Return a copy of this NDArray without autograd history. </div><div class="ttdef"><b>Definition:</b> ndarray.h:309</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_a4649b740b80a6045754cb2389cfcca61"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">mxnet::TBlob::type_flag_</a></div><div class="ttdeci">int type_flag_</div><div class="ttdoc">type flag of the tensor blob </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:48</div></div>
<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_a4f6a3ba75e30389c827b3b26a12415d1"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#a4f6a3ba75e30389c827b3b26a12415d1">mxnet::NDArrayFunctionReg::set_num_scalars</a></div><div class="ttdeci">NDArrayFunctionReg & set_num_scalars(unsigned n)</div><div class="ttdoc">set the number of scalar arguments </div><div class="ttdef"><b>Definition:</b> ndarray.h:749</div></div>
<div class="ttc" id="namespacemxnet_html_a6e14bb5f6ffc81892feca8d1da658687"><div class="ttname"><a href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">mxnet::TShape</a></div><div class="ttdeci">nnvm::TShape TShape</div><div class="ttdoc">Shape data structure used to record shape information. </div><div class="ttdef"><b>Definition:</b> base.h:111</div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a7df5a5d9956a481635fc43ca4d9a196c"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a7df5a5d9956a481635fc43ca4d9a196c">mxnet::NDArray::operator+=</a></div><div class="ttdeci">NDArray & operator+=(const NDArray &src)</div><div class="ttdoc">elementwise add to current space this mutate the current NDArray </div></div>
<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_ad7881779b7807c6357902bdaccfccde2"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">mxnet::NDArrayFunctionReg::num_mutate_vars</a></div><div class="ttdeci">unsigned num_mutate_vars</div><div class="ttdoc">number of variable mutated by this function </div><div class="ttdef"><b>Definition:</b> ndarray.h:601</div></div>
<div class="ttc" id="structmxnet_1_1RunContext_html"><div class="ttname"><a href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></div><div class="ttdoc">execution time context. The information needed in runtime for actual execution. </div><div class="ttdef"><b>Definition:</b> base.h:213</div></div>
-<div class="ttc" id="structmxnet_1_1Storage_1_1Handle_html_a428e25a6317e50c8b55307c96f8ec595"><div class="ttname"><a href="structmxnet_1_1Storage_1_1Handle.html#a428e25a6317e50c8b55307c96f8ec595">mxnet::Storage::Handle::dptr</a></div><div class="ttdeci">void * dptr</div><div class="ttdoc">Pointer to the data. </div><div class="ttdef"><b>Definition:</b> storage.h:26</div></div>
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-<div class="ttc" id="classmxnet_1_1TBlob_html_ad8bcf56e13082fc0607e21f08b14758f"><div class="ttname"><a href="classmxnet_1_1TBlob.html#ad8bcf56e13082fc0607e21f08b14758f">mxnet::TBlob::dev_mask</a></div><div class="ttdeci">int dev_mask() const</div><div class="ttdoc">device mask of the corresponding device </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:209</div></div>
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+<div class="ttc" id="classmxnet_1_1NDArray_html_a4819d2e6431ccdf4b9f8efa8eb0bd1fd"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a4819d2e6431ccdf4b9f8efa8eb0bd1fd">mxnet::NDArray::SyncCopyToCPU</a></div><div class="ttdeci">void SyncCopyToCPU(void *data, size_t size) const </div><div class="ttdoc">Do a synchronize copy to a continugous CPU memory region. </div></div>
<div class="ttc" id="classmxnet_1_1NDArray_html_a12ec978e5dbe9092d21721a657c39756"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a12ec978e5dbe9092d21721a657c39756">mxnet::NDArray::NDArray</a></div><div class="ttdeci">NDArray()</div><div class="ttdoc">default constructor </div><div class="ttdef"><b>Definition:</b> ndarray.h:62</div></div>
<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_aa0847438b4f3f600ec61d6d544755a29"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29">mxnet::NDArrayFunctionReg::num_use_vars</a></div><div class="ttdeci">unsigned num_use_vars</div><div class="ttdoc">number of variable used by this function </div><div class="ttdef"><b>Definition:</b> ndarray.h:599</div></div>
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-<div class="ttc" id="classmxnet_1_1NDArray_html_a67b0f8eb36568b4c9fbd3da2a1c4b0a3"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a67b0f8eb36568b4c9fbd3da2a1c4b0a3">mxnet::NDArray::dtype</a></div><div class="ttdeci">int dtype() const</div><div class="ttdef"><b>Definition:</b> ndarray.h:123</div></div>
<div class="ttc" id="classmxnet_1_1Storage_html_aaa387e9ae8d66a63438543d640f6cfff"><div class="ttname"><a href="classmxnet_1_1Storage.html#aaa387e9ae8d66a63438543d640f6cfff">mxnet::Storage::Alloc</a></div><div class="ttdeci">virtual Handle Alloc(size_t size, Context ctx)=0</div><div class="ttdoc">Allocate a new contiguous memory for a given size. </div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_ada618436206b59aa3ffcdb8474d5051c"><div class="ttname"><a href="classmxnet_1_1NDArray.html#ada618436206b59aa3ffcdb8474d5051c">mxnet::NDArray::Reshape</a></div><div class="ttdeci">NDArray Reshape(const TShape &shape) const </div><div class="ttdoc">Get an reshaped NDArray. </div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_aee74980eb87bf7c482d15458f5194cbe"><div class="ttname"><a href="classmxnet_1_1NDArray.html#aee74980eb87bf7c482d15458f5194cbe">mxnet::NDArray::fresh_out_grad</a></div><div class="ttdeci">bool fresh_out_grad() const </div></div>
<div class="ttc" id="classmxnet_1_1autograd_1_1AGNodeEntry_html"><div class="ttname"><a href="classmxnet_1_1autograd_1_1AGNodeEntry.html">mxnet::autograd::AGNodeEntry</a></div><div class="ttdef"><b>Definition:</b> ndarray.h:38</div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a2a15ffdbf29d4d5455a4fbb4b607880c"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a2a15ffdbf29d4d5455a4fbb4b607880c">mxnet::NDArray::is_none</a></div><div class="ttdeci">bool is_none() const </div><div class="ttdef"><b>Definition:</b> ndarray.h:127</div></div>
<div class="ttc" id="namespacemxnet_html_a89a5f0f5cfd9e1e94604a7b42dda818aaef9818c8aae9d0046b3d9033a62fcfbb"><div class="ttname"><a href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aaef9818c8aae9d0046b3d9033a62fcfbb">mxnet::kScalarArgBeforeNDArray</a></div><div class="ttdoc">all the scalar should go before use_vars </div><div class="ttdef"><b>Definition:</b> ndarray.h:583</div></div>
<div class="ttc" id="namespacemxnet_html_a51f9b094369a349c05463de2be9f0a31"><div class="ttname"><a href="namespacemxnet.html#a51f9b094369a349c05463de2be9f0a31">mxnet::SampleExponential</a></div><div class="ttdeci">void SampleExponential(real_t lambda, NDArray *out)</div><div class="ttdoc">Sample exponential distribution for each elements of out. </div></div>
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<div class="ttc" id="classmxnet_1_1TBlob_html_a4e5f4953be2c16724ecff0afc5ea2e10"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">mxnet::TBlob::dptr_</a></div><div class="ttdeci">void * dptr_</div><div class="ttdoc">pointer to the data </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:44</div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_a6e141b188f018d5d933ab99868631d5e"><div class="ttname"><a href="classmxnet_1_1Engine.html#a6e141b188f018d5d933ab99868631d5e">mxnet::Engine::NewVariable</a></div><div class="ttdeci">virtual VarHandle NewVariable()=0</div><div class="ttdoc">Allocate a new variable, the variable can then be used to schedule the operation concurrently via dep...</div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_aa7a198e3e4c80a48cf92a913009aab8f"><div class="ttname"><a href="classmxnet_1_1NDArray.html#aa7a198e3e4c80a48cf92a913009aab8f">mxnet::NDArray::operator=</a></div><div class="ttdeci">NDArray & operator=(real_t scalar)</div><div class="ttdoc">set all the elements in ndarray to be scalar </div></div>
<div class="ttc" id="namespacemxnet_html_a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd"><div class="ttname"><a href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aa334598420a4246d5412ddc346b2b57bd">mxnet::kAcceptEmptyMutateTarget</a></div><div class="ttdoc">whether this function allows the handles in the target to be empty NDArray that are not yet initializ...</div><div class="ttdef"><b>Definition:</b> ndarray.h:592</div></div>
<div class="ttc" id="classmxnet_1_1Storage_html_a96abc072908d72d3271360a7e3e4bbc2"><div class="ttname"><a href="classmxnet_1_1Storage.html#a96abc072908d72d3271360a7e3e4bbc2">mxnet::Storage::Get</a></div><div class="ttdeci">static Storage * Get()</div></div>
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+<div class="ttc" id="classmxnet_1_1NDArray_html_abfbb85eb84be64976417ed0b13b1d8ee"><div class="ttname"><a href="classmxnet_1_1NDArray.html#abfbb85eb84be64976417ed0b13b1d8ee">mxnet::NDArray::shape</a></div><div class="ttdeci">const TShape & shape() const </div><div class="ttdef"><b>Definition:</b> ndarray.h:99</div></div>
<div class="ttc" id="namespacemxnet_1_1autograd_html_a8070fc188711b03b349a39d1b286d4a0"><div class="ttname"><a href="namespacemxnet_1_1autograd.html#a8070fc188711b03b349a39d1b286d4a0">mxnet::autograd::AGNodePtr</a></div><div class="ttdeci">std::shared_ptr< AGNode > AGNodePtr</div><div class="ttdef"><b>Definition:</b> ndarray.h:36</div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_aed51bd7f294d9f2b569764a0c151d883"><div class="ttname"><a href="classmxnet_1_1Engine.html#aed51bd7f294d9f2b569764a0c151d883">mxnet::Engine::WaitForVar</a></div><div class="ttdeci">virtual void WaitForVar(VarHandle var)=0</div><div class="ttdoc">Wait for a variable. </div></div>
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+<div class="ttc" id="classmxnet_1_1NDArray_html_ad35ba01da6949494afe895b7429e4fc3"><div class="ttname"><a href="classmxnet_1_1NDArray.html#ad35ba01da6949494afe895b7429e4fc3">mxnet::NDArray::operator-=</a></div><div class="ttdeci">NDArray & operator-=(const NDArray &src)</div><div class="ttdoc">elementwise subtract from current ndarray this mutate the current NDArray </div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a5e2eb64751872f537ae3cb25f949fff1"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a5e2eb64751872f537ae3cb25f949fff1">mxnet::NDArray::ctx</a></div><div class="ttdeci">Context ctx() const </div><div class="ttdef"><b>Definition:</b> ndarray.h:117</div></div>
<div class="ttc" id="namespacemxnet_html_a5a28062f52ca576a126599e7ad487077"><div class="ttname"><a href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">mxnet::SampleGaussian</a></div><div class="ttdeci">void SampleGaussian(real_t mu, real_t sigma, NDArray *out)</div><div class="ttdoc">Sample gaussian distribution for each elements of out. </div></div>
<div class="ttc" id="storage_8h_html"><div class="ttname"><a href="storage_8h.html">storage.h</a></div><div class="ttdoc">Storage manager across multiple devices. </div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_aedd79a43d5fa4173f3ddd719fb939fbc"><div class="ttname"><a href="classmxnet_1_1NDArray.html#aedd79a43d5fa4173f3ddd719fb939fbc">mxnet::NDArray::WaitToRead</a></div><div class="ttdeci">void WaitToRead() const </div><div class="ttdoc">Block until all the pending write operations with respect to current NDArray are finished, and read can be performed. </div><div class="ttdef"><b>Definition:</b> ndarray.h:138</div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a1b6038a196c14d038bd3e6d6bbe36ad2"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a1b6038a196c14d038bd3e6d6bbe36ad2">mxnet::NDArray::dtype</a></div><div class="ttdeci">int dtype() const </div><div class="ttdef"><b>Definition:</b> ndarray.h:123</div></div>
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<div class="ttc" id="classmxnet_1_1Engine_html_a738e5192dab345ab0ec9888b095903cf"><div class="ttname"><a href="classmxnet_1_1Engine.html#a738e5192dab345ab0ec9888b095903cf">mxnet::Engine::DeleteVariable</a></div><div class="ttdeci">virtual void DeleteVariable(SyncFn delete_fn, Context exec_ctx, VarHandle var)=0</div><div class="ttdoc">Schedule the deletion of a variable. </div></div>
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<div class="ttc" id="classmxnet_1_1Engine_html_aac31510c793a12944c33f9cac6150491"><div class="ttname"><a href="classmxnet_1_1Engine.html#aac31510c793a12944c33f9cac6150491">mxnet::Engine::VarHandle</a></div><div class="ttdeci">engine::VarHandle VarHandle</div><div class="ttdoc">Variable pointer. </div><div class="ttdef"><b>Definition:</b> engine.h:84</div></div>
-<div class="ttc" id="classmxnet_1_1NDArray_html_a1cf4c49e6925d35586f75a1c9bdb51e4"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a1cf4c49e6925d35586f75a1c9bdb51e4">mxnet::NDArray::WaitToRead</a></div><div class="ttdeci">void WaitToRead() const</div><div class="ttdoc">Block until all the pending write operations with respect to current NDArray are finished, and read can be performed. </div><div class="ttdef"><b>Definition:</b> ndarray.h:138</div></div>
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<div class="ttc" id="namespacemxnet_html_ae3356844b657c9ae1b84d3ddd56bccf6"><div class="ttname"><a href="namespacemxnet.html#ae3356844b657c9ae1b84d3ddd56bccf6">mxnet::operator-</a></div><div class="ttdeci">NDArray operator-(const NDArray &lhs, const NDArray &rhs)</div><div class="ttdoc">elementwise subtraction </div></div>
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<div class="ttc" id="namespacemxnet_html_a4dafe3aae43025fd21bc44b98c18ed1e"><div class="ttname"><a href="namespacemxnet.html#a4dafe3aae43025fd21bc44b98c18ed1e">mxnet::operator+</a></div><div class="ttdeci">NDArray operator+(const NDArray &lhs, const NDArray &rhs)</div><div class="ttdoc">elementwise add </div></div>
<div class="ttc" id="namespacemxnet_html_a2846f4556c9ca9bd0f567504ce60f274"><div class="ttname"><a href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">mxnet::SampleUniform</a></div><div class="ttdeci">void SampleUniform(real_t begin, real_t end, NDArray *out)</div><div class="ttdoc">Sample uniform distribution for each elements of out. </div></div>
<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html">mxnet::NDArrayFunctionReg</a></div><div class="ttdoc">Registry entry for NDArrayFunction. </div><div class="ttdef"><b>Definition:</b> ndarray.h:595</div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_a1c2f38927e4bf7a62e23353b0bd3d619"><div class="ttname"><a href="classmxnet_1_1Engine.html#a1c2f38927e4bf7a62e23353b0bd3d619">mxnet::Engine::PushSync</a></div><div class="ttdeci">void PushSync(SyncFn exec_fn, Context exec_ctx, std::vector< VarHandle > const &const_vars, std::vector< VarHandle > const &mutable_vars, FnProperty prop=FnProperty::kNormal, int priority=0, const char *opr_name=nullptr)</div><div class="ttdoc">Pu [...]
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<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_aa57c24a22d49470496b221c72bf7a1a0"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#aa57c24a22d49470496b221c72bf7a1a0">mxnet::NDArrayFunctionReg::set_function</a></div><div class="ttdeci">NDArrayFunctionReg & set_function(void(*fbinary)(const NDArray &lhs, const NDArray &rhs, NDArray *out))</div><div class="ttdoc">set the function body to a binary NDArray function this will also auto set the parame [...]
-<div class="ttc" id="classmxnet_1_1NDArray_html_ad8599450a4ba06b5890c5d06bd024d8e"><div class="ttname"><a href="classmxnet_1_1NDArray.html#ad8599450a4ba06b5890c5d06bd024d8e">mxnet::NDArray::WaitToWrite</a></div><div class="ttdeci">void WaitToWrite() const</div><div class="ttdoc">Block until all the pending read/write operations with respect to current NDArray are finished...</div><div class="ttdef"><b>Definition:</b> ndarray.h:146</div></div>
<div class="ttc" id="structmxnet_1_1Context_html_a4bb231d70623fde216de51589347e513"><div class="ttname"><a href="structmxnet_1_1Context.html#a4bb231d70623fde216de51589347e513">mxnet::Context::CPU</a></div><div class="ttdeci">static Context CPU(int32_t dev_id=0)</div></div>
-<div class="ttc" id="classmxnet_1_1NDArray_html_ade5c291caf0e240892b1bffaf7e5c8b5"><div class="ttname"><a href="classmxnet_1_1NDArray.html#ade5c291caf0e240892b1bffaf7e5c8b5">mxnet::NDArray::data</a></div><div class="ttdeci">const TBlob & data() const</div><div class="ttdef"><b>Definition:</b> ndarray.h:105</div></div>
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<div class="ttc" id="base_8h_html"><div class="ttname"><a href="base_8h.html">base.h</a></div><div class="ttdoc">configuation of mxnet as well as basic data structure. </div></div>
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+<div class="ttc" id="classmxnet_1_1NDArray_html_a1b47078a60bf0369ff0a7df893caf65b"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a1b47078a60bf0369ff0a7df893caf65b">mxnet::NDArray::operator*=</a></div><div class="ttdeci">NDArray & operator*=(const NDArray &src)</div><div class="ttdoc">elementwise multiplication to current ndarray this mutate the current NDArray </div></div>
<div class="ttc" id="namespacemxnet_html_a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51"><div class="ttname"><a href="namespacemxnet.html#a89a5f0f5cfd9e1e94604a7b42dda818aabbc1ba0bb3f8647dcfd8bad69efdbb51">mxnet::kNDArrayArgBeforeScalar</a></div><div class="ttdoc">all the use_vars should go before scalar </div><div class="ttdef"><b>Definition:</b> ndarray.h:581</div></div>
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+<div class="ttc" id="classmxnet_1_1NDArray_html_a3896fcce89afce4c1d2f825f7853ca0c"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a3896fcce89afce4c1d2f825f7853ca0c">mxnet::NDArray::AsArray</a></div><div class="ttdeci">NDArray AsArray(const TShape &shape, int dtype) const </div><div class="ttdoc">Create a NDArray that shares memory with current one The new array must have smaller memory size than...</div><div class="ttdef"><b>Definition:</b> ndarray.h:285</div></div>
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<div class="ttc" id="classmxnet_1_1autograd_1_1AGNodeEntry_html_ada6b942a22f14e3d61fccb645f9e9a27"><div class="ttname"><a href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ada6b942a22f14e3d61fccb645f9e9a27">mxnet::autograd::AGNodeEntry::ag_node</a></div><div class="ttdeci">AGNodePtr ag_node</div><div class="ttdef"><b>Definition:</b> ndarray.h:40</div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a35c2a2c797f601fbc5d560481b11720a"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a35c2a2c797f601fbc5d560481b11720a">mxnet::NDArray::CheckAndAlloc</a></div><div class="ttdeci">void CheckAndAlloc() const </div><div class="ttdoc">Allocate the space if it is delayed allocated. This is an internal function used by system that norma...</div><div class="ttdef"><b>Definition:</b> ndarray.h:318</div></div>
<div class="ttc" id="classmxnet_1_1autograd_1_1AGNodeEntry_html_ad0fccdc6fc5d492306c0eaf6df11880a"><div class="ttname"><a href="classmxnet_1_1autograd_1_1AGNodeEntry.html#ad0fccdc6fc5d492306c0eaf6df11880a">mxnet::autograd::AGNodeEntry::index</a></div><div class="ttdeci">uint32_t index</div><div class="ttdef"><b>Definition:</b> ndarray.h:41</div></div>
<div class="ttc" id="namespacemxnet_html_abadedc1b42ee3e5b8852e63783b982ef"><div class="ttname"><a href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">mxnet::index_t</a></div><div class="ttdeci">mshadow::index_t index_t</div><div class="ttdoc">index type usually use unsigned </div><div class="ttdef"><b>Definition:</b> base.h:107</div></div>
-<div class="ttc" id="structmxnet_1_1Storage_1_1Handle_html_ab93b9db0c83e4ef3803fb587377a74a0"><div class="ttname"><a href="structmxnet_1_1Storage_1_1Handle.html#ab93b9db0c83e4ef3803fb587377a74a0">mxnet::Storage::Handle::size</a></div><div class="ttdeci">size_t size</div><div class="ttdoc">Size of the storage. </div><div class="ttdef"><b>Definition:</b> storage.h:30</div></div>
+<div class="ttc" id="classmxnet_1_1autograd_1_1AGNodeEntry_html_adf925c9b87cbd5969e142123da6c7e30"><div class="ttname"><a href="classmxnet_1_1autograd_1_1AGNodeEntry.html#adf925c9b87cbd5969e142123da6c7e30">mxnet::autograd::AGNodeEntry::is_none</a></div><div class="ttdeci">bool is_none() const </div></div>
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<div class="ttc" id="namespacemxnet_html_ad8e0dad88ce27133fd24811763b7afdb"><div class="ttname"><a href="namespacemxnet.html#ad8e0dad88ce27133fd24811763b7afdb">mxnet::SampleGenNegBinomial</a></div><div class="ttdeci">void SampleGenNegBinomial(real_t mu, real_t alpha, NDArray *out)</div><div class="ttdoc">Sample generalized negative binomial distribution for each elements of out. </div></div>
<div class="ttc" id="structmxnet_1_1Context_html"><div class="ttname"><a href="structmxnet_1_1Context.html">mxnet::Context</a></div><div class="ttdoc">Context information about the execution environment. </div><div class="ttdef"><b>Definition:</b> base.h:116</div></div>
<div class="ttc" id="namespacemxnet_html_aed55e8197182b7c66126902b2a43739a"><div class="ttname"><a href="namespacemxnet.html#aed55e8197182b7c66126902b2a43739a">mxnet::SamplePoisson</a></div><div class="ttdeci">void SamplePoisson(real_t lambda, NDArray *out)</div><div class="ttdoc">Sample Poisson distribution for each elements of out. </div></div>
<div class="ttc" id="classmxnet_1_1NDArray_html"><div class="ttname"><a href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></div><div class="ttdoc">ndarray interface </div><div class="ttdef"><b>Definition:</b> ndarray.h:59</div></div>
<div class="ttc" id="namespacemxnet_html_aeee0e64a5e59bb0c222cbf44f4266f7a"><div class="ttname"><a href="namespacemxnet.html#aeee0e64a5e59bb0c222cbf44f4266f7a">mxnet::CopyFromTo</a></div><div class="ttdeci">void CopyFromTo(const NDArray &from, NDArray *to, int priority=0)</div><div class="ttdoc">issue an copy operation from one NDArray to another the two ndarray can sit on different devices this...</div></div>
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+<div class="ttc" id="classmxnet_1_1TBlob_html_a613ad1f5ba31efcbecbd041e23cc61b2"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">mxnet::TBlob::dev_mask</a></div><div class="ttdeci">int dev_mask() const </div><div class="ttdoc">device mask of the corresponding device </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:209</div></div>
<div class="ttc" id="namespacemxnet_html_a15dfd6f3fa03dc2cf39dbab1225e1a51"><div class="ttname"><a href="namespacemxnet.html#a15dfd6f3fa03dc2cf39dbab1225e1a51">mxnet::ElementwiseSum</a></div><div class="ttdeci">void ElementwiseSum(const std::vector< NDArray > &source, NDArray *out, int priority=0)</div><div class="ttdoc">Perform elementwise sum over each data from source, store result into out. </div></div>
<div class="ttc" id="namespacemxnet_html_ab0d55b0edf98cf8f3caeb050662ae9ec"><div class="ttname"><a href="namespacemxnet.html#ab0d55b0edf98cf8f3caeb050662ae9ec">mxnet::NDArrayAPIFunction</a></div><div class="ttdeci">std::function< void(NDArray **used_vars, real_t *scalars, NDArray **mutate_vars, int num_params, char **param_keys, char **param_vals)> NDArrayAPIFunction</div><div class="ttdoc">definition of NDArray function </div><div class="ttdef"><b>Definition:</b> ndarray.h:577</di [...]
<div class="ttc" id="namespacemxnet_html_ae82f70918b2d946f7ad66a158b1733dc"><div class="ttname"><a href="namespacemxnet.html#ae82f70918b2d946f7ad66a158b1733dc">mxnet::SampleNegBinomial</a></div><div class="ttdeci">void SampleNegBinomial(int32_t k, real_t p, NDArray *out)</div><div class="ttdoc">Sample negative binomial distribution for each elements of out. </div></div>
+<div class="ttc" id="classmxnet_1_1NDArray_html_a32ed2be4ecf94c58b19bd43b48f2b1fa"><div class="ttname"><a href="classmxnet_1_1NDArray.html#a32ed2be4ecf94c58b19bd43b48f2b1fa">mxnet::NDArray::set_fresh_out_grad</a></div><div class="ttdeci">void set_fresh_out_grad(bool state) const </div></div>
<div class="ttc" id="structmxnet_1_1NDArrayFunctionReg_html_a220e1866bea35e103adbe23ff92855d9"><div class="ttname"><a href="structmxnet_1_1NDArrayFunctionReg.html#a220e1866bea35e103adbe23ff92855d9">mxnet::NDArrayFunctionReg::set_function</a></div><div class="ttdeci">NDArrayFunctionReg & set_function(void(*fgeneric)(NDArray **used_vars, real_t *s, NDArray **mutate_vars, const std::map< std::string, std::string > &param))</div><div class="ttdoc">set the function body to a una [...]
<div class="ttc" id="classmxnet_1_1TBlob_html"><div class="ttname"><a href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></div><div class="ttdoc">tensor blob class that can be used to hold tensor of any dimension, any device and any data type...</div><div class="ttdef"><b>Definition:</b> tensor_blob.h:40</div></div>
<div class="ttc" id="namespacemxnet_html_a050beaa505f11e0b844deb91efe0cac2"><div class="ttname"><a href="namespacemxnet.html#a050beaa505f11e0b844deb91efe0cac2">mxnet::SampleGamma</a></div><div class="ttdeci">void SampleGamma(real_t alpha, real_t beta, NDArray *out)</div><div class="ttdoc">Sample gamma distribution for each elements of out. </div></div>
@@ -158,9 +567,9 @@ $(function() {
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+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/object__pool_8h.html b/versions/master/doxygen/object__pool_8h.html
index ac11fd8..92ad07c 100644
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@@ -3,15 +3,16 @@
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: src/common/object_pool.h File Reference</title>
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<script type="text/javascript" src="jquery.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
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+<!-- Generated by Doxygen 1.8.6 -->
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var searchBox = new SearchBox("searchBox", "search",false,'Search');
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
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+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -69,15 +91,17 @@ $(function() {
<div class="title">object_pool.h File Reference</div> </div>
</div><!--header-->
<div class="contents">
-<div class="textblock"><code>#include <dmlc/logging.h></code><br />
-<code>#include <cstdlib></code><br />
-<code>#include <mutex></code><br />
-<code>#include <utility></code><br />
-<code>#include <vector></code><br />
+<div class="textblock"><code>#include <dmlc/logging.h></code><br/>
+<code>#include <cstdlib></code><br/>
+<code>#include <mutex></code><br/>
+<code>#include <utility></code><br/>
+<code>#include <vector></code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for object_pool.h:</div>
<div class="dyncontent">
<div class="center"><img src="object__pool_8h__incl.png" border="0" usemap="#src_2common_2object__pool_8h" alt=""/></div>
+<map name="src_2common_2object__pool_8h" id="src_2common_2object__pool_8h">
+</map>
</div>
</div>
<p><a href="object__pool_8h_source.html">Go to the source code of this file.</a></p>
@@ -85,26 +109,26 @@ Include dependency graph for object_pool.h:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1common_1_1ObjectPool.html">mxnet::common::ObjectPool< T ></a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Object pool for fast allocation and deallocation. <a href="classmxnet_1_1common_1_1ObjectPool.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Object pool for fast allocation and deallocation. <a href="classmxnet_1_1common_1_1ObjectPool.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html">mxnet::common::ObjectPoolAllocatable< T ></a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper trait class for easy allocation and deallocation. <a href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper trait class for easy allocation and deallocation. <a href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#details">More...</a><br/></td></tr>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: src/common/object_pool.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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@@ -20,7 +21,7 @@
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<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
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<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
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@@ -66,22 +88,160 @@ $(function() {
<div class="title">object_pool.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="object__pool_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="preprocessor">#ifndef MXNET_COMMON_OBJECT_POOL_H_</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#define MXNET_COMMON_OBJECT_POOL_H_</span></div><div cla [...]
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
+<a href="object__pool_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="preprocessor">#ifndef MXNET_COMMON_OBJECT_POOL_H_</span></div>
+<div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_COMMON_OBJECT_POOL_H_</span></div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor"></span><span class="preprocessor">#include <dmlc/logging.h></span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <cstdlib></span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <mutex></span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <utility></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">namespace </span>common {</div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00018"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1ObjectPool.html"> 18</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool</a> {</div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#aa5ff3f223210e5f3d0881c272462aedb">~ObjectPool</a>();</div>
+<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">template</span> <<span class="keyword">typename</span>... Args></div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  T* <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#a5d711031f61dbd06d8c6975fd49e5632">New</a>(Args&&... args);</div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#a7dcdcbe9b6764cedc28a1de2fbc8dd6f">Delete</a>(T* ptr);</div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool</a>* <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#a82b4d0c3577560a4f6f62f482b125471">Get</a>();</div>
+<div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div>
+<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">static</span> std::shared_ptr<ObjectPool> <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#ac6e5e05a710c3f094efb4ba13d36ab89">_GetSharedRef</a>();</div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">struct </span>LinkedList {</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="preprocessor">#if defined(_MSC_VER)</span></div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="preprocessor"></span> T t;</div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  LinkedList* next{<span class="keyword">nullptr</span>};</div>
+<div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="preprocessor">#else</span></div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="preprocessor"></span> <span class="keyword">union </span>{</div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  T t;</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  LinkedList* next{<span class="keyword">nullptr</span>};</div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  };</div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="preprocessor"></span> };</div>
+<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  constexpr <span class="keyword">static</span> std::size_t kPageSize = 1 << 12;</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  std::mutex m_;</div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  LinkedList* head_{<span class="keyword">nullptr</span>};</div>
+<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  std::vector<void*> allocated_;</div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool</a>();</div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordtype">void</span> AllocateChunk();</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  DISALLOW_COPY_AND_ASSIGN(<a class="code" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool</a>);</div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span> }; <span class="comment">// class ObjectPool</span></div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00098"></a><span class="lineno"><a class="line" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html"> 98</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html">ObjectPoolAllocatable</a> {</div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">template</span> <<span class="keyword">typename</span>... Args></div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keyword">static</span> T* <a class="code" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#aaf92ecb0cb6542b5c3f70ec31a31db4e">New</a>(Args&&... args);</div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#a68ed487cb6de6194a4798fdbbfd04597">Delete</a>(T* ptr);</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span> }; <span class="comment">// struct ObjectPoolAllocatable</span></div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1ObjectPool.html#aa5ff3f223210e5f3d0881c272462aedb"> 115</a></span> <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#aa5ff3f223210e5f3d0881c272462aedb">ObjectPool<T>::~ObjectPool</a>() {</div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="comment">// TODO(hotpxl): mind destruction order</span></div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// for (auto i : allocated_) {</span></div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">// free(i);</span></div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// }</span></div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span> }</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="keyword">template</span> <<span class="keyword">typename</span>... Args></div>
+<div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1ObjectPool.html#a5d711031f61dbd06d8c6975fd49e5632"> 124</a></span> T* <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#a5d711031f61dbd06d8c6975fd49e5632">ObjectPool<T>::New</a>(Args&&... args) {</div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  LinkedList* ret;</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  {</div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  std::lock_guard<std::mutex> lock{m_};</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">if</span> (head_->next == <span class="keyword">nullptr</span>) {</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  AllocateChunk();</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  }</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  ret = head_;</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  head_ = head_->next;</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">return</span> <span class="keyword">new</span> (<span class="keyword">static_cast<</span><span class="keywordtype">void</span>*<span class="keyword">></span>(ret)) T(std::forward<Args>(args)...);</div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span> }</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00138"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1ObjectPool.html#a7dcdcbe9b6764cedc28a1de2fbc8dd6f"> 138</a></span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#a7dcdcbe9b6764cedc28a1de2fbc8dd6f">ObjectPool<T>::Delete</a>(T* ptr) {</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  ptr->~T();</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">auto</span> linked_list_ptr = <span class="keyword">reinterpret_cast<</span>LinkedList*<span class="keyword">></span>(ptr);</div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  {</div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  std::lock_guard<std::mutex> lock{m_};</div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  linked_list_ptr->next = head_;</div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  head_ = linked_list_ptr;</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  }</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span> }</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00149"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1ObjectPool.html#a82b4d0c3577560a4f6f62f482b125471"> 149</a></span> <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool<T></a>* <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#a82b4d0c3577560a4f6f62f482b125471">ObjectPool<T>::Get</a>() {</div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">return</span> _GetSharedRef().get();</div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span> }</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00154"></a><span class="lineno"><a class="line" href="classmxnet_1_1common_1_1ObjectPool.html#ac6e5e05a710c3f094efb4ba13d36ab89"> 154</a></span> std::shared_ptr<ObjectPool<T> > <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html#ac6e5e05a710c3f094efb4ba13d36ab89">ObjectPool<T>::_GetSharedRef</a>() {</div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">static</span> std::shared_ptr<ObjectPool<T> > inst_ptr(<span class="keyword">new</span> <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool<T></a>());</div>
+<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">return</span> inst_ptr;</div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span> }</div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <a class="code" href="classmxnet_1_1common_1_1ObjectPool.html">ObjectPool<T>::ObjectPool</a>() {</div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  AllocateChunk();</div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span> }</div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span> <span class="keywordtype">void</span> ObjectPool<T>::AllocateChunk() {</div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  static_assert(<span class="keyword">sizeof</span>(LinkedList) <= kPageSize, <span class="stringliteral">"Object too big."</span>);</div>
+<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  static_assert(<span class="keyword">sizeof</span>(LinkedList) % <span class="keyword">alignof</span>(LinkedList) == 0, <span class="stringliteral">"ObjectPooll Invariant"</span>);</div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  static_assert(<span class="keyword">alignof</span>(LinkedList) % <span class="keyword">alignof</span>(T) == 0, <span class="stringliteral">"ObjectPooll Invariant"</span>);</div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  static_assert(kPageSize % <span class="keyword">alignof</span>(LinkedList) == 0, <span class="stringliteral">"ObjectPooll Invariant"</span>);</div>
+<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordtype">void</span>* new_chunk_ptr;</div>
+<div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="preprocessor">#ifdef _MSC_VER</span></div>
+<div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="preprocessor"></span> new_chunk_ptr = _aligned_malloc(kPageSize, kPageSize);</div>
+<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  CHECK(new_chunk_ptr != NULL) << <span class="stringliteral">"Allocation failed"</span>;</div>
+<div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="preprocessor">#else</span></div>
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+<div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="keyword">template</span> <<span class="keyword">typename</span>... Args></div>
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+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div>
+<div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div>
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+<div class="line"><a name="l00197"></a><span class="lineno"> 197</span> }</div>
+<div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div>
+<div class="line"><a name="l00199"></a><span class="lineno"> 199</span> } <span class="comment">// namespace common</span></div>
+<div class="line"><a name="l00200"></a><span class="lineno"> 200</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="preprocessor">#endif // MXNET_COMMON_OBJECT_POOL_H_</span></div>
+<div class="ttc" id="structmxnet_1_1common_1_1ObjectPoolAllocatable_html_a68ed487cb6de6194a4798fdbbfd04597"><div class="ttname"><a href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#a68ed487cb6de6194a4798fdbbfd04597">mxnet::common::ObjectPoolAllocatable::Delete</a></div><div class="ttdeci">static void Delete(T *ptr)</div><div class="ttdoc">Delete an existing object. </div><div class="ttdef"><b>Definition:</b> object_pool.h:195</div></div>
+<div class="ttc" id="structmxnet_1_1common_1_1ObjectPoolAllocatable_html_aaf92ecb0cb6542b5c3f70ec31a31db4e"><div class="ttname"><a href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#aaf92ecb0cb6542b5c3f70ec31a31db4e">mxnet::common::ObjectPoolAllocatable::New</a></div><div class="ttdeci">static T * New(Args &&...args)</div><div class="ttdoc">Create new object. </div><div class="ttdef"><b>Definition:</b> object_pool.h:190</div></div>
+<div class="ttc" id="classmxnet_1_1common_1_1ObjectPool_html_a5d711031f61dbd06d8c6975fd49e5632"><div class="ttname"><a href="classmxnet_1_1common_1_1ObjectPool.html#a5d711031f61dbd06d8c6975fd49e5632">mxnet::common::ObjectPool::New</a></div><div class="ttdeci">T * New(Args &&...args)</div><div class="ttdoc">Create new object. </div><div class="ttdef"><b>Definition:</b> object_pool.h:124</div></div>
<div class="ttc" id="classmxnet_1_1common_1_1ObjectPool_html_a82b4d0c3577560a4f6f62f482b125471"><div class="ttname"><a href="classmxnet_1_1common_1_1ObjectPool.html#a82b4d0c3577560a4f6f62f482b125471">mxnet::common::ObjectPool::Get</a></div><div class="ttdeci">static ObjectPool * Get()</div><div class="ttdoc">Get singleton instance of pool. </div><div class="ttdef"><b>Definition:</b> object_pool.h:149</div></div>
<div class="ttc" id="classmxnet_1_1common_1_1ObjectPool_html_ac6e5e05a710c3f094efb4ba13d36ab89"><div class="ttname"><a href="classmxnet_1_1common_1_1ObjectPool.html#ac6e5e05a710c3f094efb4ba13d36ab89">mxnet::common::ObjectPool::_GetSharedRef</a></div><div class="ttdeci">static std::shared_ptr< ObjectPool > _GetSharedRef()</div><div class="ttdoc">Get a shared ptr of the singleton instance of pool. </div><div class="ttdef"><b>Definition:</b> object_pool.h:154</div></div>
<div class="ttc" id="classmxnet_1_1common_1_1ObjectPool_html_a7dcdcbe9b6764cedc28a1de2fbc8dd6f"><div class="ttname"><a href="classmxnet_1_1common_1_1ObjectPool.html#a7dcdcbe9b6764cedc28a1de2fbc8dd6f">mxnet::common::ObjectPool::Delete</a></div><div class="ttdeci">void Delete(T *ptr)</div><div class="ttdoc">Delete an existing object. </div><div class="ttdef"><b>Definition:</b> object_pool.h:138</div></div>
-<div class="ttc" id="classmxnet_1_1common_1_1ObjectPool_html_aaadcbc99eaec88191b201bad9da441c6"><div class="ttname"><a href="classmxnet_1_1common_1_1ObjectPool.html#aaadcbc99eaec88191b201bad9da441c6">mxnet::common::ObjectPool::New</a></div><div class="ttdeci">T * New(Args &&... args)</div><div class="ttdoc">Create new object. </div><div class="ttdef"><b>Definition:</b> object_pool.h:124</div></div>
-<div class="ttc" id="structmxnet_1_1common_1_1ObjectPoolAllocatable_html_aa08281412ac1a31d485884fa5a7544ce"><div class="ttname"><a href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#aa08281412ac1a31d485884fa5a7544ce">mxnet::common::ObjectPoolAllocatable::New</a></div><div class="ttdeci">static T * New(Args &&... args)</div><div class="ttdoc">Create new object. </div><div class="ttdef"><b>Definition:</b> object_pool.h:190</div></div>
<div class="ttc" id="structmxnet_1_1common_1_1ObjectPoolAllocatable_html"><div class="ttname"><a href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html">mxnet::common::ObjectPoolAllocatable</a></div><div class="ttdoc">Helper trait class for easy allocation and deallocation. </div><div class="ttdef"><b>Definition:</b> object_pool.h:98</div></div>
<div class="ttc" id="classmxnet_1_1common_1_1ObjectPool_html_aa5ff3f223210e5f3d0881c272462aedb"><div class="ttname"><a href="classmxnet_1_1common_1_1ObjectPool.html#aa5ff3f223210e5f3d0881c272462aedb">mxnet::common::ObjectPool::~ObjectPool</a></div><div class="ttdeci">~ObjectPool()</div><div class="ttdoc">Destructor. </div><div class="ttdef"><b>Definition:</b> object_pool.h:115</div></div>
<div class="ttc" id="classmxnet_1_1common_1_1ObjectPool_html"><div class="ttname"><a href="classmxnet_1_1common_1_1ObjectPool.html">mxnet::common::ObjectPool</a></div><div class="ttdoc">Object pool for fast allocation and deallocation. </div><div class="ttdef"><b>Definition:</b> object_pool.h:18</div></div>
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+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/op__attr__types_8h.html b/versions/master/doxygen/op__attr__types_8h.html
index ce11600..eab3e8c 100644
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@@ -3,15 +3,16 @@
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<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/op_attr_types.h File Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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@@ -29,25 +30,46 @@
</table>
</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
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+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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@@ -74,33 +96,26 @@ $(function() {
<p>Additional operator attributes beside the ones provided by NNVM.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <mshadow/tensor.h></code><br />
-<code>#include <nnvm/op_attr_types.h></code><br />
-<code>#include <vector></code><br />
-<code>#include <functional></code><br />
-<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br />
-<code>#include "<a class="el" href="ndarray_8h_source.html">./ndarray.h</a>"</code><br />
-<code>#include "<a class="el" href="engine_8h_source.html">./engine.h</a>"</code><br />
+<div class="textblock"><code>#include <mshadow/tensor.h></code><br/>
+<code>#include <nnvm/op_attr_types.h></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <functional></code><br/>
+<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br/>
+<code>#include "<a class="el" href="ndarray_8h_source.html">./ndarray.h</a>"</code><br/>
+<code>#include "<a class="el" href="engine_8h_source.html">./engine.h</a>"</code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for op_attr_types.h:</div>
<div class="dyncontent">
<div class="center"><img src="op__attr__types_8h__incl.png" border="0" usemap="#include_2mxnet_2op__attr__types_8h" alt=""/></div>
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This graph shows which files directly or indirectly include this file:</div>
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<p><a href="op__attr__types_8h_source.html">Go to the source code of this file.</a></p>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OpContext.html">mxnet::OpContext</a></td></tr>
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">All the possible information needed by <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd" title="perform a forward operation of Operator, save the output to TBlob. ">Operator.Forward</a> and Backward This is the superset of <a class="el" href="structmxnet_1_1RunContext.html" title="execution time context. The information needed in runtime for actual execution. ">RunContext</a>. We [...]
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OpStatePtr.html">mxnet::OpStatePtr</a></td></tr>
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Namespaces</h2></td></tr>
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-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
+<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
+<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
<tr class="memitem:acb83431b596f805fabcc4ba400c62e85"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#acb83431b596f805fabcc4ba400c62e85">mxnet::FCreateOpState</a> = std::function< OpStatePtr(const NodeAttrs &attrs, Context ctx, const std::vector< TShape > &in_shape, const std::vector< int > &in_type)></td></tr>
-<tr class="memdesc:acb83431b596f805fabcc4ba400c62e85"><td class="mdescLeft"> </td><td class="mdescRight">Create a Layer style, forward/backward operator. This is easy to write code that contains state. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator [...]
+<tr class="memdesc:acb83431b596f805fabcc4ba400c62e85"><td class="mdescLeft"> </td><td class="mdescRight">Create a Layer style, forward/backward operator. This is easy to write code that contains state. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator [...]
<tr class="separator:acb83431b596f805fabcc4ba400c62e85"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a666f1055e5401ecec0278e8ba5790732"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a666f1055e5401ecec0278e8ba5790732">mxnet::FExecType</a> = std::function< ExecType(const NodeAttrs &attrs)></td></tr>
-<tr class="memdesc:a666f1055e5401ecec0278e8ba5790732"><td class="mdescLeft"> </td><td class="mdescRight">Execution mode of this operator. <a href="namespacemxnet.html#a666f1055e5401ecec0278e8ba5790732">More...</a><br /></td></tr>
+<tr class="memdesc:a666f1055e5401ecec0278e8ba5790732"><td class="mdescLeft"> </td><td class="mdescRight">Execution mode of this operator. <a href="#a666f1055e5401ecec0278e8ba5790732">More...</a><br/></td></tr>
<tr class="separator:a666f1055e5401ecec0278e8ba5790732"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9097a269dd76d833c014149ff3eaab9b"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a9097a269dd76d833c014149ff3eaab9b">mxnet::FStatefulCompute</a> = std::function< void(const OpStatePtr &state, const OpContext &ctx, const std::vector< TBlob > &inputs, const std::vector< OpReqType > &req, const std::vector< TBlob > &outputs)></td></tr>
-<tr class="memdesc:a9097a269dd76d833c014149ff3eaab9b"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for stateful operator. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is [...]
+<tr class="memdesc:a9097a269dd76d833c014149ff3eaab9b"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for stateful operator. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is [...]
<tr class="separator:a9097a269dd76d833c014149ff3eaab9b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab12f1853179b1456152a563edff844a9"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ab12f1853179b1456152a563edff844a9">mxnet::FStatefulComputeEx</a> = std::function< void(const OpStatePtr &state, const OpContext &ctx, const std::vector< NDArray > &inputs, const std::vector< OpReqType > &req, const std::vector< NDArray > &outputs)&g [...]
-<tr class="memdesc:ab12f1853179b1456152a563edff844a9"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for stateful operator using <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> interface. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class=" [...]
+<tr class="memdesc:ab12f1853179b1456152a563edff844a9"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for stateful operator using <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> interface. <a class="el" href="classmxnet_1_1OpStatePtr.html" title="Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...">OpStatePtr</a> is a pointer type, it's content is mutable even if <a class=" [...]
<tr class="separator:ab12f1853179b1456152a563edff844a9"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:afaa687cbca9e992614be285711c1d72d"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#afaa687cbca9e992614be285711c1d72d">mxnet::FResourceRequest</a> = std::function< std::vector< ResourceRequest >(const NodeAttrs &n)></td></tr>
-<tr class="memdesc:afaa687cbca9e992614be285711c1d72d"><td class="mdescLeft"> </td><td class="mdescRight">The resource request from the operator. <a href="namespacemxnet.html#afaa687cbca9e992614be285711c1d72d">More...</a><br /></td></tr>
+<tr class="memdesc:afaa687cbca9e992614be285711c1d72d"><td class="mdescLeft"> </td><td class="mdescRight">The resource request from the operator. <a href="#afaa687cbca9e992614be285711c1d72d">More...</a><br/></td></tr>
<tr class="separator:afaa687cbca9e992614be285711c1d72d"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0906e49a921265634672f555451075ed"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a0906e49a921265634672f555451075ed">mxnet::FNDArrayFunction</a> = std::function< void(const nnvm::NodeAttrs &attrs, const std::vector< NDArray > &inputs, std::vector< NDArray > *outputs)></td></tr>
-<tr class="memdesc:a0906e49a921265634672f555451075ed"><td class="mdescLeft"> </td><td class="mdescRight">Register an operator called as a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function. <a href="namespacemxnet.html#a0906e49a921265634672f555451075ed">More...</a><br /></td></tr>
+<tr class="memdesc:a0906e49a921265634672f555451075ed"><td class="mdescLeft"> </td><td class="mdescRight">Register an operator called as a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function. <a href="#a0906e49a921265634672f555451075ed">More...</a><br/></td></tr>
<tr class="separator:a0906e49a921265634672f555451075ed"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad77e2048b7e8db91a12e05b477aac13e"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ad77e2048b7e8db91a12e05b477aac13e">mxnet::FCompute</a> = std::function< void(const nnvm::NodeAttrs &attrs, const OpContext &ctx, const std::vector< TBlob > &inputs, const std::vector< OpReqType > &req, const std::vector< TBlob > &outputs)></td></tr>
-<tr class="memdesc:ad77e2048b7e8db91a12e05b477aac13e"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for simple stateless forward only operator. <a href="namespacemxnet.html#ad77e2048b7e8db91a12e05b477aac13e">More...</a><br /></td></tr>
+<tr class="memdesc:ad77e2048b7e8db91a12e05b477aac13e"><td class="mdescLeft"> </td><td class="mdescRight">Resiger a compute function for simple stateless forward only operator. <a href="#ad77e2048b7e8db91a12e05b477aac13e">More...</a><br/></td></tr>
<tr class="separator:ad77e2048b7e8db91a12e05b477aac13e"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="enum-members"></a>
@@ -150,15 +165,15 @@ Enumerations</h2></td></tr>
<a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a">mxnet::kWriteTo</a>,
<a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51">mxnet::kWriteInplace</a>,
<a class="el" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383">mxnet::kAddTo</a>
- }<tr class="memdesc:a7cd7094ef222e9ae1f520c3b6a214398"><td class="mdescLeft"> </td><td class="mdescRight">operation request type to Forward and Backward <a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:a7cd7094ef222e9ae1f520c3b6a214398"><td class="mdescLeft"> </td><td class="mdescRight">operation request type to Forward and Backward <a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">More...</a><br/></td></tr>
<tr class="separator:a7cd7094ef222e9ae1f520c3b6a214398"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad7801fb2dd68294d4c7a9e289bc071ad"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">mxnet::ExecType</a> { <a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c">mxnet::ExecType::kSync</a>,
<a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada9f2b960005d2a3a5f35ac32809d84db7">mxnet::ExecType::kAsync</a>,
<a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adafcf37e733f29d6aa14fb34fa737a809a">mxnet::ExecType::kLocal</a>,
<a class="el" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada3864860a073c64d74d3fc8e765fa6cad">mxnet::ExecType::kCrossDeviceCopy</a>
- }<tr class="memdesc:ad7801fb2dd68294d4c7a9e289bc071ad"><td class="mdescLeft"> </td><td class="mdescRight">the execution type of the operator <a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:ad7801fb2dd68294d4c7a9e289bc071ad"><td class="mdescLeft"> </td><td class="mdescRight">the execution type of the operator <a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">More...</a><br/></td></tr>
<tr class="separator:ad7801fb2dd68294d4c7a9e289bc071ad"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
@@ -167,9 +182,9 @@ Enumerations</h2></td></tr>
</div></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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--- a/versions/master/doxygen/op__attr__types_8h_source.html
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@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/op_attr_types.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
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<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
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+ </li>
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -66,23 +88,143 @@ $(function() {
<div class="title">op_attr_types.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="op__attr__types_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_OP_ATTR_TYPES_H_</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#define MXNET_OP_ATTR_TYPES_H_</span></div><div class="lin [...]
-<div class="ttc" id="classmxnet_1_1OpStatePtr_html_a6d720b8843340fa4d9a7ff7937f2e141"><div class="ttname"><a href="classmxnet_1_1OpStatePtr.html#a6d720b8843340fa4d9a7ff7937f2e141">mxnet::OpStatePtr::get_state</a></div><div class="ttdeci">T & get_state() const</div><div class="ttdef"><b>Definition:</b> op_attr_types.h:111</div></div>
+<a href="op__attr__types_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_OP_ATTR_TYPES_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_OP_ATTR_TYPES_H_</span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <mshadow/tensor.h></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <nnvm/op_attr_types.h></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <functional></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include "<a class="code" href="ndarray_8h.html">./ndarray.h</a>"</span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "<a class="code" href="engine_8h.html">./engine.h</a>"</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">using</span> nnvm::NodeAttrs;</div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div>
+<div class="line"><a name="l00026"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398"> 26</a></span> <span class="keyword">enum</span> <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> {</div>
+<div class="line"><a name="l00028"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26"> 28</a></span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26">kNullOp</a>,</div>
+<div class="line"><a name="l00030"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a"> 30</a></span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a">kWriteTo</a>,</div>
+<div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51"> 36</a></span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51">kWriteInplace</a>,</div>
+<div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383"> 38</a></span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383">kAddTo</a></div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> };</div>
+<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div>
+<div class="line"><a name="l00047"></a><span class="lineno"><a class="line" href="structmxnet_1_1OpContext.html"> 47</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1OpContext.html">OpContext</a> {</div>
+<div class="line"><a name="l00049"></a><span class="lineno"><a class="line" href="structmxnet_1_1OpContext.html#abd7405c065167e4e15af032b1e0a5c42"> 49</a></span>  <span class="keywordtype">int</span> <a class="code" href="structmxnet_1_1OpContext.html#abd7405c065167e4e15af032b1e0a5c42">is_train</a>;</div>
+<div class="line"><a name="l00051"></a><span class="lineno"><a class="line" href="structmxnet_1_1OpContext.html#a869d4c68c0028a175406f11332baf7cf"> 51</a></span>  <a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> <a class="code" href="structmxnet_1_1OpContext.html#a869d4c68c0028a175406f11332baf7cf">run_ctx</a>;</div>
+<div class="line"><a name="l00053"></a><span class="lineno"><a class="line" href="structmxnet_1_1OpContext.html#a40ce0e3d4ac21412cfafafc5b1fd32ef"> 53</a></span>  <a class="code" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">engine::CallbackOnComplete</a> <a class="code" href="structmxnet_1_1OpContext.html#a40ce0e3d4ac21412cfafafc5b1fd32ef">async_on_complete</a>;</div>
+<div class="line"><a name="l00055"></a><span class="lineno"><a class="line" href="structmxnet_1_1OpContext.html#a833e95f95cf5e3af9c193b74b500fc5b"> 55</a></span>  std::vector<Resource> <a class="code" href="structmxnet_1_1OpContext.html#a833e95f95cf5e3af9c193b74b500fc5b">requested</a>;</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">template</span><<span class="keyword">typename</span> xpu></div>
+<div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="structmxnet_1_1OpContext.html#aa700c5c28e024b42d41e15f46615f1b8"> 62</a></span>  <span class="keyword">inline</span> mshadow::Stream<xpu>* <a class="code" href="structmxnet_1_1OpContext.html#aa700c5c28e024b42d41e15f46615f1b8">get_stream</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">return</span> <a class="code" href="structmxnet_1_1OpContext.html#a869d4c68c0028a175406f11332baf7cf">run_ctx</a>.<a class="code" href="structmxnet_1_1RunContext.html#a8bcd2fc6392f95a1fdc3d98f74dbfcb9">get_stream</a><xpu>();</div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  }</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> };</div>
+<div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div>
+<div class="line"><a name="l00068"></a><span class="lineno"><a class="line" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad"> 68</a></span> <span class="keyword">enum class</span> <a class="code" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">ExecType</a> {</div>
+<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c">kSync</a>,</div>
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <a class="code" href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7">kAsync</a>,</div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adafcf37e733f29d6aa14fb34fa737a809a">kLocal</a>,</div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada3864860a073c64d74d3fc8e765fa6cad">kCrossDeviceCopy</a></div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span> };</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span> </div>
+<div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="classmxnet_1_1OpStatePtr.html"> 91</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a> {</div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="comment">/* \brief Create a OpStatePtr with state of type T.</span></div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="comment"> * \param args Arguments passed to T's constructor.</span></div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="comment"> */</span></div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keyword">template</span><<span class="keyword">typename</span> T, <span class="keyword">typename</span>... Args></div>
+<div class="line"><a name="l00097"></a><span class="lineno"><a class="line" href="classmxnet_1_1OpStatePtr.html#ae0f821c7efce61a11730ef8b93070e3e"> 97</a></span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a> <a class="code" href="classmxnet_1_1OpStatePtr.html#ae0f821c7efce61a11730ef8b93070e3e">Create</a>(Args&&... args) {</div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <a class="code" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a> ret;</div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  ret.ptr_ = std::make_shared<OpState>();</div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  ret.ptr_->var_ = <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#a6e141b188f018d5d933ab99868631d5e">NewVariable</a>();</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  ret.ptr_->state_.construct<T>(std::forward<Args>(args)...);</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">return</span> ret;</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  }</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="comment">/* \brief Get engine variable associated with this state */</span></div>
+<div class="line"><a name="l00106"></a><span class="lineno"><a class="line" href="classmxnet_1_1OpStatePtr.html#a77a1636269466569c0b6e32941ed7264"> 106</a></span>  <a class="code" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">engine::VarHandle</a> <a class="code" href="classmxnet_1_1OpStatePtr.html#a77a1636269466569c0b6e32941ed7264">get_var</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">return</span> ptr_->var_;</div>
+<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="comment">/* \brief Get state of type T */</span></div>
+<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">template</span><<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00111"></a><span class="lineno"><a class="line" href="classmxnet_1_1OpStatePtr.html#aa2ea1d7ae06008faf44a92c009b2b26e"> 111</a></span>  T& <a class="code" href="classmxnet_1_1OpStatePtr.html#aa2ea1d7ae06008faf44a92c009b2b26e">get_state</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">return</span> dmlc::get<T>(ptr_->state_);</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">/* \brief clear state */</span></div>
+<div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="classmxnet_1_1OpStatePtr.html#ae43213b3a65073a87bf489350a58d78e"> 115</a></span>  <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1OpStatePtr.html#ae43213b3a65073a87bf489350a58d78e">reset</a>() {</div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  ptr_.reset();</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">/* \brief Whether state is empty */</span></div>
+<div class="line"><a name="l00119"></a><span class="lineno"><a class="line" href="classmxnet_1_1OpStatePtr.html#a6f407d1c8d461dc474cd1c50aa53e3f8"> 119</a></span>  <span class="keyword">explicit</span> <span class="keyword">operator</span> bool()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">return</span> ptr_ ? <span class="keyword">true</span> : <span class="keyword">false</span>;</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="comment">/* \brief state structure */</span></div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">struct </span>OpState {</div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  OpState() {}</div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  OpState(<span class="keyword">const</span> OpState& other) = <span class="keyword">delete</span>;</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  OpState& operator=(<span class="keyword">const</span> OpState& other) = <span class="keyword">delete</span>;</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  ~OpState() {</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">Engine::Get</a>()-><a class="code" href="classmxnet_1_1Engine.html#a738e5192dab345ab0ec9888b095903cf">DeleteVariable</a>([](RunContext s) {}, <a class="code" href="structmxnet_1_1Context.html#a4bb231d70623fde216de51589347e513">Context::CPU</a>(), var_);</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  }</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">engine::VarHandle</a> var_;</div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  dmlc::any state_;</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  };</div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">/* \brief shared pointer to state */</span></div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  std::shared_ptr<OpState> ptr_;</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span> };</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="keyword">using</span> <a class="code" href="namespacemxnet.html#acb83431b596f805fabcc4ba400c62e85">FCreateOpState</a> = std::function<OpStatePtr (<span class="keyword">const</span> NodeAttrs& attrs,</div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  Context ctx,</div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">const</span> std::vector<TShape>& in_shape,</div>
+<div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="namespacemxnet.html#acb83431b596f805fabcc4ba400c62e85"> 156</a></span>  <span class="keyword">const</span> std::vector<int>& in_type)>;</div>
+<div class="line"><a name="l00160"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a666f1055e5401ecec0278e8ba5790732"> 160</a></span> <span class="keyword">using</span> <a class="code" href="namespacemxnet.html#a666f1055e5401ecec0278e8ba5790732">FExecType</a> = std::function<ExecType (const NodeAttrs& attrs)>;</div>
+<div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="keyword">using</span> <a class="code" href="namespacemxnet.html#a9097a269dd76d833c014149ff3eaab9b">FStatefulCompute</a> = std::function<void (<span class="keyword">const</span> <a class="code" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a>& state,</div>
+<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1OpContext.html">OpContext</a>& ctx,</div>
+<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">const</span> std::vector<TBlob>& inputs,</div>
+<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">const</span> std::vector<OpReqType>& req,</div>
+<div class="line"><a name="l00172"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a9097a269dd76d833c014149ff3eaab9b"> 172</a></span>  <span class="keyword">const</span> std::vector<TBlob>& outputs)>;</div>
+<div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="keyword">using</span> <a class="code" href="namespacemxnet.html#ab12f1853179b1456152a563edff844a9">FStatefulComputeEx</a> = std::function<void (<span class="keyword">const</span> <a class="code" href="classmxnet_1_1OpStatePtr.html">OpStatePtr</a>& state,</div>
+<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1OpContext.html">OpContext</a>& ctx,</div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">const</span> std::vector<NDArray>& inputs,</div>
+<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">const</span> std::vector<OpReqType>& req,</div>
+<div class="line"><a name="l00184"></a><span class="lineno"><a class="line" href="namespacemxnet.html#ab12f1853179b1456152a563edff844a9"> 184</a></span>  <span class="keyword">const</span> std::vector<NDArray>& outputs)>;</div>
+<div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <span class="keyword">using</span> <a class="code" href="namespacemxnet.html#afaa687cbca9e992614be285711c1d72d">FResourceRequest</a> = std::function<</div>
+<div class="line"><a name="l00191"></a><span class="lineno"><a class="line" href="namespacemxnet.html#afaa687cbca9e992614be285711c1d72d"> 191</a></span>  std::vector<ResourceRequest> (<span class="keyword">const</span> NodeAttrs& n)>;</div>
+<div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="keyword">using</span> <a class="code" href="namespacemxnet.html#a0906e49a921265634672f555451075ed">FNDArrayFunction</a> = std::function<void (<span class="keyword">const</span> nnvm::NodeAttrs& attrs,</div>
+<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">const</span> std::vector<NDArray>& inputs,</div>
+<div class="line"><a name="l00199"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a0906e49a921265634672f555451075ed"> 199</a></span>  std::vector<NDArray>* outputs)>;</div>
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span> <span class="keyword">using</span> <a class="code" href="namespacemxnet.html#ad77e2048b7e8db91a12e05b477aac13e">FCompute</a> = std::function<void (<span class="keyword">const</span> nnvm::NodeAttrs& attrs,</div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1OpContext.html">OpContext</a>& ctx,</div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keyword">const</span> std::vector<TBlob>& inputs,</div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keyword">const</span> std::vector<OpReqType>& req,</div>
+<div class="line"><a name="l00209"></a><span class="lineno"><a class="line" href="namespacemxnet.html#ad77e2048b7e8db91a12e05b477aac13e"> 209</a></span>  <span class="keyword">const</span> std::vector<TBlob>& outputs)>;</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div>
+<div class="line"><a name="l00212"></a><span class="lineno"> 212</span> <span class="preprocessor">#endif // MXNET_OP_ATTR_TYPES_H_</span></div>
+<div class="ttc" id="namespacemxnet_html_a9097a269dd76d833c014149ff3eaab9b"><div class="ttname"><a href="namespacemxnet.html#a9097a269dd76d833c014149ff3eaab9b">mxnet::FStatefulCompute</a></div><div class="ttdeci">std::function< void(const OpStatePtr &state, const OpContext &ctx, const std::vector< TBlob > &inputs, const std::vector< OpReqType > &req, const std::vector< TBlob > &outputs)> FStatefulCompute</div><div class="ttdoc">Resiger a comput [...]
<div class="ttc" id="classmxnet_1_1OpStatePtr_html_ae43213b3a65073a87bf489350a58d78e"><div class="ttname"><a href="classmxnet_1_1OpStatePtr.html#ae43213b3a65073a87bf489350a58d78e">mxnet::OpStatePtr::reset</a></div><div class="ttdeci">void reset()</div><div class="ttdef"><b>Definition:</b> op_attr_types.h:115</div></div>
<div class="ttc" id="namespacemxnet_html_ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c"><div class="ttname"><a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c">mxnet::ExecType::kSync</a></div><div class="ttdoc">Forward/Backward are synchronize calls. </div></div>
<div class="ttc" id="engine_8h_html"><div class="ttname"><a href="engine_8h.html">engine.h</a></div><div class="ttdoc">Engine that schedules all the operations according to dependency. </div></div>
<div class="ttc" id="namespacemxnet_html_a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26"><div class="ttname"><a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26">mxnet::kNullOp</a></div><div class="ttdoc">no operation, do not write anything </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:28</div></div>
-<div class="ttc" id="classmxnet_1_1OpStatePtr_html_a2484d61830b33181bf24c536ba91b790"><div class="ttname"><a href="classmxnet_1_1OpStatePtr.html#a2484d61830b33181bf24c536ba91b790">mxnet::OpStatePtr::Create</a></div><div class="ttdeci">static OpStatePtr Create(Args &&... args)</div><div class="ttdef"><b>Definition:</b> op_attr_types.h:97</div></div>
<div class="ttc" id="namespacemxnet_html_a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a"><div class="ttname"><a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a">mxnet::kWriteTo</a></div><div class="ttdoc">write gradient to provided space </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:30</div></div>
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
<div class="ttc" id="namespacemxnet_html_afaa687cbca9e992614be285711c1d72d"><div class="ttname"><a href="namespacemxnet.html#afaa687cbca9e992614be285711c1d72d">mxnet::FResourceRequest</a></div><div class="ttdeci">std::function< std::vector< ResourceRequest >(const NodeAttrs &n)> FResourceRequest</div><div class="ttdoc">The resource request from the operator. </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:191</div></div>
<div class="ttc" id="namespacemxnet_html_ab12f1853179b1456152a563edff844a9"><div class="ttname"><a href="namespacemxnet.html#ab12f1853179b1456152a563edff844a9">mxnet::FStatefulComputeEx</a></div><div class="ttdeci">std::function< void(const OpStatePtr &state, const OpContext &ctx, const std::vector< NDArray > &inputs, const std::vector< OpReqType > &req, const std::vector< NDArray > &outputs)> FStatefulComputeEx</div><div class="ttdoc">Resiger [...]
-<div class="ttc" id="structmxnet_1_1OpContext_html_a0c48c2419d23551a626ae3de6ee1f6da"><div class="ttname"><a href="structmxnet_1_1OpContext.html#a0c48c2419d23551a626ae3de6ee1f6da">mxnet::OpContext::get_stream</a></div><div class="ttdeci">mshadow::Stream< xpu > * get_stream() const</div><div class="ttdoc">get mshadow stream from Context </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:62</div></div>
+<div class="ttc" id="structmxnet_1_1RunContext_html_a8bcd2fc6392f95a1fdc3d98f74dbfcb9"><div class="ttname"><a href="structmxnet_1_1RunContext.html#a8bcd2fc6392f95a1fdc3d98f74dbfcb9">mxnet::RunContext::get_stream</a></div><div class="ttdeci">mshadow::Stream< xpu > * get_stream() const </div><div class="ttdoc">get mshadow stream from Context </div><div class="ttdef"><b>Definition:</b> base.h:226</div></div>
<div class="ttc" id="namespacemxnet_html_a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7"><div class="ttname"><a href="namespacemxnet.html#a998b74220fab2b012cf8a179650e1b3ba9f2b960005d2a3a5f35ac32809d84db7">mxnet::FnProperty::kAsync</a></div><div class="ttdoc">Asynchronous function call. </div></div>
+<div class="ttc" id="classmxnet_1_1OpStatePtr_html_a77a1636269466569c0b6e32941ed7264"><div class="ttname"><a href="classmxnet_1_1OpStatePtr.html#a77a1636269466569c0b6e32941ed7264">mxnet::OpStatePtr::get_var</a></div><div class="ttdeci">engine::VarHandle get_var() const </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:106</div></div>
<div class="ttc" id="namespacemxnet_html_ad7801fb2dd68294d4c7a9e289bc071ada3864860a073c64d74d3fc8e765fa6cad"><div class="ttname"><a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ada3864860a073c64d74d3fc8e765fa6cad">mxnet::ExecType::kCrossDeviceCopy</a></div><div class="ttdoc">Cross device copy operation, this is a special operator That indicates copy across devices...</div></div>
<div class="ttc" id="structmxnet_1_1RunContext_html"><div class="ttname"><a href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></div><div class="ttdoc">execution time context. The information needed in runtime for actual execution. </div><div class="ttdef"><b>Definition:</b> base.h:213</div></div>
<div class="ttc" id="namespacemxnet_html_ad7801fb2dd68294d4c7a9e289bc071adafcf37e733f29d6aa14fb34fa737a809a"><div class="ttname"><a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adafcf37e733f29d6aa14fb34fa737a809a">mxnet::ExecType::kLocal</a></div><div class="ttdoc">Run this operator on the scheduling thread without pushing to engine. </div></div>
-<div class="ttc" id="structmxnet_1_1RunContext_html_a9901db00632774aef42081ceda0d23f1"><div class="ttname"><a href="structmxnet_1_1RunContext.html#a9901db00632774aef42081ceda0d23f1">mxnet::RunContext::get_stream</a></div><div class="ttdeci">mshadow::Stream< xpu > * get_stream() const</div><div class="ttdoc">get mshadow stream from Context </div><div class="ttdef"><b>Definition:</b> base.h:226</div></div>
+<div class="ttc" id="classmxnet_1_1OpStatePtr_html_aa2ea1d7ae06008faf44a92c009b2b26e"><div class="ttname"><a href="classmxnet_1_1OpStatePtr.html#aa2ea1d7ae06008faf44a92c009b2b26e">mxnet::OpStatePtr::get_state</a></div><div class="ttdeci">T & get_state() const </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:111</div></div>
<div class="ttc" id="structmxnet_1_1OpContext_html_a40ce0e3d4ac21412cfafafc5b1fd32ef"><div class="ttname"><a href="structmxnet_1_1OpContext.html#a40ce0e3d4ac21412cfafafc5b1fd32ef">mxnet::OpContext::async_on_complete</a></div><div class="ttdeci">engine::CallbackOnComplete async_on_complete</div><div class="ttdoc">the callback when operation completes, used by asynchronize ops </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:53</div></div>
<div class="ttc" id="namespacemxnet_html_acb83431b596f805fabcc4ba400c62e85"><div class="ttname"><a href="namespacemxnet.html#acb83431b596f805fabcc4ba400c62e85">mxnet::FCreateOpState</a></div><div class="ttdeci">std::function< OpStatePtr(const NodeAttrs &attrs, Context ctx, const std::vector< TShape > &in_shape, const std::vector< int > &in_type)> FCreateOpState</div><div class="ttdoc">Create a Layer style, forward/backward operator. This is easy to write cod [...]
<div class="ttc" id="structmxnet_1_1OpContext_html"><div class="ttname"><a href="structmxnet_1_1OpContext.html">mxnet::OpContext</a></div><div class="ttdoc">All the possible information needed by Operator.Forward and Backward This is the superset of RunConte...</div><div class="ttdef"><b>Definition:</b> op_attr_types.h:47</div></div>
@@ -90,6 +232,7 @@ $(function() {
<div class="ttc" id="structmxnet_1_1OpContext_html_abd7405c065167e4e15af032b1e0a5c42"><div class="ttname"><a href="structmxnet_1_1OpContext.html#abd7405c065167e4e15af032b1e0a5c42">mxnet::OpContext::is_train</a></div><div class="ttdeci">int is_train</div><div class="ttdoc">whether it is training phase </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:49</div></div>
<div class="ttc" id="namespacemxnet_html_a666f1055e5401ecec0278e8ba5790732"><div class="ttname"><a href="namespacemxnet.html#a666f1055e5401ecec0278e8ba5790732">mxnet::FExecType</a></div><div class="ttdeci">std::function< ExecType(const NodeAttrs &attrs)> FExecType</div><div class="ttdoc">Execution mode of this operator. </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:160</div></div>
<div class="ttc" id="classmxnet_1_1Engine_html_a6e141b188f018d5d933ab99868631d5e"><div class="ttname"><a href="classmxnet_1_1Engine.html#a6e141b188f018d5d933ab99868631d5e">mxnet::Engine::NewVariable</a></div><div class="ttdeci">virtual VarHandle NewVariable()=0</div><div class="ttdoc">Allocate a new variable, the variable can then be used to schedule the operation concurrently via dep...</div></div>
+<div class="ttc" id="classmxnet_1_1OpStatePtr_html_ae0f821c7efce61a11730ef8b93070e3e"><div class="ttname"><a href="classmxnet_1_1OpStatePtr.html#ae0f821c7efce61a11730ef8b93070e3e">mxnet::OpStatePtr::Create</a></div><div class="ttdeci">static OpStatePtr Create(Args &&...args)</div><div class="ttdef"><b>Definition:</b> op_attr_types.h:97</div></div>
<div class="ttc" id="namespacemxnet_html_a0906e49a921265634672f555451075ed"><div class="ttname"><a href="namespacemxnet.html#a0906e49a921265634672f555451075ed">mxnet::FNDArrayFunction</a></div><div class="ttdeci">std::function< void(const nnvm::NodeAttrs &attrs, const std::vector< NDArray > &inputs, std::vector< NDArray > *outputs)> FNDArrayFunction</div><div class="ttdoc">Register an operator called as a NDArray function. </div><div class="ttdef"><b>Definition: [...]
<div class="ttc" id="classmxnet_1_1Engine_html_a738e5192dab345ab0ec9888b095903cf"><div class="ttname"><a href="classmxnet_1_1Engine.html#a738e5192dab345ab0ec9888b095903cf">mxnet::Engine::DeleteVariable</a></div><div class="ttdeci">virtual void DeleteVariable(SyncFn delete_fn, Context exec_ctx, VarHandle var)=0</div><div class="ttdoc">Schedule the deletion of a variable. </div></div>
<div class="ttc" id="namespacemxnet_1_1engine_html_a9d36c4f33eae8531586dc2edf83ae7cf"><div class="ttname"><a href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">mxnet::engine::VarHandle</a></div><div class="ttdeci">Var * VarHandle</div><div class="ttdoc">Variable pointer type, usually hold by user used to specify dependencies. </div><div class="ttdef"><b>Definition:</b> engine.h:28</div></div>
@@ -104,15 +247,14 @@ $(function() {
<div class="ttc" id="classmxnet_1_1Engine_html_ae0a23da15ef63d9479c7468e1f2f825f"><div class="ttname"><a href="classmxnet_1_1Engine.html#ae0a23da15ef63d9479c7468e1f2f825f">mxnet::Engine::Get</a></div><div class="ttdeci">static Engine * Get()</div></div>
<div class="ttc" id="namespacemxnet_html_a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383"><div class="ttname"><a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383">mxnet::kAddTo</a></div><div class="ttdoc">add to the provided space </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:38</div></div>
<div class="ttc" id="namespacemxnet_html_ad7801fb2dd68294d4c7a9e289bc071ad"><div class="ttname"><a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">mxnet::ExecType</a></div><div class="ttdeci">ExecType</div><div class="ttdoc">the execution type of the operator </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:68</div></div>
-<div class="ttc" id="structmxnet_1_1Context_html"><div class="ttname"><a href="structmxnet_1_1Context.html">mxnet::Context</a></div><div class="ttdoc">Context information about the execution environment. </div><div class="ttdef"><b>Definition:</b> base.h:116</div></div>
+<div class="ttc" id="structmxnet_1_1OpContext_html_aa700c5c28e024b42d41e15f46615f1b8"><div class="ttname"><a href="structmxnet_1_1OpContext.html#aa700c5c28e024b42d41e15f46615f1b8">mxnet::OpContext::get_stream</a></div><div class="ttdeci">mshadow::Stream< xpu > * get_stream() const </div><div class="ttdoc">get mshadow stream from Context </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:62</div></div>
<div class="ttc" id="classmxnet_1_1OpStatePtr_html"><div class="ttname"><a href="classmxnet_1_1OpStatePtr.html">mxnet::OpStatePtr</a></div><div class="ttdoc">Operator state. This is a pointer type, its content is mutable even if OpStatePtr is const...</div><div class="ttdef"><b>Definition:</b> op_attr_types.h:91</div></div>
-<div class="ttc" id="classmxnet_1_1OpStatePtr_html_af2082fb6224b78b2aac9e05f4b5365a4"><div class="ttname"><a href="classmxnet_1_1OpStatePtr.html#af2082fb6224b78b2aac9e05f4b5365a4">mxnet::OpStatePtr::get_var</a></div><div class="ttdeci">engine::VarHandle get_var() const</div><div class="ttdef"><b>Definition:</b> op_attr_types.h:106</div></div>
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+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
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diff --git a/versions/master/doxygen/operator_8h.html b/versions/master/doxygen/operator_8h.html
index e34ac88..e4ff8fb 100644
--- a/versions/master/doxygen/operator_8h.html
+++ b/versions/master/doxygen/operator_8h.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/operator.h File Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
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+ <div id="navrow1" class="tabs">
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ <li><a href="files.html"><span>File List</span></a></li>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -74,39 +96,31 @@ $(function() {
<p>Operator interface of mxnet.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/base.h></code><br />
-<code>#include <dmlc/json.h></code><br />
-<code>#include <dmlc/logging.h></code><br />
-<code>#include <dmlc/registry.h></code><br />
-<code>#include <nnvm/node.h></code><br />
-<code>#include <vector></code><br />
-<code>#include <map></code><br />
-<code>#include <string></code><br />
-<code>#include <utility></code><br />
-<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br />
-<code>#include "<a class="el" href="resource_8h_source.html">./resource.h</a>"</code><br />
-<code>#include "<a class="el" href="op__attr__types_8h_source.html">./op_attr_types.h</a>"</code><br />
+<div class="textblock"><code>#include <dmlc/base.h></code><br/>
+<code>#include <dmlc/json.h></code><br/>
+<code>#include <dmlc/logging.h></code><br/>
+<code>#include <dmlc/registry.h></code><br/>
+<code>#include <nnvm/node.h></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <map></code><br/>
+<code>#include <string></code><br/>
+<code>#include <utility></code><br/>
+<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br/>
+<code>#include "<a class="el" href="resource_8h_source.html">./resource.h</a>"</code><br/>
+<code>#include "<a class="el" href="op__attr__types_8h_source.html">./op_attr_types.h</a>"</code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for operator.h:</div>
<div class="dyncontent">
<div class="center"><img src="operator_8h__incl.png" border="0" usemap="#include_2mxnet_2operator_8h" alt=""/></div>
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This graph shows which files directly or indirectly include this file:</div>
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<p><a href="operator_8h_source.html">Go to the source code of this file.</a></p>
@@ -114,31 +128,32 @@ This graph shows which files directly or indirectly include this file:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1Operator.html">mxnet::Operator</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> interface. <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> defines basic operation unit of optimized computation graph in [...]
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> interface. <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a> defines basic operation unit of optimized computation graph in [...]
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> is a object that stores all information about <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. It als [...]
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> is a object that stores all information about <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. It als [...]
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OperatorPropertyReg.html">mxnet::OperatorPropertyReg</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> factory functions. <a href="structmxnet_1_1OperatorPropertyReg.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Registry entry for <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> factory functions. <a href="structmxnet_1_1OperatorPropertyReg.html#details">More...</a><br/></td></tr>
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Namespaces</h2></td></tr>
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-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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Macros</h2></td></tr>
-<tr class="memitem:a926db042759256e94d20696c66f65709"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="operator_8h.html#a926db042759256e94d20696c66f65709">MXNET_REGISTER_OP_PROPERTY</a>(name, OperatorPropertyType)</td></tr>
-<tr class="memdesc:a926db042759256e94d20696c66f65709"><td class="mdescLeft"> </td><td class="mdescRight">Macro to register OperatorProperty. <a href="#a926db042759256e94d20696c66f65709">More...</a><br /></td></tr>
+<tr class="memitem:a926db042759256e94d20696c66f65709"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="operator_8h.html#a926db042759256e94d20696c66f65709">MXNET_REGISTER_OP_PROPERTY</a>(name, OperatorPropertyType)</td></tr>
+<tr class="memdesc:a926db042759256e94d20696c66f65709"><td class="mdescLeft"> </td><td class="mdescRight">Macro to register OperatorProperty. <a href="#a926db042759256e94d20696c66f65709">More...</a><br/></td></tr>
<tr class="separator:a926db042759256e94d20696c66f65709"><td class="memSeparator" colspan="2"> </td></tr>
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Typedefs</h2></td></tr>
-<tr class="memitem:a6f5428fbf261a94eaac079ec24413815"><td class="memItemLeft" align="right" valign="top">typedef std::function< OperatorProperty *()> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6f5428fbf261a94eaac079ec24413815">mxnet::OperatorPropertyFactory</a></td></tr>
-<tr class="memdesc:a6f5428fbf261a94eaac079ec24413815"><td class="mdescLeft"> </td><td class="mdescRight">typedef the factory function of operator property <a href="namespacemxnet.html#a6f5428fbf261a94eaac079ec24413815">More...</a><br /></td></tr>
+<tr class="memitem:a6f5428fbf261a94eaac079ec24413815"><td class="memItemLeft" align="right" valign="top">typedef std::function<br class="typebreak"/>
+< OperatorProperty *()> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html#a6f5428fbf261a94eaac079ec24413815">mxnet::OperatorPropertyFactory</a></td></tr>
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<tr class="separator:a6f5428fbf261a94eaac079ec24413815"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
@@ -146,9 +161,7 @@ Typedefs</h2></td></tr>
<p>Copyright (c) 2015 by Contributors</p>
<dl class="section author"><dt>Author</dt><dd>Naiyan Wang </dd></dl>
</div><h2 class="groupheader">Macro Definition Documentation</h2>
-<a id="a926db042759256e94d20696c66f65709"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a926db042759256e94d20696c66f65709">◆ </a></span>MXNET_REGISTER_OP_PROPERTY</h2>
-
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-<b>Value:</b><div class="fragment"><div class="line">DMLC_REGISTRY_REGISTER(::<a class="code" href="structmxnet_1_1OperatorPropertyReg.html">mxnet::OperatorPropertyReg</a>, OperatorPropertyReg, name) \</div><div class="line"> .set_body([]() { <span class="keywordflow">return</span> <span class="keyword">new</span> OperatorPropertyType(); }) \</div><div class="line"> .set_return_type(<span class="stringliteral">"NDArray-or-Symbol"</span>) \</div><div class="line [...]
+<b>Value:</b><div class="fragment"><div class="line">DMLC_REGISTRY_REGISTER(::<a class="code" href="structmxnet_1_1OperatorPropertyReg.html">mxnet::OperatorPropertyReg</a>, OperatorPropertyReg, name) \</div>
+<div class="line"> .set_body([]() { <span class="keywordflow">return</span> <span class="keyword">new</span> OperatorPropertyType(); }) \</div>
+<div class="line"> .set_return_type(<span class="stringliteral">"NDArray-or-Symbol"</span>) \</div>
+<div class="line"> .check_name()</div>
+<div class="ttc" id="structmxnet_1_1OperatorPropertyReg_html"><div class="ttname"><a href="structmxnet_1_1OperatorPropertyReg.html">mxnet::OperatorPropertyReg</a></div><div class="ttdoc">Registry entry for OperatorProperty factory functions. </div><div class="ttdef"><b>Definition:</b> operator.h:434</div></div>
</div><!-- fragment -->
<p>Macro to register OperatorProperty. </p>
-<div class="fragment"><div class="line"><span class="comment">// example of registering a fully connected operator</span></div><div class="line">REGISTER_OP_PROPERTY(FullyConnected, FullyConnectedOpProp)</div><div class="line">.describe(<span class="stringliteral">"Fully connected layer"</span>);</div></div><!-- fragment -->
+<div class="fragment"><div class="line"><span class="comment">// example of registering a fully connected operator</span></div>
+<div class="line">REGISTER_OP_PROPERTY(FullyConnected, FullyConnectedOpProp)</div>
+<div class="line">.describe(<span class="stringliteral">"Fully connected layer"</span>);</div>
+</div><!-- fragment -->
</div>
</div>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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@@ -66,47 +88,258 @@ $(function() {
<div class="title">operator.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="operator_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_OPERATOR_H_</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#define MXNET_OPERATOR_H_</span></div><div class="line"><a name="l0000 [...]
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+<a href="operator_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#ifndef MXNET_OPERATOR_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_OPERATOR_H_</span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <dmlc/base.h></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <dmlc/json.h></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <dmlc/logging.h></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <dmlc/registry.h></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <nnvm/node.h></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <map></span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <string></span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <utility></span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include "<a class="code" href="resource_8h.html">./resource.h</a>"</span></div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include "<a class="code" href="op__attr__types_8h.html">./op_attr_types.h</a>"</span></div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="classmxnet_1_1Operator.html"> 36</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1Operator.html">Operator</a> {</div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="classmxnet_1_1Operator.html#a4d34cc792d7271bd8b5b60eaa0b55767"> 39</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1Operator.html#a4d34cc792d7271bd8b5b60eaa0b55767">~Operator</a>() {}</div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd">Forward</a>(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1OpContext.html">OpContext</a> &ctx,</div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">const</span> std::vector<TBlob> &in_data,</div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">const</span> std::vector<OpReqType> &req,</div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> std::vector<TBlob> &out_data,</div>
+<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">const</span> std::vector<TBlob> &aux_states) = 0;</div>
+<div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="classmxnet_1_1Operator.html#ad32d74b9bf56caf920b5e6379d2bb259"> 84</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Operator.html#ad32d74b9bf56caf920b5e6379d2bb259">Backward</a>(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1OpContext.html">OpContext</a> &ctx,</div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">const</span> std::vector<TBlob> &out_grad,</div>
+<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">const</span> std::vector<TBlob> &in_data,</div>
+<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">const</span> std::vector<TBlob> &out_data,</div>
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">const</span> std::vector<OpReqType> &req,</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">const</span> std::vector<TBlob> &in_grad,</div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keyword">const</span> std::vector<TBlob> &aux_states) {</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  LOG(FATAL) << <span class="stringliteral">"Backward is not implemented"</span>;</div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  }</div>
+<div class="line"><a name="l00094"></a><span class="lineno"><a class="line" href="classmxnet_1_1Operator.html#aa6b4c247a914b4659c773965248019c7"> 94</a></span>  <span class="keyword">virtual</span> <a class="code" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">ExecType</a> <a class="code" href="classmxnet_1_1Operator.html#aa6b4c247a914b4659c773965248019c7">exec_type</a>() const final { <span class="comment">// NOLINT(*) exec_type has been moved to OperatorProperty< [...]
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">return</span> <a class="code" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c">ExecType::kSync</a>;</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  }</div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span> };</div>
+<div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="preprocessor">#if DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="preprocessor"></span><span class="comment">// OperatorProperty allows C++11, while Operator do not rely on it.</span></div>
+<div class="line"><a name="l00108"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html"> 108</a></span> <span class="comment"></span><span class="keyword">class </span><a class="code" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a> {</div>
+<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00113"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a610af8e9cb560104301ccd1efa2a96fd"> 113</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1OperatorProperty.html#a610af8e9cb560104301ccd1efa2a96fd">~OperatorProperty</a>() {}</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1OperatorProperty.html#a0fb32e0ab03a86517473db0f0630856c">Init</a>(<span class="keyword">const</span> std::vector<std::pair<std::string, std::string> >& kwargs) = 0;</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">virtual</span> std::map<std::string, std::string> <a class="code" href="classmxnet_1_1OperatorProperty.html#a704ae43181c8a88bfd0a47e8005f7cb8">GetParams</a>() <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00129"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a24cc6a3763ff5f0881a2de0652810596"> 129</a></span>  <span class="keyword">virtual</span> std::vector<std::string> <a class="code" href="classmxnet_1_1OperatorProperty.html#a24cc6a3763ff5f0881a2de0652810596">ListArguments</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">return</span> {<span class="stringliteral">"data"</span>};</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  }</div>
+<div class="line"><a name="l00136"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892"> 136</a></span>  <span class="keyword">virtual</span> std::vector<std::string> <a class="code" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">ListOutputs</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">return</span> {<span class="stringliteral">"output"</span>};</div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  }</div>
+<div class="line"><a name="l00143"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094"> 143</a></span>  <span class="keyword">virtual</span> std::vector<std::string> <a class="code" href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094">ListAuxiliaryStates</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordflow">return</span> {};</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  }</div>
+<div class="line"><a name="l00147"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1"> 147</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">NumOutputs</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">return</span> this-><a class="code" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">ListOutputs</a>().size();</div>
+<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  }</div>
+<div class="line"><a name="l00162"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b"> 162</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b">NumVisibleOutputs</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordflow">return</span> <a class="code" href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">NumOutputs</a>();</div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  }</div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">virtual</span> <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1OperatorProperty.html#a543cbf36f0e4d8dc99109db71705a11c">InferShape</a>(std::vector<TShape> *in_shape,</div>
+<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  std::vector<TShape> *out_shape,</div>
+<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  std::vector<TShape> *aux_shape) <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00202"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#ad5c9f53ea70f3eeb1bf27b983f420c0b"> 202</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1OperatorProperty.html#ad5c9f53ea70f3eeb1bf27b983f420c0b">InferType</a>(std::vector<int> *in_type,</div>
+<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  std::vector<int> *out_type,</div>
+<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  std::vector<int> *aux_type)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  CHECK_LE(in_type->size(), this-><a class="code" href="classmxnet_1_1OperatorProperty.html#a24cc6a3763ff5f0881a2de0652810596">ListArguments</a>().size());</div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordtype">int</span> n_in = this-><a class="code" href="classmxnet_1_1OperatorProperty.html#a24cc6a3763ff5f0881a2de0652810596">ListArguments</a>().size();</div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> i = 0; i < in_type->size(); ++i) {</div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  CHECK(in_type->at(i) == mshadow::default_type_flag ||</div>
+<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  in_type->at(i) == -1) << <span class="stringliteral">"Unsupported data type "</span> << in_type->at(i);</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  }</div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  in_type->clear();</div>
+<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < n_in; ++i ) in_type->push_back(mshadow::default_type_flag);</div>
+<div class="line"><a name="l00213"></a><span class="lineno"> 213</span> </div>
+<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordtype">int</span> n_out = this-><a class="code" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">ListOutputs</a>().size();</div>
+<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  out_type->clear();</div>
+<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < n_out; ++i ) out_type->push_back(mshadow::default_type_flag);</div>
+<div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div>
+<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordtype">int</span> n_aux = this-><a class="code" href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094">ListAuxiliaryStates</a>().size();</div>
+<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  aux_type->clear();</div>
+<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < n_aux; ++i ) aux_type->push_back(mshadow::default_type_flag);</div>
+<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
+<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  }</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a>* <a class="code" href="classmxnet_1_1OperatorProperty.html#ab38f2f877555ba34b548efd2575cac99">Copy</a>() <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1Operator.html">Operator</a>* <a class="code" href="classmxnet_1_1OperatorProperty.html#a546ab25ddaae590ca98705ae9abc7ac0">CreateOperator</a>(<a class="code" href="structmxnet_1_1Context.html">Context</a> ctx) <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00239"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a7985004f99a931b4a6ff69df733bae72"> 239</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1Operator.html">Operator</a>* <a class="code" href="classmxnet_1_1OperatorProperty.html#a7985004f99a931b4a6ff69df733bae72">CreateOperatorEx</a>(<a class="code" href="structmxnet_1_1Context.html">Context</a> ctx, std::vector<TShape> *in_s [...]
+<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  std::vector<int> *in_type)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  std::vector<int> out_type, aux_type;</div>
+<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  std::vector<TShape> out_shape, aux_shape;</div>
+<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  out_type.resize(this-><a class="code" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">ListOutputs</a>().size());</div>
+<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  out_shape.resize(this-><a class="code" href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">ListOutputs</a>().size());</div>
+<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  aux_type.resize(this-><a class="code" href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094">ListAuxiliaryStates</a>().size());</div>
+<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  aux_shape.resize(this-><a class="code" href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094">ListAuxiliaryStates</a>().size());</div>
+<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  CHECK(<a class="code" href="classmxnet_1_1OperatorProperty.html#ad5c9f53ea70f3eeb1bf27b983f420c0b">InferType</a>(in_type, &out_type, &aux_type));</div>
+<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  CHECK(<a class="code" href="classmxnet_1_1OperatorProperty.html#a543cbf36f0e4d8dc99109db71705a11c">InferShape</a>(in_shape, &out_shape, &aux_shape));</div>
+<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordflow">return</span> <a class="code" href="classmxnet_1_1OperatorProperty.html#a546ab25ddaae590ca98705ae9abc7ac0">CreateOperator</a>(ctx);</div>
+<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  }</div>
+<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">virtual</span> std::string <a class="code" href="classmxnet_1_1OperatorProperty.html#ac6b7df90c0285a1823b463c21e926698">TypeString</a>() <span class="keyword">const</span> = 0;</div>
+<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="comment">//--------------------------------------------------------</span></div>
+<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="comment">// All the below functions are optional to override.</span></div>
+<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="comment">//--------------------------------------------------------</span></div>
+<div class="line"><a name="l00267"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a623210dd1b9495ca73f223e0b06f998c"> 267</a></span> <span class="comment"></span> <span class="keyword">virtual</span> std::vector<ResourceRequest> <a class="code" href="classmxnet_1_1OperatorProperty.html#a623210dd1b9495ca73f223e0b06f998c">ForwardResource</a>(</div>
+<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> std::vector<TShape> &in_shape)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keywordflow">return</span> std::vector<ResourceRequest>();</div>
+<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  }</div>
+<div class="line"><a name="l00278"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#ade7720a3e0ebba67d89978925a77609b"> 278</a></span>  <span class="keyword">virtual</span> std::vector<ResourceRequest> <a class="code" href="classmxnet_1_1OperatorProperty.html#ade7720a3e0ebba67d89978925a77609b">BackwardResource</a>(</div>
+<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> std::vector<TShape> &in_shape)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keywordflow">return</span> std::vector<ResourceRequest>();</div>
+<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  }</div>
+<div class="line"><a name="l00304"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#abf9e6a8d40750f3ee81fe30cbe3e2aae"> 304</a></span>  <span class="keyword">virtual</span> std::vector<int> <a class="code" href="classmxnet_1_1OperatorProperty.html#abf9e6a8d40750f3ee81fe30cbe3e2aae">DeclareBackwardDependency</a>(</div>
+<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">const</span> std::vector<int> &out_grad,</div>
+<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keyword">const</span> std::vector<int> &in_data,</div>
+<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keyword">const</span> std::vector<int> &out_data)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="comment">// By default requires to see all the things.</span></div>
+<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="comment">// remember to override this function to get a better performance.</span></div>
+<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  std::vector<int> ret = out_grad;</div>
+<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  ret.insert(ret.end(), in_data.begin(), in_data.end());</div>
+<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  ret.insert(ret.end(), out_data.begin(), out_data.end());</div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">return</span> ret;</div>
+<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  }</div>
+<div class="line"><a name="l00336"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#aad7a3e87866515a2b7100e849a2fe966"> 336</a></span>  <span class="keyword">virtual</span> std::vector<std::pair<int, void*> > <a class="code" href="classmxnet_1_1OperatorProperty.html#aad7a3e87866515a2b7100e849a2fe966">ForwardInplaceOption</a>(</div>
+<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">const</span> std::vector<int> &in_data,</div>
+<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keyword">const</span> std::vector<void*> &out_data)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keywordflow">return</span> std::vector<std::pair<int, void*> >();</div>
+<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  }</div>
+<div class="line"><a name="l00367"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a67231a5a1525ec7480de98a79dce0194"> 367</a></span>  <span class="keyword">virtual</span> std::vector<std::pair<int, void*> > <a class="code" href="classmxnet_1_1OperatorProperty.html#a67231a5a1525ec7480de98a79dce0194">BackwardInplaceOption</a>(</div>
+<div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keyword">const</span> std::vector<int> &out_grad,</div>
+<div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keyword">const</span> std::vector<int> &in_data,</div>
+<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keyword">const</span> std::vector<int> &out_data,</div>
+<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keyword">const</span> std::vector<void*> &in_grad)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keywordflow">return</span> std::vector<std::pair<int, void*> >();</div>
+<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  }</div>
+<div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keyword">template</span><<span class="keyword">typename</span> T></div>
+<div class="line"><a name="l00387"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#a9f488509e132094ce7e79125ce853042"> 387</a></span>  <span class="keyword">inline</span> std::vector<T> <a class="code" href="classmxnet_1_1OperatorProperty.html#a9f488509e132094ce7e79125ce853042">BackwardInputs</a>(<span class="keyword">const</span> std::vector<T> &out_grad,</div>
+<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keyword">const</span> std::vector<T> &in_data,</div>
+<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">const</span> std::vector<T> &out_data)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordtype">int</span> counter = 0;</div>
+<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  std::vector<int> out_grad_index(out_grad.size());</div>
+<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  std::vector<int> in_data_index(in_data.size());</div>
+<div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  std::vector<int> out_data_index(out_data.size());</div>
+<div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < out_grad_index.size(); ++i) {</div>
+<div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  out_grad_index[i] = counter++;</div>
+<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  }</div>
+<div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < in_data_index.size(); ++i) {</div>
+<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  in_data_index[i] = counter++;</div>
+<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  }</div>
+<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < out_data_index.size(); ++i) {</div>
+<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  out_data_index[i] = counter++;</div>
+<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  }</div>
+<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  std::vector<T> all_data;</div>
+<div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  all_data.insert(all_data.end(), out_grad.begin(), out_grad.end());</div>
+<div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  all_data.insert(all_data.end(), in_data.begin(), in_data.end());</div>
+<div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  all_data.insert(all_data.end(), out_data.begin(), out_data.end());</div>
+<div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div>
+<div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  std::vector<int> ret_index = this-><a class="code" href="classmxnet_1_1OperatorProperty.html#abf9e6a8d40750f3ee81fe30cbe3e2aae">DeclareBackwardDependency</a>(</div>
+<div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  out_grad_index, in_data_index, out_data_index);</div>
+<div class="line"><a name="l00410"></a><span class="lineno"> 410</span> </div>
+<div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  std::vector<T> ret(ret_index.size());</div>
+<div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < ret_index.size(); ++i) {</div>
+<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  ret[i] = all_data[ret_index[i]];</div>
+<div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  }</div>
+<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="keywordflow">return</span> ret;</div>
+<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  }</div>
+<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a> *<a class="code" href="classmxnet_1_1OperatorProperty.html#a15cf1f905b08a008ff38556ef6a35dd0">Create</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* type_name);</div>
+<div class="line"><a name="l00424"></a><span class="lineno"><a class="line" href="classmxnet_1_1OperatorProperty.html#aa5d6354ebe3e2a0c51f7f7ce4a60f664"> 424</a></span>  <span class="keyword">virtual</span> <a class="code" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">ExecType</a> <a class="code" href="classmxnet_1_1OperatorProperty.html#aa5d6354ebe3e2a0c51f7f7ce4a60f664">exec_type</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordflow">return</span> <a class="code" href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c">ExecType::kSync</a>;</div>
+<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  }</div>
+<div class="line"><a name="l00427"></a><span class="lineno"> 427</span> };</div>
+<div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div>
+<div class="line"><a name="l00430"></a><span class="lineno"><a class="line" href="namespacemxnet.html#a6f5428fbf261a94eaac079ec24413815"> 430</a></span> <span class="keyword">typedef</span> std::function<OperatorProperty *()> <a class="code" href="namespacemxnet.html#a6f5428fbf261a94eaac079ec24413815">OperatorPropertyFactory</a>;</div>
+<div class="line"><a name="l00434"></a><span class="lineno"><a class="line" href="structmxnet_1_1OperatorPropertyReg.html"> 434</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1OperatorPropertyReg.html">OperatorPropertyReg</a></div>
+<div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  : <span class="keyword">public</span> dmlc::FunctionRegEntryBase<OperatorPropertyReg,</div>
+<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  OperatorPropertyFactory> {</div>
+<div class="line"><a name="l00449"></a><span class="lineno"><a class="line" href="structmxnet_1_1OperatorPropertyReg.html#a0e3536ad5305fa1ef402c5385fb4a089"> 449</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1OperatorPropertyReg.html">OperatorPropertyReg</a>& <a class="code" href="structmxnet_1_1OperatorPropertyReg.html#a0e3536ad5305fa1ef402c5385fb4a089">set_key_var_num_args</a>(<span class="keyword">const</span> std::string &key) { < [...]
+<div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  this-><a class="code" href="structmxnet_1_1OperatorPropertyReg.html#af3aee96c6a2289e7554d7503186a540c">key_var_num_args</a> = key;</div>
+<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  }</div>
+<div class="line"><a name="l00456"></a><span class="lineno"><a class="line" href="structmxnet_1_1OperatorPropertyReg.html#ad959fdbefb6dd807ee1961a2fec7a038"> 456</a></span>  <span class="keyword">inline</span> <a class="code" href="structmxnet_1_1OperatorPropertyReg.html">OperatorPropertyReg</a>& <a class="code" href="structmxnet_1_1OperatorPropertyReg.html#ad959fdbefb6dd807ee1961a2fec7a038">check_name</a>() {</div>
+<div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="classmxnet_1_1OperatorProperty.html">OperatorProperty</a> *p = this->body();</div>
+<div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  std::string type = p-><a class="code" href="classmxnet_1_1OperatorProperty.html#ac6b7df90c0285a1823b463c21e926698">TypeString</a>();</div>
+<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="keyword">delete</span> p;</div>
+<div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  CHECK_EQ(this->name, type)</div>
+<div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  << <span class="stringliteral">"Register Name and TypeString mismatch, name=\""</span> << this->name << <span class="stringliteral">"\","</span></div>
+<div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  << <span class="stringliteral">" but TypeString=\""</span> << type <<<span class="stringliteral">"\""</span>;</div>
+<div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  }</div>
+<div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </div>
+<div class="line"><a name="l00467"></a><span class="lineno"><a class="line" href="structmxnet_1_1OperatorPropertyReg.html#af3aee96c6a2289e7554d7503186a540c"> 467</a></span>  std::string <a class="code" href="structmxnet_1_1OperatorPropertyReg.html#af3aee96c6a2289e7554d7503186a540c">key_var_num_args</a>;</div>
+<div class="line"><a name="l00468"></a><span class="lineno"> 468</span> };</div>
+<div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div>
+<div class="line"><a name="l00470"></a><span class="lineno"> 470</span> <span class="comment">//---------------------------------------------------------------------------------</span></div>
+<div class="line"><a name="l00471"></a><span class="lineno"> 471</span> <span class="comment">// The following part are API Registration of Operators</span></div>
+<div class="line"><a name="l00472"></a><span class="lineno"> 472</span> <span class="comment">// See also MXNET_REGISTER_SIMPLE_OP in operator_util.h for registering simple ops.</span></div>
+<div class="line"><a name="l00473"></a><span class="lineno"> 473</span> <span class="comment">//---------------------------------------------------------------------------------</span></div>
+<div class="line"><a name="l00484"></a><span class="lineno"><a class="line" href="operator_8h.html#a926db042759256e94d20696c66f65709"> 484</a></span> <span class="comment"></span><span class="preprocessor">#define MXNET_REGISTER_OP_PROPERTY(name, OperatorPropertyType) \</span></div>
+<div class="line"><a name="l00485"></a><span class="lineno"> 485</span> <span class="preprocessor"> DMLC_REGISTRY_REGISTER(::mxnet::OperatorPropertyReg, OperatorPropertyReg, name) \</span></div>
+<div class="line"><a name="l00486"></a><span class="lineno"> 486</span> <span class="preprocessor"> .set_body([]() { return new OperatorPropertyType(); }) \</span></div>
+<div class="line"><a name="l00487"></a><span class="lineno"> 487</span> <span class="preprocessor"> .set_return_type("NDArray-or-Symbol") \</span></div>
+<div class="line"><a name="l00488"></a><span class="lineno"> 488</span> <span class="preprocessor"> .check_name()</span></div>
+<div class="line"><a name="l00489"></a><span class="lineno"> 489</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00490"></a><span class="lineno"> 490</span> <span class="preprocessor">#endif // DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00491"></a><span class="lineno"> 491</span> <span class="preprocessor"></span>} <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00492"></a><span class="lineno"> 492</span> <span class="preprocessor">#endif // MXNET_OPERATOR_H_</span></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_abf9e6a8d40750f3ee81fe30cbe3e2aae"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#abf9e6a8d40750f3ee81fe30cbe3e2aae">mxnet::OperatorProperty::DeclareBackwardDependency</a></div><div class="ttdeci">virtual std::vector< int > DeclareBackwardDependency(const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data) const </div><div class=" [...]
+<div class="ttc" id="structmxnet_1_1OperatorPropertyReg_html_ad959fdbefb6dd807ee1961a2fec7a038"><div class="ttname"><a href="structmxnet_1_1OperatorPropertyReg.html#ad959fdbefb6dd807ee1961a2fec7a038">mxnet::OperatorPropertyReg::check_name</a></div><div class="ttdeci">OperatorPropertyReg & check_name()</div><div class="ttdoc">Check if TypeString of the type matches the registered name. </div><div class="ttdef"><b>Definition:</b> operator.h:456</div></div>
<div class="ttc" id="namespacemxnet_html_ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c"><div class="ttname"><a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071adae3bade32f12a5139b27ef40b3689b77c">mxnet::ExecType::kSync</a></div><div class="ttdoc">Forward/Backward are synchronize calls. </div></div>
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_ab9e52badb93fe965ce961803f2101aa7"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#ab9e52badb93fe965ce961803f2101aa7">mxnet::OperatorProperty::ListOutputs</a></div><div class="ttdeci">virtual std::vector< std::string > ListOutputs() const</div><div class="ttdoc">Get name of output values of Operator. </div><div class="ttdef"><b>Definition:</b> operator.h:136</div></div>
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a6c5c8df10748a7eee873d4b7eb8d684b"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a6c5c8df10748a7eee873d4b7eb8d684b">mxnet::OperatorProperty::BackwardInputs</a></div><div class="ttdeci">std::vector< T > BackwardInputs(const std::vector< T > &out_grad, const std::vector< T > &in_data, const std::vector< T > &out_data) const</div><div class="ttdoc">Get Backward Input Dependency fo [...]
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a0255256ac4e0e76b3fff917560203efe"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a0255256ac4e0e76b3fff917560203efe">mxnet::OperatorProperty::BackwardInplaceOption</a></div><div class="ttdeci">virtual std::vector< std::pair< int, void * > > BackwardInplaceOption(const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data, const [...]
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_aa5d6354ebe3e2a0c51f7f7ce4a60f664"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#aa5d6354ebe3e2a0c51f7f7ce4a60f664">mxnet::OperatorProperty::exec_type</a></div><div class="ttdeci">virtual ExecType exec_type() const </div><div class="ttdef"><b>Definition:</b> operator.h:424</div></div>
<div class="ttc" id="classmxnet_1_1Operator_html_a3dbe23d985df86da51eeb868439872dd"><div class="ttname"><a href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd">mxnet::Operator::Forward</a></div><div class="ttdeci">virtual void Forward(const OpContext &ctx, const std::vector< TBlob > &in_data, const std::vector< OpReqType > &req, const std::vector< TBlob > &out_data, const std::vector< TBlob > &aux_states)=0</div><div class="ttdo [...]
<div class="ttc" id="classmxnet_1_1Operator_html_ad32d74b9bf56caf920b5e6379d2bb259"><div class="ttname"><a href="classmxnet_1_1Operator.html#ad32d74b9bf56caf920b5e6379d2bb259">mxnet::Operator::Backward</a></div><div class="ttdeci">virtual void Backward(const OpContext &ctx, const std::vector< TBlob > &out_grad, const std::vector< TBlob > &in_data, const std::vector< TBlob > &out_data, const std::vector< OpReqType > &req, const std::vector< T [...]
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a9fc6e9f04f52b1c05f62fb7357bb95c4"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a9fc6e9f04f52b1c05f62fb7357bb95c4">mxnet::OperatorProperty::NumOutputs</a></div><div class="ttdeci">virtual int NumOutputs() const</div><div class="ttdef"><b>Definition:</b> operator.h:147</div></div>
<div class="ttc" id="op__attr__types_8h_html"><div class="ttname"><a href="op__attr__types_8h.html">op_attr_types.h</a></div><div class="ttdoc">Additional operator attributes beside the ones provided by NNVM. </div></div>
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_ad01be3a84423800af7e818b45afc748f"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#ad01be3a84423800af7e818b45afc748f">mxnet::OperatorProperty::ListArguments</a></div><div class="ttdeci">virtual std::vector< std::string > ListArguments() const</div><div class="ttdoc">Get input arguments of the Operator. </div><div class="ttdef"><b>Definition:</b> operator.h:129</div></div>
<div class="ttc" id="classmxnet_1_1Operator_html_a4d34cc792d7271bd8b5b60eaa0b55767"><div class="ttname"><a href="classmxnet_1_1Operator.html#a4d34cc792d7271bd8b5b60eaa0b55767">mxnet::Operator::~Operator</a></div><div class="ttdeci">virtual ~Operator()</div><div class="ttdoc">destructor </div><div class="ttdef"><b>Definition:</b> operator.h:39</div></div>
<div class="ttc" id="namespacemxnet_html_a6f5428fbf261a94eaac079ec24413815"><div class="ttname"><a href="namespacemxnet.html#a6f5428fbf261a94eaac079ec24413815">mxnet::OperatorPropertyFactory</a></div><div class="ttdeci">std::function< OperatorProperty *()> OperatorPropertyFactory</div><div class="ttdoc">typedef the factory function of operator property </div><div class="ttdef"><b>Definition:</b> operator.h:430</div></div>
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a006f0e2c9b3f934e1f73b242b7e8b062"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a006f0e2c9b3f934e1f73b242b7e8b062">mxnet::OperatorProperty::NumVisibleOutputs</a></div><div class="ttdeci">virtual int NumVisibleOutputs() const</div><div class="ttdoc">get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...</div><div class="ttdef"><b>Definition:</b> operator.h:162</div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a9f488509e132094ce7e79125ce853042"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a9f488509e132094ce7e79125ce853042">mxnet::OperatorProperty::BackwardInputs</a></div><div class="ttdeci">std::vector< T > BackwardInputs(const std::vector< T > &out_grad, const std::vector< T > &in_data, const std::vector< T > &out_data) const </div><div class="ttdoc">Get Backward Input Dependency f [...]
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_ade7720a3e0ebba67d89978925a77609b"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#ade7720a3e0ebba67d89978925a77609b">mxnet::OperatorProperty::BackwardResource</a></div><div class="ttdeci">virtual std::vector< ResourceRequest > BackwardResource(const std::vector< TShape > &in_shape) const </div><div class="ttdoc">Declare additional resource required in backward pass. These additional resources will [...]
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a15cf1f905b08a008ff38556ef6a35dd0"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a15cf1f905b08a008ff38556ef6a35dd0">mxnet::OperatorProperty::Create</a></div><div class="ttdeci">static OperatorProperty * Create(const char *type_name)</div><div class="ttdoc">create OperatorProperty </div></div>
<div class="ttc" id="structmxnet_1_1OperatorPropertyReg_html_a0e3536ad5305fa1ef402c5385fb4a089"><div class="ttname"><a href="structmxnet_1_1OperatorPropertyReg.html#a0e3536ad5305fa1ef402c5385fb4a089">mxnet::OperatorPropertyReg::set_key_var_num_args</a></div><div class="ttdeci">OperatorPropertyReg & set_key_var_num_args(const std::string &key)</div><div class="ttdoc">Set key_var_num_args When this is set, the API caller is required to pass in a argument with key=key_...</div><div [...]
<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a610af8e9cb560104301ccd1efa2a96fd"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a610af8e9cb560104301ccd1efa2a96fd">mxnet::OperatorProperty::~OperatorProperty</a></div><div class="ttdeci">virtual ~OperatorProperty()</div><div class="ttdoc">virtual destructor </div><div class="ttdef"><b>Definition:</b> operator.h:113</div></div>
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a149c52bd285e4fa78109a535765a9b7c"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a149c52bd285e4fa78109a535765a9b7c">mxnet::OperatorProperty::exec_type</a></div><div class="ttdeci">virtual ExecType exec_type() const</div><div class="ttdef"><b>Definition:</b> operator.h:424</div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a1b37fb362748fa5027d4b5c925131892"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a1b37fb362748fa5027d4b5c925131892">mxnet::OperatorProperty::ListOutputs</a></div><div class="ttdeci">virtual std::vector< std::string > ListOutputs() const </div><div class="ttdoc">Get name of output values of Operator. </div><div class="ttdef"><b>Definition:</b> operator.h:136</div></div>
<div class="ttc" id="structmxnet_1_1OpContext_html"><div class="ttname"><a href="structmxnet_1_1OpContext.html">mxnet::OpContext</a></div><div class="ttdoc">All the possible information needed by Operator.Forward and Backward This is the superset of RunConte...</div><div class="ttdef"><b>Definition:</b> op_attr_types.h:47</div></div>
<div class="ttc" id="classmxnet_1_1Operator_html"><div class="ttname"><a href="classmxnet_1_1Operator.html">mxnet::Operator</a></div><div class="ttdoc">Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...</div><div class="ttdef"><b>Definition:</b> operator.h:36</div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a704ae43181c8a88bfd0a47e8005f7cb8"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a704ae43181c8a88bfd0a47e8005f7cb8">mxnet::OperatorProperty::GetParams</a></div><div class="ttdeci">virtual std::map< std::string, std::string > GetParams() const =0</div><div class="ttdoc">Get a map representation of internal parameters. This can be used by Init to recover the state of Ope...</div></div>
<div class="ttc" id="resource_8h_html"><div class="ttname"><a href="resource_8h.html">resource.h</a></div><div class="ttdoc">Global resource allocation handling. </div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_aad7a3e87866515a2b7100e849a2fe966"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#aad7a3e87866515a2b7100e849a2fe966">mxnet::OperatorProperty::ForwardInplaceOption</a></div><div class="ttdeci">virtual std::vector< std::pair< int, void * > > ForwardInplaceOption(const std::vector< int > &in_data, const std::vector< void * > &out_data) const </div><div class="ttdoc">Get possible forwar [...]
<div class="ttc" id="classmxnet_1_1OperatorProperty_html"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html">mxnet::OperatorProperty</a></div><div class="ttdoc">OperatorProperty is a object that stores all information about Operator. It also contains method to g...</div><div class="ttdef"><b>Definition:</b> operator.h:108</div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a7985004f99a931b4a6ff69df733bae72"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a7985004f99a931b4a6ff69df733bae72">mxnet::OperatorProperty::CreateOperatorEx</a></div><div class="ttdeci">virtual Operator * CreateOperatorEx(Context ctx, std::vector< TShape > *in_shape, std::vector< int > *in_type) const </div><div class="ttdoc">Create a Operator on specific context and input shape/type. </div><div clas [...]
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a67231a5a1525ec7480de98a79dce0194"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a67231a5a1525ec7480de98a79dce0194">mxnet::OperatorProperty::BackwardInplaceOption</a></div><div class="ttdeci">virtual std::vector< std::pair< int, void * > > BackwardInplaceOption(const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data, const [...]
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_ad5c9f53ea70f3eeb1bf27b983f420c0b"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#ad5c9f53ea70f3eeb1bf27b983f420c0b">mxnet::OperatorProperty::InferType</a></div><div class="ttdeci">virtual bool InferType(std::vector< int > *in_type, std::vector< int > *out_type, std::vector< int > *aux_type) const </div><div class="ttdoc">infer the data types of outputs and unknown input arguments </div><div clas [...]
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a0e20dead67dad0cd76c147a997ef5094"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a0e20dead67dad0cd76c147a997ef5094">mxnet::OperatorProperty::ListAuxiliaryStates</a></div><div class="ttdeci">virtual std::vector< std::string > ListAuxiliaryStates() const </div><div class="ttdoc">Get name of auxiliary states of Operator. </div><div class="ttdef"><b>Definition:</b> operator.h:143</div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a24cc6a3763ff5f0881a2de0652810596"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a24cc6a3763ff5f0881a2de0652810596">mxnet::OperatorProperty::ListArguments</a></div><div class="ttdeci">virtual std::vector< std::string > ListArguments() const </div><div class="ttdoc">Get input arguments of the Operator. </div><div class="ttdef"><b>Definition:</b> operator.h:129</div></div>
<div class="ttc" id="classmxnet_1_1Operator_html_aa6b4c247a914b4659c773965248019c7"><div class="ttname"><a href="classmxnet_1_1Operator.html#aa6b4c247a914b4659c773965248019c7">mxnet::Operator::exec_type</a></div><div class="ttdeci">virtual ExecType exec_type() const final</div><div class="ttdef"><b>Definition:</b> operator.h:94</div></div>
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a8aa205ea6ce962bfa3fcbe73a8f3db5d"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a8aa205ea6ce962bfa3fcbe73a8f3db5d">mxnet::OperatorProperty::DeclareBackwardDependency</a></div><div class="ttdeci">virtual std::vector< int > DeclareBackwardDependency(const std::vector< int > &out_grad, const std::vector< int > &in_data, const std::vector< int > &out_data) const</div><div class="t [...]
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a0fb32e0ab03a86517473db0f0630856c"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a0fb32e0ab03a86517473db0f0630856c">mxnet::OperatorProperty::Init</a></div><div class="ttdeci">virtual void Init(const std::vector< std::pair< std::string, std::string > > &kwargs)=0</div><div class="ttdoc">Initialize the Operator by setting the parameters This function need to be called before all other fu...</div></div>
<div class="ttc" id="structmxnet_1_1OperatorPropertyReg_html_af3aee96c6a2289e7554d7503186a540c"><div class="ttname"><a href="structmxnet_1_1OperatorPropertyReg.html#af3aee96c6a2289e7554d7503186a540c">mxnet::OperatorPropertyReg::key_var_num_args</a></div><div class="ttdeci">std::string key_var_num_args</div><div class="ttdoc">The key num_args name. </div><div class="ttdef"><b>Definition:</b> operator.h:467</div></div>
<div class="ttc" id="base_8h_html"><div class="ttname"><a href="base_8h.html">base.h</a></div><div class="ttdoc">configuation of mxnet as well as basic data structure. </div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a6dc5ecd089ef01dcc154c02b20ad2f4b"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a6dc5ecd089ef01dcc154c02b20ad2f4b">mxnet::OperatorProperty::NumVisibleOutputs</a></div><div class="ttdeci">virtual int NumVisibleOutputs() const </div><div class="ttdoc">get number of visible return values during Symbol creation. If NumVisibleOutputs() = k...</div><div class="ttdef"><b>Definition:</b> operator.h:162</div></div>
<div class="ttc" id="structmxnet_1_1OperatorPropertyReg_html"><div class="ttname"><a href="structmxnet_1_1OperatorPropertyReg.html">mxnet::OperatorPropertyReg</a></div><div class="ttdoc">Registry entry for OperatorProperty factory functions. </div><div class="ttdef"><b>Definition:</b> operator.h:434</div></div>
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_aa6d3a955ffa452a368a9dd4d33ce5a25"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#aa6d3a955ffa452a368a9dd4d33ce5a25">mxnet::OperatorProperty::ListAuxiliaryStates</a></div><div class="ttdeci">virtual std::vector< std::string > ListAuxiliaryStates() const</div><div class="ttdoc">Get name of auxiliary states of Operator. </div><div class="ttdef"><b>Definition:</b> operator.h:143</div></div>
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a78989f905d04bedb41896876fd4dbac9"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a78989f905d04bedb41896876fd4dbac9">mxnet::OperatorProperty::ForwardInplaceOption</a></div><div class="ttdeci">virtual std::vector< std::pair< int, void * > > ForwardInplaceOption(const std::vector< int > &in_data, const std::vector< void *> &out_data) const</div><div class="ttdoc">Get possible forward [...]
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_af07a7f946cfadb9bea19fa3d2b621ab4"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#af07a7f946cfadb9bea19fa3d2b621ab4">mxnet::OperatorProperty::CreateOperatorEx</a></div><div class="ttdeci">virtual Operator * CreateOperatorEx(Context ctx, std::vector< TShape > *in_shape, std::vector< int > *in_type) const</div><div class="ttdoc">Create a Operator on specific context and input shape/type. </div><div class [...]
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_acec41540dde2c49ff2f2a8fc425890e8"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#acec41540dde2c49ff2f2a8fc425890e8">mxnet::OperatorProperty::BackwardResource</a></div><div class="ttdeci">virtual std::vector< ResourceRequest > BackwardResource(const std::vector< TShape > &in_shape) const</div><div class="ttdoc">Declare additional resource required in backward pass. These additional resources will b [...]
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a543cbf36f0e4d8dc99109db71705a11c"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a543cbf36f0e4d8dc99109db71705a11c">mxnet::OperatorProperty::InferShape</a></div><div class="ttdeci">virtual bool InferShape(std::vector< TShape > *in_shape, std::vector< TShape > *out_shape, std::vector< TShape > *aux_shape) const =0</div><div class="ttdoc">infer the shapes of outputs and unknown input arguments </d [...]
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a623210dd1b9495ca73f223e0b06f998c"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a623210dd1b9495ca73f223e0b06f998c">mxnet::OperatorProperty::ForwardResource</a></div><div class="ttdeci">virtual std::vector< ResourceRequest > ForwardResource(const std::vector< TShape > &in_shape) const </div><div class="ttdoc">Declare additional resource required in forward pass. These additional resources will be [...]
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a546ab25ddaae590ca98705ae9abc7ac0"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a546ab25ddaae590ca98705ae9abc7ac0">mxnet::OperatorProperty::CreateOperator</a></div><div class="ttdeci">virtual Operator * CreateOperator(Context ctx) const =0</div><div class="ttdoc">Create a Operator on specific context. </div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_ab38f2f877555ba34b548efd2575cac99"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#ab38f2f877555ba34b548efd2575cac99">mxnet::OperatorProperty::Copy</a></div><div class="ttdeci">virtual OperatorProperty * Copy() const =0</div><div class="ttdoc">Copy this OperatorProperty. </div></div>
<div class="ttc" id="namespacemxnet_html_ad7801fb2dd68294d4c7a9e289bc071ad"><div class="ttname"><a href="namespacemxnet.html#ad7801fb2dd68294d4c7a9e289bc071ad">mxnet::ExecType</a></div><div class="ttdeci">ExecType</div><div class="ttdoc">the execution type of the operator </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:68</div></div>
+<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a49e25d85c38199338a9fa8150bc86df1"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a49e25d85c38199338a9fa8150bc86df1">mxnet::OperatorProperty::NumOutputs</a></div><div class="ttdeci">virtual int NumOutputs() const </div><div class="ttdef"><b>Definition:</b> operator.h:147</div></div>
<div class="ttc" id="structmxnet_1_1Context_html"><div class="ttname"><a href="structmxnet_1_1Context.html">mxnet::Context</a></div><div class="ttdoc">Context information about the execution environment. </div><div class="ttdef"><b>Definition:</b> base.h:116</div></div>
-<div class="ttc" id="classmxnet_1_1OperatorProperty_html_a819b81e14761e9e8b9c888a63c7df1fa"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#a819b81e14761e9e8b9c888a63c7df1fa">mxnet::OperatorProperty::InferType</a></div><div class="ttdeci">virtual bool InferType(std::vector< int > *in_type, std::vector< int > *out_type, std::vector< int > *aux_type) const</div><div class="ttdoc">infer the data types of outputs and unknown input arguments </div><div class [...]
<div class="ttc" id="classmxnet_1_1OperatorProperty_html_ac6b7df90c0285a1823b463c21e926698"><div class="ttname"><a href="classmxnet_1_1OperatorProperty.html#ac6b7df90c0285a1823b463c21e926698">mxnet::OperatorProperty::TypeString</a></div><div class="ttdeci">virtual std::string TypeString() const =0</div><div class="ttdoc">return the type string of the Operator subclasses override this function. </div></div>
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diff --git a/versions/master/doxygen/operator__util_8h.html b/versions/master/doxygen/operator__util_8h.html
index 036b2de..858defc 100644
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+++ b/versions/master/doxygen/operator__util_8h.html
@@ -3,15 +3,16 @@
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+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/operator_util.h File Reference</title>
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@@ -20,7 +21,7 @@
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
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@@ -29,25 +30,46 @@
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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@@ -75,28 +97,21 @@ $(function() {
<p>Utility functions and registries to help quickly build new operators. [Deprecated] Use the register functions in this file when possible to simplify operator creations. Operators registered in this file will be exposed to both NDArray API and symbolic API.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/registry.h></code><br />
-<code>#include <dmlc/parameter.h></code><br />
-<code>#include <map></code><br />
-<code>#include <vector></code><br />
-<code>#include <string></code><br />
-<code>#include <utility></code><br />
-<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br />
-<code>#include "<a class="el" href="operator_8h_source.html">./operator.h</a>"</code><br />
-<code>#include <functional></code><br />
+<div class="textblock"><code>#include <dmlc/registry.h></code><br/>
+<code>#include <dmlc/parameter.h></code><br/>
+<code>#include <map></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <string></code><br/>
+<code>#include <utility></code><br/>
+<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br/>
+<code>#include "<a class="el" href="operator_8h_source.html">./operator.h</a>"</code><br/>
+<code>#include <functional></code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for operator_util.h:</div>
<div class="dyncontent">
<div class="center"><img src="operator__util_8h__incl.png" border="0" usemap="#include_2mxnet_2operator__util_8h" alt=""/></div>
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+<area shape="rect" id="node8" href="base_8h.html" title="configuation of mxnet as well as basic data structure. " alt="" coords="1158,395,1231,421"/><area shape="rect" id="node16" href="operator_8h.html" title="Operator interface of mxnet. " alt="" coords="833,96,929,123"/><area shape="rect" id="node20" href="resource_8h.html" title="Global resource allocation handling. " alt="" coords="1199,245,1297,272"/><area shape="rect" id="node25" href="op__attr__types_8h.html" title="Additional op [...]
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@@ -104,109 +119,109 @@ Include dependency graph for operator_util.h:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html">mxnet::op::GradFunctionArgument</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">super class of all gradient function argument <a href="structmxnet_1_1op_1_1GradFunctionArgument.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">super class of all gradient function argument <a href="structmxnet_1_1op_1_1GradFunctionArgument.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1Input0.html">mxnet::op::Input0</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">First input to the function. <a href="structmxnet_1_1op_1_1Input0.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">First input to the function. <a href="structmxnet_1_1op_1_1Input0.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1Input1.html">mxnet::op::Input1</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Second input to the function. <a href="structmxnet_1_1op_1_1Input1.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Second input to the function. <a href="structmxnet_1_1op_1_1Input1.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1OutputValue.html">mxnet::op::OutputValue</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Ouput value of the function to the function. <a href="structmxnet_1_1op_1_1OutputValue.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Ouput value of the function to the function. <a href="structmxnet_1_1op_1_1OutputValue.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1OutputGrad.html">mxnet::op::OutputGrad</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Gradient of output value. <a href="structmxnet_1_1op_1_1OutputGrad.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Gradient of output value. <a href="structmxnet_1_1op_1_1OutputGrad.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html">mxnet::op::EnvArguments</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Environment arguments that is used by the function. These can be things like scalar arguments when add a value with scalar. <a href="structmxnet_1_1op_1_1EnvArguments.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Environment arguments that is used by the function. These can be things like scalar arguments when add a value with scalar. <a href="structmxnet_1_1op_1_1EnvArguments.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">mxnet::op::SimpleOpRegEntry</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">registry entry to register simple operators via functions. <a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">registry entry to register simple operators via functions. <a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1op_1_1SimpleOpRegistry.html">mxnet::op::SimpleOpRegistry</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">registry for <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> functions <a href="classmxnet_1_1op_1_1SimpleOpRegistry.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">registry for <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> functions <a href="classmxnet_1_1op_1_1SimpleOpRegistry.html#details">More...</a><br/></td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
+<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
+<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacemxnet_1_1op"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a></td></tr>
-<tr class="memdesc:namespacemxnet_1_1op"><td class="mdescLeft"> </td><td class="mdescRight">namespace of arguments <br /></td></tr>
+<tr class="memitem:namespacemxnet_1_1op"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html">mxnet::op</a></td></tr>
+<tr class="memdesc:namespacemxnet_1_1op"><td class="mdescLeft"> </td><td class="mdescRight">namespace of arguments <br/></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="define-members"></a>
Macros</h2></td></tr>
-<tr class="memitem:a0e0ffc581a0c4a40815110163625b517"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="operator__util_8h.html#a0e0ffc581a0c4a40815110163625b517">ASSIGN_DISPATCH</a>(out, req, exp)</td></tr>
-<tr class="memdesc:a0e0ffc581a0c4a40815110163625b517"><td class="mdescLeft"> </td><td class="mdescRight">assign the expression to out according to request <a href="#a0e0ffc581a0c4a40815110163625b517">More...</a><br /></td></tr>
+<tr class="memitem:a0e0ffc581a0c4a40815110163625b517"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="operator__util_8h.html#a0e0ffc581a0c4a40815110163625b517">ASSIGN_DISPATCH</a>(out, req, exp)</td></tr>
+<tr class="memdesc:a0e0ffc581a0c4a40815110163625b517"><td class="mdescLeft"> </td><td class="mdescRight">assign the expression to out according to request <a href="#a0e0ffc581a0c4a40815110163625b517">More...</a><br/></td></tr>
<tr class="separator:a0e0ffc581a0c4a40815110163625b517"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad815e6121771b9c0e292674c7b966f22"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="operator__util_8h.html#ad815e6121771b9c0e292674c7b966f22">MXNET_SPECIAL_MAX_NDIM</a>   5</td></tr>
-<tr class="memdesc:ad815e6121771b9c0e292674c7b966f22"><td class="mdescLeft"> </td><td class="mdescRight">Maximum ndim supported for special operators like broadcasting with non contiguous lhs/rhs. <a href="#ad815e6121771b9c0e292674c7b966f22">More...</a><br /></td></tr>
+<tr class="memdesc:ad815e6121771b9c0e292674c7b966f22"><td class="mdescLeft"> </td><td class="mdescRight">Maximum ndim supported for special operators like broadcasting with non contiguous lhs/rhs. <a href="#ad815e6121771b9c0e292674c7b966f22">More...</a><br/></td></tr>
<tr class="separator:ad815e6121771b9c0e292674c7b966f22"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a7c62fb645da1e054596c58315aeadc2c"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="operator__util_8h.html#a7c62fb645da1e054596c58315aeadc2c">MXNET_REGISTER_SIMPLE_OP</a>(Name, DEV)</td></tr>
-<tr class="memdesc:a7c62fb645da1e054596c58315aeadc2c"><td class="mdescLeft"> </td><td class="mdescRight">Macro to register simple operator to both imperative and symbolic API. <a href="#a7c62fb645da1e054596c58315aeadc2c">More...</a><br /></td></tr>
+<tr class="memitem:a7c62fb645da1e054596c58315aeadc2c"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="operator__util_8h.html#a7c62fb645da1e054596c58315aeadc2c">MXNET_REGISTER_SIMPLE_OP</a>(Name, DEV)</td></tr>
+<tr class="memdesc:a7c62fb645da1e054596c58315aeadc2c"><td class="mdescLeft"> </td><td class="mdescRight">Macro to register simple operator to both imperative and symbolic API. <a href="#a7c62fb645da1e054596c58315aeadc2c">More...</a><br/></td></tr>
<tr class="separator:a7c62fb645da1e054596c58315aeadc2c"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
-<tr class="memitem:a65e8bade5ae0539689b705b921ed9a9d"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a65e8bade5ae0539689b705b921ed9a9d">mxnet::op::SourceFunction</a>) (const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</td></tr>
-<tr class="memdesc:a65e8bade5ae0539689b705b921ed9a9d"><td class="mdescLeft"> </td><td class="mdescRight">source function that generate output based on env The result container is pre-allocated with the correct shape. <a href="namespacemxnet_1_1op.html#a65e8bade5ae0539689b705b921ed9a9d">More...</a><br /></td></tr>
-<tr class="separator:a65e8bade5ae0539689b705b921ed9a9d"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a3419f1ab0d1a62068a5e6c59efdc0fc9"><td class="memItemLeft" align="right" valign="top">typedef TShape(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a3419f1ab0d1a62068a5e6c59efdc0fc9">mxnet::op::SourceShapeFunction</a>) (const EnvArguments &env)</td></tr>
-<tr class="memdesc:a3419f1ab0d1a62068a5e6c59efdc0fc9"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape. <a href="namespacemxnet_1_1op.html#a3419f1ab0d1a62068a5e6c59efdc0fc9">More...</a><br /></td></tr>
-<tr class="separator:a3419f1ab0d1a62068a5e6c59efdc0fc9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:aa37d5216112b72e8110fc8654860910b"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#aa37d5216112b72e8110fc8654860910b">mxnet::op::UnaryFunction</a>) (const TBlob &src, const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</td></tr>
-<tr class="memdesc:aa37d5216112b72e8110fc8654860910b"><td class="mdescLeft"> </td><td class="mdescRight">Unary function that takes a src and save result to ret. The result container is pre-allocated with the correct shape. <a href="namespacemxnet_1_1op.html#aa37d5216112b72e8110fc8654860910b">More...</a><br /></td></tr>
-<tr class="separator:aa37d5216112b72e8110fc8654860910b"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a2d6fbc6ef1e22ea4433b3cf44ad3cef7"><td class="memItemLeft" align="right" valign="top">typedef TShape(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a2d6fbc6ef1e22ea4433b3cf44ad3cef7">mxnet::op::UnaryShapeFunction</a>) (const TShape &src, const EnvArguments &env)</td></tr>
-<tr class="memdesc:a2d6fbc6ef1e22ea4433b3cf44ad3cef7"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape given source. <a href="namespacemxnet_1_1op.html#a2d6fbc6ef1e22ea4433b3cf44ad3cef7">More...</a><br /></td></tr>
-<tr class="separator:a2d6fbc6ef1e22ea4433b3cf44ad3cef7"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a1e5767bffea568a1c38379060cbb68b4"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a1e5767bffea568a1c38379060cbb68b4">mxnet::op::UnaryGradFunctionT0</a>) (const OutputGrad &out_grad, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</td></tr>
-<tr class="memdesc:a1e5767bffea568a1c38379060cbb68b4"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes output value of function and computes gradient wrt to input. <a href="namespacemxnet_1_1op.html#a1e5767bffea568a1c38379060cbb68b4">More...</a><br /></td></tr>
-<tr class="separator:a1e5767bffea568a1c38379060cbb68b4"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a6ad11641816278f8fdb6fe87c7fd3740"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a6ad11641816278f8fdb6fe87c7fd3740">mxnet::op::UnaryGradFunctionT1</a>) (const OutputGrad &out_grad, const OutputValue &out_value, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</td></tr>
-<tr class="memdesc:a6ad11641816278f8fdb6fe87c7fd3740"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes output value of function and computes gradient wrt to input. <a href="namespacemxnet_1_1op.html#a6ad11641816278f8fdb6fe87c7fd3740">More...</a><br /></td></tr>
-<tr class="separator:a6ad11641816278f8fdb6fe87c7fd3740"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ac29102128f7e7785493bbb5b7db80997"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#ac29102128f7e7785493bbb5b7db80997">mxnet::op::UnaryGradFunctionT2</a>) (const OutputGrad &out_grad, const Input0 &in_data0, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</td></tr>
-<tr class="memdesc:ac29102128f7e7785493bbb5b7db80997"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes input value of function and computes gradient wrt to input. <a href="namespacemxnet_1_1op.html#ac29102128f7e7785493bbb5b7db80997">More...</a><br /></td></tr>
-<tr class="separator:ac29102128f7e7785493bbb5b7db80997"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a880eff3ebc68810ef133a6d8b77facc9"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a880eff3ebc68810ef133a6d8b77facc9">mxnet::op::BinaryFunction</a>) (const TBlob &lhs, const TBlob &rhs, const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</td></tr>
-<tr class="memdesc:a880eff3ebc68810ef133a6d8b77facc9"><td class="mdescLeft"> </td><td class="mdescRight">Binary function that takes lhs, rhs and save result to ret. The result container is pre-allocated with the correct shape. <a href="namespacemxnet_1_1op.html#a880eff3ebc68810ef133a6d8b77facc9">More...</a><br /></td></tr>
-<tr class="separator:a880eff3ebc68810ef133a6d8b77facc9"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a78c7bd2075894ac41351feca1b1e5c94"><td class="memItemLeft" align="right" valign="top">typedef TShape(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a78c7bd2075894ac41351feca1b1e5c94">mxnet::op::BinaryShapeFunction</a>) (const TShape &lhs, const TShape &rhs, const EnvArguments &env)</td></tr>
-<tr class="memdesc:a78c7bd2075894ac41351feca1b1e5c94"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape given source shapes. <a href="namespacemxnet_1_1op.html#a78c7bd2075894ac41351feca1b1e5c94">More...</a><br /></td></tr>
-<tr class="separator:a78c7bd2075894ac41351feca1b1e5c94"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:abcef841f759a7874ef50f21ae0226c8a"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#abcef841f759a7874ef50f21ae0226c8a">mxnet::op::BinaryGradFunctionT0</a>) (const OutputGrad &out_grad, const EnvArguments &env, TBlob *lhs_grad, TBlob *rhs_grad, OpReqType req_lhs_grad, OpReqType req_rhs_grad, RunContext ctx)</td></tr>
-<tr class="memdesc:abcef841f759a7874ef50f21ae0226c8a"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes only output gradient and computes gradient wrt to input. We support total gradient as a whole to make it easy to combine a few ops. <a href="namespacemxnet_1_1op.html#abcef841f759a7874ef50f21ae0226c8a">More...</a><br /></td></tr>
-<tr class="separator:abcef841f759a7874ef50f21ae0226c8a"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a027d1f190d964f7494816733aff6c3f2"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a027d1f190d964f7494816733aff6c3f2">mxnet::op::BinaryGradFunctionT1</a>) (const OutputGrad &out_grad, const Input0 &lhs, const Input1 &rhs, const EnvArguments &env, TBlob *lhs_grad, TBlob *rhs_grad, OpReqType req_lhs_grad, OpReqType req_rhs_grad, RunContext ct [...]
-<tr class="memdesc:a027d1f190d964f7494816733aff6c3f2"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes inputs of function anod computes gradient wrt to input. <a href="namespacemxnet_1_1op.html#a027d1f190d964f7494816733aff6c3f2">More...</a><br /></td></tr>
-<tr class="separator:a027d1f190d964f7494816733aff6c3f2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8dc66fcca4ca52343570931503fca2c3"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a8dc66fcca4ca52343570931503fca2c3">mxnet::op::SourceFunction</a> )(const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</td></tr>
+<tr class="memdesc:a8dc66fcca4ca52343570931503fca2c3"><td class="mdescLeft"> </td><td class="mdescRight">source function that generate output based on env The result container is pre-allocated with the correct shape. <a href="#a8dc66fcca4ca52343570931503fca2c3">More...</a><br/></td></tr>
+<tr class="separator:a8dc66fcca4ca52343570931503fca2c3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af71f289b0df41398bcbac75d37435a07"><td class="memItemLeft" align="right" valign="top">typedef TShape(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#af71f289b0df41398bcbac75d37435a07">mxnet::op::SourceShapeFunction</a> )(const EnvArguments &env)</td></tr>
+<tr class="memdesc:af71f289b0df41398bcbac75d37435a07"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape. <a href="#af71f289b0df41398bcbac75d37435a07">More...</a><br/></td></tr>
+<tr class="separator:af71f289b0df41398bcbac75d37435a07"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af5075d1f55b857402d9b9da07c164f11"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11">mxnet::op::UnaryFunction</a> )(const TBlob &src, const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</td></tr>
+<tr class="memdesc:af5075d1f55b857402d9b9da07c164f11"><td class="mdescLeft"> </td><td class="mdescRight">Unary function that takes a src and save result to ret. The result container is pre-allocated with the correct shape. <a href="#af5075d1f55b857402d9b9da07c164f11">More...</a><br/></td></tr>
+<tr class="separator:af5075d1f55b857402d9b9da07c164f11"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab004eaba5bbfd4a05d7ddeb54ce1c311"><td class="memItemLeft" align="right" valign="top">typedef TShape(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311">mxnet::op::UnaryShapeFunction</a> )(const TShape &src, const EnvArguments &env)</td></tr>
+<tr class="memdesc:ab004eaba5bbfd4a05d7ddeb54ce1c311"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape given source. <a href="#ab004eaba5bbfd4a05d7ddeb54ce1c311">More...</a><br/></td></tr>
+<tr class="separator:ab004eaba5bbfd4a05d7ddeb54ce1c311"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5e30ef3bfc75cf4b111af7b1c74dab1b"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b">mxnet::op::UnaryGradFunctionT0</a> )(const OutputGrad &out_grad, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</td></tr>
+<tr class="memdesc:a5e30ef3bfc75cf4b111af7b1c74dab1b"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes output value of function and computes gradient wrt to input. <a href="#a5e30ef3bfc75cf4b111af7b1c74dab1b">More...</a><br/></td></tr>
+<tr class="separator:a5e30ef3bfc75cf4b111af7b1c74dab1b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a362db0d0023a59204d018c32458ae4e3"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3">mxnet::op::UnaryGradFunctionT1</a> )(const OutputGrad &out_grad, const OutputValue &out_value, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</td></tr>
+<tr class="memdesc:a362db0d0023a59204d018c32458ae4e3"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes output value of function and computes gradient wrt to input. <a href="#a362db0d0023a59204d018c32458ae4e3">More...</a><br/></td></tr>
+<tr class="separator:a362db0d0023a59204d018c32458ae4e3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6b53969567339c4b97a8e0da51580c11"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11">mxnet::op::UnaryGradFunctionT2</a> )(const OutputGrad &out_grad, const Input0 &in_data0, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</td></tr>
+<tr class="memdesc:a6b53969567339c4b97a8e0da51580c11"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes input value of function and computes gradient wrt to input. <a href="#a6b53969567339c4b97a8e0da51580c11">More...</a><br/></td></tr>
+<tr class="separator:a6b53969567339c4b97a8e0da51580c11"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a97e7c6f5bbc6cdf02d443df6a36167e0"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0">mxnet::op::BinaryFunction</a> )(const TBlob &lhs, const TBlob &rhs, const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</td></tr>
+<tr class="memdesc:a97e7c6f5bbc6cdf02d443df6a36167e0"><td class="mdescLeft"> </td><td class="mdescRight">Binary function that takes lhs, rhs and save result to ret. The result container is pre-allocated with the correct shape. <a href="#a97e7c6f5bbc6cdf02d443df6a36167e0">More...</a><br/></td></tr>
+<tr class="separator:a97e7c6f5bbc6cdf02d443df6a36167e0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a05df0e7fd54e37144430654f8c200db5"><td class="memItemLeft" align="right" valign="top">typedef TShape(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5">mxnet::op::BinaryShapeFunction</a> )(const TShape &lhs, const TShape &rhs, const EnvArguments &env)</td></tr>
+<tr class="memdesc:a05df0e7fd54e37144430654f8c200db5"><td class="mdescLeft"> </td><td class="mdescRight">Shape inference function to get the correct shape given source shapes. <a href="#a05df0e7fd54e37144430654f8c200db5">More...</a><br/></td></tr>
+<tr class="separator:a05df0e7fd54e37144430654f8c200db5"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad1ec651ba6eee751c85b957df8972fc3"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3">mxnet::op::BinaryGradFunctionT0</a> )(const OutputGrad &out_grad, const EnvArguments &env, TBlob *lhs_grad, TBlob *rhs_grad, OpReqType req_lhs_grad, OpReqType req_rhs_grad, RunContext ctx)</td></tr>
+<tr class="memdesc:ad1ec651ba6eee751c85b957df8972fc3"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes only output gradient and computes gradient wrt to input. We support total gradient as a whole to make it easy to combine a few ops. <a href="#ad1ec651ba6eee751c85b957df8972fc3">More...</a><br/></td></tr>
+<tr class="separator:ad1ec651ba6eee751c85b957df8972fc3"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2ed3ee83974ac57efd4b251a85dca849"><td class="memItemLeft" align="right" valign="top">typedef void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849">mxnet::op::BinaryGradFunctionT1</a> )(const OutputGrad &out_grad, const Input0 &lhs, const Input1 &rhs, const EnvArguments &env, TBlob *lhs_grad, TBlob *rhs_grad, OpReqType req_lhs_grad, OpReqType req_rhs_grad, RunContext ct [...]
+<tr class="memdesc:a2ed3ee83974ac57efd4b251a85dca849"><td class="mdescLeft"> </td><td class="mdescRight">Gradient function that takes inputs of function anod computes gradient wrt to input. <a href="#a2ed3ee83974ac57efd4b251a85dca849">More...</a><br/></td></tr>
+<tr class="separator:a2ed3ee83974ac57efd4b251a85dca849"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="enum-members"></a>
Enumerations</h2></td></tr>
-<tr class="memitem:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">mxnet::op::SimpleOpInplaceOption</a> { <br />
+<tr class="memitem:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">mxnet::op::SimpleOpInplaceOption</a> { <br/>
  <a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2">mxnet::op::kNoInplace</a>,
<a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde">mxnet::op::kInplaceInOut</a>,
<a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daab7974a6a3982dae42ba00af90dcabd80">mxnet::op::kInplaceOutIn</a>,
<a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa31a764e0ff5966a10e26f7fbff308c18">mxnet::op::kInplaceLhsOut</a>,
-<br />
+<br/>
  <a class="el" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa01a199e9f1e7d97724be8aa618d5a3d4">mxnet::op::kInplaceOutLhs</a>
-<br />
- }<tr class="memdesc:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set inplace of operator <a href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">More...</a><br /></td></tr>
-</td></tr>
+<br/>
+ }</td></tr>
+<tr class="memdesc:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set inplace of operator <a href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">More...</a><br/></td></tr>
<tr class="separator:a3c3b8c77865c7c715cfe05ee2b2fa8da"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae7310844fa858f1ec6b6428dd6977a13"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">mxnet::op::SimpleOpScalarOption</a> { <a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13a44de4ba9d059034c023bc755cdc6026a">mxnet::op::kScalarBeforeArray</a>,
<a class="el" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7">mxnet::op::kArrayBeforeScalar</a>
- }<tr class="memdesc:ae7310844fa858f1ec6b6428dd6977a13"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set symbolic registration <a href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:ae7310844fa858f1ec6b6428dd6977a13"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set symbolic registration <a href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">More...</a><br/></td></tr>
<tr class="separator:ae7310844fa858f1ec6b6428dd6977a13"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a01bb0bb658bedb4d58bf7d11e4b17268"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">mxnet::op::SimpleOpRegOption</a> { <a class="el" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b">mxnet::op::kNotRegisterSymbolic</a>,
<a class="el" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1">mxnet::op::kRegisterSymbolic</a>
- }<tr class="memdesc:a01bb0bb658bedb4d58bf7d11e4b17268"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set symbolic registration <a href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:a01bb0bb658bedb4d58bf7d11e4b17268"><td class="mdescLeft"> </td><td class="mdescRight">options in the registry to set symbolic registration <a href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">More...</a><br/></td></tr>
<tr class="separator:a01bb0bb658bedb4d58bf7d11e4b17268"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
@@ -214,9 +229,7 @@ Enumerations</h2></td></tr>
<p>Copyright (c) 2015 by Contributors</p>
<dl class="section author"><dt>Author</dt><dd>Tianqi Chen </dd></dl>
</div><h2 class="groupheader">Macro Definition Documentation</h2>
-<a id="a0e0ffc581a0c4a40815110163625b517"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a0e0ffc581a0c4a40815110163625b517">◆ </a></span>ASSIGN_DISPATCH</h2>
-
+<a class="anchor" id="a0e0ffc581a0c4a40815110163625b517"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -245,7 +258,22 @@ Enumerations</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<b>Value:</b><div class="fragment"><div class="line">{ \</div><div class="line"> switch (req) { \</div><div class="line"> case <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26">kNullOp</a>: \</div><div class="line"> break; \</div><div class="line"> case <a class="code" href="namespacemxnet.html#a7cd7094ef222e [...]
+<b>Value:</b><div class="fragment"><div class="line">{ \</div>
+<div class="line"> switch (req) { \</div>
+<div class="line"> case <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26">kNullOp</a>: \</div>
+<div class="line"> break; \</div>
+<div class="line"> case <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a">kWriteTo</a>: \</div>
+<div class="line"> case <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51">kWriteInplace</a>: \</div>
+<div class="line"> (out) = (exp); \</div>
+<div class="line"> break; \</div>
+<div class="line"> case <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383">kAddTo</a>: \</div>
+<div class="line"> (out) += (exp); \</div>
+<div class="line"> break; \</div>
+<div class="line"> default: \</div>
+<div class="line"> LOG(FATAL) << <span class="stringliteral">"not reached"</span>; \</div>
+<div class="line"> } \</div>
+<div class="line"> }</div>
+<div class="ttc" id="namespacemxnet_html_a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26"><div class="ttname"><a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a001b85096e07108e174fe4c406368a26">mxnet::kNullOp</a></div><div class="ttdoc">no operation, do not write anything </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:28</div></div>
<div class="ttc" id="namespacemxnet_html_a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a"><div class="ttname"><a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a03b4c19f34d3add7deecd7ef6c54b42a">mxnet::kWriteTo</a></div><div class="ttdoc">write gradient to provided space </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:30</div></div>
<div class="ttc" id="namespacemxnet_html_a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51"><div class="ttname"><a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a640322face587ca12684378d0f053d51">mxnet::kWriteInplace</a></div><div class="ttdoc">perform an inplace write, Target shares memory with one of input arguments. This option only happen w...</div><div class="ttdef"><b>Definition:</b> op_attr_types.h:36</div></div>
<div class="ttc" id="namespacemxnet_html_a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383"><div class="ttname"><a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398a1b850737dba4d2c42a2687d14c10f383">mxnet::kAddTo</a></div><div class="ttdoc">add to the provided space </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:38</div></div>
@@ -269,9 +297,7 @@ Enumerations</h2></td></tr>
</div>
</div>
-<a id="a7c62fb645da1e054596c58315aeadc2c"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a7c62fb645da1e054596c58315aeadc2c">◆ </a></span>MXNET_REGISTER_SIMPLE_OP</h2>
-
+<a class="anchor" id="a7c62fb645da1e054596c58315aeadc2c"></a>
<div class="memitem">
<div class="memproto">
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@@ -294,17 +320,26 @@ Enumerations</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<b>Value:</b><div class="fragment"><div class="line">static ::mxnet::op::SimpleOpRegEntry & \</div><div class="line"> __make_ ## SimpleOpRegEntry ## _ ## Name ## __ ## DEV ##__ = <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a374ec19d647f622c80a79fb3d541e7ec">\</a></div><div class="line"><a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a374ec19d647f622c80a79fb3d541e7ec"> ::mxnet::op::SimpleOpRegistry::Get< [...]
+<b>Value:</b><div class="fragment"><div class="line">static ::mxnet::op::SimpleOpRegEntry & \</div>
+<div class="line"> __make_ ## SimpleOpRegEntry ## _ ## Name ## __ ## DEV ##__ = <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a374ec19d647f622c80a79fb3d541e7ec">\</a></div>
+<div class="line"><a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a374ec19d647f622c80a79fb3d541e7ec"> ::mxnet::op::SimpleOpRegistry::Get</a>()-><a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a376e3412e9f425db998c384e5d3f0523">__REGISTER_OR_FIND__</a>(#Name)</div>
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegistry_html_a374ec19d647f622c80a79fb3d541e7ec"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a374ec19d647f622c80a79fb3d541e7ec">mxnet::op::SimpleOpRegistry::Get</a></div><div class="ttdeci">static SimpleOpRegistry * Get()</div></div>
<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegistry_html_a376e3412e9f425db998c384e5d3f0523"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a376e3412e9f425db998c384e5d3f0523">mxnet::op::SimpleOpRegistry::__REGISTER_OR_FIND__</a></div><div class="ttdeci">SimpleOpRegEntry & __REGISTER_OR_FIND__(char const *name)</div><div class="ttdoc">Internal function to register a name function under name. </div></div>
</div><!-- fragment -->
<p>Macro to register simple operator to both imperative and symbolic API. </p>
<p>see src/operator/elementwise_unary_op-inl.h for example</p>
-<div class="fragment"><div class="line"><span class="comment">// example of registering a sigmoid operator on GPU</span></div><div class="line"><span class="comment">// MySigmoid is of type UnaryFunction,</span></div><div class="line"><span class="comment">// MySigmoidGrad is of type UnaryGradFunctionT2</span></div><div class="line"></div><div class="line"><a class="code" href="operator__util_8h.html#a7c62fb645da1e054596c58315aeadc2c">MXNET_REGISTER_SIMPLE_OP</a>(sigmoid, <a class="code" [...]
+<div class="fragment"><div class="line"><span class="comment">// example of registering a sigmoid operator on GPU</span></div>
+<div class="line"><span class="comment">// MySigmoid is of type UnaryFunction,</span></div>
+<div class="line"><span class="comment">// MySigmoidGrad is of type UnaryGradFunctionT2</span></div>
+<div class="line"></div>
+<div class="line"><a class="code" href="operator__util_8h.html#a7c62fb645da1e054596c58315aeadc2c">MXNET_REGISTER_SIMPLE_OP</a>(sigmoid, <a class="code" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">cpu</a>)</div>
+<div class="line">.set_function(MySigmoid<gpu>, <span class="keyword">true</span>)</div>
+<div class="line">.set_gradient(MySigmoidGrad<gpu>, <span class="keyword">true</span>)</div>
+<div class="line">.describe(<span class="stringliteral">"Sigmoid function"</span>);</div>
+</div><!-- fragment -->
</div>
</div>
-<a id="ad815e6121771b9c0e292674c7b966f22"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad815e6121771b9c0e292674c7b966f22">◆ </a></span>MXNET_SPECIAL_MAX_NDIM</h2>
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@@ -321,9 +356,9 @@ Enumerations</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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+++ b/versions/master/doxygen/operator__util_8h_source.html
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<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/operator_util.h Source File</title>
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<script type="text/javascript" src="jquery.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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@@ -66,60 +88,280 @@ $(function() {
<div class="title">operator_util.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="operator__util_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#ifndef MXNET_OPERATOR_UTIL_H_</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#define MXNET_OPERATOR_UTIL_H_</span></div><div class="line [...]
-<div class="ttc" id="namespacemxnet_1_1op_html_ac29102128f7e7785493bbb5b7db80997"><div class="ttname"><a href="namespacemxnet_1_1op.html#ac29102128f7e7785493bbb5b7db80997">mxnet::op::UnaryGradFunctionT2</a></div><div class="ttdeci">void(* UnaryGradFunctionT2)(const OutputGrad &out_grad, const Input0 &in_data0, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</div><div class="ttdoc">Gradient function that takes input value of function and computes gradie [...]
+<a href="operator__util_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#ifndef MXNET_OPERATOR_UTIL_H_</span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_OPERATOR_UTIL_H_</span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#ifdef _MSC_VER</span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor"></span><span class="preprocessor">#pragma warning(disable:4503) // disable warning: decorated name length exceeded.</span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor"></span><span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <dmlc/registry.h></span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <dmlc/parameter.h></span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <map></span></div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#include <string></span></div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include <utility></span></div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include "<a class="code" href="operator_8h.html">./operator.h</a>"</span></div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div>
+<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#if DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor"></span><span class="preprocessor">#include <functional></span></div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00033"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html"> 33</a></span> <span class="keyword">namespace </span>op {</div>
+<div class="line"><a name="l00035"></a><span class="lineno"><a class="line" href="structmxnet_1_1op_1_1GradFunctionArgument.html"> 35</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1op_1_1GradFunctionArgument.html">GradFunctionArgument</a> {</div>
+<div class="line"><a name="l00037"></a><span class="lineno"><a class="line" href="structmxnet_1_1op_1_1GradFunctionArgument.html#ab9214ef3cfcf53b375a2e8f716e66929"> 37</a></span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a> <a class="code" href="structmxnet_1_1op_1_1GradFunctionArgument.html#ab9214ef3cfcf53b375a2e8f716e66929">data</a>;</div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> };</div>
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+<div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="structmxnet_1_1op_1_1OutputGrad.html"> 48</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a> : <a class="code" href="structmxnet_1_1op_1_1GradFunctionArgument.html">GradFunctionArgument</a> {};</div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div>
+<div class="line"><a name="l00054"></a><span class="lineno"><a class="line" href="structmxnet_1_1op_1_1EnvArguments.html"> 54</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a> {</div>
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+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> };</div>
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+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* ret,</div>
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+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div>
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+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div>
+<div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11"> 92</a></span> <span class="keyword">typedef</span> void (*<a class="code" href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11">UnaryFunction</a>)(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>& src,</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a>& env,</div>
+<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* ret,</div>
+<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req,</div>
+<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> ctx);</div>
+<div class="line"><a name="l00103"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311"> 103</a></span> <span class="keyword">typedef</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> (*<a class="code" href="namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311">UnaryShapeFunction</a>)(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc818 [...]
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a>& env);</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div>
+<div class="line"><a name="l00114"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b"> 114</a></span> <span class="keyword">typedef</span> void (*<a class="code" href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b">UnaryGradFunctionT0</a>)(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a>& out_grad,</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a>& env,</div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* in_grad,</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req,</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> ctx);</div>
+<div class="line"><a name="l00128"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3"> 128</a></span> <span class="keyword">typedef</span> void (*<a class="code" href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3">UnaryGradFunctionT1</a>)(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a>& out_grad,</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1OutputValue.html">OutputValue</a>& out_value,</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a>& env,</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* in_grad,</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req,</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> ctx);</div>
+<div class="line"><a name="l00143"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11"> 143</a></span> <span class="keyword">typedef</span> void (*<a class="code" href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11">UnaryGradFunctionT2</a>)(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a>& out_grad,</div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1Input0.html">Input0</a>& in_data0,</div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a>& env,</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* in_grad,</div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req,</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> ctx);</div>
+<div class="line"><a name="l00159"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0"> 159</a></span> <span class="keyword">typedef</span> void (*<a class="code" href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0">BinaryFunction</a>)(<span class="keyword">const</span> <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>& lhs,</div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">const</span> <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>& rhs,</div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a>& env,</div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* ret,</div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req,</div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> ctx);</div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div>
+<div class="line"><a name="l00173"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5"> 173</a></span> <span class="keyword">typedef</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> (*<a class="code" href="namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5">BinaryShapeFunction</a>)(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81 [...]
+<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>& rhs,</div>
+<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a>& env);</div>
+<div class="line"><a name="l00187"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3"> 187</a></span> <span class="keyword">typedef</span> void (*<a class="code" href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3">BinaryGradFunctionT0</a>)(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a>& out_grad,</div>
+<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a>& env,</div>
+<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* lhs_grad,</div>
+<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* rhs_grad,</div>
+<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req_lhs_grad,</div>
+<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req_rhs_grad,</div>
+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> ctx);</div>
+<div class="line"><a name="l00206"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849"> 206</a></span> <span class="keyword">typedef</span> void (*<a class="code" href="namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849">BinaryGradFunctionT1</a>)(<span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1OutputGrad.html">OutputGrad</a>& out_grad,</div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1Input0.html">Input0</a>& lhs,</div>
+<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1Input1.html">Input1</a>& rhs,</div>
+<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1op_1_1EnvArguments.html">EnvArguments</a>& env,</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* lhs_grad,</div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a>* rhs_grad,</div>
+<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req_lhs_grad,</div>
+<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">OpReqType</a> req_rhs_grad,</div>
+<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="structmxnet_1_1RunContext.html">RunContext</a> ctx);</div>
+<div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div>
+<div class="line"><a name="l00217"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da"> 217</a></span> <span class="keyword">enum</span> <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> {</div>
+<div class="line"><a name="l00219"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2"> 219</a></span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2">kNoInplace</a>,</div>
+<div class="line"><a name="l00221"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde"> 221</a></span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde">kInplaceInOut</a>,</div>
+<div class="line"><a name="l00223"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daab7974a6a3982dae42ba00af90dcabd80"> 223</a></span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daab7974a6a3982dae42ba00af90dcabd80">kInplaceOutIn</a>,</div>
+<div class="line"><a name="l00225"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa31a764e0ff5966a10e26f7fbff308c18"> 225</a></span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa31a764e0ff5966a10e26f7fbff308c18">kInplaceLhsOut</a>,</div>
+<div class="line"><a name="l00227"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa01a199e9f1e7d97724be8aa618d5a3d4"> 227</a></span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa01a199e9f1e7d97724be8aa618d5a3d4">kInplaceOutLhs</a></div>
+<div class="line"><a name="l00228"></a><span class="lineno"> 228</span> };</div>
+<div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div>
+<div class="line"><a name="l00231"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13"> 231</a></span> <span class="keyword">enum</span> <a class="code" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">SimpleOpScalarOption</a> {</div>
+<div class="line"><a name="l00232"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13a44de4ba9d059034c023bc755cdc6026a"> 232</a></span>  <a class="code" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13a44de4ba9d059034c023bc755cdc6026a">kScalarBeforeArray</a>,</div>
+<div class="line"><a name="l00233"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7"> 233</a></span>  <a class="code" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7">kArrayBeforeScalar</a></div>
+<div class="line"><a name="l00234"></a><span class="lineno"> 234</span> };</div>
+<div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div>
+<div class="line"><a name="l00237"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268"> 237</a></span> <span class="keyword">enum</span> <a class="code" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">SimpleOpRegOption</a> {</div>
+<div class="line"><a name="l00238"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b"> 238</a></span>  <a class="code" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b">kNotRegisterSymbolic</a>,</div>
+<div class="line"><a name="l00239"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1"> 239</a></span>  <a class="code" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1">kRegisterSymbolic</a></div>
+<div class="line"><a name="l00240"></a><span class="lineno"> 240</span> };</div>
+<div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div>
+<div class="line"><a name="l00243"></a><span class="lineno"><a class="line" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html"> 243</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">SimpleOpRegEntry</a> {</div>
+<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00246"></a><span class="lineno"><a class="line" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3"> 246</a></span>  <span class="keyword">typedef</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">SimpleOpRegEntry</a> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">TSelf</a>;</div>
+<div class="line"><a name="l00248"></a><span class="lineno"><a class="line" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a02c180f08b215e9553d3c93fa783f9a4"> 248</a></span>  std::string <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a02c180f08b215e9553d3c93fa783f9a4">name</a>;</div>
+<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac258ea0f99d31b36e7dd682c25b3aeca">set_symbol_op_name</a>(<span class="keywordtype">char</span> <span class="keyword">const</span>* symbol_name) = 0;</div>
+<div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a0725f1b670044149a088355ffeb6977f">set_enable_scalar</a>(</div>
+<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordtype">bool</span> enable_scalar,</div>
+<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <a class="code" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">SimpleOpScalarOption</a> type_mask = <a class="code" href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7">kArrayBeforeScalar</a>) = 0;</div>
+<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a4cdffb8337b41a643c05632b29e65a2b">set_enable_kwargs</a>(<span class="keywordtype">bool</span> enable_kwargs) = 0;</div>
+<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#af67ac155e01e797e7f633789453fb5f3">set_resource_request</a>(</div>
+<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keyword">const</span> std::vector<ResourceRequest>& reqs) = 0;</div>
+<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#af67ac155e01e797e7f633789453fb5f3">set_resource_request</a>(<a class="code" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> req) = 0;</div>
+<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a3be9c65fc5f747b6a05e3c2889d8f72b">set_shape_function</a>(<a class="code" href="namespacemxnet_1_1op.html#af71f289b0df41398bcbac75d37435a07">SourceShapeFunction</a> fshapeinfer) = 0;</div>
+<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a3be9c65fc5f747b6a05e3c2889d8f72b">set_shape_function</a>(<a class="code" href="namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311">UnaryShapeFunction</a> fshapeinfer) = 0;</div>
+<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a3be9c65fc5f747b6a05e3c2889d8f72b">set_shape_function</a>(<a class="code" href="namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5">BinaryShapeFunction</a> fshapeinfer) = 0;</div>
+<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1f9bcff5039f065f73c7ec3a2558d799">set_function</a>(</div>
+<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordtype">int</span> dev_mask,</div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="namespacemxnet_1_1op.html#a8dc66fcca4ca52343570931503fca2c3">SourceFunction</a> fsource,</div>
+<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <a class="code" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">SimpleOpRegOption</a> register_symbolic = <a class="code" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1">kRegisterSymbolic</a>) = 0;</div>
+<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1f9bcff5039f065f73c7ec3a2558d799">set_function</a>(</div>
+<div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keywordtype">int</span> dev_mask,</div>
+<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <a class="code" href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11">UnaryFunction</a> funary,</div>
+<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> inplace_in_out,</div>
+<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <a class="code" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">SimpleOpRegOption</a> register_symbolic = <a class="code" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1">kRegisterSymbolic</a>) = 0;</div>
+<div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1f9bcff5039f065f73c7ec3a2558d799">set_function</a>(</div>
+<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keywordtype">int</span> dev_mask,</div>
+<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0">BinaryFunction</a> fbinary,</div>
+<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> inplace_lhs_out,</div>
+<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <a class="code" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">SimpleOpRegOption</a> register_symbolic = <a class="code" href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1">kRegisterSymbolic</a>) = 0;</div>
+<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ab8fe122e15142dc85c679071acf53302">set_gradient</a>(<span class="keywordtype">int</span> dev_mask,</div>
+<div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <a class="code" href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b">UnaryGradFunctionT0</a> fgrad,</div>
+<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> inplace_out_in_grad) = 0;</div>
+<div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ab8fe122e15142dc85c679071acf53302">set_gradient</a>(<span class="keywordtype">int</span> dev_mask,</div>
+<div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3">UnaryGradFunctionT1</a> fgrad,</div>
+<div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> inplace_out_in_grad) = 0;</div>
+<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ab8fe122e15142dc85c679071acf53302">set_gradient</a>(<span class="keywordtype">int</span> dev_mask,</div>
+<div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <a class="code" href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11">UnaryGradFunctionT2</a> fgrad,</div>
+<div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> inplace_out_in_grad) = 0;</div>
+<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ab8fe122e15142dc85c679071acf53302">set_gradient</a>(<span class="keywordtype">int</span> dev_mask,</div>
+<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <a class="code" href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3">BinaryGradFunctionT0</a> fgrad,</div>
+<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> inplace_out_lhs_grad) = 0;</div>
+<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ab8fe122e15142dc85c679071acf53302">set_gradient</a>(<span class="keywordtype">int</span> dev_mask,</div>
+<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <a class="code" href="namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849">BinaryGradFunctionT1</a> fgrad,</div>
+<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <a class="code" href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">SimpleOpInplaceOption</a> inplace_out_lhs_grad) = 0;</div>
+<div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1cce9a6aaa175711b6be5b864b055f6a">describe</a>(<span class="keyword">const</span> std::string &description) = 0;</div>
+<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">TSelf</a>& <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a405bfb5929d459d45d93c2eaa645bfb5">add_arguments</a>(<span class="keyword">const</span> std::vector<dmlc::ParamFieldInfo> &args) = 0;</div>
+<div class="line"><a name="l00398"></a><span class="lineno"><a class="line" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a94815080350681ad70e7815da1bc0eae"> 398</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a94815080350681ad70e7815da1bc0eae">~SimpleOpRegEntry</a>() {}</div>
+<div class="line"><a name="l00399"></a><span class="lineno"> 399</span> };</div>
+<div class="line"><a name="l00400"></a><span class="lineno"> 400</span> </div>
+<div class="line"><a name="l00402"></a><span class="lineno"><a class="line" href="classmxnet_1_1op_1_1SimpleOpRegistry.html"> 402</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html">SimpleOpRegistry</a> {</div>
+<div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">SimpleOpRegEntry</a> &<a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a376e3412e9f425db998c384e5d3f0523">__REGISTER_OR_FIND__</a>(<span class="keywordtype">char</span> <span class="keyword">const</span>* name);</div>
+<div class="line"><a name="l00415"></a><span class="lineno"><a class="line" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a8ee596c99eef39fbbfffca181bf7f26e"> 415</a></span>  <span class="keyword">inline</span> <span class="keyword">static</span> <span class="keyword">const</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">SimpleOpRegEntry</a> *<a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a8ee596c99eef39fbbfffca181bf7f26e">Find</a>(<span c [...]
+<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keywordflow">return</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a374ec19d647f622c80a79fb3d541e7ec">Get</a>()->fmap_.at(name);</div>
+<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  }</div>
+<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html">SimpleOpRegistry</a>* <a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a374ec19d647f622c80a79fb3d541e7ec">Get</a>();</div>
+<div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div>
+<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="comment">// destructor</span></div>
+<div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  ~<a class="code" href="classmxnet_1_1op_1_1SimpleOpRegistry.html">SimpleOpRegistry</a>();</div>
+<div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  std::map<std::string, SimpleOpRegEntry*> fmap_;</div>
+<div class="line"><a name="l00426"></a><span class="lineno"> 426</span> };</div>
+<div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div>
+<div class="line"><a name="l00436"></a><span class="lineno"><a class="line" href="operator__util_8h.html#a0e0ffc581a0c4a40815110163625b517"> 436</a></span> <span class="preprocessor">#define ASSIGN_DISPATCH(out, req, exp) \</span></div>
+<div class="line"><a name="l00437"></a><span class="lineno"> 437</span> <span class="preprocessor"> { \</span></div>
+<div class="line"><a name="l00438"></a><span class="lineno"> 438</span> <span class="preprocessor"> switch (req) { \</span></div>
+<div class="line"><a name="l00439"></a><span class="lineno"> 439</span> <span class="preprocessor"> case kNullOp: \</span></div>
+<div class="line"><a name="l00440"></a><span class="lineno"> 440</span> <span class="preprocessor"> break; \</span></div>
+<div class="line"><a name="l00441"></a><span class="lineno"> 441</span> <span class="preprocessor"> case kWriteTo: \</span></div>
+<div class="line"><a name="l00442"></a><span class="lineno"> 442</span> <span class="preprocessor"> case kWriteInplace: \</span></div>
+<div class="line"><a name="l00443"></a><span class="lineno"> 443</span> <span class="preprocessor"> (out) = (exp); \</span></div>
+<div class="line"><a name="l00444"></a><span class="lineno"> 444</span> <span class="preprocessor"> break; \</span></div>
+<div class="line"><a name="l00445"></a><span class="lineno"> 445</span> <span class="preprocessor"> case kAddTo: \</span></div>
+<div class="line"><a name="l00446"></a><span class="lineno"> 446</span> <span class="preprocessor"> (out) += (exp); \</span></div>
+<div class="line"><a name="l00447"></a><span class="lineno"> 447</span> <span class="preprocessor"> break; \</span></div>
+<div class="line"><a name="l00448"></a><span class="lineno"> 448</span> <span class="preprocessor"> default: \</span></div>
+<div class="line"><a name="l00449"></a><span class="lineno"> 449</span> <span class="preprocessor"> LOG(FATAL) << "not reached"; \</span></div>
+<div class="line"><a name="l00450"></a><span class="lineno"> 450</span> <span class="preprocessor"> } \</span></div>
+<div class="line"><a name="l00451"></a><span class="lineno"> 451</span> <span class="preprocessor"> }</span></div>
+<div class="line"><a name="l00452"></a><span class="lineno"> 452</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00456"></a><span class="lineno"><a class="line" href="operator__util_8h.html#ad815e6121771b9c0e292674c7b966f22"> 456</a></span> <span class="preprocessor">#define MXNET_SPECIAL_MAX_NDIM 5</span></div>
+<div class="line"><a name="l00457"></a><span class="lineno"> 457</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div>
+<div class="line"><a name="l00459"></a><span class="lineno"> 459</span> <span class="comment">//--------------------------------------------------------------</span></div>
+<div class="line"><a name="l00460"></a><span class="lineno"> 460</span> <span class="comment">// The following part are API Registration of Simple Operators</span></div>
+<div class="line"><a name="l00461"></a><span class="lineno"> 461</span> <span class="comment">//--------------------------------------------------------------</span></div>
+<div class="line"><a name="l00479"></a><span class="lineno"><a class="line" href="operator__util_8h.html#a7c62fb645da1e054596c58315aeadc2c"> 479</a></span> <span class="comment"></span><span class="preprocessor">#define MXNET_REGISTER_SIMPLE_OP(Name, DEV) \</span></div>
+<div class="line"><a name="l00480"></a><span class="lineno"> 480</span> <span class="preprocessor"> static ::mxnet::op::SimpleOpRegEntry & \</span></div>
+<div class="line"><a name="l00481"></a><span class="lineno"> 481</span> <span class="preprocessor"> __make_ ## SimpleOpRegEntry ## _ ## Name ## __ ## DEV ##__ = \</span></div>
+<div class="line"><a name="l00482"></a><span class="lineno"> 482</span> <span class="preprocessor"> ::mxnet::op::SimpleOpRegistry::Get()->__REGISTER_OR_FIND__(#Name)</span></div>
+<div class="line"><a name="l00483"></a><span class="lineno"> 483</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00484"></a><span class="lineno"> 484</span> } <span class="comment">// namespace op</span></div>
+<div class="line"><a name="l00485"></a><span class="lineno"> 485</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00486"></a><span class="lineno"> 486</span> <span class="preprocessor">#endif // MXNET_OPERATOR_UTIL_H_</span></div>
+<div class="ttc" id="namespacemxnet_1_1op_html_a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1"><div class="ttname"><a href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a534486ab7c3f672542bfa09b7b6982d1">mxnet::op::kRegisterSymbolic</a></div><div class="ttdef"><b>Definition:</b> operator_util.h:239</div></div>
<div class="ttc" id="structmxnet_1_1op_1_1OutputGrad_html"><div class="ttname"><a href="structmxnet_1_1op_1_1OutputGrad.html">mxnet::op::OutputGrad</a></div><div class="ttdoc">Gradient of output value. </div><div class="ttdef"><b>Definition:</b> operator_util.h:48</div></div>
-<div class="ttc" id="namespacemxnet_1_1op_html_a3419f1ab0d1a62068a5e6c59efdc0fc9"><div class="ttname"><a href="namespacemxnet_1_1op.html#a3419f1ab0d1a62068a5e6c59efdc0fc9">mxnet::op::SourceShapeFunction</a></div><div class="ttdeci">TShape(* SourceShapeFunction)(const EnvArguments &env)</div><div class="ttdoc">Shape inference function to get the correct shape. </div><div class="ttdef"><b>Definition:</b> operator_util.h:81</div></div>
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_af67ac155e01e797e7f633789453fb5f3"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#af67ac155e01e797e7f633789453fb5f3">mxnet::op::SimpleOpRegEntry::set_resource_request</a></div><div class="ttdeci">virtual TSelf & set_resource_request(const std::vector< ResourceRequest > &reqs)=0</div><div class="ttdoc">set resource request By default there is no resource request. The resource will be prese [...]
<div class="ttc" id="namespacemxnet_1_1op_html_a01bb0bb658bedb4d58bf7d11e4b17268"><div class="ttname"><a href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268">mxnet::op::SimpleOpRegOption</a></div><div class="ttdeci">SimpleOpRegOption</div><div class="ttdoc">options in the registry to set symbolic registration </div><div class="ttdef"><b>Definition:</b> operator_util.h:237</div></div>
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_a1f9bcff5039f065f73c7ec3a2558d799"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1f9bcff5039f065f73c7ec3a2558d799">mxnet::op::SimpleOpRegEntry::set_function</a></div><div class="ttdeci">virtual TSelf & set_function(int dev_mask, SourceFunction fsource, SimpleOpRegOption register_symbolic=kRegisterSymbolic)=0</div><div class="ttdoc">set function of the function to be fsource </div></div>
<div class="ttc" id="namespacemxnet_1_1op_html_ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7"><div class="ttname"><a href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13aa4cab4197f615e92b1fdb61c51e1b9c7">mxnet::op::kArrayBeforeScalar</a></div><div class="ttdef"><b>Definition:</b> operator_util.h:233</div></div>
-<div class="ttc" id="namespacemxnet_1_1op_html_a880eff3ebc68810ef133a6d8b77facc9"><div class="ttname"><a href="namespacemxnet_1_1op.html#a880eff3ebc68810ef133a6d8b77facc9">mxnet::op::BinaryFunction</a></div><div class="ttdeci">void(* BinaryFunction)(const TBlob &lhs, const TBlob &rhs, const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</div><div class="ttdoc">Binary function that takes lhs, rhs and save result to ret. The result container is pre-allocated wit. [...]
-<div class="ttc" id="namespacemxnet_1_1op_html_aa37d5216112b72e8110fc8654860910b"><div class="ttname"><a href="namespacemxnet_1_1op.html#aa37d5216112b72e8110fc8654860910b">mxnet::op::UnaryFunction</a></div><div class="ttdeci">void(* UnaryFunction)(const TBlob &src, const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</div><div class="ttdoc">Unary function that takes a src and save result to ret. The result container is pre-allocated with th...</div><div class="ttde [...]
-<div class="ttc" id="namespacemxnet_1_1op_html_a78c7bd2075894ac41351feca1b1e5c94"><div class="ttname"><a href="namespacemxnet_1_1op.html#a78c7bd2075894ac41351feca1b1e5c94">mxnet::op::BinaryShapeFunction</a></div><div class="ttdeci">TShape(* BinaryShapeFunction)(const TShape &lhs, const TShape &rhs, const EnvArguments &env)</div><div class="ttdoc">Shape inference function to get the correct shape given source shapes. </div><div class="ttdef"><b>Definition:</b> operator_util.h: [...]
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
-<div class="ttc" id="namespacemxnet_1_1op_html_a027d1f190d964f7494816733aff6c3f2"><div class="ttname"><a href="namespacemxnet_1_1op.html#a027d1f190d964f7494816733aff6c3f2">mxnet::op::BinaryGradFunctionT1</a></div><div class="ttdeci">void(* BinaryGradFunctionT1)(const OutputGrad &out_grad, const Input0 &lhs, const Input1 &rhs, const EnvArguments &env, TBlob *lhs_grad, TBlob *rhs_grad, OpReqType req_lhs_grad, OpReqType req_rhs_grad, RunContext ctx)</div><div class="ttdoc">G [...]
+<div class="ttc" id="namespacemxnet_1_1op_html_a05df0e7fd54e37144430654f8c200db5"><div class="ttname"><a href="namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5">mxnet::op::BinaryShapeFunction</a></div><div class="ttdeci">TShape(* BinaryShapeFunction)(const TShape &lhs, const TShape &rhs, const EnvArguments &env)</div><div class="ttdoc">Shape inference function to get the correct shape given source shapes. </div><div class="ttdef"><b>Definition:</b> operator_util.h: [...]
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_a4cdffb8337b41a643c05632b29e65a2b"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a4cdffb8337b41a643c05632b29e65a2b">mxnet::op::SimpleOpRegEntry::set_enable_kwargs</a></div><div class="ttdeci">virtual TSelf & set_enable_kwargs(bool enable_kwargs)=0</div><div class="ttdoc">set whether to enable kwargs A function cannot have both kwargs and scalar arguments. Default: this is set to false </div></div>
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_ac258ea0f99d31b36e7dd682c25b3aeca"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac258ea0f99d31b36e7dd682c25b3aeca">mxnet::op::SimpleOpRegEntry::set_symbol_op_name</a></div><div class="ttdeci">virtual TSelf & set_symbol_op_name(char const *symbol_name)=0</div><div class="ttdoc">set a seperate name for symbol This must be called before set_function. Default: this is set to be sa...</div></div>
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_a405bfb5929d459d45d93c2eaa645bfb5"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a405bfb5929d459d45d93c2eaa645bfb5">mxnet::op::SimpleOpRegEntry::add_arguments</a></div><div class="ttdeci">virtual TSelf & add_arguments(const std::vector< dmlc::ParamFieldInfo > &args)=0</div><div class="ttdoc">Describe the function. </div></div>
+<div class="ttc" id="namespacemxnet_1_1op_html_a362db0d0023a59204d018c32458ae4e3"><div class="ttname"><a href="namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3">mxnet::op::UnaryGradFunctionT1</a></div><div class="ttdeci">void(* UnaryGradFunctionT1)(const OutputGrad &out_grad, const OutputValue &out_value, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</div><div class="ttdoc">Gradient function that takes output value of function and computes [...]
<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegistry_html"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegistry.html">mxnet::op::SimpleOpRegistry</a></div><div class="ttdoc">registry for TBlob functions </div><div class="ttdef"><b>Definition:</b> operator_util.h:402</div></div>
<div class="ttc" id="structmxnet_1_1ResourceRequest_html"><div class="ttname"><a href="structmxnet_1_1ResourceRequest.html">mxnet::ResourceRequest</a></div><div class="ttdoc">The resources that can be requested by Operator. </div><div class="ttdef"><b>Definition:</b> resource.h:18</div></div>
<div class="ttc" id="namespacemxnet_html_a4421326e33f2a848cb2fa3e70ab24ff4"><div class="ttname"><a href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">mxnet::real_t</a></div><div class="ttdeci">mshadow::default_real_t real_t</div><div class="ttdoc">data type that will be used to store ndarray </div><div class="ttdef"><b>Definition:</b> base.h:109</div></div>
<div class="ttc" id="namespacemxnet_html_a6e14bb5f6ffc81892feca8d1da658687"><div class="ttname"><a href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">mxnet::TShape</a></div><div class="ttdeci">nnvm::TShape TShape</div><div class="ttdoc">Shape data structure used to record shape information. </div><div class="ttdef"><b>Definition:</b> base.h:111</div></div>
<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_a02c180f08b215e9553d3c93fa783f9a4"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a02c180f08b215e9553d3c93fa783f9a4">mxnet::op::SimpleOpRegEntry::name</a></div><div class="ttdeci">std::string name</div><div class="ttdoc">name of the operator </div><div class="ttdef"><b>Definition:</b> operator_util.h:248</div></div>
<div class="ttc" id="namespacemxnet_1_1op_html_a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde"><div class="ttname"><a href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa2c7771fd11a989ca2480963611405bde">mxnet::op::kInplaceInOut</a></div><div class="ttdoc">in unary forward, allow inplace in with out </div><div class="ttdef"><b>Definition:</b> operator_util.h:221</div></div>
+<div class="ttc" id="namespacemxnet_1_1op_html_a6b53969567339c4b97a8e0da51580c11"><div class="ttname"><a href="namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11">mxnet::op::UnaryGradFunctionT2</a></div><div class="ttdeci">void(* UnaryGradFunctionT2)(const OutputGrad &out_grad, const Input0 &in_data0, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</div><div class="ttdoc">Gradient function that takes input value of function and computes gradie [...]
<div class="ttc" id="structmxnet_1_1RunContext_html"><div class="ttname"><a href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></div><div class="ttdoc">execution time context. The information needed in runtime for actual execution. </div><div class="ttdef"><b>Definition:</b> base.h:213</div></div>
<div class="ttc" id="structmxnet_1_1op_1_1EnvArguments_html_a74dfe03602a990b6552c8f7a01b2ecb5"><div class="ttname"><a href="structmxnet_1_1op_1_1EnvArguments.html#a74dfe03602a990b6552c8f7a01b2ecb5">mxnet::op::EnvArguments::scalar</a></div><div class="ttdeci">real_t scalar</div><div class="ttdoc">scalar argument, if enabled </div><div class="ttdef"><b>Definition:</b> operator_util.h:56</div></div>
<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegistry_html_a8ee596c99eef39fbbfffca181bf7f26e"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a8ee596c99eef39fbbfffca181bf7f26e">mxnet::op::SimpleOpRegistry::Find</a></div><div class="ttdeci">static const SimpleOpRegEntry * Find(const std::string &name)</div><div class="ttdoc">Find the entry with corresponding name. </div><div class="ttdef"><b>Definition:</b> operator_util.h:415</div></div>
<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_a94815080350681ad70e7815da1bc0eae"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a94815080350681ad70e7815da1bc0eae">mxnet::op::SimpleOpRegEntry::~SimpleOpRegEntry</a></div><div class="ttdeci">virtual ~SimpleOpRegEntry()</div><div class="ttdoc">virtual destructor </div><div class="ttdef"><b>Definition:</b> operator_util.h:398</div></div>
+<div class="ttc" id="namespacemxnet_1_1op_html_af71f289b0df41398bcbac75d37435a07"><div class="ttname"><a href="namespacemxnet_1_1op.html#af71f289b0df41398bcbac75d37435a07">mxnet::op::SourceShapeFunction</a></div><div class="ttdeci">TShape(* SourceShapeFunction)(const EnvArguments &env)</div><div class="ttdoc">Shape inference function to get the correct shape. </div><div class="ttdef"><b>Definition:</b> operator_util.h:81</div></div>
<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html">mxnet::op::SimpleOpRegEntry</a></div><div class="ttdoc">registry entry to register simple operators via functions. </div><div class="ttdef"><b>Definition:</b> operator_util.h:243</div></div>
<div class="ttc" id="operator_8h_html"><div class="ttname"><a href="operator_8h.html">operator.h</a></div><div class="ttdoc">Operator interface of mxnet. </div></div>
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_a1cce9a6aaa175711b6be5b864b055f6a"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a1cce9a6aaa175711b6be5b864b055f6a">mxnet::op::SimpleOpRegEntry::describe</a></div><div class="ttdeci">virtual TSelf & describe(const std::string &description)=0</div><div class="ttdoc">Describe the function. </div></div>
<div class="ttc" id="structmxnet_1_1op_1_1Input1_html"><div class="ttname"><a href="structmxnet_1_1op_1_1Input1.html">mxnet::op::Input1</a></div><div class="ttdoc">Second input to the function. </div><div class="ttdef"><b>Definition:</b> operator_util.h:43</div></div>
+<div class="ttc" id="namespacemxnet_1_1op_html_ab004eaba5bbfd4a05d7ddeb54ce1c311"><div class="ttname"><a href="namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311">mxnet::op::UnaryShapeFunction</a></div><div class="ttdeci">TShape(* UnaryShapeFunction)(const TShape &src, const EnvArguments &env)</div><div class="ttdoc">Shape inference function to get the correct shape given source. </div><div class="ttdef"><b>Definition:</b> operator_util.h:103</div></div>
<div class="ttc" id="namespacemxnet_1_1op_html_a3c3b8c77865c7c715cfe05ee2b2fa8daab7974a6a3982dae42ba00af90dcabd80"><div class="ttname"><a href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daab7974a6a3982dae42ba00af90dcabd80">mxnet::op::kInplaceOutIn</a></div><div class="ttdoc">in unary backward, allow inplace out_grad with in_grad </div><div class="ttdef"><b>Definition:</b> operator_util.h:223</div></div>
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_a3be9c65fc5f747b6a05e3c2889d8f72b"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a3be9c65fc5f747b6a05e3c2889d8f72b">mxnet::op::SimpleOpRegEntry::set_shape_function</a></div><div class="ttdeci">virtual TSelf & set_shape_function(SourceShapeFunction fshapeinfer)=0</div><div class="ttdoc">set source inference function. </div></div>
<div class="ttc" id="structmxnet_1_1op_1_1GradFunctionArgument_html"><div class="ttname"><a href="structmxnet_1_1op_1_1GradFunctionArgument.html">mxnet::op::GradFunctionArgument</a></div><div class="ttdoc">super class of all gradient function argument </div><div class="ttdef"><b>Definition:</b> operator_util.h:35</div></div>
<div class="ttc" id="structmxnet_1_1op_1_1GradFunctionArgument_html_ab9214ef3cfcf53b375a2e8f716e66929"><div class="ttname"><a href="structmxnet_1_1op_1_1GradFunctionArgument.html#ab9214ef3cfcf53b375a2e8f716e66929">mxnet::op::GradFunctionArgument::data</a></div><div class="ttdeci">TBlob data</div><div class="ttdoc">The real data. </div><div class="ttdef"><b>Definition:</b> operator_util.h:37</div></div>
<div class="ttc" id="namespacemxnet_1_1op_html_a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2"><div class="ttname"><a href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa048c01bf1598f31850baeb2152b005a2">mxnet::op::kNoInplace</a></div><div class="ttdoc">do not allow inplace in arguments </div><div class="ttdef"><b>Definition:</b> operator_util.h:219</div></div>
-<div class="ttc" id="namespacemxnet_1_1op_html_a6ad11641816278f8fdb6fe87c7fd3740"><div class="ttname"><a href="namespacemxnet_1_1op.html#a6ad11641816278f8fdb6fe87c7fd3740">mxnet::op::UnaryGradFunctionT1</a></div><div class="ttdeci">void(* UnaryGradFunctionT1)(const OutputGrad &out_grad, const OutputValue &out_value, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</div><div class="ttdoc">Gradient function that takes output value of function and computes [...]
+<div class="ttc" id="namespacemxnet_1_1op_html_af5075d1f55b857402d9b9da07c164f11"><div class="ttname"><a href="namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11">mxnet::op::UnaryFunction</a></div><div class="ttdeci">void(* UnaryFunction)(const TBlob &src, const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</div><div class="ttdoc">Unary function that takes a src and save result to ret. The result container is pre-allocated with th...</div><div class="ttde [...]
<div class="ttc" id="namespacemxnet_1_1op_html_a3c3b8c77865c7c715cfe05ee2b2fa8da"><div class="ttname"><a href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8da">mxnet::op::SimpleOpInplaceOption</a></div><div class="ttdeci">SimpleOpInplaceOption</div><div class="ttdoc">options in the registry to set inplace of operator </div><div class="ttdef"><b>Definition:</b> operator_util.h:217</div></div>
-<div class="ttc" id="namespacemxnet_1_1op_html_a65e8bade5ae0539689b705b921ed9a9d"><div class="ttname"><a href="namespacemxnet_1_1op.html#a65e8bade5ae0539689b705b921ed9a9d">mxnet::op::SourceFunction</a></div><div class="ttdeci">void(* SourceFunction)(const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</div><div class="ttdoc">source function that generate output based on env The result container is pre-allocated with the corr...</div><div class="ttdef"><b>Definition:</b [...]
+<div class="ttc" id="namespacemxnet_1_1op_html_a5e30ef3bfc75cf4b111af7b1c74dab1b"><div class="ttname"><a href="namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b">mxnet::op::UnaryGradFunctionT0</a></div><div class="ttdeci">void(* UnaryGradFunctionT0)(const OutputGrad &out_grad, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</div><div class="ttdoc">Gradient function that takes output value of function and computes gradient wrt to input...</div><di [...]
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_a0725f1b670044149a088355ffeb6977f"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#a0725f1b670044149a088355ffeb6977f">mxnet::op::SimpleOpRegEntry::set_enable_scalar</a></div><div class="ttdeci">virtual TSelf & set_enable_scalar(bool enable_scalar, SimpleOpScalarOption type_mask=kArrayBeforeScalar)=0</div><div class="ttdoc">set number of scalar arguments needed to be passed in env A function cannot h [...]
+<div class="ttc" id="namespacemxnet_1_1op_html_ad1ec651ba6eee751c85b957df8972fc3"><div class="ttname"><a href="namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3">mxnet::op::BinaryGradFunctionT0</a></div><div class="ttdeci">void(* BinaryGradFunctionT0)(const OutputGrad &out_grad, const EnvArguments &env, TBlob *lhs_grad, TBlob *rhs_grad, OpReqType req_lhs_grad, OpReqType req_rhs_grad, RunContext ctx)</div><div class="ttdoc">Gradient function that takes only output gradie [...]
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegistry_html_a374ec19d647f622c80a79fb3d541e7ec"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a374ec19d647f622c80a79fb3d541e7ec">mxnet::op::SimpleOpRegistry::Get</a></div><div class="ttdeci">static SimpleOpRegistry * Get()</div></div>
<div class="ttc" id="namespacemxnet_html_a7cd7094ef222e9ae1f520c3b6a214398"><div class="ttname"><a href="namespacemxnet.html#a7cd7094ef222e9ae1f520c3b6a214398">mxnet::OpReqType</a></div><div class="ttdeci">OpReqType</div><div class="ttdoc">operation request type to Forward and Backward </div><div class="ttdef"><b>Definition:</b> op_attr_types.h:26</div></div>
<div class="ttc" id="structmxnet_1_1op_1_1EnvArguments_html"><div class="ttname"><a href="structmxnet_1_1op_1_1EnvArguments.html">mxnet::op::EnvArguments</a></div><div class="ttdoc">Environment arguments that is used by the function. These can be things like scalar arguments when ad...</div><div class="ttdef"><b>Definition:</b> operator_util.h:54</div></div>
<div class="ttc" id="structmxnet_1_1op_1_1OutputValue_html"><div class="ttname"><a href="structmxnet_1_1op_1_1OutputValue.html">mxnet::op::OutputValue</a></div><div class="ttdoc">Ouput value of the function to the function. </div><div class="ttdef"><b>Definition:</b> operator_util.h:46</div></div>
<div class="ttc" id="namespacemxnet_1_1op_html_ae7310844fa858f1ec6b6428dd6977a13"><div class="ttname"><a href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13">mxnet::op::SimpleOpScalarOption</a></div><div class="ttdeci">SimpleOpScalarOption</div><div class="ttdoc">options in the registry to set symbolic registration </div><div class="ttdef"><b>Definition:</b> operator_util.h:231</div></div>
<div class="ttc" id="base_8h_html"><div class="ttname"><a href="base_8h.html">base.h</a></div><div class="ttdoc">configuation of mxnet as well as basic data structure. </div></div>
-<div class="ttc" id="namespacemxnet_1_1op_html_a1e5767bffea568a1c38379060cbb68b4"><div class="ttname"><a href="namespacemxnet_1_1op.html#a1e5767bffea568a1c38379060cbb68b4">mxnet::op::UnaryGradFunctionT0</a></div><div class="ttdeci">void(* UnaryGradFunctionT0)(const OutputGrad &out_grad, const EnvArguments &env, TBlob *in_grad, OpReqType req, RunContext ctx)</div><div class="ttdoc">Gradient function that takes output value of function and computes gradient wrt to input...</div><di [...]
-<div class="ttc" id="namespacemxnet_1_1op_html_abcef841f759a7874ef50f21ae0226c8a"><div class="ttname"><a href="namespacemxnet_1_1op.html#abcef841f759a7874ef50f21ae0226c8a">mxnet::op::BinaryGradFunctionT0</a></div><div class="ttdeci">void(* BinaryGradFunctionT0)(const OutputGrad &out_grad, const EnvArguments &env, TBlob *lhs_grad, TBlob *rhs_grad, OpReqType req_lhs_grad, OpReqType req_rhs_grad, RunContext ctx)</div><div class="ttdoc">Gradient function that takes only output gradie [...]
+<div class="ttc" id="namespacemxnet_1_1op_html_a97e7c6f5bbc6cdf02d443df6a36167e0"><div class="ttname"><a href="namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0">mxnet::op::BinaryFunction</a></div><div class="ttdeci">void(* BinaryFunction)(const TBlob &lhs, const TBlob &rhs, const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</div><div class="ttdoc">Binary function that takes lhs, rhs and save result to ret. The result container is pre-allocated wit. [...]
+<div class="ttc" id="namespacemxnet_1_1op_html_a2ed3ee83974ac57efd4b251a85dca849"><div class="ttname"><a href="namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849">mxnet::op::BinaryGradFunctionT1</a></div><div class="ttdeci">void(* BinaryGradFunctionT1)(const OutputGrad &out_grad, const Input0 &lhs, const Input1 &rhs, const EnvArguments &env, TBlob *lhs_grad, TBlob *rhs_grad, OpReqType req_lhs_grad, OpReqType req_rhs_grad, RunContext ctx)</div><div class="ttdoc">G [...]
<div class="ttc" id="structmxnet_1_1op_1_1Input0_html"><div class="ttname"><a href="structmxnet_1_1op_1_1Input0.html">mxnet::op::Input0</a></div><div class="ttdoc">First input to the function. </div><div class="ttdef"><b>Definition:</b> operator_util.h:41</div></div>
<div class="ttc" id="structmxnet_1_1op_1_1EnvArguments_html_a9dc6ddeef01eb50bc77517bec1195d5d"><div class="ttname"><a href="structmxnet_1_1op_1_1EnvArguments.html#a9dc6ddeef01eb50bc77517bec1195d5d">mxnet::op::EnvArguments::kwargs</a></div><div class="ttdeci">std::vector< std::pair< std::string, std::string > > kwargs</div><div class="ttdoc">keyword arguments </div><div class="ttdef"><b>Definition:</b> operator_util.h:58</div></div>
<div class="ttc" id="namespacemxnet_1_1op_html_ae7310844fa858f1ec6b6428dd6977a13a44de4ba9d059034c023bc755cdc6026a"><div class="ttname"><a href="namespacemxnet_1_1op.html#ae7310844fa858f1ec6b6428dd6977a13a44de4ba9d059034c023bc755cdc6026a">mxnet::op::kScalarBeforeArray</a></div><div class="ttdef"><b>Definition:</b> operator_util.h:232</div></div>
<div class="ttc" id="namespacemxnet_1_1op_html_a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b"><div class="ttname"><a href="namespacemxnet_1_1op.html#a01bb0bb658bedb4d58bf7d11e4b17268a929ea2e4ea4bffd5119a8bbf296e616b">mxnet::op::kNotRegisterSymbolic</a></div><div class="ttdef"><b>Definition:</b> operator_util.h:238</div></div>
<div class="ttc" id="namespacemxnet_1_1op_html_a3c3b8c77865c7c715cfe05ee2b2fa8daa31a764e0ff5966a10e26f7fbff308c18"><div class="ttname"><a href="namespacemxnet_1_1op.html#a3c3b8c77865c7c715cfe05ee2b2fa8daa31a764e0ff5966a10e26f7fbff308c18">mxnet::op::kInplaceLhsOut</a></div><div class="ttdoc">in binary forward, allow inplace left operand with out </div><div class="ttdef"><b>Definition:</b> operator_util.h:225</div></div>
+<div class="ttc" id="namespacemxnet_1_1op_html_a8dc66fcca4ca52343570931503fca2c3"><div class="ttname"><a href="namespacemxnet_1_1op.html#a8dc66fcca4ca52343570931503fca2c3">mxnet::op::SourceFunction</a></div><div class="ttdeci">void(* SourceFunction)(const EnvArguments &env, TBlob *ret, OpReqType req, RunContext ctx)</div><div class="ttdoc">source function that generate output based on env The result container is pre-allocated with the corr...</div><div class="ttdef"><b>Definition:</b [...]
+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegistry_html_a376e3412e9f425db998c384e5d3f0523"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegistry.html#a376e3412e9f425db998c384e5d3f0523">mxnet::op::SimpleOpRegistry::__REGISTER_OR_FIND__</a></div><div class="ttdeci">SimpleOpRegEntry & __REGISTER_OR_FIND__(char const *name)</div><div class="ttdoc">Internal function to register a name function under name. </div></div>
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+<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_ab8fe122e15142dc85c679071acf53302"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ab8fe122e15142dc85c679071acf53302">mxnet::op::SimpleOpRegEntry::set_gradient</a></div><div class="ttdeci">virtual TSelf & set_gradient(int dev_mask, UnaryGradFunctionT0 fgrad, SimpleOpInplaceOption inplace_out_in_grad)=0</div><div class="ttdoc">set gradient of the function of this function. </div></div>
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<div class="ttc" id="classmxnet_1_1op_1_1SimpleOpRegEntry_html_ac2fd6ca9171b44a1ab131430cc5866a3"><div class="ttname"><a href="classmxnet_1_1op_1_1SimpleOpRegEntry.html#ac2fd6ca9171b44a1ab131430cc5866a3">mxnet::op::SimpleOpRegEntry::TSelf</a></div><div class="ttdeci">SimpleOpRegEntry TSelf</div><div class="ttdoc">declare self type </div><div class="ttdef"><b>Definition:</b> operator_util.h:246</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html"><div class="ttname"><a href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></div><div class="ttdoc">tensor blob class that can be used to hold tensor of any dimension, any device and any data type...</div><div class="ttdef"><b>Definition:</b> tensor_blob.h:40</div></div>
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</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<tbody>
<tr style="height: 56px;">
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<p>Global resource allocation handling.
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
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<script type="text/javascript" src="jquery.js"></script>
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ <li><a href="files.html"><span>File List</span></a></li>
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@@ -66,37 +88,116 @@ $(function() {
<div class="title">resource.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="resource_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_RESOURCE_H_</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#define MXNET_RESOURCE_H_</span></div><div class="line"><a name="l0000 [...]
+<a href="resource_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_RESOURCE_H_</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_RESOURCE_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <dmlc/logging.h></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include "<a class="code" href="engine_8h.html">./engine.h</a>"</span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div>
+<div class="line"><a name="l00018"></a><span class="lineno"><a class="line" href="structmxnet_1_1ResourceRequest.html"> 18</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> {</div>
+<div class="line"><a name="l00020"></a><span class="lineno"><a class="line" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630"> 20</a></span>  <span class="keyword">enum</span> <a class="code" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630">Type</a> {</div>
+<div class="line"><a name="l00022"></a><span class="lineno"><a class="line" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630adfccd3e369be7104e7300557f620f6b9"> 22</a></span>  <a class="code" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630adfccd3e369be7104e7300557f620f6b9">kRandom</a>,</div>
+<div class="line"><a name="l00024"></a><span class="lineno"><a class="line" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630aae5d4509a9b382744ea13d2daa8aa3f3"> 24</a></span>  <a class="code" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630aae5d4509a9b382744ea13d2daa8aa3f3">kTempSpace</a></div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  };</div>
+<div class="line"><a name="l00027"></a><span class="lineno"><a class="line" href="structmxnet_1_1ResourceRequest.html#ad67cb809ea1e0db24629ffbd3db00a63"> 27</a></span>  <a class="code" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630">Type</a> <a class="code" href="structmxnet_1_1ResourceRequest.html#ad67cb809ea1e0db24629ffbd3db00a63">type</a>;</div>
+<div class="line"><a name="l00029"></a><span class="lineno"><a class="line" href="structmxnet_1_1ResourceRequest.html#a0191250c5891ba487f354df4caf3604d"> 29</a></span>  <a class="code" href="structmxnet_1_1ResourceRequest.html#a0191250c5891ba487f354df4caf3604d">ResourceRequest</a>() {}</div>
+<div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="structmxnet_1_1ResourceRequest.html#aa01dd9fd716dfbb68c2ee01f5a11aea5"> 34</a></span>  <a class="code" href="structmxnet_1_1ResourceRequest.html#aa01dd9fd716dfbb68c2ee01f5a11aea5">ResourceRequest</a>(<a class="code" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630">Type</a> <a class="code" href="structmxnet_1_1ResourceRequest.html#ad67cb809ea1e0db24629ffbd3db00a63">type</a [...]
+<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  : type(type) {}</div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> };</div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div>
+<div class="line"><a name="l00044"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html"> 44</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1Resource.html">Resource</a> {</div>
+<div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#ae841e25cf5ee4aed895ace7fa080edc4"> 46</a></span>  <a class="code" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> <a class="code" href="structmxnet_1_1Resource.html#ae841e25cf5ee4aed895ace7fa080edc4">req</a>;</div>
+<div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#a5a8048bdda73fefa3af322aaaba50eb8"> 48</a></span>  <a class="code" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">engine::VarHandle</a> <a class="code" href="structmxnet_1_1Resource.html#a5a8048bdda73fefa3af322aaaba50eb8">var</a>;</div>
+<div class="line"><a name="l00050"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#aa1fc8578c0fa849afe7da29c1cf1308b"> 50</a></span>  int32_t <a class="code" href="structmxnet_1_1Resource.html#aa1fc8578c0fa849afe7da29c1cf1308b">id</a>;</div>
+<div class="line"><a name="l00055"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#a7e9576783b7e9023375ffebd76a11806"> 55</a></span>  <span class="keywordtype">void</span> *<a class="code" href="structmxnet_1_1Resource.html#a7e9576783b7e9023375ffebd76a11806">ptr_</a>;</div>
+<div class="line"><a name="l00057"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#a76a2cc8f2a94b67b3533a048a80396aa"> 57</a></span>  <a class="code" href="structmxnet_1_1Resource.html#a76a2cc8f2a94b67b3533a048a80396aa">Resource</a>() : <a class="code" href="structmxnet_1_1Resource.html#aa1fc8578c0fa849afe7da29c1cf1308b">id</a>(0) {}</div>
+<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">template</span><<span class="keyword">typename</span> xpu, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00065"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#a86039963ae5d682ffc287aefd520f6f9"> 65</a></span>  <span class="keyword">inline</span> mshadow::Random<xpu, DType>* <a class="code" href="structmxnet_1_1Resource.html#a86039963ae5d682ffc287aefd520f6f9">get_random</a>(</div>
+<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  mshadow::Stream<xpu> *stream)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  CHECK_EQ(<a class="code" href="structmxnet_1_1Resource.html#ae841e25cf5ee4aed895ace7fa080edc4">req</a>.<a class="code" href="structmxnet_1_1ResourceRequest.html#ad67cb809ea1e0db24629ffbd3db00a63">type</a>, <a class="code" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630adfccd3e369be7104e7300557f620f6b9">ResourceRequest::kRandom</a>);</div>
+<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  mshadow::Random<xpu, DType> *ret =</div>
+<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">static_cast<</span>mshadow::Random<xpu, DType>*<span class="keyword">></span>(<a class="code" href="structmxnet_1_1Resource.html#a7e9576783b7e9023375ffebd76a11806">ptr_</a>);</div>
+<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  ret->set_stream(stream);</div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">return</span> ret;</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  }</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">template</span><<span class="keyword">typename</span> xpu, <span class="keywordtype">int</span> ndim></div>
+<div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#ab2be3ebdd3c5f5fa181ac4f6cdd69508"> 90</a></span>  <span class="keyword">inline</span> mshadow::Tensor<xpu, ndim, real_t> <a class="code" href="structmxnet_1_1Resource.html#ab2be3ebdd3c5f5fa181ac4f6cdd69508">get_space</a>(</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  mshadow::Shape<ndim> shape, mshadow::Stream<xpu> *stream)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">return</span> get_space_typed<xpu, ndim, real_t>(shape, stream);</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  }</div>
+<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> ndim></div>
+<div class="line"><a name="l00103"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#a92724193fad2a6c5e2a63687e63e3d80"> 103</a></span>  <span class="keyword">inline</span> mshadow::Tensor<cpu, ndim, real_t> <a class="code" href="structmxnet_1_1Resource.html#a92724193fad2a6c5e2a63687e63e3d80">get_host_space</a>(</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  mshadow::Shape<ndim> shape)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">return</span> get_host_space_typed<cpu, ndim, real_t>(shape);</div>
+<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  }</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">template</span><<span class="keyword">typename</span> xpu, <span class="keywordtype">int</span> ndim, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00118"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#a84e66895d72f9e99ae994de86539a77e"> 118</a></span>  <span class="keyword">inline</span> mshadow::Tensor<xpu, ndim, DType> <a class="code" href="structmxnet_1_1Resource.html#a84e66895d72f9e99ae994de86539a77e">get_space_typed</a>(</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  mshadow::Shape<ndim> shape, mshadow::Stream<xpu> *stream)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  CHECK_EQ(<a class="code" href="structmxnet_1_1Resource.html#ae841e25cf5ee4aed895ace7fa080edc4">req</a>.<a class="code" href="structmxnet_1_1ResourceRequest.html#ad67cb809ea1e0db24629ffbd3db00a63">type</a>, <a class="code" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630aae5d4509a9b382744ea13d2daa8aa3f3">ResourceRequest::kTempSpace</a>);</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">return</span> mshadow::Tensor<xpu, ndim, DType>(</div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keyword">reinterpret_cast<</span>DType*<span class="keyword">></span>(<a class="code" href="structmxnet_1_1Resource.html#af57d32e959227f49d54d6eb6a5c62875">get_space_internal</a>(shape.Size() * <span class="keyword">sizeof</span>(DType))),</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  shape, shape[ndim - 1], stream);</div>
+<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  }</div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> ndim, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00135"></a><span class="lineno"><a class="line" href="structmxnet_1_1Resource.html#a7c6dff75a9e05a7394b02c84b79a14b2"> 135</a></span>  <span class="keyword">inline</span> mshadow::Tensor<cpu, ndim, DType> <a class="code" href="structmxnet_1_1Resource.html#a7c6dff75a9e05a7394b02c84b79a14b2">get_host_space_typed</a>(</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  mshadow::Shape<ndim> shape)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">return</span> mshadow::Tensor<cpu, ndim, DType>(</div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">reinterpret_cast<</span>DType*<span class="keyword">></span>(<a class="code" href="structmxnet_1_1Resource.html#af6e0224f60b5e9afd3c883972863537f">get_host_space_internal</a>(shape.Size() * <span class="keyword">sizeof</span>(DType))),</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  shape, shape[ndim - 1], NULL);</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordtype">void</span>* <a class="code" href="structmxnet_1_1Resource.html#af57d32e959227f49d54d6eb6a5c62875">get_space_internal</a>(<span class="keywordtype">size_t</span> size) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordtype">void</span> *<a class="code" href="structmxnet_1_1Resource.html#af6e0224f60b5e9afd3c883972863537f">get_host_space_internal</a>(<span class="keywordtype">size_t</span> size) <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span> };</div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div>
+<div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="classmxnet_1_1ResourceManager.html"> 156</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1ResourceManager.html">ResourceManager</a> {</div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keyword">virtual</span> <a class="code" href="structmxnet_1_1Resource.html">Resource</a> <a class="code" href="classmxnet_1_1ResourceManager.html#a276e4e3ae229a118e50a03955f3f1a08">Request</a>(<a class="code" href="structmxnet_1_1Context.html">Context</a> ctx, <span class="keyword">const</span> <a class="code" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> &req) = 0;</div>
+<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1ResourceManager.html#a7aa335bc9231a8eb7cd04de22e033603">SeedRandom</a>(uint32_t seed) = 0;</div>
+<div class="line"><a name="l00173"></a><span class="lineno"><a class="line" href="classmxnet_1_1ResourceManager.html#a2b53c0843ddbaf7ccb9e06f448b20cac"> 173</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1ResourceManager.html#a2b53c0843ddbaf7ccb9e06f448b20cac">~ResourceManager</a>() DMLC_THROW_EXCEPTION {}</div>
+<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1ResourceManager.html">ResourceManager</a> *<a class="code" href="classmxnet_1_1ResourceManager.html#a5712bb055045ba9fb45dd3cdb812abb8">Get</a>();</div>
+<div class="line"><a name="l00178"></a><span class="lineno"> 178</span> };</div>
+<div class="line"><a name="l00179"></a><span class="lineno"> 179</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="preprocessor">#endif // MXNET_RESOURCE_H_</span></div>
+<div class="ttc" id="engine_8h_html"><div class="ttname"><a href="engine_8h.html">engine.h</a></div><div class="ttdoc">Engine that schedules all the operations according to dependency. </div></div>
+<div class="ttc" id="classmxnet_1_1ResourceManager_html_a276e4e3ae229a118e50a03955f3f1a08"><div class="ttname"><a href="classmxnet_1_1ResourceManager.html#a276e4e3ae229a118e50a03955f3f1a08">mxnet::ResourceManager::Request</a></div><div class="ttdeci">virtual Resource Request(Context ctx, const ResourceRequest &req)=0</div><div class="ttdoc">Get resource of requested type. </div></div>
<div class="ttc" id="structmxnet_1_1Resource_html_a5a8048bdda73fefa3af322aaaba50eb8"><div class="ttname"><a href="structmxnet_1_1Resource.html#a5a8048bdda73fefa3af322aaaba50eb8">mxnet::Resource::var</a></div><div class="ttdeci">engine::VarHandle var</div><div class="ttdoc">engine variable </div><div class="ttdef"><b>Definition:</b> resource.h:48</div></div>
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
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<div class="ttc" id="structmxnet_1_1ResourceRequest_html"><div class="ttname"><a href="structmxnet_1_1ResourceRequest.html">mxnet::ResourceRequest</a></div><div class="ttdoc">The resources that can be requested by Operator. </div><div class="ttdef"><b>Definition:</b> resource.h:18</div></div>
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<div class="ttc" id="structmxnet_1_1Resource_html_ae841e25cf5ee4aed895ace7fa080edc4"><div class="ttname"><a href="structmxnet_1_1Resource.html#ae841e25cf5ee4aed895ace7fa080edc4">mxnet::Resource::req</a></div><div class="ttdeci">ResourceRequest req</div><div class="ttdoc">The original request. </div><div class="ttdef"><b>Definition:</b> resource.h:46</div></div>
<div class="ttc" id="structmxnet_1_1ResourceRequest_html_ad67cb809ea1e0db24629ffbd3db00a63"><div class="ttname"><a href="structmxnet_1_1ResourceRequest.html#ad67cb809ea1e0db24629ffbd3db00a63">mxnet::ResourceRequest::type</a></div><div class="ttdeci">Type type</div><div class="ttdoc">type of resources </div><div class="ttdef"><b>Definition:</b> resource.h:27</div></div>
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<div class="ttc" id="structmxnet_1_1Resource_html"><div class="ttname"><a href="structmxnet_1_1Resource.html">mxnet::Resource</a></div><div class="ttdoc">Resources used by mxnet operations. A resource is something special other than NDArray, but will still participate. </div><div class="ttdef"><b>Definition:</b> resource.h:44</div></div>
+<div class="ttc" id="structmxnet_1_1Resource_html_a86039963ae5d682ffc287aefd520f6f9"><div class="ttname"><a href="structmxnet_1_1Resource.html#a86039963ae5d682ffc287aefd520f6f9">mxnet::Resource::get_random</a></div><div class="ttdeci">mshadow::Random< xpu, DType > * get_random(mshadow::Stream< xpu > *stream) const </div><div class="ttdoc">Get random number generator. </div><div class="ttdef"><b>Definition:</b> resource.h:65</div></div>
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<div class="ttc" id="namespacemxnet_1_1engine_html_a9d36c4f33eae8531586dc2edf83ae7cf"><div class="ttname"><a href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">mxnet::engine::VarHandle</a></div><div class="ttdeci">Var * VarHandle</div><div class="ttdoc">Variable pointer type, usually hold by user used to specify dependencies. </div><div class="ttdef"><b>Definition:</b> engine.h:28</div></div>
<div class="ttc" id="base_8h_html"><div class="ttname"><a href="base_8h.html">base.h</a></div><div class="ttdoc">configuation of mxnet as well as basic data structure. </div></div>
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<div class="ttc" id="structmxnet_1_1ResourceRequest_html_aba2ee7560afb89ebdd00c7c735149630adfccd3e369be7104e7300557f620f6b9"><div class="ttname"><a href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630adfccd3e369be7104e7300557f620f6b9">mxnet::ResourceRequest::kRandom</a></div><div class="ttdoc">mshadow::Random<xpu> object </div><div class="ttdef"><b>Definition:</b> resource.h:22</div></div>
<div class="ttc" id="structmxnet_1_1Resource_html_a7e9576783b7e9023375ffebd76a11806"><div class="ttname"><a href="structmxnet_1_1Resource.html#a7e9576783b7e9023375ffebd76a11806">mxnet::Resource::ptr_</a></div><div class="ttdeci">void * ptr_</div><div class="ttdoc">pointer to the resource, do not use directly, access using member functions </div><div class="ttdef"><b>Definition:</b> resource.h:55</div></div>
+<div class="ttc" id="structmxnet_1_1Resource_html_af57d32e959227f49d54d6eb6a5c62875"><div class="ttname"><a href="structmxnet_1_1Resource.html#af57d32e959227f49d54d6eb6a5c62875">mxnet::Resource::get_space_internal</a></div><div class="ttdeci">void * get_space_internal(size_t size) const </div><div class="ttdoc">internal function to get space from resources. </div></div>
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<div class="ttc" id="structmxnet_1_1Context_html"><div class="ttname"><a href="structmxnet_1_1Context.html">mxnet::Context</a></div><div class="ttdoc">Context information about the execution environment. </div><div class="ttdef"><b>Definition:</b> base.h:116</div></div>
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html><head><title></title>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
+<meta name="generator" content="Doxygen 1.8.6">
<link rel="stylesheet" type="text/css" href="search.css"/>
<script type="text/javascript" src="related_1.js"></script>
<script type="text/javascript" src="search.js"></script>
diff --git a/versions/master/doxygen/search/related_2.html b/versions/master/doxygen/search/related_2.html
index 60d1ede..06b8cbf 100644
--- a/versions/master/doxygen/search/related_2.html
+++ b/versions/master/doxygen/search/related_2.html
@@ -1,7 +1,7 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html><head><title></title>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
+<meta name="generator" content="Doxygen 1.8.6">
<link rel="stylesheet" type="text/css" href="search.css"/>
<script type="text/javascript" src="related_2.js"></script>
<script type="text/javascript" src="search.js"></script>
diff --git a/versions/master/doxygen/search/search.css b/versions/master/doxygen/search/search.css
index 3cf9df9..4d7612f 100644
--- a/versions/master/doxygen/search/search.css
+++ b/versions/master/doxygen/search/search.css
@@ -6,12 +6,14 @@
#MSearchBox {
white-space : nowrap;
+ position: absolute;
float: none;
+ display: inline;
margin-top: 8px;
right: 0px;
width: 170px;
- height: 24px;
z-index: 102;
+ background-color: white;
}
#MSearchBox .left
@@ -46,13 +48,12 @@
height:19px;
background:url('search_m.png') repeat-x;
border:none;
- width:115px;
+ width:111px;
margin-left:20px;
padding-left:4px;
color: #909090;
outline: none;
font: 9pt Arial, Verdana, sans-serif;
- -webkit-border-radius: 0px;
}
#FSearchBox #MSearchField {
@@ -63,7 +64,7 @@
display:block;
position:absolute;
right:10px;
- top:8px;
+ top:0px;
width:20px;
height:19px;
background:url('search_r.png') no-repeat;
@@ -101,7 +102,7 @@
left: 0; top: 0;
border: 1px solid #90A5CE;
background-color: #F9FAFC;
- z-index: 10001;
+ z-index: 1;
padding-top: 4px;
padding-bottom: 4px;
-moz-border-radius: 4px;
@@ -164,7 +165,6 @@ iframe#MSearchResults {
left: 0; top: 0;
border: 1px solid #000;
background-color: #EEF1F7;
- z-index:10000;
}
/* ----------------------------------- */
diff --git a/versions/master/doxygen/search/search.js b/versions/master/doxygen/search/search.js
index dedce3b..4c99d5c 100644
--- a/versions/master/doxygen/search/search.js
+++ b/versions/master/doxygen/search/search.js
@@ -1,3 +1,40 @@
+// Search script generated by doxygen
+// Copyright (C) 2009 by Dimitri van Heesch.
+
+// The code in this file is loosly based on main.js, part of Natural Docs,
+// which is Copyright (C) 2003-2008 Greg Valure
+// Natural Docs is licensed under the GPL.
+
+var indexSectionsWithContent =
+{
+ 0: ":_abcdefghiklmnoprstuvw~",
+ 1: "acdefghiklmnorstu",
+ 2: "dm",
+ 3: "bceiklmnorstu",
+ 4: "_abcdefgilmnoprstvw~",
+ 5: "abcdefiklnprstuv",
+ 6: "abcdefgikmnoprstuv",
+ 7: "cdefnost",
+ 8: "k",
+ 9: ":an",
+ 10: "acdmp"
+};
+
+var indexSectionNames =
+{
+ 0: "all",
+ 1: "classes",
+ 2: "namespaces",
+ 3: "files",
+ 4: "functions",
+ 5: "variables",
+ 6: "typedefs",
+ 7: "enums",
+ 8: "enumvalues",
+ 9: "related",
+ 10: "defines"
+};
+
function convertToId(search)
{
var result = '';
@@ -9,11 +46,11 @@ function convertToId(search)
{
result+=c;
}
- else if (cn<16)
+ else if (cn<16)
{
result+="_0"+cn.toString(16);
}
- else
+ else
{
result+="_"+cn.toString(16);
}
@@ -52,14 +89,14 @@ function getYPos(item)
/* A class handling everything associated with the search panel.
Parameters:
- name - The name of the global variable that will be
+ name - The name of the global variable that will be
storing this instance. Is needed to be able to set timeouts.
resultPath - path to use for external files
*/
function SearchBox(name, resultsPath, inFrame, label)
{
if (!name || !resultsPath) { alert("Missing parameters to SearchBox."); }
-
+
// ---------- Instance variables
this.name = name;
this.resultsPath = resultsPath;
@@ -136,7 +173,7 @@ function SearchBox(name, resultsPath, inFrame, label)
}
// stop selection hide timer
- if (this.hideTimeout)
+ if (this.hideTimeout)
{
clearTimeout(this.hideTimeout);
this.hideTimeout=0;
@@ -165,7 +202,7 @@ function SearchBox(name, resultsPath, inFrame, label)
if (e.shiftKey==1)
{
this.OnSearchSelectShow();
- var win=this.DOMSearchSelectWindow();
+ var win=this.DOMSearchSelectWindow();
for (i=0;i<win.childNodes.length;i++)
{
var child = win.childNodes[i]; // get span within a
@@ -216,7 +253,7 @@ function SearchBox(name, resultsPath, inFrame, label)
this.SelectItemCount = function(id)
{
var count=0;
- var win=this.DOMSearchSelectWindow();
+ var win=this.DOMSearchSelectWindow();
for (i=0;i<win.childNodes.length;i++)
{
var child = win.childNodes[i]; // get span within a
@@ -231,7 +268,7 @@ function SearchBox(name, resultsPath, inFrame, label)
this.SelectItemSet = function(id)
{
var i,j=0;
- var win=this.DOMSearchSelectWindow();
+ var win=this.DOMSearchSelectWindow();
for (i=0;i<win.childNodes.length;i++)
{
var child = win.childNodes[i]; // get span within a
@@ -335,7 +372,7 @@ function SearchBox(name, resultsPath, inFrame, label)
hasResultsPage = false;
}
- window.frames.MSearchResults.location = resultsPageWithSearch;
+ window.frames.MSearchResults.location = resultsPageWithSearch;
var domPopupSearchResultsWindow = this.DOMPopupSearchResultsWindow();
if (domPopupSearchResultsWindow.style.display!='block')
@@ -369,12 +406,12 @@ function SearchBox(name, resultsPath, inFrame, label)
// -------- Activation Functions
- // Activates or deactivates the search panel, resetting things to
- // their default values if necessary.
+ // Activates or deactivates the search panel, resetting things to
+ // their default values if necessary.
this.Activate = function(isActive)
{
if (isActive || // open it
- this.DOMPopupSearchResultsWindow().style.display == 'block'
+ this.DOMPopupSearchResultsWindow().style.display == 'block'
)
{
this.DOMSearchBox().className = 'MSearchBoxActive';
@@ -382,8 +419,8 @@ function SearchBox(name, resultsPath, inFrame, label)
var searchField = this.DOMSearchField();
if (searchField.value == this.searchLabel) // clear "Search" term upon entry
- {
- searchField.value = '';
+ {
+ searchField.value = '';
this.searchActive = true;
}
}
@@ -422,12 +459,12 @@ function SearchResults(name)
}
if (element.nodeName == 'DIV' && element.hasChildNodes())
- {
- element = element.firstChild;
+ {
+ element = element.firstChild;
}
else if (element.nextSibling)
- {
- element = element.nextSibling;
+ {
+ element = element.nextSibling;
}
else
{
@@ -438,8 +475,8 @@ function SearchResults(name)
while (element && element!=parentElement && !element.nextSibling);
if (element && element!=parentElement)
- {
- element = element.nextSibling;
+ {
+ element = element.nextSibling;
}
}
}
@@ -492,7 +529,7 @@ function SearchResults(name)
var rowMatchName = row.id.toLowerCase();
rowMatchName = rowMatchName.replace(/^sr\d*_/, ''); // strip 'sr123_'
- if (search.length<=rowMatchName.length &&
+ if (search.length<=rowMatchName.length &&
rowMatchName.substr(0, search.length)==search)
{
row.style.display = 'block';
@@ -563,7 +600,7 @@ function SearchResults(name)
this.ProcessKeys = function(e)
{
- if (e.type == "keydown")
+ if (e.type == "keydown")
{
this.repeatOn = false;
this.lastKey = e.keyCode;
@@ -584,7 +621,7 @@ function SearchResults(name)
return this.lastKey!=0;
}
- this.Nav = function(evt,itemIndex)
+ this.Nav = function(evt,itemIndex)
{
var e = (evt) ? evt : window.event; // for IE
if (e.keyCode==13) return true;
@@ -598,7 +635,7 @@ function SearchResults(name)
{
var child = this.FindChildElement(focusItem.parentNode.parentNode.id);
if (child && child.style.display == 'block') // children visible
- {
+ {
var n=0;
var tmpElem;
while (1) // search for last child
@@ -691,7 +728,7 @@ function SearchResults(name)
if (elem)
{
elem.focus();
- }
+ }
}
else if (this.lastKey==27) // Escape
{
@@ -774,18 +811,3 @@ function createResults()
}
}
-function init_search()
-{
- var results = document.getElementById("MSearchSelectWindow");
- for (var key in indexSectionLabels)
- {
- var link = document.createElement('a');
- link.setAttribute('class','SelectItem');
- link.setAttribute('onclick','searchBox.OnSelectItem('+key+')');
- link.href='javascript:void(0)';
- link.innerHTML='<span class="SelectionMark"> </span>'+indexSectionLabels[key];
- results.appendChild(link);
- }
- searchBox.OnSelectItem(0);
-}
-
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index 05722e1..8d6b78f 100644
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<html><head><title></title>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
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<link rel="stylesheet" type="text/css" href="search.css"/>
<script type="text/javascript" src="typedefs_0.js"></script>
<script type="text/javascript" src="search.js"></script>
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index b77c533..455fe2b 100644
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<html><head><title></title>
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<script type="text/javascript" src="search.js"></script>
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index 0a40193..bfa33e5 100644
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- ['binaryshapefunction',['BinaryShapeFunction',['../namespacemxnet_1_1op.html#a78c7bd2075894ac41351feca1b1e5c94',1,'mxnet::op']]]
+ ['binaryfunction',['BinaryFunction',['../namespacemxnet_1_1op.html#a97e7c6f5bbc6cdf02d443df6a36167e0',1,'mxnet::op']]],
+ ['binarygradfunctiont0',['BinaryGradFunctionT0',['../namespacemxnet_1_1op.html#ad1ec651ba6eee751c85b957df8972fc3',1,'mxnet::op']]],
+ ['binarygradfunctiont1',['BinaryGradFunctionT1',['../namespacemxnet_1_1op.html#a2ed3ee83974ac57efd4b251a85dca849',1,'mxnet::op']]],
+ ['binaryshapefunction',['BinaryShapeFunction',['../namespacemxnet_1_1op.html#a05df0e7fd54e37144430654f8c200db5',1,'mxnet::op']]]
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index 8813055..2c23b36 100644
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<html><head><title></title>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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<link rel="stylesheet" type="text/css" href="search.css"/>
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index 4538289..8e3f031 100644
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- ['unaryshapefunction',['UnaryShapeFunction',['../namespacemxnet_1_1op.html#a2d6fbc6ef1e22ea4433b3cf44ad3cef7',1,'mxnet::op']]],
+ ['unaryfunction',['UnaryFunction',['../namespacemxnet_1_1op.html#af5075d1f55b857402d9b9da07c164f11',1,'mxnet::op']]],
+ ['unarygradfunctiont0',['UnaryGradFunctionT0',['../namespacemxnet_1_1op.html#a5e30ef3bfc75cf4b111af7b1c74dab1b',1,'mxnet::op']]],
+ ['unarygradfunctiont1',['UnaryGradFunctionT1',['../namespacemxnet_1_1op.html#a362db0d0023a59204d018c32458ae4e3',1,'mxnet::op']]],
+ ['unarygradfunctiont2',['UnaryGradFunctionT2',['../namespacemxnet_1_1op.html#a6b53969567339c4b97a8e0da51580c11',1,'mxnet::op']]],
+ ['unaryshapefunction',['UnaryShapeFunction',['../namespacemxnet_1_1op.html#ab004eaba5bbfd4a05d7ddeb54ce1c311',1,'mxnet::op']]],
['unknownbound',['UnknownBound',['../structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html#a0cf05a5a2f3e1ac49ebdef497701055c',1,'mxnet::common::helper::UniqueIf< T[]>']]],
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index 170f24a..3cd5c34 100644
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<html><head><title></title>
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diff --git a/versions/master/doxygen/search/typedefs_2.html b/versions/master/doxygen/search/typedefs_2.html
index 076311d..fac5dba 100644
--- a/versions/master/doxygen/search/typedefs_2.html
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<html><head><title></title>
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index 8ef9c6a..42d1bce 100644
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['cpu',['cpu',['../namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3',1,'mxnet']]],
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- ['customoppropcreator',['CustomOpPropCreator',['../c__api_8h.html#a470eba0beb11ed2fd858c50c9b2be33a',1,'c_api.h']]]
+ ['customopbwddepfunc',['CustomOpBwdDepFunc',['../c__api_8h.html#ae8feb033b715209653ea91e089d060b0',1,'c_api.h']]],
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+ ['customopdelfunc',['CustomOpDelFunc',['../c__api_8h.html#a557fa6ce86e94109ce7797b2cce33e23',1,'c_api.h']]],
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+ <ul class="tablist">
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<p>Storage manager across multiple devices.
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-<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br />
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> manager across multiple devices. <a href="classmxnet_1_1Storage.html#details">More...</a><br/></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Storage_1_1Handle.html">mxnet::Storage::Handle</a></td></tr>
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+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> handle. <a href="structmxnet_1_1Storage_1_1Handle.html#details">More...</a><br/></td></tr>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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<script type="text/javascript" src="jquery.js"></script>
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
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@@ -66,9 +88,33 @@ $(function() {
<div class="title">storage.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="storage_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_STORAGE_H_</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#define MXNET_STORAGE_H_</span></div><div class="line"><a name="l00008"> [...]
+<a href="storage_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_STORAGE_H_</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_STORAGE_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <memory></span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div>
+<div class="line"><a name="l00017"></a><span class="lineno"><a class="line" href="classmxnet_1_1Storage.html"> 17</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1Storage.html">Storage</a> {</div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00022"></a><span class="lineno"><a class="line" href="structmxnet_1_1Storage_1_1Handle.html"> 22</a></span>  <span class="keyword">struct </span><a class="code" href="structmxnet_1_1Storage_1_1Handle.html">Handle</a> {</div>
+<div class="line"><a name="l00026"></a><span class="lineno"><a class="line" href="structmxnet_1_1Storage_1_1Handle.html#a428e25a6317e50c8b55307c96f8ec595"> 26</a></span>  <span class="keywordtype">void</span>* <a class="code" href="structmxnet_1_1Storage_1_1Handle.html#a428e25a6317e50c8b55307c96f8ec595">dptr</a>;</div>
+<div class="line"><a name="l00030"></a><span class="lineno"><a class="line" href="structmxnet_1_1Storage_1_1Handle.html#ab93b9db0c83e4ef3803fb587377a74a0"> 30</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="structmxnet_1_1Storage_1_1Handle.html#ab93b9db0c83e4ef3803fb587377a74a0">size</a>;</div>
+<div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="structmxnet_1_1Storage_1_1Handle.html#ae0a4202582aa12a598f5c55ef51ad4c8"> 34</a></span>  <a class="code" href="structmxnet_1_1Context.html">Context</a> <a class="code" href="structmxnet_1_1Storage_1_1Handle.html#ae0a4202582aa12a598f5c55ef51ad4c8">ctx</a>;</div>
+<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  };</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">virtual</span> <a class="code" href="structmxnet_1_1Storage_1_1Handle.html">Handle</a> <a class="code" href="classmxnet_1_1Storage.html#aaa387e9ae8d66a63438543d640f6cfff">Alloc</a>(<span class="keywordtype">size_t</span> size, <a class="code" href="structmxnet_1_1Context.html">Context</a> ctx) = 0;</div>
+<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Storage.html#a5bdbd7b3ab712cd9e087ab007bdc64dd">Free</a>(<a class="code" href="structmxnet_1_1Storage_1_1Handle.html">Handle</a> handle) = 0;</div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classmxnet_1_1Storage.html#a098f8ebacd25f5203b2dc4c1796aec64">DirectFree</a>(<a class="code" href="structmxnet_1_1Storage_1_1Handle.html">Handle</a> handle) = 0;</div>
+<div class="line"><a name="l00061"></a><span class="lineno"><a class="line" href="classmxnet_1_1Storage.html#af1e1bfea9236ccfba64344226416b04a"> 61</a></span>  <span class="keyword">virtual</span> <a class="code" href="classmxnet_1_1Storage.html#af1e1bfea9236ccfba64344226416b04a">~Storage</a>() {}</div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keyword">static</span> <a class="code" href="classmxnet_1_1Storage.html">Storage</a>* <a class="code" href="classmxnet_1_1Storage.html#a96abc072908d72d3271360a7e3e4bbc2">Get</a>();</div>
+<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">static</span> std::shared_ptr<Storage> <a class="code" href="classmxnet_1_1Storage.html#a4c5e9c239447ee223ab912716d53d7c7">_GetSharedRef</a>();</div>
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span> }; <span class="comment">// class Storage</span></div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="preprocessor">#endif // MXNET_STORAGE_H_</span></div>
+<div class="ttc" id="classmxnet_1_1Storage_html_a098f8ebacd25f5203b2dc4c1796aec64"><div class="ttname"><a href="classmxnet_1_1Storage.html#a098f8ebacd25f5203b2dc4c1796aec64">mxnet::Storage::DirectFree</a></div><div class="ttdeci">virtual void DirectFree(Handle handle)=0</div><div class="ttdoc">Free storage directly, without putting it into memory pool. This can synchronization of all previous ...</div></div>
<div class="ttc" id="classmxnet_1_1Storage_html_a4c5e9c239447ee223ab912716d53d7c7"><div class="ttname"><a href="classmxnet_1_1Storage.html#a4c5e9c239447ee223ab912716d53d7c7">mxnet::Storage::_GetSharedRef</a></div><div class="ttdeci">static std::shared_ptr< Storage > _GetSharedRef()</div><div class="ttdoc">Get shared pointer reference to storage singleton. Most user should not call this function...</div></div>
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
<div class="ttc" id="classmxnet_1_1Storage_html"><div class="ttname"><a href="classmxnet_1_1Storage.html">mxnet::Storage</a></div><div class="ttdoc">Storage manager across multiple devices. </div><div class="ttdef"><b>Definition:</b> storage.h:17</div></div>
<div class="ttc" id="classmxnet_1_1Storage_html_af1e1bfea9236ccfba64344226416b04a"><div class="ttname"><a href="classmxnet_1_1Storage.html#af1e1bfea9236ccfba64344226416b04a">mxnet::Storage::~Storage</a></div><div class="ttdeci">virtual ~Storage()</div><div class="ttdoc">Destructor. </div><div class="ttdef"><b>Definition:</b> storage.h:61</div></div>
<div class="ttc" id="classmxnet_1_1Storage_html_a5bdbd7b3ab712cd9e087ab007bdc64dd"><div class="ttname"><a href="classmxnet_1_1Storage.html#a5bdbd7b3ab712cd9e087ab007bdc64dd">mxnet::Storage::Free</a></div><div class="ttdeci">virtual void Free(Handle handle)=0</div><div class="ttdoc">Free storage. </div></div>
@@ -83,9 +129,9 @@ $(function() {
</div><!-- fragment --></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/structMXCallbackList-members.html b/versions/master/doxygen/structMXCallbackList-members.html
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<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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@@ -65,15 +89,15 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="structMXCallbackList.html">MXCallbackList</a>, including all inherited members.</p>
<table class="directory">
- <tr class="even"><td class="entry"><a class="el" href="structMXCallbackList.html#a3957dba7b237dc6baff2ba578c652f76">callbacks</a></td><td class="entry"><a class="el" href="structMXCallbackList.html">MXCallbackList</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structMXCallbackList.html#ad56325e57f5fffc2b920eb3f8f378199">callbacks</a></td><td class="entry"><a class="el" href="structMXCallbackList.html">MXCallbackList</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="structMXCallbackList.html#a332323a5f414bd3f5e423788804a0dcd">contexts</a></td><td class="entry"><a class="el" href="structMXCallbackList.html">MXCallbackList</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structMXCallbackList.html#aa593dcc4096dcc38a2af477e057d8861">num_callbacks</a></td><td class="entry"><a class="el" href="structMXCallbackList.html">MXCallbackList</a></td><td class="entry"></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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+ <ul class="tablist">
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+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
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onmouseover="return searchBox.OnSearchSelectShow()"
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<div class="dyncontent">
<div class="center"><img src="structMXCallbackList__coll__graph.png" border="0" usemap="#MXCallbackList_coll__map" alt="Collaboration graph"/></div>
+<map name="MXCallbackList_coll__map" id="MXCallbackList_coll__map">
+</map>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:aa593dcc4096dcc38a2af477e057d8861"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="structMXCallbackList.html#aa593dcc4096dcc38a2af477e057d8861">num_callbacks</a></td></tr>
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-<tr class="memitem:a3957dba7b237dc6baff2ba578c652f76"><td class="memItemLeft" align="right" valign="top">int(** </td><td class="memItemRight" valign="bottom"><a class="el" href="structMXCallbackList.html#a3957dba7b237dc6baff2ba578c652f76">callbacks</a> )(void)</td></tr>
-<tr class="separator:a3957dba7b237dc6baff2ba578c652f76"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad56325e57f5fffc2b920eb3f8f378199"><td class="memItemLeft" align="right" valign="top">int(** </td><td class="memItemRight" valign="bottom"><a class="el" href="structMXCallbackList.html#ad56325e57f5fffc2b920eb3f8f378199">callbacks</a> )(void)</td></tr>
+<tr class="separator:ad56325e57f5fffc2b920eb3f8f378199"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a332323a5f414bd3f5e423788804a0dcd"><td class="memItemLeft" align="right" valign="top">void ** </td><td class="memItemRight" valign="bottom"><a class="el" href="structMXCallbackList.html#a332323a5f414bd3f5e423788804a0dcd">contexts</a></td></tr>
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<h2 class="groupheader">Member Data Documentation</h2>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<script type="text/javascript" src="jquery.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<table cellspacing="0" cellpadding="0">
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- $(document).ready(function() { init_search(); });
-});
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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+ <tr><td class="entry"><a class="el" href="structNDArrayOpInfo.html#a139351ad186cde67a376d2f9d6682baf">infer_shape</a></td><td class="entry"><a class="el" href="structNDArrayOpInfo.html">NDArrayOpInfo</a></td><td class="entry"></td></tr>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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- $(document).ready(function() { init_search(); });
-});
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
-<tr class="memitem:ad39c62943775f93fd5cb1fdc39aa38a5"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#ad39c62943775f93fd5cb1fdc39aa38a5">forward</a> )(int, void **, int *, void *)</td></tr>
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-<tr class="memitem:ac8a4bb0b4a71f372e5abb8ded36dd61c"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#ac8a4bb0b4a71f372e5abb8ded36dd61c">backward</a> )(int, void **, int *, void *)</td></tr>
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-<tr class="memitem:a526a6092e581aea808b180758c2c4610"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#a526a6092e581aea808b180758c2c4610">infer_shape</a> )(int, int *, unsigned **, void *)</td></tr>
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-<tr class="memitem:ab99abece52b77824dde5df98837f4f7b"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#ab99abece52b77824dde5df98837f4f7b">list_outputs</a> )(char ***, void *)</td></tr>
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-<tr class="memitem:abc701d8c032a1c5b9358de8ab060d9c2"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#abc701d8c032a1c5b9358de8ab060d9c2">list_arguments</a> )(char ***, void *)</td></tr>
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-<tr class="memitem:af73bb5dbfdc64e2442b98398f0b63ce0"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#af73bb5dbfdc64e2442b98398f0b63ce0">declare_backward_dependency</a> )(const int *, const int *, const int *, int *, int **, void *)</td></tr>
-<tr class="separator:af73bb5dbfdc64e2442b98398f0b63ce0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac11f40fb25fa4abd275b6f1c7b3d3213"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#ac11f40fb25fa4abd275b6f1c7b3d3213">forward</a> )(int, void **, int *, void *)</td></tr>
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+<tr class="memitem:a139351ad186cde67a376d2f9d6682baf"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#a139351ad186cde67a376d2f9d6682baf">infer_shape</a> )(int, int *, unsigned **, void *)</td></tr>
+<tr class="separator:a139351ad186cde67a376d2f9d6682baf"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:acd6f9dbac22ba35f0f9ac5062dc5c8ec"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#acd6f9dbac22ba35f0f9ac5062dc5c8ec">list_outputs</a> )(char ***, void *)</td></tr>
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+<tr class="memitem:a4327dd20a65eb3a7241f67ede18f9950"><td class="memItemLeft" align="right" valign="top">bool(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#a4327dd20a65eb3a7241f67ede18f9950">declare_backward_dependency</a> )(const int *, const int *, const int *, int *, int **, void *)</td></tr>
+<tr class="separator:a4327dd20a65eb3a7241f67ede18f9950"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae40b148a45687b8e4e9d475fef1ed13a"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#ae40b148a45687b8e4e9d475fef1ed13a">p_forward</a></td></tr>
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<tr class="memitem:afb5ee1e890c2499a5a845b21f4df518f"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structNDArrayOpInfo.html#afb5ee1e890c2499a5a845b21f4df518f">p_backward</a></td></tr>
@@ -101,93 +127,79 @@ Public Attributes</h2></td></tr>
<tr class="separator:af17150d574bc8ca7e7a82b9cb4965f05"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<h2 class="groupheader">Member Data Documentation</h2>
-<a id="ac8a4bb0b4a71f372e5abb8ded36dd61c"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ac8a4bb0b4a71f372e5abb8ded36dd61c">◆ </a></span>backward</h2>
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<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">bool(* NDArrayOpInfo::backward) (int, void **, int *, void *)</td>
+ <td class="memname">bool(* NDArrayOpInfo::backward)(int, void **, int *, void *)</td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
-<a id="af73bb5dbfdc64e2442b98398f0b63ce0"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#af73bb5dbfdc64e2442b98398f0b63ce0">◆ </a></span>declare_backward_dependency</h2>
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<div class="memitem">
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<table class="memname">
<tr>
- <td class="memname">bool(* NDArrayOpInfo::declare_backward_dependency) (const int *, const int *, const int *, int *, int **, void *)</td>
+ <td class="memname">bool(* NDArrayOpInfo::declare_backward_dependency)(const int *, const int *, const int *, int *, int **, void *)</td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
-<a id="ad39c62943775f93fd5cb1fdc39aa38a5"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad39c62943775f93fd5cb1fdc39aa38a5">◆ </a></span>forward</h2>
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<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">bool(* NDArrayOpInfo::forward) (int, void **, int *, void *)</td>
+ <td class="memname">bool(* NDArrayOpInfo::forward)(int, void **, int *, void *)</td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
-<a id="a526a6092e581aea808b180758c2c4610"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a526a6092e581aea808b180758c2c4610">◆ </a></span>infer_shape</h2>
-
+<a class="anchor" id="a139351ad186cde67a376d2f9d6682baf"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">bool(* NDArrayOpInfo::infer_shape) (int, int *, unsigned **, void *)</td>
+ <td class="memname">bool(* NDArrayOpInfo::infer_shape)(int, int *, unsigned **, void *)</td>
</tr>
</table>
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</div>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#abc701d8c032a1c5b9358de8ab060d9c2">◆ </a></span>list_arguments</h2>
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<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">bool(* NDArrayOpInfo::list_arguments) (char ***, void *)</td>
+ <td class="memname">bool(* NDArrayOpInfo::list_arguments)(char ***, void *)</td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
-<a id="ab99abece52b77824dde5df98837f4f7b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ab99abece52b77824dde5df98837f4f7b">◆ </a></span>list_outputs</h2>
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<div class="memitem">
<div class="memproto">
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<tr>
- <td class="memname">bool(* NDArrayOpInfo::list_outputs) (char ***, void *)</td>
+ <td class="memname">bool(* NDArrayOpInfo::list_outputs)(char ***, void *)</td>
</tr>
</table>
</div><div class="memdoc">
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#afb5ee1e890c2499a5a845b21f4df518f">◆ </a></span>p_backward</h2>
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+<a class="anchor" id="afb5ee1e890c2499a5a845b21f4df518f"></a>
<div class="memitem">
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@@ -199,9 +211,7 @@ Public Attributes</h2></td></tr>
</div>
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-<a id="af17150d574bc8ca7e7a82b9cb4965f05"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#af17150d574bc8ca7e7a82b9cb4965f05">◆ </a></span>p_declare_backward_dependency</h2>
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+<a class="anchor" id="af17150d574bc8ca7e7a82b9cb4965f05"></a>
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@@ -213,9 +223,7 @@ Public Attributes</h2></td></tr>
</div>
</div>
-<a id="ae40b148a45687b8e4e9d475fef1ed13a"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ae40b148a45687b8e4e9d475fef1ed13a">◆ </a></span>p_forward</h2>
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+<a class="anchor" id="ae40b148a45687b8e4e9d475fef1ed13a"></a>
<div class="memitem">
<div class="memproto">
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@@ -227,9 +235,7 @@ Public Attributes</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#afe27107d0470918edda2d2c5c62fd8d7">◆ </a></span>p_infer_shape</h2>
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+<a class="anchor" id="afe27107d0470918edda2d2c5c62fd8d7"></a>
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@@ -241,9 +247,7 @@ Public Attributes</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#aad953647557d6f2cb13ced8c00e4da0b">◆ </a></span>p_list_arguments</h2>
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+<a class="anchor" id="aad953647557d6f2cb13ced8c00e4da0b"></a>
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@@ -255,9 +259,7 @@ Public Attributes</h2></td></tr>
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-<a id="aa7ba9b33d0e48357207d6ce6c62707ba"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aa7ba9b33d0e48357207d6ce6c62707ba">◆ </a></span>p_list_outputs</h2>
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@@ -275,9 +277,9 @@ Public Attributes</h2></td></tr>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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index 4fda29f..b0fcd55 100644
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<title>mxnet: Member List</title>
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-<script type="text/javascript" src="search/searchdata.js"></script>
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
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+ <td style="padding-left: 0.5em;">
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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@@ -65,11 +89,11 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="structNativeOpInfo.html">NativeOpInfo</a>, including all inherited members.</p>
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- <tr><td class="entry"><a class="el" href="structNativeOpInfo.html#ae4deb88375716334d58f3dc28e693d2d">forward</a></td><td class="entry"><a class="el" href="structNativeOpInfo.html">NativeOpInfo</a></td><td class="entry"></td></tr>
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- <tr><td class="entry"><a class="el" href="structNativeOpInfo.html#af25c58d65a2350cab76403f1ce21922e">list_arguments</a></td><td class="entry"><a class="el" href="structNativeOpInfo.html">NativeOpInfo</a></td><td class="entry"></td></tr>
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+ <tr class="even"><td class="entry"><a class="el" href="structNativeOpInfo.html#a2d571a373ff0ca0da5d96295cc03f465">backward</a></td><td class="entry"><a class="el" href="structNativeOpInfo.html">NativeOpInfo</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="structNativeOpInfo.html#a65f675dec9ef798a96122e91d4cf3fa8">forward</a></td><td class="entry"><a class="el" href="structNativeOpInfo.html">NativeOpInfo</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structNativeOpInfo.html#af4c12b25f9d708835619b768db5805ff">infer_shape</a></td><td class="entry"><a class="el" href="structNativeOpInfo.html">NativeOpInfo</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="structNativeOpInfo.html#ab1f5d01bbb1d6507c23e640ac3eea834">list_arguments</a></td><td class="entry"><a class="el" href="structNativeOpInfo.html">NativeOpInfo</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structNativeOpInfo.html#a1dfb6e3206e76dcf755ae0bca655081b">list_outputs</a></td><td class="entry"><a class="el" href="structNativeOpInfo.html">NativeOpInfo</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="structNativeOpInfo.html#a1f9c9294685a6e3e953acd26728e0692">p_infer_shape</a></td><td class="entry"><a class="el" href="structNativeOpInfo.html">NativeOpInfo</a></td><td class="entry"></td></tr>
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</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/structNativeOpInfo.html b/versions/master/doxygen/structNativeOpInfo.html
index 0c7b96e..2ccaf7c 100644
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: NativeOpInfo Struct Reference</title>
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<script type="text/javascript" src="jquery.js"></script>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
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<table cellspacing="0" cellpadding="0">
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+ <td style="padding-left: 0.5em;">
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</div>
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@@ -29,25 +30,48 @@
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+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
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Collaboration diagram for NativeOpInfo:</div>
<div class="dyncontent">
<div class="center"><img src="structNativeOpInfo__coll__graph.png" border="0" usemap="#NativeOpInfo_coll__map" alt="Collaboration graph"/></div>
+<map name="NativeOpInfo_coll__map" id="NativeOpInfo_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
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-<tr class="memitem:a6a324c4b74a64b295c404ff235fac1e1"><td class="memItemLeft" align="right" valign="top">void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNativeOpInfo.html#a6a324c4b74a64b295c404ff235fac1e1">list_outputs</a> )(char ***, void *)</td></tr>
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-<tr class="memitem:af25c58d65a2350cab76403f1ce21922e"><td class="memItemLeft" align="right" valign="top">void(* </td><td class="memItemRight" valign="bottom"><a class="el" href="structNativeOpInfo.html#af25c58d65a2350cab76403f1ce21922e">list_arguments</a> )(char ***, void *)</td></tr>
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<tr class="memitem:acf0017f1f716c2e88ce9feedc9b4759f"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structNativeOpInfo.html#acf0017f1f716c2e88ce9feedc9b4759f">p_forward</a></td></tr>
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<h2 class="groupheader">Member Data Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a2faec6cfc4510b455de950a4b104ea9f">◆ </a></span>backward</h2>
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<div class="memproto">
<table class="memname">
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<div class="memproto">
<table class="memname">
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- <td class="memname">void(* NativeOpInfo::forward) (int, float **, int *, unsigned **, int *, void *)</td>
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-<h2 class="memtitle"><span class="permalink"><a href="#a2d8f03e8586a1a1cb6b46ab8872a214a">◆ </a></span>infer_shape</h2>
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<div class="memproto">
<table class="memname">
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</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#af25c58d65a2350cab76403f1ce21922e">◆ </a></span>list_arguments</h2>
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<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
- <td class="memname">void(* NativeOpInfo::list_arguments) (char ***, void *)</td>
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</tr>
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</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a6a324c4b74a64b295c404ff235fac1e1">◆ </a></span>list_outputs</h2>
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<div class="memitem">
<div class="memproto">
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<tr>
- <td class="memname">void(* NativeOpInfo::list_outputs) (char ***, void *)</td>
+ <td class="memname">void(* NativeOpInfo::list_outputs)(char ***, void *)</td>
</tr>
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</div>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a60beae73be72aeb308b55aeed8cbd3c1">◆ </a></span>p_backward</h2>
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@@ -181,9 +195,7 @@ Public Attributes</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#acf0017f1f716c2e88ce9feedc9b4759f">◆ </a></span>p_forward</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a1f9c9294685a6e3e953acd26728e0692">◆ </a></span>p_infer_shape</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a0e5c803876bbcc5d8162b6ae30bce231">◆ </a></span>p_list_arguments</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a32ec41926eee694c1bfb8e8b5697a146">◆ </a></span>p_list_outputs</h2>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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onkeydown="return searchBox.OnSearchSelectKey(event)">
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
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@@ -74,7 +98,7 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a420d4e662a5129b202507f42c991391c">CPUPinned</a>(int32_t dev_id=-1)</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">Create</a>(DeviceType dev_type, int32_t dev_id=-1)</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a></td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a4182c1a2f910064d058832c57489d8f5">dev_mask</a>() const</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe">dev_mask</a>() const </td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a></td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">DeviceType</a> enum name</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Context.html#ac58071fc1c0647bcab7aca4a39ae6a29">FromString</a>(std::string str)</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">static</span></td></tr>
@@ -85,16 +109,16 @@ $(function() {
<tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a958219ae412395685ef1d82af0dbbe67">kMaxDevID</a></td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a7433936d1daf38f7dced0de49fbe6a4b">kMaxDevType</a></td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a882305327a53b9b9a82f5802b4b013e3">Load</a>(dmlc::Stream *strm)</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a7e84dc72147472e72b50e8545780fd1e">operator!=</a>(const Context &b) const</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#afd07473e20810635cb3fffc216d76494">operator<</a>(const Context &b) const</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a8b8d414a429c12ab55cfa9ed13539393">operator==</a>(const Context &b) const</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a9ea910ddb7e15d51320806b1666bf6c5">Save</a>(dmlc::Stream *strm) const</td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a10fded12664afacb95f75c07bb77bfe6">operator!=</a>(const Context &b) const </td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a1862b54dcfd20332eb2365451f47c678">operator<</a>(const Context &b) const </td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Context.html#ad59267de7b56e3aebe86a6d0e9fca2f0">operator==</a>(const Context &b) const </td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="structmxnet_1_1Context.html#a90542e0930244392f9a81ed0f206b612">Save</a>(dmlc::Stream *strm) const </td><td class="entry"><a class="el" href="structmxnet_1_1Context.html">mxnet::Context</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
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-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/structmxnet_1_1Context.html b/versions/master/doxygen/structmxnet_1_1Context.html
index c244232..bc3d932 100644
--- a/versions/master/doxygen/structmxnet_1_1Context.html
+++ b/versions/master/doxygen/structmxnet_1_1Context.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::Context Struct Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -82,6 +106,8 @@ $(function() {
Collaboration diagram for mxnet::Context:</div>
<div class="dyncontent">
<div class="center"><img src="structmxnet_1_1Context__coll__graph.png" border="0" usemap="#mxnet_1_1Context_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1Context_coll__map" id="mxnet_1_1Context_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
@@ -89,38 +115,38 @@ Public Types</h2></td></tr>
<tr class="memitem:a611c227b5e04a2777c3e4507bb76f60e"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">DeviceType</a> { <a class="el" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140">kCPU</a> = cpu::kDevMask,
<a class="el" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eab21bed4d5cee411db15283d7529f28cf">kGPU</a> = gpu::kDevMask,
<a class="el" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34">kCPUPinned</a> = 3
- }<tr class="memdesc:a611c227b5e04a2777c3e4507bb76f60e"><td class="mdescLeft"> </td><td class="mdescRight">Type of device. <a href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">More...</a><br /></td></tr>
-</td></tr>
+ }</td></tr>
+<tr class="memdesc:a611c227b5e04a2777c3e4507bb76f60e"><td class="mdescLeft"> </td><td class="mdescRight">Type of device. <a href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">More...</a><br/></td></tr>
<tr class="separator:a611c227b5e04a2777c3e4507bb76f60e"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a290642a21363bef82bcb32cf7f98b884"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a290642a21363bef82bcb32cf7f98b884">Context</a> ()</td></tr>
-<tr class="memdesc:a290642a21363bef82bcb32cf7f98b884"><td class="mdescLeft"> </td><td class="mdescRight">default constructor <a href="#a290642a21363bef82bcb32cf7f98b884">More...</a><br /></td></tr>
+<tr class="memdesc:a290642a21363bef82bcb32cf7f98b884"><td class="mdescLeft"> </td><td class="mdescRight">default constructor <a href="#a290642a21363bef82bcb32cf7f98b884">More...</a><br/></td></tr>
<tr class="separator:a290642a21363bef82bcb32cf7f98b884"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a4182c1a2f910064d058832c57489d8f5"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a4182c1a2f910064d058832c57489d8f5">dev_mask</a> () const</td></tr>
-<tr class="memdesc:a4182c1a2f910064d058832c57489d8f5"><td class="mdescLeft"> </td><td class="mdescRight">Get corresponding device mask. <a href="#a4182c1a2f910064d058832c57489d8f5">More...</a><br /></td></tr>
-<tr class="separator:a4182c1a2f910064d058832c57489d8f5"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:afd07473e20810635cb3fffc216d76494"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#afd07473e20810635cb3fffc216d76494">operator<</a> (const <a class="el" href="structmxnet_1_1Context.html">Context</a> &b) const</td></tr>
-<tr class="memdesc:afd07473e20810635cb3fffc216d76494"><td class="mdescLeft"> </td><td class="mdescRight">Comparator, used to enable <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> as std::map key. <a href="#afd07473e20810635cb3fffc216d76494">More...</a><br /></td></tr>
-<tr class="separator:afd07473e20810635cb3fffc216d76494"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a8b8d414a429c12ab55cfa9ed13539393"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a8b8d414a429c12ab55cfa9ed13539393">operator==</a> (const <a class="el" href="structmxnet_1_1Context.html">Context</a> &b) const</td></tr>
-<tr class="memdesc:a8b8d414a429c12ab55cfa9ed13539393"><td class="mdescLeft"> </td><td class="mdescRight">check if current context equals another one <a href="#a8b8d414a429c12ab55cfa9ed13539393">More...</a><br /></td></tr>
-<tr class="separator:a8b8d414a429c12ab55cfa9ed13539393"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a7e84dc72147472e72b50e8545780fd1e"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a7e84dc72147472e72b50e8545780fd1e">operator!=</a> (const <a class="el" href="structmxnet_1_1Context.html">Context</a> &b) const</td></tr>
-<tr class="memdesc:a7e84dc72147472e72b50e8545780fd1e"><td class="mdescLeft"> </td><td class="mdescRight">check if current context not equals another one <a href="#a7e84dc72147472e72b50e8545780fd1e">More...</a><br /></td></tr>
-<tr class="separator:a7e84dc72147472e72b50e8545780fd1e"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a9ea910ddb7e15d51320806b1666bf6c5"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a9ea910ddb7e15d51320806b1666bf6c5">Save</a> (dmlc::Stream *strm) const</td></tr>
-<tr class="memdesc:a9ea910ddb7e15d51320806b1666bf6c5"><td class="mdescLeft"> </td><td class="mdescRight">save the content into binary stream <a href="#a9ea910ddb7e15d51320806b1666bf6c5">More...</a><br /></td></tr>
-<tr class="separator:a9ea910ddb7e15d51320806b1666bf6c5"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4f6fd8608a4abc9673cb6da4654f84fe"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe">dev_mask</a> () const </td></tr>
+<tr class="memdesc:a4f6fd8608a4abc9673cb6da4654f84fe"><td class="mdescLeft"> </td><td class="mdescRight">Get corresponding device mask. <a href="#a4f6fd8608a4abc9673cb6da4654f84fe">More...</a><br/></td></tr>
+<tr class="separator:a4f6fd8608a4abc9673cb6da4654f84fe"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1862b54dcfd20332eb2365451f47c678"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a1862b54dcfd20332eb2365451f47c678">operator<</a> (const <a class="el" href="structmxnet_1_1Context.html">Context</a> &b) const </td></tr>
+<tr class="memdesc:a1862b54dcfd20332eb2365451f47c678"><td class="mdescLeft"> </td><td class="mdescRight">Comparator, used to enable <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> as std::map key. <a href="#a1862b54dcfd20332eb2365451f47c678">More...</a><br/></td></tr>
+<tr class="separator:a1862b54dcfd20332eb2365451f47c678"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad59267de7b56e3aebe86a6d0e9fca2f0"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#ad59267de7b56e3aebe86a6d0e9fca2f0">operator==</a> (const <a class="el" href="structmxnet_1_1Context.html">Context</a> &b) const </td></tr>
+<tr class="memdesc:ad59267de7b56e3aebe86a6d0e9fca2f0"><td class="mdescLeft"> </td><td class="mdescRight">check if current context equals another one <a href="#ad59267de7b56e3aebe86a6d0e9fca2f0">More...</a><br/></td></tr>
+<tr class="separator:ad59267de7b56e3aebe86a6d0e9fca2f0"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a10fded12664afacb95f75c07bb77bfe6"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a10fded12664afacb95f75c07bb77bfe6">operator!=</a> (const <a class="el" href="structmxnet_1_1Context.html">Context</a> &b) const </td></tr>
+<tr class="memdesc:a10fded12664afacb95f75c07bb77bfe6"><td class="mdescLeft"> </td><td class="mdescRight">check if current context not equals another one <a href="#a10fded12664afacb95f75c07bb77bfe6">More...</a><br/></td></tr>
+<tr class="separator:a10fded12664afacb95f75c07bb77bfe6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a90542e0930244392f9a81ed0f206b612"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a90542e0930244392f9a81ed0f206b612">Save</a> (dmlc::Stream *strm) const </td></tr>
+<tr class="memdesc:a90542e0930244392f9a81ed0f206b612"><td class="mdescLeft"> </td><td class="mdescRight">save the content into binary stream <a href="#a90542e0930244392f9a81ed0f206b612">More...</a><br/></td></tr>
+<tr class="separator:a90542e0930244392f9a81ed0f206b612"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a882305327a53b9b9a82f5802b4b013e3"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a882305327a53b9b9a82f5802b4b013e3">Load</a> (dmlc::Stream *strm)</td></tr>
-<tr class="memdesc:a882305327a53b9b9a82f5802b4b013e3"><td class="mdescLeft"> </td><td class="mdescRight">load the content from binary stream <a href="#a882305327a53b9b9a82f5802b4b013e3">More...</a><br /></td></tr>
+<tr class="memdesc:a882305327a53b9b9a82f5802b4b013e3"><td class="mdescLeft"> </td><td class="mdescRight">load the content from binary stream <a href="#a882305327a53b9b9a82f5802b4b013e3">More...</a><br/></td></tr>
<tr class="separator:a882305327a53b9b9a82f5802b4b013e3"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:a7cea13406ae21ae557d3455f54670a4a"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="structmxnet_1_1Context.html">Context</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a7cea13406ae21ae557d3455f54670a4a">Create</a> (<a class="el" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">DeviceType</a> <a class="el" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0 [...]
-<tr class="memdesc:a7cea13406ae21ae557d3455f54670a4a"><td class="mdescLeft"> </td><td class="mdescRight">Create a new context. <a href="#a7cea13406ae21ae557d3455f54670a4a">More...</a><br /></td></tr>
+<tr class="memdesc:a7cea13406ae21ae557d3455f54670a4a"><td class="mdescLeft"> </td><td class="mdescRight">Create a new context. <a href="#a7cea13406ae21ae557d3455f54670a4a">More...</a><br/></td></tr>
<tr class="separator:a7cea13406ae21ae557d3455f54670a4a"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4bb231d70623fde216de51589347e513"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="structmxnet_1_1Context.html">Context</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a4bb231d70623fde216de51589347e513">CPU</a> (int32_t <a class="el" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a>=0)</td></tr>
<tr class="separator:a4bb231d70623fde216de51589347e513"><td class="memSeparator" colspan="2"> </td></tr>
@@ -134,27 +160,25 @@ Static Public Member Functions</h2></td></tr>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:a66040df23c2a5663f29bc50aa0bbace3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1Context.html#a611c227b5e04a2777c3e4507bb76f60e">DeviceType</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a66040df23c2a5663f29bc50aa0bbace3">dev_type</a></td></tr>
-<tr class="memdesc:a66040df23c2a5663f29bc50aa0bbace3"><td class="mdescLeft"> </td><td class="mdescRight">the device type we run the op on <a href="#a66040df23c2a5663f29bc50aa0bbace3">More...</a><br /></td></tr>
+<tr class="memdesc:a66040df23c2a5663f29bc50aa0bbace3"><td class="mdescLeft"> </td><td class="mdescRight">the device type we run the op on <a href="#a66040df23c2a5663f29bc50aa0bbace3">More...</a><br/></td></tr>
<tr class="separator:a66040df23c2a5663f29bc50aa0bbace3"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8486dcc599dffe447e2f098b170d3db9"><td class="memItemLeft" align="right" valign="top">int32_t </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a8486dcc599dffe447e2f098b170d3db9">dev_id</a></td></tr>
-<tr class="memdesc:a8486dcc599dffe447e2f098b170d3db9"><td class="mdescLeft"> </td><td class="mdescRight">device id we are going to run it on <a href="#a8486dcc599dffe447e2f098b170d3db9">More...</a><br /></td></tr>
+<tr class="memdesc:a8486dcc599dffe447e2f098b170d3db9"><td class="mdescLeft"> </td><td class="mdescRight">device id we are going to run it on <a href="#a8486dcc599dffe447e2f098b170d3db9">More...</a><br/></td></tr>
<tr class="separator:a8486dcc599dffe447e2f098b170d3db9"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-attribs"></a>
Static Public Attributes</h2></td></tr>
<tr class="memitem:a7433936d1daf38f7dced0de49fbe6a4b"><td class="memItemLeft" align="right" valign="top">static const int32_t </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a7433936d1daf38f7dced0de49fbe6a4b">kMaxDevType</a> = 4</td></tr>
-<tr class="memdesc:a7433936d1daf38f7dced0de49fbe6a4b"><td class="mdescLeft"> </td><td class="mdescRight">the maximal device type <a href="#a7433936d1daf38f7dced0de49fbe6a4b">More...</a><br /></td></tr>
+<tr class="memdesc:a7433936d1daf38f7dced0de49fbe6a4b"><td class="mdescLeft"> </td><td class="mdescRight">the maximal device type <a href="#a7433936d1daf38f7dced0de49fbe6a4b">More...</a><br/></td></tr>
<tr class="separator:a7433936d1daf38f7dced0de49fbe6a4b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a958219ae412395685ef1d82af0dbbe67"><td class="memItemLeft" align="right" valign="top">static const int32_t </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Context.html#a958219ae412395685ef1d82af0dbbe67">kMaxDevID</a> = 16</td></tr>
-<tr class="memdesc:a958219ae412395685ef1d82af0dbbe67"><td class="mdescLeft"> </td><td class="mdescRight">the maximal device index <a href="#a958219ae412395685ef1d82af0dbbe67">More...</a><br /></td></tr>
+<tr class="memdesc:a958219ae412395685ef1d82af0dbbe67"><td class="mdescLeft"> </td><td class="mdescRight">the maximal device index <a href="#a958219ae412395685ef1d82af0dbbe67">More...</a><br/></td></tr>
<tr class="separator:a958219ae412395685ef1d82af0dbbe67"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> information about the execution environment. </p>
</div><h2 class="groupheader">Member Enumeration Documentation</h2>
-<a id="a611c227b5e04a2777c3e4507bb76f60e"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a611c227b5e04a2777c3e4507bb76f60e">◆ </a></span>DeviceType</h2>
-
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@@ -166,17 +190,18 @@ Static Public Attributes</h2></td></tr>
<p>Type of device. </p>
<table class="fieldtable">
-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140"></a>kCPU </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a611c227b5e04a2777c3e4507bb76f60eab21bed4d5cee411db15283d7529f28cf"></a>kGPU </td><td class="fielddoc"></td></tr>
-<tr><td class="fieldname"><a id="a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34"></a>kCPUPinned </td><td class="fielddoc"></td></tr>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="a611c227b5e04a2777c3e4507bb76f60ea1aced5778bb0d64d4ce6bd4c0a86d140"></a>kCPU</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a611c227b5e04a2777c3e4507bb76f60eab21bed4d5cee411db15283d7529f28cf"></a>kGPU</em> </td><td class="fielddoc">
+</td></tr>
+<tr><td class="fieldname"><em><a class="anchor" id="a611c227b5e04a2777c3e4507bb76f60eaa2e12b86e0f957dc14d45863eaa47f34"></a>kCPUPinned</em> </td><td class="fielddoc">
+</td></tr>
</table>
</div>
</div>
<h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a290642a21363bef82bcb32cf7f98b884"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a290642a21363bef82bcb32cf7f98b884">◆ </a></span>Context()</h2>
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@@ -202,9 +227,7 @@ Static Public Attributes</h2></td></tr>
</div>
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<h2 class="groupheader">Member Function Documentation</h2>
-<a id="a4bb231d70623fde216de51589347e513"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a4bb231d70623fde216de51589347e513">◆ </a></span>CPU()</h2>
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@@ -229,9 +252,7 @@ Static Public Attributes</h2></td></tr>
</div>
</div>
-<a id="a420d4e662a5129b202507f42c991391c"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a420d4e662a5129b202507f42c991391c">◆ </a></span>CPUPinned()</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -252,7 +273,8 @@ Static Public Attributes</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<p>Create a pinned CPU context. </p><dl class="params"><dt>Parameters</dt><dd>
+<p>Create a pinned CPU context. </p>
+<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">dev_id</td><td>the device id for corresponding GPU. </td></tr>
</table>
@@ -262,9 +284,7 @@ Static Public Attributes</h2></td></tr>
</div>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a7cea13406ae21ae557d3455f54670a4a">◆ </a></span>Create()</h2>
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@@ -307,9 +327,7 @@ Static Public Attributes</h2></td></tr>
</div>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a4182c1a2f910064d058832c57489d8f5">◆ </a></span>dev_mask()</h2>
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@@ -335,9 +353,7 @@ Static Public Attributes</h2></td></tr>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#ac58071fc1c0647bcab7aca4a39ae6a29">◆ </a></span>FromString()</h2>
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<div class="memitem">
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@@ -358,7 +374,8 @@ Static Public Attributes</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<p>Create a context from string of the format [cpu|gpu|cpu_pinned](n) </p><dl class="params"><dt>Parameters</dt><dd>
+<p>Create a context from string of the format [cpu|gpu|cpu_pinned](n) </p>
+<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">str</td><td>the string pattern </td></tr>
</table>
@@ -368,9 +385,7 @@ Static Public Attributes</h2></td></tr>
</div>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a39d1a2b59bce135da127475995a58671">◆ </a></span>GPU()</h2>
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<table class="mlabels">
@@ -391,7 +406,8 @@ Static Public Attributes</h2></td></tr>
</tr>
</table>
</div><div class="memdoc">
-<p>Create a GPU context. </p><dl class="params"><dt>Parameters</dt><dd>
+<p>Create a GPU context. </p>
+<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">dev_id</td><td>the device id. </td></tr>
</table>
@@ -401,9 +417,7 @@ Static Public Attributes</h2></td></tr>
</div>
</div>
-<a id="a882305327a53b9b9a82f5802b4b013e3"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a882305327a53b9b9a82f5802b4b013e3">◆ </a></span>Load()</h2>
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@@ -436,9 +450,7 @@ Static Public Attributes</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a7e84dc72147472e72b50e8545780fd1e">◆ </a></span>operator!=()</h2>
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@@ -471,9 +483,7 @@ Static Public Attributes</h2></td></tr>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#afd07473e20810635cb3fffc216d76494">◆ </a></span>operator<()</h2>
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@@ -506,9 +516,7 @@ Static Public Attributes</h2></td></tr>
</div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a8b8d414a429c12ab55cfa9ed13539393">◆ </a></span>operator==()</h2>
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@@ -541,9 +549,7 @@ Static Public Attributes</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a9ea910ddb7e15d51320806b1666bf6c5">◆ </a></span>Save()</h2>
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@@ -576,9 +582,7 @@ Static Public Attributes</h2></td></tr>
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<h2 class="groupheader">Member Data Documentation</h2>
-<a id="a8486dcc599dffe447e2f098b170d3db9"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a8486dcc599dffe447e2f098b170d3db9">◆ </a></span>dev_id</h2>
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@@ -592,9 +596,7 @@ Static Public Attributes</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a66040df23c2a5663f29bc50aa0bbace3">◆ </a></span>dev_type</h2>
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@@ -608,9 +610,7 @@ Static Public Attributes</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a958219ae412395685ef1d82af0dbbe67">◆ </a></span>kMaxDevID</h2>
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@@ -632,9 +632,7 @@ Static Public Attributes</h2></td></tr>
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-<h2 class="memtitle"><span class="permalink"><a href="#a7433936d1daf38f7dced0de49fbe6a4b">◆ </a></span>kMaxDevType</h2>
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@@ -662,9 +660,9 @@ Static Public Attributes</h2></td></tr>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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@@ -20,7 +21,7 @@
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Collaboration diagram for mxnet::DataBatch:</div>
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<div class="center"><img src="structmxnet_1_1DataBatch__coll__graph.png" border="0" usemap="#mxnet_1_1DataBatch_coll__map" alt="Collaboration graph"/></div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:acc98dd6f48816ca7462352a929e48959"><td class="memItemLeft" align="right" valign="top">std::vector< <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataBatch.html#acc98dd6f48816ca7462352a929e48959">data</a></td></tr>
-<tr class="memdesc:acc98dd6f48816ca7462352a929e48959"><td class="mdescLeft"> </td><td class="mdescRight">content of dense data, if this <a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> is dense <a href="#acc98dd6f48816ca7462352a929e48959">More...</a><br /></td></tr>
+<tr class="memdesc:acc98dd6f48816ca7462352a929e48959"><td class="mdescLeft"> </td><td class="mdescRight">content of dense data, if this <a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> is dense <a href="#acc98dd6f48816ca7462352a929e48959">More...</a><br/></td></tr>
<tr class="separator:acc98dd6f48816ca7462352a929e48959"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a26a2ca473161c2c2a914cbc7ae4eb74e"><td class="memItemLeft" align="right" valign="top">std::vector< uint64_t > </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataBatch.html#a26a2ca473161c2c2a914cbc7ae4eb74e">index</a></td></tr>
-<tr class="memdesc:a26a2ca473161c2c2a914cbc7ae4eb74e"><td class="mdescLeft"> </td><td class="mdescRight">index of image data <a href="#a26a2ca473161c2c2a914cbc7ae4eb74e">More...</a><br /></td></tr>
+<tr class="memdesc:a26a2ca473161c2c2a914cbc7ae4eb74e"><td class="mdescLeft"> </td><td class="mdescRight">index of image data <a href="#a26a2ca473161c2c2a914cbc7ae4eb74e">More...</a><br/></td></tr>
<tr class="separator:a26a2ca473161c2c2a914cbc7ae4eb74e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2e9faeff00dd061f04a88c3ea5758aa2"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataBatch.html#a2e9faeff00dd061f04a88c3ea5758aa2">extra_data</a></td></tr>
-<tr class="memdesc:a2e9faeff00dd061f04a88c3ea5758aa2"><td class="mdescLeft"> </td><td class="mdescRight">extra data to be fed to the network <a href="#a2e9faeff00dd061f04a88c3ea5758aa2">More...</a><br /></td></tr>
+<tr class="memdesc:a2e9faeff00dd061f04a88c3ea5758aa2"><td class="mdescLeft"> </td><td class="mdescRight">extra data to be fed to the network <a href="#a2e9faeff00dd061f04a88c3ea5758aa2">More...</a><br/></td></tr>
<tr class="separator:a2e9faeff00dd061f04a88c3ea5758aa2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aad93df826690100bb16689952fc83558"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataBatch.html#aad93df826690100bb16689952fc83558">num_batch_padd</a></td></tr>
-<tr class="memdesc:aad93df826690100bb16689952fc83558"><td class="mdescLeft"> </td><td class="mdescRight">num of example padded to batch <a href="#aad93df826690100bb16689952fc83558">More...</a><br /></td></tr>
+<tr class="memdesc:aad93df826690100bb16689952fc83558"><td class="mdescLeft"> </td><td class="mdescRight">num of example padded to batch <a href="#aad93df826690100bb16689952fc83558">More...</a><br/></td></tr>
<tr class="separator:aad93df826690100bb16689952fc83558"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="structmxnet_1_1DataBatch.html" title="DataBatch of NDArray, returned by Iterator. ">DataBatch</a> of <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, returned by Iterator. </p>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:a548411c4a3dc77475c5ae6bfa6ee3564"><td class="memItemLeft" align="right" valign="top">unsigned </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataInst.html#a548411c4a3dc77475c5ae6bfa6ee3564">index</a></td></tr>
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+<tr class="memdesc:a548411c4a3dc77475c5ae6bfa6ee3564"><td class="mdescLeft"> </td><td class="mdescRight">unique id for instance <a href="#a548411c4a3dc77475c5ae6bfa6ee3564">More...</a><br/></td></tr>
<tr class="separator:a548411c4a3dc77475c5ae6bfa6ee3564"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a3511bbb64add286c9cd1b03c70d4b111"><td class="memItemLeft" align="right" valign="top">std::vector< <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataInst.html#a3511bbb64add286c9cd1b03c70d4b111">data</a></td></tr>
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+<tr class="memdesc:a3511bbb64add286c9cd1b03c70d4b111"><td class="mdescLeft"> </td><td class="mdescRight">content of data <a href="#a3511bbb64add286c9cd1b03c70d4b111">More...</a><br/></td></tr>
<tr class="separator:a3511bbb64add286c9cd1b03c70d4b111"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a311b93ed629984fdcb29cb346d3062ee"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1DataInst.html#a311b93ed629984fdcb29cb346d3062ee">extra_data</a></td></tr>
-<tr class="memdesc:a311b93ed629984fdcb29cb346d3062ee"><td class="mdescLeft"> </td><td class="mdescRight">extra data to be fed to the network <a href="#a311b93ed629984fdcb29cb346d3062ee">More...</a><br /></td></tr>
+<tr class="memdesc:a311b93ed629984fdcb29cb346d3062ee"><td class="mdescLeft"> </td><td class="mdescRight">extra data to be fed to the network <a href="#a311b93ed629984fdcb29cb346d3062ee">More...</a><br/></td></tr>
<tr class="separator:a311b93ed629984fdcb29cb346d3062ee"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>a single data instance </p>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Registry entry for DataIterator factory functions. </p>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
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+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ alt=""/>
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ <li><a href="annotated.html"><span>Class List</span></a></li>
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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+ </ul>
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@@ -79,70 +103,72 @@ $(function() {
Inheritance diagram for mxnet::NDArrayFunctionReg:</div>
<div class="dyncontent">
<div class="center"><img src="structmxnet_1_1NDArrayFunctionReg__inherit__graph.png" border="0" usemap="#mxnet_1_1NDArrayFunctionReg_inherit__map" alt="Inheritance graph"/></div>
+<map name="mxnet_1_1NDArrayFunctionReg_inherit__map" id="mxnet_1_1NDArrayFunctionReg_inherit__map">
+</map>
</div>
<div class="dynheader">
Collaboration diagram for mxnet::NDArrayFunctionReg:</div>
<div class="dyncontent">
<div class="center"><img src="structmxnet_1_1NDArrayFunctionReg__coll__graph.png" border="0" usemap="#mxnet_1_1NDArrayFunctionReg_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1NDArrayFunctionReg_coll__map" id="mxnet_1_1NDArrayFunctionReg_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:abd540887b52ba89572ad6a18b4ac739c"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#abd540887b52ba89572ad6a18b4ac739c">NDArrayFunctionReg</a> ()</td></tr>
-<tr class="memdesc:abd540887b52ba89572ad6a18b4ac739c"><td class="mdescLeft"> </td><td class="mdescRight">constructor <a href="#abd540887b52ba89572ad6a18b4ac739c">More...</a><br /></td></tr>
+<tr class="memdesc:abd540887b52ba89572ad6a18b4ac739c"><td class="mdescLeft"> </td><td class="mdescRight">constructor <a href="#abd540887b52ba89572ad6a18b4ac739c">More...</a><br/></td></tr>
<tr class="separator:abd540887b52ba89572ad6a18b4ac739c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa606f0cec91a2e069553f98d7e7addcd"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#aa606f0cec91a2e069553f98d7e7addcd">set_function</a> (void(*fsetvalue)(const <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> &rhs, <a class="el" hr [...]
-<tr class="memdesc:aa606f0cec91a2e069553f98d7e7addcd"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> setvalue function this will also auto set the parameters correctly <a href="#aa606f0cec91a2e069553f98d7e7addcd">More...</a><br /></td></tr>
+<tr class="memdesc:aa606f0cec91a2e069553f98d7e7addcd"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> setvalue function this will also auto set the parameters correctly <a href="#aa606f0cec91a2e069553f98d7e7addcd">More...</a><br/></td></tr>
<tr class="separator:aa606f0cec91a2e069553f98d7e7addcd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa7f4633d042179d90c067aeb8da62060"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#aa7f4633d042179d90c067aeb8da62060">set_function</a> (void(*fternary)(const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="classmxnet_1_1NDA [...]
-<tr class="memdesc:aa7f4633d042179d90c067aeb8da62060"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a ternary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#aa7f4633d042179d90c067aeb8da62060">More...</a><br /></td></tr>
+<tr class="memdesc:aa7f4633d042179d90c067aeb8da62060"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a ternary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#aa7f4633d042179d90c067aeb8da62060">More...</a><br/></td></tr>
<tr class="separator:aa7f4633d042179d90c067aeb8da62060"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa57c24a22d49470496b221c72bf7a1a0"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#aa57c24a22d49470496b221c72bf7a1a0">set_function</a> (void(*fbinary)(const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="classmxnet_1_1NDAr [...]
-<tr class="memdesc:aa57c24a22d49470496b221c72bf7a1a0"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a binary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#aa57c24a22d49470496b221c72bf7a1a0">More...</a><br /></td></tr>
+<tr class="memdesc:aa57c24a22d49470496b221c72bf7a1a0"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a binary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#aa57c24a22d49470496b221c72bf7a1a0">More...</a><br/></td></tr>
<tr class="separator:aa57c24a22d49470496b221c72bf7a1a0"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af6fc4419b30c74139eceb04e7d5758f5"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#af6fc4419b30c74139eceb04e7d5758f5">set_function</a> (void(*fscalar)(const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &lhs, const <a class="el" href="namespacemxnet.htm [...]
-<tr class="memdesc:af6fc4419b30c74139eceb04e7d5758f5"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a binary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#af6fc4419b30c74139eceb04e7d5758f5">More...</a><br /></td></tr>
+<tr class="memdesc:af6fc4419b30c74139eceb04e7d5758f5"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a binary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#af6fc4419b30c74139eceb04e7d5758f5">More...</a><br/></td></tr>
<tr class="separator:af6fc4419b30c74139eceb04e7d5758f5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5f4c0751bc3111a8707b160d0e99e1af"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#a5f4c0751bc3111a8707b160d0e99e1af">set_function</a> (void(*funary)(const <a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> &src, <a class="el" href="classmxnet_1_1NDArray.htm [...]
-<tr class="memdesc:a5f4c0751bc3111a8707b160d0e99e1af"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a unary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#a5f4c0751bc3111a8707b160d0e99e1af">More...</a><br /></td></tr>
+<tr class="memdesc:a5f4c0751bc3111a8707b160d0e99e1af"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a unary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#a5f4c0751bc3111a8707b160d0e99e1af">More...</a><br/></td></tr>
<tr class="separator:a5f4c0751bc3111a8707b160d0e99e1af"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a220e1866bea35e103adbe23ff92855d9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#a220e1866bea35e103adbe23ff92855d9">set_function</a> (void(*fgeneric)(<a class="el" href="classmxnet_1_1NDArray.html">NDArray</a> **used_vars, <a class="el" href="namespacemxnet.html#a44213 [...]
-<tr class="memdesc:a220e1866bea35e103adbe23ff92855d9"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a unary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#a220e1866bea35e103adbe23ff92855d9">More...</a><br /></td></tr>
+<tr class="memdesc:a220e1866bea35e103adbe23ff92855d9"><td class="mdescLeft"> </td><td class="mdescRight">set the function body to a unary <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a> function this will also auto set the parameters correctly <a href="#a220e1866bea35e103adbe23ff92855d9">More...</a><br/></td></tr>
<tr class="separator:a220e1866bea35e103adbe23ff92855d9"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a970756506340ff688dbb447858b1963c"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#a970756506340ff688dbb447858b1963c">set_num_use_vars</a> (unsigned n)</td></tr>
-<tr class="memdesc:a970756506340ff688dbb447858b1963c"><td class="mdescLeft"> </td><td class="mdescRight">set the number of mutate variables <a href="#a970756506340ff688dbb447858b1963c">More...</a><br /></td></tr>
+<tr class="memdesc:a970756506340ff688dbb447858b1963c"><td class="mdescLeft"> </td><td class="mdescRight">set the number of mutate variables <a href="#a970756506340ff688dbb447858b1963c">More...</a><br/></td></tr>
<tr class="separator:a970756506340ff688dbb447858b1963c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af0a5efd67efe9b7c14abd49652e79a14"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#af0a5efd67efe9b7c14abd49652e79a14">set_num_mutate_vars</a> (unsigned n)</td></tr>
-<tr class="memdesc:af0a5efd67efe9b7c14abd49652e79a14"><td class="mdescLeft"> </td><td class="mdescRight">set the number of mutate variables <a href="#af0a5efd67efe9b7c14abd49652e79a14">More...</a><br /></td></tr>
+<tr class="memdesc:af0a5efd67efe9b7c14abd49652e79a14"><td class="mdescLeft"> </td><td class="mdescRight">set the number of mutate variables <a href="#af0a5efd67efe9b7c14abd49652e79a14">More...</a><br/></td></tr>
<tr class="separator:af0a5efd67efe9b7c14abd49652e79a14"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4f6a3ba75e30389c827b3b26a12415d1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#a4f6a3ba75e30389c827b3b26a12415d1">set_num_scalars</a> (unsigned n)</td></tr>
-<tr class="memdesc:a4f6a3ba75e30389c827b3b26a12415d1"><td class="mdescLeft"> </td><td class="mdescRight">set the number of scalar arguments <a href="#a4f6a3ba75e30389c827b3b26a12415d1">More...</a><br /></td></tr>
+<tr class="memdesc:a4f6a3ba75e30389c827b3b26a12415d1"><td class="mdescLeft"> </td><td class="mdescRight">set the number of scalar arguments <a href="#a4f6a3ba75e30389c827b3b26a12415d1">More...</a><br/></td></tr>
<tr class="separator:a4f6a3ba75e30389c827b3b26a12415d1"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:acc3656ba948c28d3d155736ce064a3f4"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html">NDArrayFunctionReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#acc3656ba948c28d3d155736ce064a3f4">set_type_mask</a> (int tmask)</td></tr>
-<tr class="memdesc:acc3656ba948c28d3d155736ce064a3f4"><td class="mdescLeft"> </td><td class="mdescRight">set type mask <a href="#acc3656ba948c28d3d155736ce064a3f4">More...</a><br /></td></tr>
+<tr class="memdesc:acc3656ba948c28d3d155736ce064a3f4"><td class="mdescLeft"> </td><td class="mdescRight">set type mask <a href="#acc3656ba948c28d3d155736ce064a3f4">More...</a><br/></td></tr>
<tr class="separator:acc3656ba948c28d3d155736ce064a3f4"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:aa0847438b4f3f600ec61d6d544755a29"><td class="memItemLeft" align="right" valign="top">unsigned </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#aa0847438b4f3f600ec61d6d544755a29">num_use_vars</a></td></tr>
-<tr class="memdesc:aa0847438b4f3f600ec61d6d544755a29"><td class="mdescLeft"> </td><td class="mdescRight">number of variable used by this function <a href="#aa0847438b4f3f600ec61d6d544755a29">More...</a><br /></td></tr>
+<tr class="memdesc:aa0847438b4f3f600ec61d6d544755a29"><td class="mdescLeft"> </td><td class="mdescRight">number of variable used by this function <a href="#aa0847438b4f3f600ec61d6d544755a29">More...</a><br/></td></tr>
<tr class="separator:aa0847438b4f3f600ec61d6d544755a29"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad7881779b7807c6357902bdaccfccde2"><td class="memItemLeft" align="right" valign="top">unsigned </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#ad7881779b7807c6357902bdaccfccde2">num_mutate_vars</a></td></tr>
-<tr class="memdesc:ad7881779b7807c6357902bdaccfccde2"><td class="mdescLeft"> </td><td class="mdescRight">number of variable mutated by this function <a href="#ad7881779b7807c6357902bdaccfccde2">More...</a><br /></td></tr>
+<tr class="memdesc:ad7881779b7807c6357902bdaccfccde2"><td class="mdescLeft"> </td><td class="mdescRight">number of variable mutated by this function <a href="#ad7881779b7807c6357902bdaccfccde2">More...</a><br/></td></tr>
<tr class="separator:ad7881779b7807c6357902bdaccfccde2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9591e2abf6cbca0903aec582b96b4d95"><td class="memItemLeft" align="right" valign="top">unsigned </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#a9591e2abf6cbca0903aec582b96b4d95">num_scalars</a></td></tr>
-<tr class="memdesc:a9591e2abf6cbca0903aec582b96b4d95"><td class="mdescLeft"> </td><td class="mdescRight">number of scalars used by this function <a href="#a9591e2abf6cbca0903aec582b96b4d95">More...</a><br /></td></tr>
+<tr class="memdesc:a9591e2abf6cbca0903aec582b96b4d95"><td class="mdescLeft"> </td><td class="mdescRight">number of scalars used by this function <a href="#a9591e2abf6cbca0903aec582b96b4d95">More...</a><br/></td></tr>
<tr class="separator:a9591e2abf6cbca0903aec582b96b4d95"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a0b24615f30e4fbe69ee75c7b319735bf"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1NDArrayFunctionReg.html#a0b24615f30e4fbe69ee75c7b319735bf">type_mask</a></td></tr>
-<tr class="memdesc:a0b24615f30e4fbe69ee75c7b319735bf"><td class="mdescLeft"> </td><td class="mdescRight">information on how function should be called from API <a href="#a0b24615f30e4fbe69ee75c7b319735bf">More...</a><br /></td></tr>
+<tr class="memdesc:a0b24615f30e4fbe69ee75c7b319735bf"><td class="mdescLeft"> </td><td class="mdescRight">information on how function should be called from API <a href="#a0b24615f30e4fbe69ee75c7b319735bf">More...</a><br/></td></tr>
<tr class="separator:a0b24615f30e4fbe69ee75c7b319735bf"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Registry entry for NDArrayFunction. </p>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="abd540887b52ba89572ad6a18b4ac739c"></a>
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<h2 class="groupheader">Member Function Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#aa57c24a22d49470496b221c72bf7a1a0">◆ </a></span>set_function() <span class="overload">[3/6]</span></h2>
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- <tr><td class="entry"><a class="el" href="structmxnet_1_1OpContext.html#a0c48c2419d23551a626ae3de6ee1f6da">get_stream</a>() const</td><td class="entry"><a class="el" href="structmxnet_1_1OpContext.html">mxnet::OpContext</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="structmxnet_1_1OpContext.html#aa700c5c28e024b42d41e15f46615f1b8">get_stream</a>() const </td><td class="entry"><a class="el" href="structmxnet_1_1OpContext.html">mxnet::OpContext</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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@@ -80,41 +104,36 @@ Collaboration diagram for mxnet::OpContext:</div>
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Public Member Functions</h2></td></tr>
-<tr class="memitem:a0c48c2419d23551a626ae3de6ee1f6da"><td class="memTemplParams" colspan="2">template<typename xpu > </td></tr>
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-<tr class="memdesc:a0c48c2419d23551a626ae3de6ee1f6da"><td class="mdescLeft"> </td><td class="mdescRight">get mshadow stream from <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> <a href="#a0c48c2419d23551a626ae3de6ee1f6da">More...</a><br /></td></tr>
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+<tr class="memitem:aa700c5c28e024b42d41e15f46615f1b8"><td class="memTemplParams" colspan="2">template<typename xpu > </td></tr>
+<tr class="memitem:aa700c5c28e024b42d41e15f46615f1b8"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Stream< xpu > * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OpContext.html#aa700c5c28e024b42d41e15f46615f1b8">get_stream</a> () const </td></tr>
+<tr class="memdesc:aa700c5c28e024b42d41e15f46615f1b8"><td class="mdescLeft"> </td><td class="mdescRight">get mshadow stream from <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> <a href="#aa700c5c28e024b42d41e15f46615f1b8">More...</a><br/></td></tr>
+<tr class="separator:aa700c5c28e024b42d41e15f46615f1b8"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:abd7405c065167e4e15af032b1e0a5c42"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OpContext.html#abd7405c065167e4e15af032b1e0a5c42">is_train</a></td></tr>
-<tr class="memdesc:abd7405c065167e4e15af032b1e0a5c42"><td class="mdescLeft"> </td><td class="mdescRight">whether it is training phase <a href="#abd7405c065167e4e15af032b1e0a5c42">More...</a><br /></td></tr>
+<tr class="memdesc:abd7405c065167e4e15af032b1e0a5c42"><td class="mdescLeft"> </td><td class="mdescRight">whether it is training phase <a href="#abd7405c065167e4e15af032b1e0a5c42">More...</a><br/></td></tr>
<tr class="separator:abd7405c065167e4e15af032b1e0a5c42"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a869d4c68c0028a175406f11332baf7cf"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1RunContext.html">RunContext</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OpContext.html#a869d4c68c0028a175406f11332baf7cf">run_ctx</a></td></tr>
-<tr class="memdesc:a869d4c68c0028a175406f11332baf7cf"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1RunContext.html" title="execution time context. The information needed in runtime for actual execution. ">RunContext</a> related resources. <a href="#a869d4c68c0028a175406f11332baf7cf">More...</a><br /></td></tr>
+<tr class="memdesc:a869d4c68c0028a175406f11332baf7cf"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1RunContext.html" title="execution time context. The information needed in runtime for actual execution. ">RunContext</a> related resources. <a href="#a869d4c68c0028a175406f11332baf7cf">More...</a><br/></td></tr>
<tr class="separator:a869d4c68c0028a175406f11332baf7cf"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a40ce0e3d4ac21412cfafafc5b1fd32ef"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1engine_1_1CallbackOnComplete.html">engine::CallbackOnComplete</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OpContext.html#a40ce0e3d4ac21412cfafafc5b1fd32ef">async_on_complete</a></td></tr>
-<tr class="memdesc:a40ce0e3d4ac21412cfafafc5b1fd32ef"><td class="mdescLeft"> </td><td class="mdescRight">the callback when operation completes, used by asynchronize ops <a href="#a40ce0e3d4ac21412cfafafc5b1fd32ef">More...</a><br /></td></tr>
+<tr class="memdesc:a40ce0e3d4ac21412cfafafc5b1fd32ef"><td class="mdescLeft"> </td><td class="mdescRight">the callback when operation completes, used by asynchronize ops <a href="#a40ce0e3d4ac21412cfafafc5b1fd32ef">More...</a><br/></td></tr>
<tr class="separator:a40ce0e3d4ac21412cfafafc5b1fd32ef"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a833e95f95cf5e3af9c193b74b500fc5b"><td class="memItemLeft" align="right" valign="top">std::vector< <a class="el" href="structmxnet_1_1Resource.html">Resource</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OpContext.html#a833e95f95cf5e3af9c193b74b500fc5b">requested</a></td></tr>
-<tr class="memdesc:a833e95f95cf5e3af9c193b74b500fc5b"><td class="mdescLeft"> </td><td class="mdescRight">Resources requested by the operator. <a href="#a833e95f95cf5e3af9c193b74b500fc5b">More...</a><br /></td></tr>
+<tr class="memdesc:a833e95f95cf5e3af9c193b74b500fc5b"><td class="mdescLeft"> </td><td class="mdescRight">Resources requested by the operator. <a href="#a833e95f95cf5e3af9c193b74b500fc5b">More...</a><br/></td></tr>
<tr class="separator:a833e95f95cf5e3af9c193b74b500fc5b"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>All the possible information needed by <a class="el" href="classmxnet_1_1Operator.html#a3dbe23d985df86da51eeb868439872dd" title="perform a forward operation of Operator, save the output to TBlob. ">Operator.Forward</a> and Backward This is the superset of <a class="el" href="structmxnet_1_1RunContext.html" title="execution time context. The information needed in runtime for actual execution. ">RunContext</a>. We use this data structure to bookkeep everything nee [...]
-<dl class="section see"><dt>See also</dt><dd><a class="el" href="structmxnet_1_1Resource.html" title="Resources used by mxnet operations. A resource is something special other than NDArray, but will still participate. ">Resource</a> </dd></dl>
+<dl class="section see"><dt>See Also</dt><dd><a class="el" href="structmxnet_1_1Resource.html" title="Resources used by mxnet operations. A resource is something special other than NDArray, but will still participate. ">Resource</a> </dd></dl>
</div><h2 class="groupheader">Member Function Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a0c48c2419d23551a626ae3de6ee1f6da">◆ </a></span>get_stream()</h2>
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<div class="memproto">
<div class="memtemplate">
@@ -149,9 +168,7 @@ template<typename xpu > </div>
</div>
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<h2 class="groupheader">Member Data Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a40ce0e3d4ac21412cfafafc5b1fd32ef">◆ </a></span>async_on_complete</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#abd7405c065167e4e15af032b1e0a5c42">◆ </a></span>is_train</h2>
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<div class="memproto">
<table class="memname">
@@ -181,9 +196,7 @@ template<typename xpu > </div>
</div>
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-<a id="a833e95f95cf5e3af9c193b74b500fc5b"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a833e95f95cf5e3af9c193b74b500fc5b">◆ </a></span>requested</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -197,9 +210,7 @@ template<typename xpu > </div>
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-<h2 class="memtitle"><span class="permalink"><a href="#a869d4c68c0028a175406f11332baf7cf">◆ </a></span>run_ctx</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -219,9 +230,9 @@ template<typename xpu > </div>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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@@ -79,34 +103,36 @@ $(function() {
Inheritance diagram for mxnet::OperatorPropertyReg:</div>
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<div class="center"><img src="structmxnet_1_1OperatorPropertyReg__inherit__graph.png" border="0" usemap="#mxnet_1_1OperatorPropertyReg_inherit__map" alt="Inheritance graph"/></div>
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<div class="dynheader">
Collaboration diagram for mxnet::OperatorPropertyReg:</div>
<div class="dyncontent">
<div class="center"><img src="structmxnet_1_1OperatorPropertyReg__coll__graph.png" border="0" usemap="#mxnet_1_1OperatorPropertyReg_coll__map" alt="Collaboration graph"/></div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a0e3536ad5305fa1ef402c5385fb4a089"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1OperatorPropertyReg.html">OperatorPropertyReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OperatorPropertyReg.html#a0e3536ad5305fa1ef402c5385fb4a089">set_key_var_num_args</a> (const std::string &key)</td></tr>
-<tr class="memdesc:a0e3536ad5305fa1ef402c5385fb4a089"><td class="mdescLeft"> </td><td class="mdescRight">Set key_var_num_args When this is set, the API caller is required to pass in a argument with key=key_num_args.c_str(), and value=num_args. num_args is number of positional argument when calling the function. <a href="#a0e3536ad5305fa1ef402c5385fb4a089">More...</a><br /></td></tr>
+<tr class="memdesc:a0e3536ad5305fa1ef402c5385fb4a089"><td class="mdescLeft"> </td><td class="mdescRight">Set key_var_num_args When this is set, the API caller is required to pass in a argument with key=key_num_args.c_str(), and value=num_args. num_args is number of positional argument when calling the function. <a href="#a0e3536ad5305fa1ef402c5385fb4a089">More...</a><br/></td></tr>
<tr class="separator:a0e3536ad5305fa1ef402c5385fb4a089"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ad959fdbefb6dd807ee1961a2fec7a038"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1OperatorPropertyReg.html">OperatorPropertyReg</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OperatorPropertyReg.html#ad959fdbefb6dd807ee1961a2fec7a038">check_name</a> ()</td></tr>
-<tr class="memdesc:ad959fdbefb6dd807ee1961a2fec7a038"><td class="mdescLeft"> </td><td class="mdescRight">Check if TypeString of the type matches the registered name. <a href="#ad959fdbefb6dd807ee1961a2fec7a038">More...</a><br /></td></tr>
+<tr class="memdesc:ad959fdbefb6dd807ee1961a2fec7a038"><td class="mdescLeft"> </td><td class="mdescRight">Check if TypeString of the type matches the registered name. <a href="#ad959fdbefb6dd807ee1961a2fec7a038">More...</a><br/></td></tr>
<tr class="separator:ad959fdbefb6dd807ee1961a2fec7a038"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:af3aee96c6a2289e7554d7503186a540c"><td class="memItemLeft" align="right" valign="top">std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1OperatorPropertyReg.html#af3aee96c6a2289e7554d7503186a540c">key_var_num_args</a></td></tr>
-<tr class="memdesc:af3aee96c6a2289e7554d7503186a540c"><td class="mdescLeft"> </td><td class="mdescRight">The key num_args name. <a href="#af3aee96c6a2289e7554d7503186a540c">More...</a><br /></td></tr>
+<tr class="memdesc:af3aee96c6a2289e7554d7503186a540c"><td class="mdescLeft"> </td><td class="mdescRight">The key num_args name. <a href="#af3aee96c6a2289e7554d7503186a540c">More...</a><br/></td></tr>
<tr class="separator:af3aee96c6a2289e7554d7503186a540c"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Registry entry for <a class="el" href="classmxnet_1_1OperatorProperty.html" title="OperatorProperty is a object that stores all information about Operator. It also contains method to g...">OperatorProperty</a> factory functions. </p>
</div><h2 class="groupheader">Member Function Documentation</h2>
-<a id="ad959fdbefb6dd807ee1961a2fec7a038"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ad959fdbefb6dd807ee1961a2fec7a038">◆ </a></span>check_name()</h2>
-
+<a class="anchor" id="ad959fdbefb6dd807ee1961a2fec7a038"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -131,9 +157,7 @@ Public Attributes</h2></td></tr>
</div>
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-<a id="a0e3536ad5305fa1ef402c5385fb4a089"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a0e3536ad5305fa1ef402c5385fb4a089">◆ </a></span>set_key_var_num_args()</h2>
-
+<a class="anchor" id="a0e3536ad5305fa1ef402c5385fb4a089"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -167,9 +191,7 @@ Public Attributes</h2></td></tr>
</div>
</div>
<h2 class="groupheader">Member Data Documentation</h2>
-<a id="af3aee96c6a2289e7554d7503186a540c"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#af3aee96c6a2289e7554d7503186a540c">◆ </a></span>key_var_num_args</h2>
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+<a class="anchor" id="af3aee96c6a2289e7554d7503186a540c"></a>
<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -189,9 +211,9 @@ Public Attributes</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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diff --git a/versions/master/doxygen/structmxnet_1_1Resource-members.html b/versions/master/doxygen/structmxnet_1_1Resource-members.html
index 6b45401..0294d00 100644
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
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@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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@@ -29,25 +30,48 @@
</table>
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-$(function() {
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- $(document).ready(function() { init_search(); });
-});
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ </li>
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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<!-- window showing the filter options -->
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onmouseover="return searchBox.OnSearchSelectShow()"
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@@ -69,13 +93,13 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a>, including all inherited members.</p>
<table class="directory">
- <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a71ac977a7b8dd6abbee947df43c85ec1">get_host_space</a>(mshadow::Shape< ndim > shape) const</td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a220125a23ff2a0666ec0ec498a71c19b">get_host_space_internal</a>(size_t size) const</td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a3dc6c8f2b200e2e045fe0ff74c18e5f7">get_host_space_typed</a>(mshadow::Shape< ndim > shape) const</td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a18ed29b378bdfad93358c275967afe60">get_random</a>(mshadow::Stream< xpu > *stream) const</td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a3c3ced58b1ed0e4fdb0f8e62c6c4a362">get_space</a>(mshadow::Shape< ndim > shape, mshadow::Stream< xpu > *stream) const</td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a9e78ffb2885f9411ecb52683d935e9c1">get_space_internal</a>(size_t size) const</td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a95f1c70975a646f9db28140798ae87d0">get_space_typed</a>(mshadow::Shape< ndim > shape, mshadow::Stream< xpu > *stream) const</td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a92724193fad2a6c5e2a63687e63e3d80">get_host_space</a>(mshadow::Shape< ndim > shape) const </td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#af6e0224f60b5e9afd3c883972863537f">get_host_space_internal</a>(size_t size) const </td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a7c6dff75a9e05a7394b02c84b79a14b2">get_host_space_typed</a>(mshadow::Shape< ndim > shape) const </td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a86039963ae5d682ffc287aefd520f6f9">get_random</a>(mshadow::Stream< xpu > *stream) const </td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#ab2be3ebdd3c5f5fa181ac4f6cdd69508">get_space</a>(mshadow::Shape< ndim > shape, mshadow::Stream< xpu > *stream) const </td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#af57d32e959227f49d54d6eb6a5c62875">get_space_internal</a>(size_t size) const </td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a84e66895d72f9e99ae994de86539a77e">get_space_typed</a>(mshadow::Shape< ndim > shape, mshadow::Stream< xpu > *stream) const </td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#aa1fc8578c0fa849afe7da29c1cf1308b">id</a></td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#a7e9576783b7e9023375ffebd76a11806">ptr_</a></td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="structmxnet_1_1Resource.html#ae841e25cf5ee4aed895ace7fa080edc4">req</a></td><td class="entry"><a class="el" href="structmxnet_1_1Resource.html">mxnet::Resource</a></td><td class="entry"></td></tr>
@@ -84,9 +108,9 @@ $(function() {
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/structmxnet_1_1Resource.html b/versions/master/doxygen/structmxnet_1_1Resource.html
index 7b91968..5f353f5 100644
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<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::Resource Struct Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
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+<!-- Generated by Doxygen 1.8.6 -->
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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@@ -80,63 +104,62 @@ Collaboration diagram for mxnet::Resource:</div>
<div class="dyncontent">
<div class="center"><img src="structmxnet_1_1Resource__coll__graph.png" border="0" usemap="#mxnet_1_1Resource_coll__map" alt="Collaboration graph"/></div>
<map name="mxnet_1_1Resource_coll__map" id="mxnet_1_1Resource_coll__map">
-<area shape="rect" id="node2" href="structmxnet_1_1ResourceRequest.html" title="The resources that can be requested by Operator. " alt="" coords="13,5,185,92"/>
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+<area shape="rect" id="node2" href="structmxnet_1_1ResourceRequest.html" title="The resources that can be requested by Operator. " alt="" coords="17,6,196,98"/></map>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a76a2cc8f2a94b67b3533a048a80396aa"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a76a2cc8f2a94b67b3533a048a80396aa">Resource</a> ()</td></tr>
-<tr class="memdesc:a76a2cc8f2a94b67b3533a048a80396aa"><td class="mdescLeft"> </td><td class="mdescRight">default constructor <a href="#a76a2cc8f2a94b67b3533a048a80396aa">More...</a><br /></td></tr>
+<tr class="memdesc:a76a2cc8f2a94b67b3533a048a80396aa"><td class="mdescLeft"> </td><td class="mdescRight">default constructor <a href="#a76a2cc8f2a94b67b3533a048a80396aa">More...</a><br/></td></tr>
<tr class="separator:a76a2cc8f2a94b67b3533a048a80396aa"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a18ed29b378bdfad93358c275967afe60"><td class="memTemplParams" colspan="2">template<typename xpu , typename DType > </td></tr>
-<tr class="memitem:a18ed29b378bdfad93358c275967afe60"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Random< xpu, DType > * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a18ed29b378bdfad93358c275967afe60">get_random</a> (mshadow::Stream< xpu > *stream) const</td></tr>
-<tr class="memdesc:a18ed29b378bdfad93358c275967afe60"><td class="mdescLeft"> </td><td class="mdescRight">Get random number generator. <a href="#a18ed29b378bdfad93358c275967afe60">More...</a><br /></td></tr>
-<tr class="separator:a18ed29b378bdfad93358c275967afe60"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a3c3ced58b1ed0e4fdb0f8e62c6c4a362"><td class="memTemplParams" colspan="2">template<typename xpu , int ndim> </td></tr>
-<tr class="memitem:a3c3ced58b1ed0e4fdb0f8e62c6c4a362"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< xpu, ndim, <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a3c3ced58b1ed0e4fdb0f8e62c6c4a362">get_space</a> (mshadow::Shape< ndim > shape, mshadow::Stream< xpu > *stream) const</td></tr>
-<tr class="memdesc:a3c3ced58b1ed0e4fdb0f8e62c6c4a362"><td class="mdescLeft"> </td><td class="mdescRight">Get space requested as mshadow Tensor. The caller can request arbitrary size. <a href="#a3c3ced58b1ed0e4fdb0f8e62c6c4a362">More...</a><br /></td></tr>
-<tr class="separator:a3c3ced58b1ed0e4fdb0f8e62c6c4a362"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a71ac977a7b8dd6abbee947df43c85ec1"><td class="memTemplParams" colspan="2">template<int ndim> </td></tr>
-<tr class="memitem:a71ac977a7b8dd6abbee947df43c85ec1"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< <a class="el" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">cpu</a>, ndim, <a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a71ac977a7b8dd6abbee947df43c85ec1">get_host_space</a> (mshadow::Shape< nd [...]
-<tr class="memdesc:a71ac977a7b8dd6abbee947df43c85ec1"><td class="mdescLeft"> </td><td class="mdescRight">Get cpu space requested as mshadow Tensor. The caller can request arbitrary size. <a href="#a71ac977a7b8dd6abbee947df43c85ec1">More...</a><br /></td></tr>
-<tr class="separator:a71ac977a7b8dd6abbee947df43c85ec1"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a95f1c70975a646f9db28140798ae87d0"><td class="memTemplParams" colspan="2">template<typename xpu , int ndim, typename DType > </td></tr>
-<tr class="memitem:a95f1c70975a646f9db28140798ae87d0"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< xpu, ndim, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a95f1c70975a646f9db28140798ae87d0">get_space_typed</a> (mshadow::Shape< ndim > shape, mshadow::Stream< xpu > *stream) const</td></tr>
-<tr class="memdesc:a95f1c70975a646f9db28140798ae87d0"><td class="mdescLeft"> </td><td class="mdescRight">Get space requested as mshadow Tensor in specified type. The caller can request arbitrary size. <a href="#a95f1c70975a646f9db28140798ae87d0">More...</a><br /></td></tr>
-<tr class="separator:a95f1c70975a646f9db28140798ae87d0"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a3dc6c8f2b200e2e045fe0ff74c18e5f7"><td class="memTemplParams" colspan="2">template<int ndim, typename DType > </td></tr>
-<tr class="memitem:a3dc6c8f2b200e2e045fe0ff74c18e5f7"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< <a class="el" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">cpu</a>, ndim, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a3dc6c8f2b200e2e045fe0ff74c18e5f7">get_host_space_typed</a> (mshadow::Shape< ndim > shape) const</td></tr>
-<tr class="memdesc:a3dc6c8f2b200e2e045fe0ff74c18e5f7"><td class="mdescLeft"> </td><td class="mdescRight">Get CPU space as mshadow Tensor in specified type. The caller can request arbitrary size. <a href="#a3dc6c8f2b200e2e045fe0ff74c18e5f7">More...</a><br /></td></tr>
-<tr class="separator:a3dc6c8f2b200e2e045fe0ff74c18e5f7"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a9e78ffb2885f9411ecb52683d935e9c1"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a9e78ffb2885f9411ecb52683d935e9c1">get_space_internal</a> (size_t size) const</td></tr>
-<tr class="memdesc:a9e78ffb2885f9411ecb52683d935e9c1"><td class="mdescLeft"> </td><td class="mdescRight">internal function to get space from resources. <a href="#a9e78ffb2885f9411ecb52683d935e9c1">More...</a><br /></td></tr>
-<tr class="separator:a9e78ffb2885f9411ecb52683d935e9c1"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a220125a23ff2a0666ec0ec498a71c19b"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a220125a23ff2a0666ec0ec498a71c19b">get_host_space_internal</a> (size_t size) const</td></tr>
-<tr class="memdesc:a220125a23ff2a0666ec0ec498a71c19b"><td class="mdescLeft"> </td><td class="mdescRight">internal function to get cpu space from resources. <a href="#a220125a23ff2a0666ec0ec498a71c19b">More...</a><br /></td></tr>
-<tr class="separator:a220125a23ff2a0666ec0ec498a71c19b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a86039963ae5d682ffc287aefd520f6f9"><td class="memTemplParams" colspan="2">template<typename xpu , typename DType > </td></tr>
+<tr class="memitem:a86039963ae5d682ffc287aefd520f6f9"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Random< xpu, DType > * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a86039963ae5d682ffc287aefd520f6f9">get_random</a> (mshadow::Stream< xpu > *stream) const </td></tr>
+<tr class="memdesc:a86039963ae5d682ffc287aefd520f6f9"><td class="mdescLeft"> </td><td class="mdescRight">Get random number generator. <a href="#a86039963ae5d682ffc287aefd520f6f9">More...</a><br/></td></tr>
+<tr class="separator:a86039963ae5d682ffc287aefd520f6f9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab2be3ebdd3c5f5fa181ac4f6cdd69508"><td class="memTemplParams" colspan="2">template<typename xpu , int ndim> </td></tr>
+<tr class="memitem:ab2be3ebdd3c5f5fa181ac4f6cdd69508"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< xpu, ndim, <br class="typebreak"/>
+<a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#ab2be3ebdd3c5f5fa181ac4f6cdd69508">get_space</a> (mshadow::Shape< ndim > shape, mshadow::Stream< xpu > *stream) const </td></tr>
+<tr class="memdesc:ab2be3ebdd3c5f5fa181ac4f6cdd69508"><td class="mdescLeft"> </td><td class="mdescRight">Get space requested as mshadow Tensor. The caller can request arbitrary size. <a href="#ab2be3ebdd3c5f5fa181ac4f6cdd69508">More...</a><br/></td></tr>
+<tr class="separator:ab2be3ebdd3c5f5fa181ac4f6cdd69508"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a92724193fad2a6c5e2a63687e63e3d80"><td class="memTemplParams" colspan="2">template<int ndim> </td></tr>
+<tr class="memitem:a92724193fad2a6c5e2a63687e63e3d80"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< <a class="el" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">cpu</a>, ndim, <br class="typebreak"/>
+<a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a92724193fad2a6c5e2a63687e63e3d80">get_host_space</a> (mshadow::Shape< ndim > shape) const </td></tr>
+<tr class="memdesc:a92724193fad2a6c5e2a63687e63e3d80"><td class="mdescLeft"> </td><td class="mdescRight">Get cpu space requested as mshadow Tensor. The caller can request arbitrary size. <a href="#a92724193fad2a6c5e2a63687e63e3d80">More...</a><br/></td></tr>
+<tr class="separator:a92724193fad2a6c5e2a63687e63e3d80"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a84e66895d72f9e99ae994de86539a77e"><td class="memTemplParams" colspan="2">template<typename xpu , int ndim, typename DType > </td></tr>
+<tr class="memitem:a84e66895d72f9e99ae994de86539a77e"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< xpu, ndim, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a84e66895d72f9e99ae994de86539a77e">get_space_typed</a> (mshadow::Shape< ndim > shape, mshadow::Stream< xpu > *stream) const </td></tr>
+<tr class="memdesc:a84e66895d72f9e99ae994de86539a77e"><td class="mdescLeft"> </td><td class="mdescRight">Get space requested as mshadow Tensor in specified type. The caller can request arbitrary size. <a href="#a84e66895d72f9e99ae994de86539a77e">More...</a><br/></td></tr>
+<tr class="separator:a84e66895d72f9e99ae994de86539a77e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7c6dff75a9e05a7394b02c84b79a14b2"><td class="memTemplParams" colspan="2">template<int ndim, typename DType > </td></tr>
+<tr class="memitem:a7c6dff75a9e05a7394b02c84b79a14b2"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Tensor< <a class="el" href="namespacemxnet.html#a850af9d76d9d4beaeb5234c1434973e3">cpu</a>, ndim, DType > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a7c6dff75a9e05a7394b02c84b79a14b2">get_host_space_typed</a> (mshadow::Shape< ndim > shape) const </td></tr>
+<tr class="memdesc:a7c6dff75a9e05a7394b02c84b79a14b2"><td class="mdescLeft"> </td><td class="mdescRight">Get CPU space as mshadow Tensor in specified type. The caller can request arbitrary size. <a href="#a7c6dff75a9e05a7394b02c84b79a14b2">More...</a><br/></td></tr>
+<tr class="separator:a7c6dff75a9e05a7394b02c84b79a14b2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af57d32e959227f49d54d6eb6a5c62875"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#af57d32e959227f49d54d6eb6a5c62875">get_space_internal</a> (size_t size) const </td></tr>
+<tr class="memdesc:af57d32e959227f49d54d6eb6a5c62875"><td class="mdescLeft"> </td><td class="mdescRight">internal function to get space from resources. <a href="#af57d32e959227f49d54d6eb6a5c62875">More...</a><br/></td></tr>
+<tr class="separator:af57d32e959227f49d54d6eb6a5c62875"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af6e0224f60b5e9afd3c883972863537f"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#af6e0224f60b5e9afd3c883972863537f">get_host_space_internal</a> (size_t size) const </td></tr>
+<tr class="memdesc:af6e0224f60b5e9afd3c883972863537f"><td class="mdescLeft"> </td><td class="mdescRight">internal function to get cpu space from resources. <a href="#af6e0224f60b5e9afd3c883972863537f">More...</a><br/></td></tr>
+<tr class="separator:af6e0224f60b5e9afd3c883972863537f"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:ae841e25cf5ee4aed895ace7fa080edc4"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1ResourceRequest.html">ResourceRequest</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#ae841e25cf5ee4aed895ace7fa080edc4">req</a></td></tr>
-<tr class="memdesc:ae841e25cf5ee4aed895ace7fa080edc4"><td class="mdescLeft"> </td><td class="mdescRight">The original request. <a href="#ae841e25cf5ee4aed895ace7fa080edc4">More...</a><br /></td></tr>
+<tr class="memdesc:ae841e25cf5ee4aed895ace7fa080edc4"><td class="mdescLeft"> </td><td class="mdescRight">The original request. <a href="#ae841e25cf5ee4aed895ace7fa080edc4">More...</a><br/></td></tr>
<tr class="separator:ae841e25cf5ee4aed895ace7fa080edc4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5a8048bdda73fefa3af322aaaba50eb8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacemxnet_1_1engine.html#a9d36c4f33eae8531586dc2edf83ae7cf">engine::VarHandle</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a5a8048bdda73fefa3af322aaaba50eb8">var</a></td></tr>
-<tr class="memdesc:a5a8048bdda73fefa3af322aaaba50eb8"><td class="mdescLeft"> </td><td class="mdescRight">engine variable <a href="#a5a8048bdda73fefa3af322aaaba50eb8">More...</a><br /></td></tr>
+<tr class="memdesc:a5a8048bdda73fefa3af322aaaba50eb8"><td class="mdescLeft"> </td><td class="mdescRight">engine variable <a href="#a5a8048bdda73fefa3af322aaaba50eb8">More...</a><br/></td></tr>
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<tr class="memitem:aa1fc8578c0fa849afe7da29c1cf1308b"><td class="memItemLeft" align="right" valign="top">int32_t </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#aa1fc8578c0fa849afe7da29c1cf1308b">id</a></td></tr>
-<tr class="memdesc:aa1fc8578c0fa849afe7da29c1cf1308b"><td class="mdescLeft"> </td><td class="mdescRight">identifier of id information, used for debug purpose <a href="#aa1fc8578c0fa849afe7da29c1cf1308b">More...</a><br /></td></tr>
+<tr class="memdesc:aa1fc8578c0fa849afe7da29c1cf1308b"><td class="mdescLeft"> </td><td class="mdescRight">identifier of id information, used for debug purpose <a href="#aa1fc8578c0fa849afe7da29c1cf1308b">More...</a><br/></td></tr>
<tr class="separator:aa1fc8578c0fa849afe7da29c1cf1308b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7e9576783b7e9023375ffebd76a11806"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Resource.html#a7e9576783b7e9023375ffebd76a11806">ptr_</a></td></tr>
-<tr class="memdesc:a7e9576783b7e9023375ffebd76a11806"><td class="mdescLeft"> </td><td class="mdescRight">pointer to the resource, do not use directly, access using member functions <a href="#a7e9576783b7e9023375ffebd76a11806">More...</a><br /></td></tr>
+<tr class="memdesc:a7e9576783b7e9023375ffebd76a11806"><td class="mdescLeft"> </td><td class="mdescRight">pointer to the resource, do not use directly, access using member functions <a href="#a7e9576783b7e9023375ffebd76a11806">More...</a><br/></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Resources used by mxnet operations. A resource is something special other than <a class="el" href="classmxnet_1_1NDArray.html" title="ndarray interface ">NDArray</a>, but will still participate. </p>
</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a76a2cc8f2a94b67b3533a048a80396aa"></a>
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<h2 class="groupheader">Member Function Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a7e9576783b7e9023375ffebd76a11806">◆ </a></span>ptr_</h2>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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+ <ul class="tablist">
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+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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diff --git a/versions/master/doxygen/structmxnet_1_1ResourceRequest.html b/versions/master/doxygen/structmxnet_1_1ResourceRequest.html
index ed73cc8..8f29342 100644
--- a/versions/master/doxygen/structmxnet_1_1ResourceRequest.html
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<table cellspacing="0" cellpadding="0">
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
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Collaboration diagram for mxnet::ResourceRequest:</div>
<div class="dyncontent">
<div class="center"><img src="structmxnet_1_1ResourceRequest__coll__graph.png" border="0" usemap="#mxnet_1_1ResourceRequest_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1ResourceRequest_coll__map" id="mxnet_1_1ResourceRequest_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:aba2ee7560afb89ebdd00c7c735149630"><td class="memItemLeft" align="right" valign="top">enum  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630">Type</a> { <a class="el" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630adfccd3e369be7104e7300557f620f6b9">kRandom</a>,
<a class="el" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630aae5d4509a9b382744ea13d2daa8aa3f3">kTempSpace</a>
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-</td></tr>
+ }</td></tr>
+<tr class="memdesc:aba2ee7560afb89ebdd00c7c735149630"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structmxnet_1_1Resource.html" title="Resources used by mxnet operations. A resource is something special other than NDArray, but will still participate. ">Resource</a> type, indicating what the pointer type is. <a href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630">More...</a><br/></td></tr>
<tr class="separator:aba2ee7560afb89ebdd00c7c735149630"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a0191250c5891ba487f354df4caf3604d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1ResourceRequest.html#a0191250c5891ba487f354df4caf3604d">ResourceRequest</a> ()</td></tr>
-<tr class="memdesc:a0191250c5891ba487f354df4caf3604d"><td class="mdescLeft"> </td><td class="mdescRight">default constructor <a href="#a0191250c5891ba487f354df4caf3604d">More...</a><br /></td></tr>
+<tr class="memdesc:a0191250c5891ba487f354df4caf3604d"><td class="mdescLeft"> </td><td class="mdescRight">default constructor <a href="#a0191250c5891ba487f354df4caf3604d">More...</a><br/></td></tr>
<tr class="separator:a0191250c5891ba487f354df4caf3604d"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa01dd9fd716dfbb68c2ee01f5a11aea5"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1ResourceRequest.html#aa01dd9fd716dfbb68c2ee01f5a11aea5">ResourceRequest</a> (<a class="el" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630">Type</a> <a class="el" href="structmxnet_1_1ResourceRequest.html#ad67cb809ea1e0db24629ffbd3db00a63">type</a>)</td></tr>
-<tr class="memdesc:aa01dd9fd716dfbb68c2ee01f5a11aea5"><td class="mdescLeft"> </td><td class="mdescRight">constructor, allow implicit conversion <a href="#aa01dd9fd716dfbb68c2ee01f5a11aea5">More...</a><br /></td></tr>
+<tr class="memdesc:aa01dd9fd716dfbb68c2ee01f5a11aea5"><td class="mdescLeft"> </td><td class="mdescRight">constructor, allow implicit conversion <a href="#aa01dd9fd716dfbb68c2ee01f5a11aea5">More...</a><br/></td></tr>
<tr class="separator:aa01dd9fd716dfbb68c2ee01f5a11aea5"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:ad67cb809ea1e0db24629ffbd3db00a63"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1ResourceRequest.html#aba2ee7560afb89ebdd00c7c735149630">Type</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1ResourceRequest.html#ad67cb809ea1e0db24629ffbd3db00a63">type</a></td></tr>
-<tr class="memdesc:ad67cb809ea1e0db24629ffbd3db00a63"><td class="mdescLeft"> </td><td class="mdescRight">type of resources <a href="#ad67cb809ea1e0db24629ffbd3db00a63">More...</a><br /></td></tr>
+<tr class="memdesc:ad67cb809ea1e0db24629ffbd3db00a63"><td class="mdescLeft"> </td><td class="mdescRight">type of resources <a href="#ad67cb809ea1e0db24629ffbd3db00a63">More...</a><br/></td></tr>
<tr class="separator:ad67cb809ea1e0db24629ffbd3db00a63"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>The resources that can be requested by <a class="el" href="classmxnet_1_1Operator.html" title="Operator interface. Operator defines basic operation unit of optimized computation graph in mxnet...">Operator</a>. </p>
</div><h2 class="groupheader">Member Enumeration Documentation</h2>
-<a id="aba2ee7560afb89ebdd00c7c735149630"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#aba2ee7560afb89ebdd00c7c735149630">◆ </a></span>Type</h2>
-
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<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -122,18 +146,18 @@ Public Attributes</h2></td></tr>
<p><a class="el" href="structmxnet_1_1Resource.html" title="Resources used by mxnet operations. A resource is something special other than NDArray, but will still participate. ">Resource</a> type, indicating what the pointer type is. </p>
<table class="fieldtable">
-<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="aba2ee7560afb89ebdd00c7c735149630adfccd3e369be7104e7300557f620f6b9"></a>kRandom </td><td class="fielddoc"><p>mshadow::Random<xpu> object </p>
+<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><em><a class="anchor" id="aba2ee7560afb89ebdd00c7c735149630adfccd3e369be7104e7300557f620f6b9"></a>kRandom</em> </td><td class="fielddoc">
+<p>mshadow::Random<xpu> object </p>
</td></tr>
-<tr><td class="fieldname"><a id="aba2ee7560afb89ebdd00c7c735149630aae5d4509a9b382744ea13d2daa8aa3f3"></a>kTempSpace </td><td class="fielddoc"><p>A dynamic temp space that can be arbitrary size. </p>
+<tr><td class="fieldname"><em><a class="anchor" id="aba2ee7560afb89ebdd00c7c735149630aae5d4509a9b382744ea13d2daa8aa3f3"></a>kTempSpace</em> </td><td class="fielddoc">
+<p>A dynamic temp space that can be arbitrary size. </p>
</td></tr>
</table>
</div>
</div>
<h2 class="groupheader">Constructor & Destructor Documentation</h2>
-<a id="a0191250c5891ba487f354df4caf3604d"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a0191250c5891ba487f354df4caf3604d">◆ </a></span>ResourceRequest() <span class="overload">[1/2]</span></h2>
-
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<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -158,9 +182,7 @@ Public Attributes</h2></td></tr>
</div>
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<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -193,9 +215,7 @@ Public Attributes</h2></td></tr>
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<h2 class="groupheader">Member Data Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#ad67cb809ea1e0db24629ffbd3db00a63">◆ </a></span>type</h2>
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<div class="memitem">
<div class="memproto">
<table class="memname">
@@ -215,9 +235,9 @@ Public Attributes</h2></td></tr>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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diff --git a/versions/master/doxygen/structmxnet_1_1ResourceRequest__coll__graph.md5 b/versions/master/doxygen/structmxnet_1_1ResourceRequest__coll__graph.md5
index 07547d4..8f11bc2 100644
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diff --git a/versions/master/doxygen/structmxnet_1_1ResourceRequest__coll__graph.png b/versions/master/doxygen/structmxnet_1_1ResourceRequest__coll__graph.png
index 09b1adc..4035469 100644
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index 7c93e60..51b8865 100644
--- a/versions/master/doxygen/structmxnet_1_1Resource__coll__graph.map
+++ b/versions/master/doxygen/structmxnet_1_1Resource__coll__graph.map
@@ -1,3 +1,3 @@
<map id="mxnet::Resource" name="mxnet::Resource">
-<area shape="rect" id="node2" href="$structmxnet_1_1ResourceRequest.html" title="The resources that can be requested by Operator. " alt="" coords="13,5,185,92"/>
+<area shape="rect" id="node2" href="$structmxnet_1_1ResourceRequest.html" title="The resources that can be requested by Operator. " alt="" coords="17,6,196,98"/>
</map>
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index 995ac2f..c0315f4 100644
--- a/versions/master/doxygen/structmxnet_1_1Resource__coll__graph.md5
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diff --git a/versions/master/doxygen/structmxnet_1_1Resource__coll__graph.png b/versions/master/doxygen/structmxnet_1_1Resource__coll__graph.png
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diff --git a/versions/master/doxygen/structmxnet_1_1RunContext-members.html b/versions/master/doxygen/structmxnet_1_1RunContext-members.html
index fc0bc46..f6c75d4 100644
--- a/versions/master/doxygen/structmxnet_1_1RunContext-members.html
+++ b/versions/master/doxygen/structmxnet_1_1RunContext-members.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
-<script type="text/javascript" src="menu.js"></script>
-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
+ onblur="searchBox.OnSearchFieldFocus(false)"
+ onkeyup="searchBox.OnSearchFieldChange(event)"/>
+ </span><span class="right">
+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
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@@ -70,15 +94,15 @@ $(function() {
<p>This is the complete list of members for <a class="el" href="structmxnet_1_1RunContext.html">mxnet::RunContext</a>, including all inherited members.</p>
<table class="directory">
<tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html#ace5970ed7c25ccc98ffd0dcf35fd3c88">ctx</a></td><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html#a323fa88ecd7bfb7d5f7d9ca727101d3e">get_ctx</a>() const</td><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html#a9901db00632774aef42081ceda0d23f1">get_stream</a>() const</td><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html#a5da6c1a11d0e6ea09dbe718cf6a4ef8d">get_ctx</a>() const </td><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html#a8bcd2fc6392f95a1fdc3d98f74dbfcb9">get_stream</a>() const </td><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html#a5c4294a44199f4056915e32a7f70fe0e">stream</a></td><td class="entry"><a class="el" href="structmxnet_1_1RunContext.html">mxnet::RunContext</a></td><td class="entry"></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
diff --git a/versions/master/doxygen/structmxnet_1_1RunContext.html b/versions/master/doxygen/structmxnet_1_1RunContext.html
index ee4d0f5..c5cb310 100644
--- a/versions/master/doxygen/structmxnet_1_1RunContext.html
+++ b/versions/master/doxygen/structmxnet_1_1RunContext.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::RunContext Struct Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,48 @@
</table>
</div>
<!-- end header part -->
-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
-<script type="text/javascript" src="menudata.js"></script>
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-<script type="text/javascript">
-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
+ alt=""/>
+ <input type="text" id="MSearchField" value="Search" accesskey="S"
+ onfocus="searchBox.OnSearchFieldFocus(true)"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -80,35 +104,32 @@ Collaboration diagram for mxnet::RunContext:</div>
<div class="dyncontent">
<div class="center"><img src="structmxnet_1_1RunContext__coll__graph.png" border="0" usemap="#mxnet_1_1RunContext_coll__map" alt="Collaboration graph"/></div>
<map name="mxnet_1_1RunContext_coll__map" id="mxnet_1_1RunContext_coll__map">
-<area shape="rect" id="node2" href="structmxnet_1_1Context.html" title="Context information about the execution environment. " alt="" coords="14,5,132,265"/>
-</map>
+<area shape="rect" id="node2" href="structmxnet_1_1Context.html" title="Context information about the execution environment. " alt="" coords="15,5,139,288"/></map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
-<tr class="memitem:a9901db00632774aef42081ceda0d23f1"><td class="memTemplParams" colspan="2">template<typename xpu > </td></tr>
-<tr class="memitem:a9901db00632774aef42081ceda0d23f1"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Stream< xpu > * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1RunContext.html#a9901db00632774aef42081ceda0d23f1">get_stream</a> () const</td></tr>
-<tr class="memdesc:a9901db00632774aef42081ceda0d23f1"><td class="mdescLeft"> </td><td class="mdescRight">get mshadow stream from <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> <a href="#a9901db00632774aef42081ceda0d23f1">More...</a><br /></td></tr>
-<tr class="separator:a9901db00632774aef42081ceda0d23f1"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a323fa88ecd7bfb7d5f7d9ca727101d3e"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structmxnet_1_1Context.html">Context</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1RunContext.html#a323fa88ecd7bfb7d5f7d9ca727101d3e">get_ctx</a> () const</td></tr>
-<tr class="memdesc:a323fa88ecd7bfb7d5f7d9ca727101d3e"><td class="mdescLeft"> </td><td class="mdescRight">get the base <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> from <a class="el" href="structmxnet_1_1RunContext.html" title="execution time context. The information needed in runtime for actual execution. ">RunContext</a> <a href="#a323fa88ecd7bfb7d5f7d9ca727101d3e">More...</a><br /></td></tr>
-<tr class="separator:a323fa88ecd7bfb7d5f7d9ca727101d3e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a8bcd2fc6392f95a1fdc3d98f74dbfcb9"><td class="memTemplParams" colspan="2">template<typename xpu > </td></tr>
+<tr class="memitem:a8bcd2fc6392f95a1fdc3d98f74dbfcb9"><td class="memTemplItemLeft" align="right" valign="top">mshadow::Stream< xpu > * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1RunContext.html#a8bcd2fc6392f95a1fdc3d98f74dbfcb9">get_stream</a> () const </td></tr>
+<tr class="memdesc:a8bcd2fc6392f95a1fdc3d98f74dbfcb9"><td class="mdescLeft"> </td><td class="mdescRight">get mshadow stream from <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> <a href="#a8bcd2fc6392f95a1fdc3d98f74dbfcb9">More...</a><br/></td></tr>
+<tr class="separator:a8bcd2fc6392f95a1fdc3d98f74dbfcb9"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5da6c1a11d0e6ea09dbe718cf6a4ef8d"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structmxnet_1_1Context.html">Context</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1RunContext.html#a5da6c1a11d0e6ea09dbe718cf6a4ef8d">get_ctx</a> () const </td></tr>
+<tr class="memdesc:a5da6c1a11d0e6ea09dbe718cf6a4ef8d"><td class="mdescLeft"> </td><td class="mdescRight">get the base <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> from <a class="el" href="structmxnet_1_1RunContext.html" title="execution time context. The information needed in runtime for actual execution. ">RunContext</a> <a href="#a5da6c1a11d0e6ea09dbe718cf6a4ef8d">More...</a><br/></td></tr>
+<tr class="separator:a5da6c1a11d0e6ea09dbe718cf6a4ef8d"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:ace5970ed7c25ccc98ffd0dcf35fd3c88"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1Context.html">Context</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1RunContext.html#ace5970ed7c25ccc98ffd0dcf35fd3c88">ctx</a></td></tr>
-<tr class="memdesc:ace5970ed7c25ccc98ffd0dcf35fd3c88"><td class="mdescLeft"> </td><td class="mdescRight">base <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> <a href="#ace5970ed7c25ccc98ffd0dcf35fd3c88">More...</a><br /></td></tr>
+<tr class="memdesc:ace5970ed7c25ccc98ffd0dcf35fd3c88"><td class="mdescLeft"> </td><td class="mdescRight">base <a class="el" href="structmxnet_1_1Context.html" title="Context information about the execution environment. ">Context</a> <a href="#ace5970ed7c25ccc98ffd0dcf35fd3c88">More...</a><br/></td></tr>
<tr class="separator:ace5970ed7c25ccc98ffd0dcf35fd3c88"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a5c4294a44199f4056915e32a7f70fe0e"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1RunContext.html#a5c4294a44199f4056915e32a7f70fe0e">stream</a></td></tr>
-<tr class="memdesc:a5c4294a44199f4056915e32a7f70fe0e"><td class="mdescLeft"> </td><td class="mdescRight">the stream of the device, can be NULL or Stream<gpu>* in GPU mode <a href="#a5c4294a44199f4056915e32a7f70fe0e">More...</a><br /></td></tr>
+<tr class="memdesc:a5c4294a44199f4056915e32a7f70fe0e"><td class="mdescLeft"> </td><td class="mdescRight">the stream of the device, can be NULL or Stream<gpu>* in GPU mode <a href="#a5c4294a44199f4056915e32a7f70fe0e">More...</a><br/></td></tr>
<tr class="separator:a5c4294a44199f4056915e32a7f70fe0e"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>execution time context. The information needed in runtime for actual execution. </p>
</div><h2 class="groupheader">Member Function Documentation</h2>
-<a id="a323fa88ecd7bfb7d5f7d9ca727101d3e"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a323fa88ecd7bfb7d5f7d9ca727101d3e">◆ </a></span>get_ctx()</h2>
-
+<a class="anchor" id="a5da6c1a11d0e6ea09dbe718cf6a4ef8d"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
@@ -133,9 +154,7 @@ Public Attributes</h2></td></tr>
</div>
</div>
-<a id="a9901db00632774aef42081ceda0d23f1"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#a9901db00632774aef42081ceda0d23f1">◆ </a></span>get_stream()</h2>
-
+<a class="anchor" id="a8bcd2fc6392f95a1fdc3d98f74dbfcb9"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -170,9 +189,7 @@ template<typename xpu > </div>
</div>
</div>
<h2 class="groupheader">Member Data Documentation</h2>
-<a id="ace5970ed7c25ccc98ffd0dcf35fd3c88"></a>
-<h2 class="memtitle"><span class="permalink"><a href="#ace5970ed7c25ccc98ffd0dcf35fd3c88">◆ </a></span>ctx</h2>
-
+<a class="anchor" id="ace5970ed7c25ccc98ffd0dcf35fd3c88"></a>
<div class="memitem">
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Public Attributes</h2></td></tr>
<tr class="memitem:a428e25a6317e50c8b55307c96f8ec595"><td class="memItemLeft" align="right" valign="top">void * </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Storage_1_1Handle.html#a428e25a6317e50c8b55307c96f8ec595">dptr</a></td></tr>
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+<tr class="memdesc:ab93b9db0c83e4ef3803fb587377a74a0"><td class="mdescLeft"> </td><td class="mdescRight">Size of the storage. <a href="#ab93b9db0c83e4ef3803fb587377a74a0">More...</a><br/></td></tr>
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<tr class="memitem:ae0a4202582aa12a598f5c55ef51ad4c8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmxnet_1_1Context.html">Context</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1Storage_1_1Handle.html#ae0a4202582aa12a598f5c55ef51ad4c8">ctx</a></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p><a class="el" href="classmxnet_1_1Storage.html" title="Storage manager across multiple devices. ">Storage</a> handle. </p>
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+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
+ </div>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
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<p>This is the complete list of members for <a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html">mxnet::common::ObjectPoolAllocatable< T ></a>, including all inherited members.</p>
<table class="directory">
<tr class="even"><td class="entry"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#a68ed487cb6de6194a4798fdbbfd04597">Delete</a>(T *ptr)</td><td class="entry"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html">mxnet::common::ObjectPoolAllocatable< T ></a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#aa08281412ac1a31d485884fa5a7544ce">New</a>(Args &&... args)</td><td class="entry"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html">mxnet::common::ObjectPoolAllocatable< T ></a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#aaf92ecb0cb6542b5c3f70ec31a31db4e">New</a>(Args &&...args)</td><td class="entry"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html">mxnet::common::ObjectPoolAllocatable< T ></a></td><td class="entry"><span class="mlabel">static</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
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<title>mxnet: mxnet::common::ObjectPoolAllocatable< T > Struct Template Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+<script type="text/javascript">
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
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</div>
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
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-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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Collaboration diagram for mxnet::common::ObjectPoolAllocatable< T >:</div>
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<div class="center"><img src="structmxnet_1_1common_1_1ObjectPoolAllocatable__coll__graph.png" border="0" usemap="#mxnet_1_1common_1_1ObjectPoolAllocatable_3_01T_01_4_coll__map" alt="Collaboration graph"/></div>
+<map name="mxnet_1_1common_1_1ObjectPoolAllocatable_3_01T_01_4_coll__map" id="mxnet_1_1common_1_1ObjectPoolAllocatable_3_01T_01_4_coll__map">
+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
-<tr class="memitem:aa08281412ac1a31d485884fa5a7544ce"><td class="memTemplParams" colspan="2">template<typename... Args> </td></tr>
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-<tr class="memdesc:aa08281412ac1a31d485884fa5a7544ce"><td class="mdescLeft"> </td><td class="mdescRight">Create new object. <a href="#aa08281412ac1a31d485884fa5a7544ce">More...</a><br /></td></tr>
-<tr class="separator:aa08281412ac1a31d485884fa5a7544ce"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aaf92ecb0cb6542b5c3f70ec31a31db4e"><td class="memTemplParams" colspan="2">template<typename... Args> </td></tr>
+<tr class="memitem:aaf92ecb0cb6542b5c3f70ec31a31db4e"><td class="memTemplItemLeft" align="right" valign="top">static T * </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#aaf92ecb0cb6542b5c3f70ec31a31db4e">New</a> (Args &&...args)</td></tr>
+<tr class="memdesc:aaf92ecb0cb6542b5c3f70ec31a31db4e"><td class="mdescLeft"> </td><td class="mdescRight">Create new object. <a href="#aaf92ecb0cb6542b5c3f70ec31a31db4e">More...</a><br/></td></tr>
+<tr class="separator:aaf92ecb0cb6542b5c3f70ec31a31db4e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a68ed487cb6de6194a4798fdbbfd04597"><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1ObjectPoolAllocatable.html#a68ed487cb6de6194a4798fdbbfd04597">Delete</a> (T *ptr)</td></tr>
-<tr class="memdesc:a68ed487cb6de6194a4798fdbbfd04597"><td class="mdescLeft"> </td><td class="mdescRight">Delete an existing object. <a href="#a68ed487cb6de6194a4798fdbbfd04597">More...</a><br /></td></tr>
+<tr class="memdesc:a68ed487cb6de6194a4798fdbbfd04597"><td class="mdescLeft"> </td><td class="mdescRight">Delete an existing object. <a href="#a68ed487cb6de6194a4798fdbbfd04597">More...</a><br/></td></tr>
<tr class="separator:a68ed487cb6de6194a4798fdbbfd04597"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
-<div class="textblock"><h3>template<typename T><br />
+<div class="textblock"><h3>template<typename T><br/>
struct mxnet::common::ObjectPoolAllocatable< T ></h3>
<p>Helper trait class for easy allocation and deallocation. </p>
</div><h2 class="groupheader">Member Function Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a68ed487cb6de6194a4798fdbbfd04597">◆ </a></span>Delete()</h2>
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<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -133,9 +157,7 @@ template<typename T > </div>
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-<h2 class="memtitle"><span class="permalink"><a href="#aa08281412ac1a31d485884fa5a7544ce">◆ </a></span>New()</h2>
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<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -172,9 +194,9 @@ template<typename... Args> </div>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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<title>mxnet: Member List</title>
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<script type="text/javascript" src="jquery.js"></script>
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<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<table cellspacing="0" cellpadding="0">
<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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+ <span class="left">
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<head>
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<title>mxnet: mxnet::common::helper::UniqueIf< T > Struct Template Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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@@ -20,7 +21,7 @@
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<tbody>
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- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:a98153aaeb43a71efa85b5cf753505cac"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html#a98153aaeb43a71efa85b5cf753505cac">SingleObject</a> = std::unique_ptr< T ></td></tr>
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+<tr class="memdesc:a98153aaeb43a71efa85b5cf753505cac"><td class="mdescLeft"> </td><td class="mdescRight">Type of <code>T</code>. <a href="#a98153aaeb43a71efa85b5cf753505cac">More...</a><br/></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
-<div class="textblock"><h3>template<class T><br />
+<div class="textblock"><h3>template<class T><br/>
struct mxnet::common::helper::UniqueIf< T ></h3>
<p>Helper for non-array type <code>T</code>. </p>
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-<h2 class="memtitle"><span class="permalink"><a href="#a98153aaeb43a71efa85b5cf753505cac">◆ </a></span>SingleObject</h2>
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<div class="memproto">
<div class="memtemplate">
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<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<title>mxnet: Member List</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<link href="doxygen.css" rel="stylesheet" type="text/css" />
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<body>
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<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
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+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
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+<li class="navelem"><a class="el" href="namespacemxnet.html">mxnet</a></li><li class="navelem"><a class="el" href="namespacemxnet_1_1common.html">common</a></li><li class="navelem"><a class="el" href="namespacemxnet_1_1common_1_1helper.html">helper</a></li><li class="navelem"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html">UniqueIf< T[]></a></li> </ul>
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<div class="header">
@@ -73,9 +97,9 @@ $(function() {
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
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diff --git a/versions/master/doxygen/structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html b/versions/master/doxygen/structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html
index 2c8984f..87202b4 100644
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+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: mxnet::common::helper::UniqueIf< T[]> Struct Template Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
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+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
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-$(function() {
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- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
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+ <li><a href="files.html"><span>Files</span></a></li>
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+ <ul class="tablist">
+ <li><a href="annotated.html"><span>Class List</span></a></li>
+ <li><a href="classes.html"><span>Class Index</span></a></li>
+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
+ <li><a href="functions.html"><span>Class Members</span></a></li>
+ </ul>
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<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
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<div class="header">
@@ -78,23 +102,23 @@ $(function() {
Collaboration diagram for mxnet::common::helper::UniqueIf< T[]>:</div>
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+</map>
</div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:a0cf05a5a2f3e1ac49ebdef497701055c"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html#a0cf05a5a2f3e1ac49ebdef497701055c">UnknownBound</a> = std::unique_ptr< T[]></td></tr>
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+<tr class="memdesc:a0cf05a5a2f3e1ac49ebdef497701055c"><td class="mdescLeft"> </td><td class="mdescRight">Type of <code>T</code>. <a href="#a0cf05a5a2f3e1ac49ebdef497701055c">More...</a><br/></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
-<div class="textblock"><h3>template<class T><br />
+<div class="textblock"><h3>template<class T><br/>
struct mxnet::common::helper::UniqueIf< T[]></h3>
<p>Helper for an array of unknown bound <code>T</code>. </p>
</div><h2 class="groupheader">Member Typedef Documentation</h2>
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-<h2 class="memtitle"><span class="permalink"><a href="#a0cf05a5a2f3e1ac49ebdef497701055c">◆ </a></span>UnknownBound</h2>
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<div class="memitem">
<div class="memproto">
<div class="memtemplate">
@@ -116,9 +140,9 @@ template<class T > </div>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
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<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
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<link href="search/search.css" rel="stylesheet" type="text/css"/>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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- $(document).ready(function() { init_search(); });
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+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li class="current"><a href="annotated.html"><span>Classes</span></a></li>
+ <li><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ onmouseout="return searchBox.OnSearchSelectHide()"
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<img class="footer" src="doxygen.png" alt="doxygen"/>
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Collaboration diagram for mxnet::common::helper::UniqueIf< T[kSize]>:</div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:af31c1728c4b497d304680d69d4e61421"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html#af31c1728c4b497d304680d69d4e61421">KnownBound</a> = void</td></tr>
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+<tr class="memdesc:af31c1728c4b497d304680d69d4e61421"><td class="mdescLeft"> </td><td class="mdescRight">Type of <code>T</code>. <a href="#af31c1728c4b497d304680d69d4e61421">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
-<div class="textblock"><h3>template<class T, size_t kSize><br />
+<div class="textblock"><h3>template<class T, size_t kSize><br/>
struct mxnet::common::helper::UniqueIf< T[kSize]></h3>
<p>Helper for an array of known bound <code>T</code>. </p>
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-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:a74dfe03602a990b6552c8f7a01b2ecb5"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html#a74dfe03602a990b6552c8f7a01b2ecb5">scalar</a></td></tr>
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+<tr class="memdesc:a74dfe03602a990b6552c8f7a01b2ecb5"><td class="mdescLeft"> </td><td class="mdescRight">scalar argument, if enabled <a href="#a74dfe03602a990b6552c8f7a01b2ecb5">More...</a><br/></td></tr>
<tr class="separator:a74dfe03602a990b6552c8f7a01b2ecb5"><td class="memSeparator" colspan="2"> </td></tr>
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+<tr class="memitem:a9dc6ddeef01eb50bc77517bec1195d5d"><td class="memItemLeft" align="right" valign="top">std::vector< std::pair<br class="typebreak"/>
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<tr class="memitem:a6b62df4767fd50d6a1fd9ebd44193bef"><td class="memItemLeft" align="right" valign="top">std::vector< <a class="el" href="structmxnet_1_1Resource.html">Resource</a> > </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1EnvArguments.html#a6b62df4767fd50d6a1fd9ebd44193bef">resource</a></td></tr>
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+<tr class="memdesc:a6b62df4767fd50d6a1fd9ebd44193bef"><td class="mdescLeft"> </td><td class="mdescRight">pointer to the resources requested <a href="#a6b62df4767fd50d6a1fd9ebd44193bef">More...</a><br/></td></tr>
<tr class="separator:a6b62df4767fd50d6a1fd9ebd44193bef"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Environment arguments that is used by the function. These can be things like scalar arguments when add a value with scalar. </p>
</div><h2 class="groupheader">Member Data Documentation</h2>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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@@ -79,33 +103,26 @@ Inheritance diagram for mxnet::op::GradFunctionArgument:</div>
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Public Attributes</h2></td></tr>
<tr class="memitem:ab9214ef3cfcf53b375a2e8f716e66929"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1op_1_1GradFunctionArgument.html#ab9214ef3cfcf53b375a2e8f716e66929">data</a></td></tr>
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+<tr class="memdesc:ab9214ef3cfcf53b375a2e8f716e66929"><td class="mdescLeft"> </td><td class="mdescRight">The real data. <a href="#ab9214ef3cfcf53b375a2e8f716e66929">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>super class of all gradient function argument </p>
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<hr class="footer"/><address class="footer"><small>
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+ <li><a href="inherits.html"><span>Class Hierarchy</span></a></li>
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+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
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diff --git a/versions/master/doxygen/structmxnet_1_1op_1_1OutputValue.html b/versions/master/doxygen/structmxnet_1_1op_1_1OutputValue.html
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@@ -78,24 +102,21 @@ Inheritance diagram for mxnet::op::OutputValue:</div>
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Collaboration diagram for mxnet::op::OutputValue:</div>
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<table class="memberdecls">
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+<tr class="memdesc:ab9214ef3cfcf53b375a2e8f716e66929 inherit pub_attribs_structmxnet_1_1op_1_1GradFunctionArgument"><td class="mdescLeft"> </td><td class="mdescRight">The real data. <a href="#ab9214ef3cfcf53b375a2e8f716e66929">More...</a><br/></td></tr>
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<hr class="footer"/><address class="footer"><small>
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<p>TBlob class that holds common representation of arbirary dimension tensor, can be used to transformed to normal fixed dimenson tensor.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <dmlc/logging.h></code><br />
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-<code>#include <dlpack/dlpack.h></code><br />
-<code>#include <vector></code><br />
-<code>#include <iostream></code><br />
-<code>#include <utility></code><br />
-<code>#include <algorithm></code><br />
-<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br />
+<div class="textblock"><code>#include <dmlc/logging.h></code><br/>
+<code>#include <dmlc/json.h></code><br/>
+<code>#include <dlpack/dlpack.h></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <iostream></code><br/>
+<code>#include <utility></code><br/>
+<code>#include <algorithm></code><br/>
+<code>#include "<a class="el" href="base_8h_source.html">./base.h</a>"</code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for tensor_blob.h:</div>
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<p><a href="tensor__blob_8h_source.html">Go to the source code of this file.</a></p>
@@ -95,29 +116,29 @@ Include dependency graph for tensor_blob.h:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> itself do not involve any arithmentic operations, but it can be converted to tens [...]
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">tensor blob class that can be used to hold tensor of any dimension, any device and any data type, This is a weak type that can be used to transfer data through interface <a class="el" href="classmxnet_1_1TBlob.html" title="tensor blob class that can be used to hold tensor of any dimension, any device and any data type...">TBlob</a> itself do not involve any arithmentic operations, but it can be converted to tens [...]
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>TBlob class that holds common representation of arbirary dimension tensor, can be used to transformed to normal fixed dimenson tensor. </p>
@@ -126,9 +147,9 @@ Functions</h2></td></tr>
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<head>
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-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: include/mxnet/tensor_blob.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
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<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
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<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
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+ <td style="padding-left: 0.5em;">
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@@ -29,25 +30,46 @@
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
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+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
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+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
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@@ -66,51 +88,319 @@ $(function() {
<div class="title">tensor_blob.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="tensor__blob_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#ifndef MXNET_TENSOR_BLOB_H_</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#define MXNET_TENSOR_BLOB_H_</span></div><div class="line"><a n [...]
+<a href="tensor__blob_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#ifndef MXNET_TENSOR_BLOB_H_</span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_TENSOR_BLOB_H_</span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <dmlc/logging.h></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <dmlc/json.h></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <dlpack/dlpack.h></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <iostream></span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <utility></span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <algorithm></span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "<a class="code" href="base_8h.html">./base.h</a>"</span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#if MXNET_USE_MKL2017 == 1</span></div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor"></span><span class="preprocessor">#include <mkl_memory.h></span></div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor"></span><span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="comment">/* Forward declaration for friend declaration in TBlob */</span></div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">class </span>NDArray;</div>
+<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div>
+<div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html"> 40</a></span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1TBlob.html">TBlob</a> {</div>
+<div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#ad41557e205cf8f4d6ef70b60587c37cb"> 41</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classmxnet_1_1NDArray.html">NDArray</a>;</div>
+<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00044"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10"> 44</a></span>  <span class="keywordtype">void</span> *<a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>;</div>
+<div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f"> 46</a></span>  <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>;</div>
+<div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61"> 48</a></span>  <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a>;</div>
+<div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div>
+<div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="preprocessor"></span> std::shared_ptr<MKLMemHolder> Mkl_mem_;</div>
+<div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00055"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#adcdc6de67f81454f0a142bfc9fd38c9e"> 55</a></span>  <a class="code" href="classmxnet_1_1TBlob.html#adcdc6de67f81454f0a142bfc9fd38c9e">TBlob</a>(<span class="keywordtype">void</span>)</div>
+<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  : <a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>(NULL),</div>
+<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a>(mshadow::DataType<<a class="code" href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">real_t</a>>::kFlag) {</div>
+<div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="preprocessor"></span> Mkl_mem_ = NULL;</div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="preprocessor"></span> SetDLTensor(cpu::kDevMask, 0);</div>
+<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  }</div>
+<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">template</span><<span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#af89975f6a6de34624a1ee7fbc43df8fa"> 71</a></span>  <a class="code" href="classmxnet_1_1TBlob.html#af89975f6a6de34624a1ee7fbc43df8fa">TBlob</a>(DType *<a class="code" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">dptr</a>, <span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a> &shape, [...]
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  : <a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>(dptr), <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>(shape),</div>
+<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a>(mshadow::DataType<DType>::kFlag) {</div>
+<div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="preprocessor"></span> Mkl_mem_ = NULL;</div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="preprocessor"></span> SetDLTensor(dev_mask, <a class="code" href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd">dev_id</a>);</div>
+<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</div>
+<div class="line"><a name="l00087"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a660f447fbe40782e1db6a993ab1e37b4"> 87</a></span>  <a class="code" href="classmxnet_1_1TBlob.html#a660f447fbe40782e1db6a993ab1e37b4">TBlob</a>(<span class="keywordtype">void</span> *<a class="code" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">dptr</a>, <span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1d [...]
+<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  : <a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>(dptr), <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>(shape), <a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a>(type_flag) {</div>
+<div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00090"></a><span class="lineno"> 90</span> <span class="preprocessor"></span> Mkl_mem_ = NULL;</div>
+<div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="preprocessor"></span> SetDLTensor(dev_mask, <a class="code" href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd">dev_id</a>);</div>
+<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  }</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keywordtype">int</span> dim, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00102"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#aae92a89345aad79c8c3b451b9e3b84a5"> 102</a></span>  <a class="code" href="classmxnet_1_1TBlob.html#aae92a89345aad79c8c3b451b9e3b84a5">TBlob</a>(<span class="keyword">const</span> mshadow::Tensor<Device, dim, DType> &src) { <span class="comment">// NOLINT(*)</span></div>
+<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  *<span class="keyword">this</span> = src;</div>
+<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  }</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keywordtype">int</span> dim, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00114"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#ad3263d9d1ef40678cd592be822d1c7be"> 114</a></span>  <span class="keyword">inline</span> <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a> &<a class="code" href="classmxnet_1_1TBlob.html#ad3263d9d1ef40678cd592be822d1c7be">operator=</a>(<span class="keyword">const</span> mshadow::Tensor<Device, dim, DType> &src) {</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a> = src.dptr_;</div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a> = src.shape_;</div>
+<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a> = mshadow::DataType<DType>::kFlag;</div>
+<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  SetDLTensor(Device::kDevMask, -1);</div>
+<div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="preprocessor"></span> Mkl_mem_ = NULL;</div>
+<div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="preprocessor"></span> <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
+<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  }</div>
+<div class="line"><a name="l00127"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#aa6cfdf8823a601602b6b56011af4b874"> 127</a></span>  <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="classmxnet_1_1TBlob.html#aa6cfdf8823a601602b6b56011af4b874">CheckContiguous</a>(<span class="keywordtype">void</span>)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
+<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  }</div>
+<div class="line"><a name="l00135"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a0c66b10f5a17528cc858762b60125789"> 135</a></span>  <span class="keyword">inline</span> <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a> <a class="code" href="classmxnet_1_1TBlob.html#a0c66b10f5a17528cc858762b60125789">reshape</a>(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>& shape)<spa [...]
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  CHECK_EQ(this-><a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.Size(), shape.Size()) << <span class="stringliteral">"Shape size mismatch "</span></div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  << this-><a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.Size() << <span class="stringliteral">" v.s. "</span> << shape.Size();</div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="classmxnet_1_1TBlob.html">TBlob</a> ret(this-><a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>, shape, this-><a class="code" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>(), this-><a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a>, this-><a class="code" [...]
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">return</span> ret;</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00149"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a23cc57be3b2c40a269c8c9f242fa0532"> 149</a></span>  <span class="keyword">inline</span> mshadow::Tensor<Device, 2, DType> <a class="code" href="classmxnet_1_1TBlob.html#a23cc57be3b2c40a269c8c9f242fa0532">FlatTo2D</a>(</div>
+<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  mshadow::Stream<Device> *stream = NULL)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  CHECK(Device::kDevMask == this-><a class="code" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>())</div>
+<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  << <span class="stringliteral">"TBlob.get: device type do not match specified type"</span>;</div>
+<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  CHECK(mshadow::DataType<DType>::kFlag == <a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a>)</div>
+<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  << <span class="stringliteral">"TBlob.get_with_shape: data type do not match specified type."</span></div>
+<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  << <span class="stringliteral">"Expected: "</span> << <a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a> << <span class="stringliteral">" v.s. given "</span> << mshadow::DataType<DType>::kFlag;</div>
+<div class="line"><a name="l00156"></a><span class="lineno"> 156</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00157"></a><span class="lineno"> 157</span> <span class="preprocessor"></span> <span class="keywordflow">if</span> (Mkl_mem_ != <span class="keyword">nullptr</span>) {</div>
+<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  Mkl_mem_->check_and_prv_to_cpu(<a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>);</div>
+<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  }</div>
+<div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="preprocessor"></span> <span class="keywordflow">return</span> mshadow::Tensor<Device, 2, DType>(<span class="keyword">static_cast<</span>DType*<span class="keyword">></span>(<a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>),</div>
+<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.FlatTo2D(),</div>
+<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>[<a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.ndim() - 1],</div>
+<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  stream);</div>
+<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  }</div>
+<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00174"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a95bf7032c2b90627510a89ac8bb2946f"> 174</a></span>  <span class="keyword">inline</span> mshadow::Tensor<Device, 1, DType> <a class="code" href="classmxnet_1_1TBlob.html#a95bf7032c2b90627510a89ac8bb2946f">FlatTo1D</a>(</div>
+<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  mshadow::Stream<Device> *stream = NULL)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">return</span> this->get_with_shape<Device, 1, DType>(</div>
+<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  mshadow::Shape1(<a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.Size()), stream);</div>
+<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  }</div>
+<div class="line"><a name="l00180"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de"> 180</a></span>  <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">ndim</a>(<span class="keywordtype">void</span>)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordflow">return</span> <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.ndim();</div>
+<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  }</div>
+<div class="line"><a name="l00188"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a523fd58883de4b9cd0b1a2852daa64ae"> 188</a></span>  <span class="keyword">inline</span> <a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> <a class="code" href="classmxnet_1_1TBlob.html#a523fd58883de4b9cd0b1a2852daa64ae">size</a>(<a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> idx)<span class="keyw [...]
+<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keywordflow">return</span> <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>[idx];</div>
+<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  }</div>
+<div class="line"><a name="l00192"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a3b04db1348834e1576cfb2ed54959703"> 192</a></span>  <span class="keyword">inline</span> <a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> <a class="code" href="classmxnet_1_1TBlob.html#a3b04db1348834e1576cfb2ed54959703">Size</a>(<span class="keywordtype">void</span>)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keywordflow">return</span> <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.Size();</div>
+<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  }</div>
+<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keyword">template</span><<span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00197"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53"> 197</a></span>  <span class="keyword">inline</span> DType* <a class="code" href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">dptr</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  CHECK(mshadow::DataType<DType>::kFlag == <a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a>)</div>
+<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  << <span class="stringliteral">"TBlob.get_with_shape: data type do not match specified type."</span></div>
+<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  << <span class="stringliteral">"Expected: "</span> << <a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a> << <span class="stringliteral">" v.s. given "</span> << mshadow::DataType<DType>::kFlag;</div>
+<div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="preprocessor">#if MKL_EXPERIMENTAL == 1</span></div>
+<div class="line"><a name="l00202"></a><span class="lineno"> 202</span> <span class="preprocessor"></span> <span class="keywordflow">if</span> (Mkl_mem_ != <span class="keyword">nullptr</span>) {</div>
+<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  Mkl_mem_->check_and_prv_to_cpu(<a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>);</div>
+<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  }</div>
+<div class="line"><a name="l00205"></a><span class="lineno"> 205</span> <span class="preprocessor">#endif</span></div>
+<div class="line"><a name="l00206"></a><span class="lineno"> 206</span> <span class="preprocessor"></span> <span class="keywordflow">return</span> <span class="keyword">static_cast<</span>DType*<span class="keyword">></span>(<a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>);</div>
+<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  }</div>
+<div class="line"><a name="l00209"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2"> 209</a></span>  <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">return</span> dltensor_.ctx.device_type;</div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  }</div>
+<div class="line"><a name="l00213"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd"> 213</a></span>  <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd">dev_id</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordflow">return</span> dltensor_.ctx.device_id;</div>
+<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  }</div>
+<div class="line"><a name="l00220"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#a519111d4450efa8e58cc10cfc1dbeca7"> 220</a></span>  <span class="keyword">inline</span> <span class="keyword">const</span> DLTensor& <a class="code" href="classmxnet_1_1TBlob.html#a519111d4450efa8e58cc10cfc1dbeca7">dltensor</a>()<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordflow">return</span> dltensor_;</div>
+<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  }</div>
+<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div>
+<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keywordtype">int</span> dim, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00234"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#acb2e058394548527844e219c7e3c480d"> 234</a></span>  <span class="keyword">inline</span> mshadow::Tensor<Device, dim, DType> <span class="keyword">get</span>(mshadow::Stream<Device> *stream = NULL) <span class="keyword">const</span> {</div>
+<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  CHECK(Device::kDevMask == this-><a class="code" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>())</div>
+<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  << <span class="stringliteral">"TBlob.get: device type do not match specified type"</span>;</div>
+<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keywordflow">return</span> mshadow::Tensor<Device, dim, DType>(dptr<DType>(),</div>
+<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.get<dim>(), <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>[<a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.ndim() - 1], stream);</div>
+<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  }</div>
+<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keywordtype">int</span> dim, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00251"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#abbdbdc8d02c15e854cc7f5fe5dc09f61"> 251</a></span>  <span class="keyword">inline</span> mshadow::Tensor<Device, dim, DType> <a class="code" href="classmxnet_1_1TBlob.html#abbdbdc8d02c15e854cc7f5fe5dc09f61">get_with_shape</a>(</div>
+<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keyword">const</span> mshadow::Shape<dim> &shape,</div>
+<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  mshadow::Stream<Device> *stream = NULL)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  CHECK(Device::kDevMask == this-><a class="code" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>())</div>
+<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  << <span class="stringliteral">"TBlob.get: device type do not match specified type"</span>;</div>
+<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  CHECK_EQ(this-><a class="code" href="classmxnet_1_1TBlob.html#aa6cfdf8823a601602b6b56011af4b874">CheckContiguous</a>(), <span class="keyword">true</span>) << <span class="stringliteral">"TBlob.get_reshape: must be contiguous"</span>;</div>
+<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  CHECK_EQ(this-><a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.Size(), shape.Size())</div>
+<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  << <span class="stringliteral">"TBlob.get_with_shape: new and old shape do not match total elements"</span>;</div>
+<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">return</span> mshadow::Tensor<Device, dim, DType>(dptr<DType>(), shape,</div>
+<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  shape[dim - 1], stream);</div>
+<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  }</div>
+<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00272"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#ac5cd4953869cb66ed374854a34da40f8"> 272</a></span>  <span class="keyword">inline</span> mshadow::Tensor<Device, 3, DType> <a class="code" href="classmxnet_1_1TBlob.html#ac5cd4953869cb66ed374854a34da40f8">FlatTo3D</a>(</div>
+<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordtype">int</span> axis, mshadow::Stream<Device> *stream = NULL)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordflow">return</span> this->get_with_shape<Device, 3, DType>(</div>
+<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  this-><a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.FlatTo3D(axis), stream);</div>
+<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  }</div>
+<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00288"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#ada83447eb80b913592e9ac2bf8c5740f"> 288</a></span>  <span class="keyword">inline</span> mshadow::Tensor<Device, 3, DType> <a class="code" href="classmxnet_1_1TBlob.html#ada83447eb80b913592e9ac2bf8c5740f">FlatTo3D</a>(</div>
+<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordtype">int</span> axis_begin, <span class="keywordtype">int</span> axis_end,</div>
+<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  mshadow::Stream<Device> *stream = NULL)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keywordflow">return</span> this->get_with_shape<Device, 3, DType>(</div>
+<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  this-><a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.FlatTo3D(axis_begin, axis_end), stream);</div>
+<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  }</div>
+<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keywordtype">int</span> dim, <span class="keyword">typename</span> DType></div>
+<div class="line"><a name="l00304"></a><span class="lineno"><a class="line" href="classmxnet_1_1TBlob.html#aa2f8e15eb1fc21e159c496ec0d499b6f"> 304</a></span>  <span class="keyword">inline</span> mshadow::Tensor<Device, dim, DType> <a class="code" href="classmxnet_1_1TBlob.html#aa2f8e15eb1fc21e159c496ec0d499b6f">FlatToKD</a>(</div>
+<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  mshadow::Stream<Device> *stream = NULL)<span class="keyword"> const </span>{</div>
+<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  mshadow::Shape<dim> shape;</div>
+<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  shape[0] = 1;</div>
+<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="comment">// Pad higher dimensions in case dim > ndim()</span></div>
+<div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < dim - <a class="code" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">ndim</a>(); ++i) {</div>
+<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  shape[i] = 1;</div>
+<div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  }</div>
+<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="comment">// Collapse higher dimensions in case dim < ndim()</span></div>
+<div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < <a class="code" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">ndim</a>() - dim + 1; ++i) {</div>
+<div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  shape[0] *= <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>[i];</div>
+<div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  }</div>
+<div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="comment">// Preserve lower dimensions.</span></div>
+<div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = std::max(0, <a class="code" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">ndim</a>() - dim + 1); i < <a class="code" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">ndim</a>(); ++i) {</div>
+<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  shape[i - <a class="code" href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">ndim</a>() + dim] = <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>[i];</div>
+<div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  }</div>
+<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keywordflow">return</span> this->get_with_shape<Device, dim, DType>(shape, stream);</div>
+<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  }</div>
+<div class="line"><a name="l00322"></a><span class="lineno"> 322</span> </div>
+<div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keyword">static</span> DLDataType DTypeTransform(<span class="keywordtype">int</span> type_flag) {</div>
+<div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keyword">static</span> std::unordered_map<int, DLDataType></div>
+<div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  MSHADOW_DTYPE_TO_DLPACK_DTYPE = {</div>
+<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  {0, {2, 32, 1}}, <span class="comment">// Float32</span></div>
+<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  {1, {2, 64, 1}}, <span class="comment">// Float64</span></div>
+<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  {2, {2, 16, 1}}, <span class="comment">// Float16</span></div>
+<div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  {3, {1, 8, 1}}, <span class="comment">// UInt8</span></div>
+<div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  {4, {0, 32, 1}}, <span class="comment">// Int32</span></div>
+<div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  {5, {0, 8, 1}} <span class="comment">// Int8</span></div>
+<div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  };</div>
+<div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keywordflow">return</span> MSHADOW_DTYPE_TO_DLPACK_DTYPE[type_flag];</div>
+<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  }</div>
+<div class="line"><a name="l00336"></a><span class="lineno"> 336</span> </div>
+<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> SetDLTensor(<span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>, <span class="keywordtype">int</span> <a class="code" href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd">dev_id</a>) {</div>
+<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  dltensor_.data = <a class="code" href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">dptr_</a>;</div>
+<div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  dltensor_.ctx = DLContext{<span class="keyword">static_cast<</span>DLDeviceType<span class="keyword">></span>(<a class="code" href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">dev_mask</a>), dev_id};</div>
+<div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  dltensor_.ndim = <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.ndim();</div>
+<div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  dltensor_.dtype = DTypeTransform(<a class="code" href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">type_flag_</a>);</div>
+<div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  dltensor_.shape = <a class="code" href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">shape_</a>.data();</div>
+<div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  dltensor_.strides = NULL;</div>
+<div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  dltensor_.byte_offset = 0;</div>
+<div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  }</div>
+<div class="line"><a name="l00346"></a><span class="lineno"> 346</span> </div>
+<div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  DLTensor dltensor_;</div>
+<div class="line"><a name="l00350"></a><span class="lineno"> 350</span> };</div>
+<div class="line"><a name="l00351"></a><span class="lineno"> 351</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00352"></a><span class="lineno"> 352</span> </div>
+<div class="line"><a name="l00353"></a><span class="lineno"> 353</span> <span class="keyword">namespace </span>dmlc {</div>
+<div class="line"><a name="l00354"></a><span class="lineno"> 354</span> <span class="comment">// Add a few patches to support TShape in dmlc/parameter.</span></div>
+<div class="line"><a name="l00355"></a><span class="lineno"> 355</span> <a class="code" href="namespacedmlc.html#aa4d7485dc06589628bb4488ec9e8b1c9">DMLC_DECLARE_TYPE_NAME</a>(<a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">mxnet::TShape</a>, <span class="stringliteral">"Shape(tuple)"</span>);</div>
+<div class="line"><a name="l00356"></a><span class="lineno"> 356</span> <a class="code" href="namespacedmlc.html#aa4d7485dc06589628bb4488ec9e8b1c9">DMLC_DECLARE_TYPE_NAME</a>(nnvm::Tuple<int>, <span class="stringliteral">"Shape(tuple)"</span>);</div>
+<div class="line"><a name="l00357"></a><span class="lineno"> 357</span> <a class="code" href="namespacedmlc.html#aa4d7485dc06589628bb4488ec9e8b1c9">DMLC_DECLARE_TYPE_NAME</a>(nnvm::Tuple<dmlc::optional<int>>, <span class="stringliteral">"Shape(tuple)"</span>);</div>
+<div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div>
+<div class="line"><a name="l00359"></a><span class="lineno"><a class="line" href="namespacedmlc_1_1parameter.html"> 359</a></span> <span class="keyword">namespace </span>parameter {</div>
+<div class="line"><a name="l00360"></a><span class="lineno"> 360</span> </div>
+<div class="line"><a name="l00361"></a><span class="lineno"> 361</span> <span class="keyword">template</span><></div>
+<div class="line"><a name="l00362"></a><span class="lineno"><a class="line" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html"> 362</a></span> <span class="keyword">class </span>FieldEntry<mxnet::<a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">TShape</a>></div>
+<div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  : <span class="keyword">public</span> FieldEntryBase<FieldEntry<mxnet::TShape>, mxnet::TShape> {</div>
+<div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00365"></a><span class="lineno"><a class="line" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a0755f0abe443e4b57da234ec7b49fde0"> 365</a></span>  <a class="code" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a0755f0abe443e4b57da234ec7b49fde0">FieldEntry</a>() : enforce_nonzero_(false), expect_ndim_(0) {}</div>
+<div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="comment">// parent class</span></div>
+<div class="line"><a name="l00367"></a><span class="lineno"><a class="line" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#ac6a42c993f66fceb5022b330e5672b6c"> 367</a></span>  <span class="keyword">typedef</span> FieldEntryBase<FieldEntry<mxnet::TShape>, <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">mxnet::TShape</a>> <a class="code" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#ac6a42c9 [...]
+<div class="line"><a name="l00368"></a><span class="lineno"> 368</span> </div>
+<div class="line"><a name="l00369"></a><span class="lineno"><a class="line" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a63affafc149cac4c39702c55cf94fdcf"> 369</a></span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a63affafc149cac4c39702c55cf94fdcf">Check</a>(<span class="keywordtype">void</span> *head)<span class="keyword"> const </ [...]
+<div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  Parent::Check(head);</div>
+<div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <a class="code" href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">mxnet::TShape</a> &v = this->Get(head);</div>
+<div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keywordflow">if</span> (expect_ndim_ != 0 && v.ndim() != expect_ndim_) {</div>
+<div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  std::ostringstream os;</div>
+<div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  os << <span class="stringliteral">"value "</span> << v << <span class="stringliteral">"for Parameter "</span> << this->key_</div>
+<div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  << <span class="stringliteral">" has wrong dimensions, expected dimension="</span> << expect_ndim_;</div>
+<div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keywordflow">throw</span> dmlc::ParamError(os.str());</div>
+<div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  }</div>
+<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">if</span> (enforce_nonzero_) {</div>
+<div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keywordflow">for</span> (<a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">mxnet::index_t</a> i = 0; i < v.ndim(); ++i) {</div>
+<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keywordflow">if</span> (v[i] == 0U) {</div>
+<div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  std::ostringstream os;</div>
+<div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  os << <span class="stringliteral">"value "</span> << v << <span class="stringliteral">"for Parameter "</span> << this->key_</div>
+<div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  << <span class="stringliteral">" is invalid, the input shape must be nonzero in all dimensions"</span>;</div>
+<div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="keywordflow">throw</span> dmlc::ParamError(os.str());</div>
+<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  }</div>
+<div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  }</div>
+<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  }</div>
+<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  }</div>
+<div class="line"><a name="l00389"></a><span class="lineno"><a class="line" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#afa1da539bf9bcb64d3b5a55aec23e8c2"> 389</a></span>  <span class="keyword">inline</span> <a class="code" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html">FieldEntry<mxnet::TShape></a> &<a class="code" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#afa1da539bf9bcb64d3b5a55aec2 [...]
+<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  this->enforce_nonzero_ = <span class="keyword">true</span>;</div>
+<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">return</span> this-><span class="keyword">self</span>();</div>
+<div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  }</div>
+<div class="line"><a name="l00393"></a><span class="lineno"><a class="line" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a9e17b866c2cbae2c0fb1af2003c0cfde"> 393</a></span>  <span class="keyword">inline</span> <a class="code" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html">FieldEntry<mxnet::TShape></a> &<a class="code" href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a9e17b866c2cbae2c0fb1af2003c [...]
+<div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  expect_ndim_ = ndim;</div>
+<div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keywordflow">return</span> this-><span class="keyword">self</span>();</div>
+<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  }</div>
+<div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div>
+<div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="comment">// whether all the entries need to be nonzero</span></div>
+<div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordtype">bool</span> enforce_nonzero_;</div>
+<div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="comment">// expected number of dimension, default = 0 means no restriction.</span></div>
+<div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <a class="code" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">mxnet::index_t</a> expect_ndim_;</div>
+<div class="line"><a name="l00403"></a><span class="lineno"> 403</span> };</div>
+<div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div>
+<div class="line"><a name="l00405"></a><span class="lineno"> 405</span> } <span class="comment">// namespace parameter</span></div>
+<div class="line"><a name="l00406"></a><span class="lineno"> 406</span> } <span class="comment">// namespace dmlc</span></div>
+<div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div>
+<div class="line"><a name="l00408"></a><span class="lineno"> 408</span> <span class="preprocessor">#endif // MXNET_TENSOR_BLOB_H_</span></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_ad3263d9d1ef40678cd592be822d1c7be"><div class="ttname"><a href="classmxnet_1_1TBlob.html#ad3263d9d1ef40678cd592be822d1c7be">mxnet::TBlob::operator=</a></div><div class="ttdeci">TBlob & operator=(const mshadow::Tensor< Device, dim, DType > &src)</div><div class="ttdoc">assignment from tensor </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:114</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_a3b89df7835e39c652889b5cdb39235fa"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a3b89df7835e39c652889b5cdb39235fa">mxnet::TBlob::reshape</a></div><div class="ttdeci">TBlob reshape(const TShape &shape) const</div><div class="ttdoc">reshape to shape </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:135</div></div>
+<div class="ttc" id="namespacedmlc_html_aa4d7485dc06589628bb4488ec9e8b1c9"><div class="ttname"><a href="namespacedmlc.html#aa4d7485dc06589628bb4488ec9e8b1c9">dmlc::DMLC_DECLARE_TYPE_NAME</a></div><div class="ttdeci">DMLC_DECLARE_TYPE_NAME(nnvm::Tuple< dmlc::optional< int >>,"Shape(tuple)")</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_a6cc7a57d9c33fefc08630e81951a004f"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a6cc7a57d9c33fefc08630e81951a004f">mxnet::TBlob::shape_</a></div><div class="ttdeci">TShape shape_</div><div class="ttdoc">shape of the tensor </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:46</div></div>
-<div class="ttc" id="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4_html_a6795bc6e9e883adf946f69e16c644788"><div class="ttname"><a href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a6795bc6e9e883adf946f69e16c644788">dmlc::parameter::FieldEntry< mxnet::TShape >::Check</a></div><div class="ttdeci">virtual void Check(void *head) const</div><div class="ttdef"><b>Definition:</b> tensor_blob.h:369</div></div>
<div class="ttc" id="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4_html_a9e17b866c2cbae2c0fb1af2003c0cfde"><div class="ttname"><a href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a9e17b866c2cbae2c0fb1af2003c0cfde">dmlc::parameter::FieldEntry< mxnet::TShape >::set_expect_ndim</a></div><div class="ttdeci">FieldEntry< mxnet::TShape > & set_expect_ndim(mxnet::index_t ndim)</div><div class="ttdef"><b>Definition:</b> tensor_blob.h:393</di [...]
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_aefabf95412878b86b1130035386beee8"><div class="ttname"><a href="classmxnet_1_1TBlob.html#aefabf95412878b86b1130035386beee8">mxnet::TBlob::FlatTo3D</a></div><div class="ttdeci">mshadow::Tensor< Device, 3, DType > FlatTo3D(int axis, mshadow::Stream< Device > *stream=NULL) const</div><div class="ttdoc">flatten the tensor to 3 dimension, collapse the dimension before and after specified axis...</div><div class="ttdef"><b>Definition:</ [...]
<div class="ttc" id="namespacemxnet_html_a4421326e33f2a848cb2fa3e70ab24ff4"><div class="ttname"><a href="namespacemxnet.html#a4421326e33f2a848cb2fa3e70ab24ff4">mxnet::real_t</a></div><div class="ttdeci">mshadow::default_real_t real_t</div><div class="ttdoc">data type that will be used to store ndarray </div><div class="ttdef"><b>Definition:</b> base.h:109</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_adcdc6de67f81454f0a142bfc9fd38c9e"><div class="ttname"><a href="classmxnet_1_1TBlob.html#adcdc6de67f81454f0a142bfc9fd38c9e">mxnet::TBlob::TBlob</a></div><div class="ttdeci">TBlob(void)</div><div class="ttdoc">storing mkl chunk buffer blob, use for experimental only </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:55</div></div>
<div class="ttc" id="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4_html"><div class="ttname"><a href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html">dmlc::parameter::FieldEntry< mxnet::TShape ></a></div><div class="ttdef"><b>Definition:</b> tensor_blob.h:362</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_a4649b740b80a6045754cb2389cfcca61"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a4649b740b80a6045754cb2389cfcca61">mxnet::TBlob::type_flag_</a></div><div class="ttdeci">int type_flag_</div><div class="ttdoc">type flag of the tensor blob </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:48</div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_abbdbdc8d02c15e854cc7f5fe5dc09f61"><div class="ttname"><a href="classmxnet_1_1TBlob.html#abbdbdc8d02c15e854cc7f5fe5dc09f61">mxnet::TBlob::get_with_shape</a></div><div class="ttdeci">mshadow::Tensor< Device, dim, DType > get_with_shape(const mshadow::Shape< dim > &shape, mshadow::Stream< Device > *stream=NULL) const </div><div class="ttdoc">fetch a tensor in given shape If size do not match the stored size, an error will [...]
<div class="ttc" id="namespacemxnet_html_a6e14bb5f6ffc81892feca8d1da658687"><div class="ttname"><a href="namespacemxnet.html#a6e14bb5f6ffc81892feca8d1da658687">mxnet::TShape</a></div><div class="ttdeci">nnvm::TShape TShape</div><div class="ttdoc">Shape data structure used to record shape information. </div><div class="ttdef"><b>Definition:</b> base.h:111</div></div>
<div class="ttc" id="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4_html_afa1da539bf9bcb64d3b5a55aec23e8c2"><div class="ttname"><a href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#afa1da539bf9bcb64d3b5a55aec23e8c2">dmlc::parameter::FieldEntry< mxnet::TShape >::enforce_nonzero</a></div><div class="ttdeci">FieldEntry< mxnet::TShape > & enforce_nonzero()</div><div class="ttdef"><b>Definition:</b> tensor_blob.h:389</div></div>
<div class="ttc" id="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4_html_ac6a42c993f66fceb5022b330e5672b6c"><div class="ttname"><a href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#ac6a42c993f66fceb5022b330e5672b6c">dmlc::parameter::FieldEntry< mxnet::TShape >::Parent</a></div><div class="ttdeci">FieldEntryBase< FieldEntry< mxnet::TShape >, mxnet::TShape > Parent</div><div class="ttdef"><b>Definition:</b> tensor_blob.h:367</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_a77a8929e46d06e5d6a0026bcc9e48851"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a77a8929e46d06e5d6a0026bcc9e48851">mxnet::TBlob::ndim</a></div><div class="ttdeci">int ndim(void) const</div><div class="ttdoc">return number of dimension of the tensor inside </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:180</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_ad8bcf56e13082fc0607e21f08b14758f"><div class="ttname"><a href="classmxnet_1_1TBlob.html#ad8bcf56e13082fc0607e21f08b14758f">mxnet::TBlob::dev_mask</a></div><div class="ttdeci">int dev_mask() const</div><div class="ttdoc">device mask of the corresponding device </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:209</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_a44c93b8a96203a1a85fa9d4b85e123c7"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a44c93b8a96203a1a85fa9d4b85e123c7">mxnet::TBlob::size</a></div><div class="ttdeci">index_t size(index_t idx) const</div><div class="ttdoc">return size of i-th dimension, start counting from highest dimension </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:188</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_ae1dfdf1a1bc6ca78e4e06fe4aa71e074"><div class="ttname"><a href="classmxnet_1_1TBlob.html#ae1dfdf1a1bc6ca78e4e06fe4aa71e074">mxnet::TBlob::FlatTo3D</a></div><div class="ttdeci">mshadow::Tensor< Device, 3, DType > FlatTo3D(int axis_begin, int axis_end, mshadow::Stream< Device > *stream=NULL) const</div><div class="ttdoc">flatten the tensor to 3 dimension, collapse the dimension: [0, axis_begin), [axis_begin, axis_end], (axis_end, nd [...]
+<div class="ttc" id="classmxnet_1_1TBlob_html_a95bf7032c2b90627510a89ac8bb2946f"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a95bf7032c2b90627510a89ac8bb2946f">mxnet::TBlob::FlatTo1D</a></div><div class="ttdeci">mshadow::Tensor< Device, 1, DType > FlatTo1D(mshadow::Stream< Device > *stream=NULL) const </div><div class="ttdoc">flatten the tensor to 1 dimension, collapse all the dimensions together. </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:174</div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_a523fd58883de4b9cd0b1a2852daa64ae"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a523fd58883de4b9cd0b1a2852daa64ae">mxnet::TBlob::size</a></div><div class="ttdeci">index_t size(index_t idx) const </div><div class="ttdoc">return size of i-th dimension, start counting from highest dimension </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:188</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_a4e5f4953be2c16724ecff0afc5ea2e10"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a4e5f4953be2c16724ecff0afc5ea2e10">mxnet::TBlob::dptr_</a></div><div class="ttdeci">void * dptr_</div><div class="ttdoc">pointer to the data </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:44</div></div>
-<div class="ttc" id="namespacedmlc_html"><div class="ttname"><a href="namespacedmlc.html">dmlc</a></div><div class="ttdef"><b>Definition:</b> ndarray.h:778</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_ae3601040650637a7ac05b3f00f0adbee"><div class="ttname"><a href="classmxnet_1_1TBlob.html#ae3601040650637a7ac05b3f00f0adbee">mxnet::TBlob::get_with_shape</a></div><div class="ttdeci">mshadow::Tensor< Device, dim, DType > get_with_shape(const mshadow::Shape< dim > &shape, mshadow::Stream< Device > *stream=NULL) const</div><div class="ttdoc">fetch a tensor in given shape If size do not match the stored size, an error will b [...]
-<div class="ttc" id="namespacedmlc_html_a136a79c5af9aa9662eda4d4894a59e7f"><div class="ttname"><a href="namespacedmlc.html#a136a79c5af9aa9662eda4d4894a59e7f">dmlc::DMLC_DECLARE_TYPE_NAME</a></div><div class="ttdeci">DMLC_DECLARE_TYPE_NAME(nnvm::Tuple< dmlc::optional< int >>, "Shape(tuple)")</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_a5be50f29087afef8d48942ea8fe39cbd"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a5be50f29087afef8d48942ea8fe39cbd">mxnet::TBlob::FlatToKD</a></div><div class="ttdeci">mshadow::Tensor< Device, dim, DType > FlatToKD(mshadow::Stream< Device > *stream=NULL) const</div><div class="ttdoc">flatten the tensor to specified number of dimensions, collapse the highest dimensions or pad with hig...</div><div class="ttdef"><b>Definitio [...]
+<div class="ttc" id="classmxnet_1_1TBlob_html_a3449ca0a9130e87c343130b041b68c53"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a3449ca0a9130e87c343130b041b68c53">mxnet::TBlob::dptr</a></div><div class="ttdeci">DType * dptr() const </div><div class="ttdoc">get pointer in dtype </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:197</div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_a3be3b640e59a480b8ac5369d73b117de"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a3be3b640e59a480b8ac5369d73b117de">mxnet::TBlob::ndim</a></div><div class="ttdeci">int ndim(void) const </div><div class="ttdoc">return number of dimension of the tensor inside </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:180</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_aae92a89345aad79c8c3b451b9e3b84a5"><div class="ttname"><a href="classmxnet_1_1TBlob.html#aae92a89345aad79c8c3b451b9e3b84a5">mxnet::TBlob::TBlob</a></div><div class="ttdeci">TBlob(const mshadow::Tensor< Device, dim, DType > &src)</div><div class="ttdoc">constructor from tensor </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:102</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_a4b0a53821dec20c558a030ed3df14dda"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a4b0a53821dec20c558a030ed3df14dda">mxnet::TBlob::dltensor</a></div><div class="ttdeci">const DLTensor & dltensor() const</div><div class="ttdoc">return the corresponding DLTensor </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:220</div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_a519111d4450efa8e58cc10cfc1dbeca7"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a519111d4450efa8e58cc10cfc1dbeca7">mxnet::TBlob::dltensor</a></div><div class="ttdeci">const DLTensor & dltensor() const </div><div class="ttdoc">return the corresponding DLTensor </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:220</div></div>
<div class="ttc" id="base_8h_html"><div class="ttname"><a href="base_8h.html">base.h</a></div><div class="ttdoc">configuation of mxnet as well as basic data structure. </div></div>
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+<div class="ttc" id="classmxnet_1_1TBlob_html_ada83447eb80b913592e9ac2bf8c5740f"><div class="ttname"><a href="classmxnet_1_1TBlob.html#ada83447eb80b913592e9ac2bf8c5740f">mxnet::TBlob::FlatTo3D</a></div><div class="ttdeci">mshadow::Tensor< Device, 3, DType > FlatTo3D(int axis_begin, int axis_end, mshadow::Stream< Device > *stream=NULL) const </div><div class="ttdoc">flatten the tensor to 3 dimension, collapse the dimension: [0, axis_begin), [axis_begin, axis_end], (axis_end, n [...]
+<div class="ttc" id="classmxnet_1_1TBlob_html_aa2f8e15eb1fc21e159c496ec0d499b6f"><div class="ttname"><a href="classmxnet_1_1TBlob.html#aa2f8e15eb1fc21e159c496ec0d499b6f">mxnet::TBlob::FlatToKD</a></div><div class="ttdeci">mshadow::Tensor< Device, dim, DType > FlatToKD(mshadow::Stream< Device > *stream=NULL) const </div><div class="ttdoc">flatten the tensor to specified number of dimensions, collapse the highest dimensions or pad with hig...</div><div class="ttdef"><b>Definiti [...]
+<div class="ttc" id="classmxnet_1_1TBlob_html_a23cc57be3b2c40a269c8c9f242fa0532"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a23cc57be3b2c40a269c8c9f242fa0532">mxnet::TBlob::FlatTo2D</a></div><div class="ttdeci">mshadow::Tensor< Device, 2, DType > FlatTo2D(mshadow::Stream< Device > *stream=NULL) const </div><div class="ttdoc">flatten the tensor to 2 dimension, collapse the higher dimensions together </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:149</div></div>
+<div class="ttc" id="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4_html_a63affafc149cac4c39702c55cf94fdcf"><div class="ttname"><a href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a63affafc149cac4c39702c55cf94fdcf">dmlc::parameter::FieldEntry< mxnet::TShape >::Check</a></div><div class="ttdeci">virtual void Check(void *head) const </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:369</div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_a3b04db1348834e1576cfb2ed54959703"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a3b04db1348834e1576cfb2ed54959703">mxnet::TBlob::Size</a></div><div class="ttdeci">index_t Size(void) const </div><div class="ttdoc">total number of elements in the tensor </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:192</div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_aa6cfdf8823a601602b6b56011af4b874"><div class="ttname"><a href="classmxnet_1_1TBlob.html#aa6cfdf8823a601602b6b56011af4b874">mxnet::TBlob::CheckContiguous</a></div><div class="ttdeci">bool CheckContiguous(void) const </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:127</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_af89975f6a6de34624a1ee7fbc43df8fa"><div class="ttname"><a href="classmxnet_1_1TBlob.html#af89975f6a6de34624a1ee7fbc43df8fa">mxnet::TBlob::TBlob</a></div><div class="ttdeci">TBlob(DType *dptr, const TShape &shape, int dev_mask, int dev_id=-1)</div><div class="ttdoc">constructor that construct TBlob from contiguous memory </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:71</div></div>
-<div class="ttc" id="namespacemshadow_html"><div class="ttname"><a href="namespacemshadow.html">mshadow</a></div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_ac5cd4953869cb66ed374854a34da40f8"><div class="ttname"><a href="classmxnet_1_1TBlob.html#ac5cd4953869cb66ed374854a34da40f8">mxnet::TBlob::FlatTo3D</a></div><div class="ttdeci">mshadow::Tensor< Device, 3, DType > FlatTo3D(int axis, mshadow::Stream< Device > *stream=NULL) const </div><div class="ttdoc">flatten the tensor to 3 dimension, collapse the dimension before and after specified axis...</div><div class="ttdef"><b>Definition:< [...]
<div class="ttc" id="namespacemxnet_html_abadedc1b42ee3e5b8852e63783b982ef"><div class="ttname"><a href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">mxnet::index_t</a></div><div class="ttdeci">mshadow::index_t index_t</div><div class="ttdoc">index type usually use unsigned </div><div class="ttdef"><b>Definition:</b> base.h:107</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html_a660f447fbe40782e1db6a993ab1e37b4"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a660f447fbe40782e1db6a993ab1e37b4">mxnet::TBlob::TBlob</a></div><div class="ttdeci">TBlob(void *dptr, const TShape &shape, int dev_mask, int type_flag, int dev_id=-1)</div><div class="ttdoc">constructor that construct TBlob from contiguous memory </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:87</div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_a0c66b10f5a17528cc858762b60125789"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a0c66b10f5a17528cc858762b60125789">mxnet::TBlob::reshape</a></div><div class="ttdeci">TBlob reshape(const TShape &shape) const </div><div class="ttdoc">reshape to shape </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:135</div></div>
<div class="ttc" id="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4_html_a0755f0abe443e4b57da234ec7b49fde0"><div class="ttname"><a href="classdmlc_1_1parameter_1_1FieldEntry_3_01mxnet_1_1TShape_01_4.html#a0755f0abe443e4b57da234ec7b49fde0">dmlc::parameter::FieldEntry< mxnet::TShape >::FieldEntry</a></div><div class="ttdeci">FieldEntry()</div><div class="ttdef"><b>Definition:</b> tensor_blob.h:365</div></div>
<div class="ttc" id="classmxnet_1_1NDArray_html"><div class="ttname"><a href="classmxnet_1_1NDArray.html">mxnet::NDArray</a></div><div class="ttdoc">ndarray interface </div><div class="ttdef"><b>Definition:</b> ndarray.h:59</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_add31f43a251cecde199e245f3ff8383a"><div class="ttname"><a href="classmxnet_1_1TBlob.html#add31f43a251cecde199e245f3ff8383a">mxnet::TBlob::FlatTo1D</a></div><div class="ttdeci">mshadow::Tensor< Device, 1, DType > FlatTo1D(mshadow::Stream< Device > *stream=NULL) const</div><div class="ttdoc">flatten the tensor to 1 dimension, collapse all the dimensions together. </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:174</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_a7b45f281d954cc716267a7b3ec0f514c"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a7b45f281d954cc716267a7b3ec0f514c">mxnet::TBlob::Size</a></div><div class="ttdeci">index_t Size(void) const</div><div class="ttdoc">total number of elements in the tensor </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:192</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_a3e4b61ee072b4d875a90e8153ef50721"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a3e4b61ee072b4d875a90e8153ef50721">mxnet::TBlob::FlatTo2D</a></div><div class="ttdeci">mshadow::Tensor< Device, 2, DType > FlatTo2D(mshadow::Stream< Device > *stream=NULL) const</div><div class="ttdoc">flatten the tensor to 2 dimension, collapse the higher dimensions together </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:149</div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_a613ad1f5ba31efcbecbd041e23cc61b2"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a613ad1f5ba31efcbecbd041e23cc61b2">mxnet::TBlob::dev_mask</a></div><div class="ttdeci">int dev_mask() const </div><div class="ttdoc">device mask of the corresponding device </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:209</div></div>
<div class="ttc" id="classmxnet_1_1TBlob_html"><div class="ttname"><a href="classmxnet_1_1TBlob.html">mxnet::TBlob</a></div><div class="ttdoc">tensor blob class that can be used to hold tensor of any dimension, any device and any data type...</div><div class="ttdef"><b>Definition:</b> tensor_blob.h:40</div></div>
-<div class="ttc" id="classmxnet_1_1TBlob_html_ad931186405f2b40a327278a8f5ff57cd"><div class="ttname"><a href="classmxnet_1_1TBlob.html#ad931186405f2b40a327278a8f5ff57cd">mxnet::TBlob::dptr</a></div><div class="ttdeci">DType * dptr() const</div><div class="ttdoc">get pointer in dtype </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:197</div></div>
+<div class="ttc" id="classmxnet_1_1TBlob_html_a5dc3a399b0119175643da0d35fde7edd"><div class="ttname"><a href="classmxnet_1_1TBlob.html#a5dc3a399b0119175643da0d35fde7edd">mxnet::TBlob::dev_id</a></div><div class="ttdeci">int dev_id() const </div><div class="ttdoc">device index of the corresponding device </div><div class="ttdef"><b>Definition:</b> tensor_blob.h:213</div></div>
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+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -74,24 +96,22 @@ $(function() {
<p>Basic utilility functions.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <memory></code><br />
-<code>#include <vector></code><br />
-<code>#include <type_traits></code><br />
-<code>#include <utility></code><br />
-<code>#include <random></code><br />
-<code>#include <string></code><br />
-<code>#include <thread></code><br />
-<code>#include <algorithm></code><br />
-<code>#include <dmlc/logging.h></code><br />
-<code>#include <<a class="el" href="engine_8h_source.html">mxnet/engine.h</a>></code><br />
+<div class="textblock"><code>#include <memory></code><br/>
+<code>#include <vector></code><br/>
+<code>#include <type_traits></code><br/>
+<code>#include <utility></code><br/>
+<code>#include <random></code><br/>
+<code>#include <string></code><br/>
+<code>#include <thread></code><br/>
+<code>#include <algorithm></code><br/>
+<code>#include <dmlc/logging.h></code><br/>
+<code>#include <<a class="el" href="engine_8h_source.html">mxnet/engine.h</a>></code><br/>
</div><div class="textblock"><div class="dynheader">
Include dependency graph for utils.h:</div>
<div class="dyncontent">
<div class="center"><img src="utils_8h__incl.png" border="0" usemap="#src_2common_2utils_8h" alt=""/></div>
<map name="src_2common_2utils_8h" id="src_2common_2utils_8h">
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-<area shape="rect" id="node14" href="base_8h.html" title="configuation of mxnet as well as basic data structure. " alt="" coords="499,152,568,177"/>
-</map>
+<area shape="rect" id="node11" href="engine_8h.html" title="Engine that schedules all the operations according to dependency. " alt="" coords="353,80,476,107"/><area shape="rect" id="node14" href="base_8h.html" title="configuation of mxnet as well as basic data structure. " alt="" coords="533,155,605,181"/></map>
</div>
</div>
<p><a href="utils_8h_source.html">Go to the source code of this file.</a></p>
@@ -99,30 +119,30 @@ Include dependency graph for utils.h:</div>
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html">mxnet::common::helper::UniqueIf< T ></a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for non-array type <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for non-array type <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html">mxnet::common::helper::UniqueIf< T[]></a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for an array of unknown bound <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for an array of unknown bound <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html">mxnet::common::helper::UniqueIf< T[kSize]></a></td></tr>
-<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for an array of known bound <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html#details">More...</a><br /></td></tr>
+<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Helper for an array of known bound <code>T</code>. <a href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html#details">More...</a><br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
-<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
-<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br /></td></tr>
+<tr class="memitem:namespacemxnet"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet.html">mxnet</a></td></tr>
+<tr class="memdesc:namespacemxnet"><td class="mdescLeft"> </td><td class="mdescRight">namespace of mxnet <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacemxnet_1_1common"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html">mxnet::common</a></td></tr>
+<tr class="memitem:namespacemxnet_1_1common"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html">mxnet::common</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacemxnet_1_1common_1_1helper"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common_1_1helper.html">mxnet::common::helper</a></td></tr>
-<tr class="memdesc:namespacemxnet_1_1common_1_1helper"><td class="mdescLeft"> </td><td class="mdescRight">Helper functions. <br /></td></tr>
+<tr class="memitem:namespacemxnet_1_1common_1_1helper"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common_1_1helper.html">mxnet::common::helper</a></td></tr>
+<tr class="memdesc:namespacemxnet_1_1common_1_1helper"><td class="mdescLeft"> </td><td class="mdescRight">Helper functions. <br/></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="typedef-members"></a>
Typedefs</h2></td></tr>
<tr class="memitem:aceba8f2d6de65796966d7f1d341dcf33"><td class="memItemLeft" align="right" valign="top">typedef std::mt19937 </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html#aceba8f2d6de65796966d7f1d341dcf33">mxnet::common::RANDOM_ENGINE</a></td></tr>
-<tr class="memdesc:aceba8f2d6de65796966d7f1d341dcf33"><td class="mdescLeft"> </td><td class="mdescRight">Random <a class="el" href="classmxnet_1_1Engine.html" title="Dependency engine that schedules operations. ">Engine</a>. <a href="namespacemxnet_1_1common.html#aceba8f2d6de65796966d7f1d341dcf33">More...</a><br /></td></tr>
+<tr class="memdesc:aceba8f2d6de65796966d7f1d341dcf33"><td class="mdescLeft"> </td><td class="mdescRight">Random <a class="el" href="classmxnet_1_1Engine.html" title="Dependency engine that schedules operations. ">Engine</a>. <a href="#aceba8f2d6de65796966d7f1d341dcf33">More...</a><br/></td></tr>
<tr class="separator:aceba8f2d6de65796966d7f1d341dcf33"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
@@ -131,18 +151,18 @@ Functions</h2></td></tr>
<tr class="separator:abc7580a95e3de5cf38d5bc2617c6d541"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a56555edcc52c5b25bed90bd05e00b5fa"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html#a56555edcc52c5b25bed90bd05e00b5fa">mxnet::common::GetExecNumMatchColor</a> ()</td></tr>
<tr class="separator:a56555edcc52c5b25bed90bd05e00b5fa"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:ad56ff4f856437bba25d38027aecfe766"><td class="memTemplParams" colspan="2">template<class T , class... Args> </td></tr>
-<tr class="memitem:ad56ff4f856437bba25d38027aecfe766"><td class="memTemplItemLeft" align="right" valign="top">helper::UniqueIf< T >::SingleObject </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html#ad56ff4f856437bba25d38027aecfe766">mxnet::common::MakeUnique</a> (Args &&... args)</td></tr>
-<tr class="memdesc:ad56ff4f856437bba25d38027aecfe766"><td class="mdescLeft"> </td><td class="mdescRight">Constructs an object of type <code>T</code> and wraps it in a <code>std</code><code>::</code><code>unique_ptr</code>. <a href="namespacemxnet_1_1common.html#ad56ff4f856437bba25d38027aecfe766">More...</a><br /></td></tr>
-<tr class="separator:ad56ff4f856437bba25d38027aecfe766"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9d9abe40c84b9924e51d5d57b3f0b7d6"><td class="memTemplParams" colspan="2">template<class T , class... Args> </td></tr>
+<tr class="memitem:a9d9abe40c84b9924e51d5d57b3f0b7d6"><td class="memTemplItemLeft" align="right" valign="top">helper::UniqueIf< T >::SingleObject </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html#a9d9abe40c84b9924e51d5d57b3f0b7d6">mxnet::common::MakeUnique</a> (Args &&...args)</td></tr>
+<tr class="memdesc:a9d9abe40c84b9924e51d5d57b3f0b7d6"><td class="mdescLeft"> </td><td class="mdescRight">Constructs an object of type <code>T</code> and wraps it in a <code>std</code><code>::</code><code>unique_ptr</code>. <a href="#a9d9abe40c84b9924e51d5d57b3f0b7d6">More...</a><br/></td></tr>
+<tr class="separator:a9d9abe40c84b9924e51d5d57b3f0b7d6"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a39b3453302aa3aa56230fbc88005861d"><td class="memTemplParams" colspan="2">template<class T > </td></tr>
<tr class="memitem:a39b3453302aa3aa56230fbc88005861d"><td class="memTemplItemLeft" align="right" valign="top">helper::UniqueIf< T >::UnknownBound </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html#a39b3453302aa3aa56230fbc88005861d">mxnet::common::MakeUnique</a> (size_t n)</td></tr>
-<tr class="memdesc:a39b3453302aa3aa56230fbc88005861d"><td class="mdescLeft"> </td><td class="mdescRight">Constructs an object of type <code>T</code> and wraps it in a <code>std</code><code>::</code><code>unique_ptr</code>. <a href="namespacemxnet_1_1common.html#a39b3453302aa3aa56230fbc88005861d">More...</a><br /></td></tr>
+<tr class="memdesc:a39b3453302aa3aa56230fbc88005861d"><td class="mdescLeft"> </td><td class="mdescRight">Constructs an object of type <code>T</code> and wraps it in a <code>std</code><code>::</code><code>unique_ptr</code>. <a href="#a39b3453302aa3aa56230fbc88005861d">More...</a><br/></td></tr>
<tr class="separator:a39b3453302aa3aa56230fbc88005861d"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:adb63438b723f69cdf56f7919275f4596"><td class="memTemplParams" colspan="2">template<class T , class... Args> </td></tr>
-<tr class="memitem:adb63438b723f69cdf56f7919275f4596"><td class="memTemplItemLeft" align="right" valign="top">helper::UniqueIf< T >::KnownBound </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html#adb63438b723f69cdf56f7919275f4596">mxnet::common::MakeUnique</a> (Args &&... args)=delete</td></tr>
-<tr class="memdesc:adb63438b723f69cdf56f7919275f4596"><td class="mdescLeft"> </td><td class="mdescRight">Constructs an object of type <code>T</code> and wraps it in a <code>std</code><code>::</code><code>unique_ptr</code>. <a href="namespacemxnet_1_1common.html#adb63438b723f69cdf56f7919275f4596">More...</a><br /></td></tr>
-<tr class="separator:adb63438b723f69cdf56f7919275f4596"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6ca270747bb7ab942c61d364f14f3042"><td class="memTemplParams" colspan="2">template<class T , class... Args> </td></tr>
+<tr class="memitem:a6ca270747bb7ab942c61d364f14f3042"><td class="memTemplItemLeft" align="right" valign="top">helper::UniqueIf< T >::KnownBound </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html#a6ca270747bb7ab942c61d364f14f3042">mxnet::common::MakeUnique</a> (Args &&...args)=delete</td></tr>
+<tr class="memdesc:a6ca270747bb7ab942c61d364f14f3042"><td class="mdescLeft"> </td><td class="mdescRight">Constructs an object of type <code>T</code> and wraps it in a <code>std</code><code>::</code><code>unique_ptr</code>. <a href="#a6ca270747bb7ab942c61d364f14f3042">More...</a><br/></td></tr>
+<tr class="separator:a6ca270747bb7ab942c61d364f14f3042"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a1abcb76ccd9022ebacdacd69c821cdc2"><td class="memTemplParams" colspan="2">template<typename FCompType > </td></tr>
<tr class="memitem:a1abcb76ccd9022ebacdacd69c821cdc2"><td class="memTemplItemLeft" align="right" valign="top">FCompType </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common.html#a1abcb76ccd9022ebacdacd69c821cdc2">mxnet::common::GetFCompute</a> (const nnvm::Op *op, const std::string &name, const Context &ctx)</td></tr>
<tr class="separator:a1abcb76ccd9022ebacdacd69c821cdc2"><td class="memSeparator" colspan="2"> </td></tr>
@@ -153,9 +173,9 @@ Functions</h2></td></tr>
</div></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
-Generated on Fri Jul 28 2017 11:44:45 for mxnet by  <a href="http://www.doxygen.org/index.html">
+Generated on Mon Aug 7 2017 07:09:28 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
-</a> 1.8.13
+</a> 1.8.6
</small></address>
</body>
</html>
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index 452b2f3..1274006 100644
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index f7f2981..7e831e9 100644
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index ad4f65a..712acb9 100644
--- a/versions/master/doxygen/utils_8h_source.html
+++ b/versions/master/doxygen/utils_8h_source.html
@@ -3,15 +3,16 @@
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
-<meta name="generator" content="Doxygen 1.8.13"/>
-<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<meta name="generator" content="Doxygen 1.8.6"/>
<title>mxnet: src/common/utils.h Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+ $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
</head>
<body>
@@ -20,7 +21,7 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
- <td id="projectalign" style="padding-left: 0.5em;">
+ <td style="padding-left: 0.5em;">
<div id="projectname">mxnet
</div>
</td>
@@ -29,25 +30,46 @@
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</div>
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-<!-- Generated by Doxygen 1.8.13 -->
+<!-- Generated by Doxygen 1.8.6 -->
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-$(function() {
- initMenu('',true,false,'search.php','Search');
- $(document).ready(function() { init_search(); });
-});
-</script>
-<div id="main-nav"></div>
+ <div id="navrow1" class="tabs">
+ <ul class="tablist">
+ <li><a href="index.html"><span>Main Page</span></a></li>
+ <li><a href="namespaces.html"><span>Namespaces</span></a></li>
+ <li><a href="annotated.html"><span>Classes</span></a></li>
+ <li class="current"><a href="files.html"><span>Files</span></a></li>
+ <li>
+ <div id="MSearchBox" class="MSearchBoxInactive">
+ <span class="left">
+ <img id="MSearchSelect" src="search/mag_sel.png"
+ onmouseover="return searchBox.OnSearchSelectShow()"
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+ <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+ </span>
+ </div>
+ </li>
+ </ul>
+ </div>
+ <div id="navrow2" class="tabs2">
+ <ul class="tablist">
+ <li><a href="files.html"><span>File List</span></a></li>
+ <li><a href="globals.html"><span>File Members</span></a></li>
+ </ul>
+ </div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
-</div>
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark"> </span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark"> </span>Classes</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark"> </span>Namespaces</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)">< [...]
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
@@ -66,26 +88,117 @@ $(function() {
<div class="title">utils.h</div> </div>
</div><!--header-->
<div class="contents">
-<a href="utils_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_COMMON_UTILS_H_</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#define MXNET_COMMON_UTILS_H_</span></div><div class="line"><a name=" [...]
-<div class="ttc" id="namespacemxnet_html"><div class="ttname"><a href="namespacemxnet.html">mxnet</a></div><div class="ttdoc">namespace of mxnet </div><div class="ttdef"><b>Definition:</b> base.h:101</div></div>
+<a href="utils_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div>
+<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#ifndef MXNET_COMMON_UTILS_H_</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor"></span><span class="preprocessor">#define MXNET_COMMON_UTILS_H_</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#if DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor"></span><span class="preprocessor">#include <memory></span></div>
+<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <vector></span></div>
+<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <type_traits></span></div>
+<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <utility></span></div>
+<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <random></span></div>
+<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <string></span></div>
+<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <thread></span></div>
+<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <algorithm></span></div>
+<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#endif // DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <dmlc/logging.h></span></div>
+<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <<a class="code" href="engine_8h.html">mxnet/engine.h</a>></span></div>
+<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div>
+<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">namespace </span>mxnet {</div>
+<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">namespace </span>common {</div>
+<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div>
+<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#if DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor"></span><span class="comment">// heuristic to dermine number of threads per GPU</span></div>
+<div class="line"><a name="l00028"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common.html#abc7580a95e3de5cf38d5bc2617c6d541"> 28</a></span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="namespacemxnet_1_1common.html#abc7580a95e3de5cf38d5bc2617c6d541">GetNumThreadPerGPU</a>() {</div>
+<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="comment">// This is resource efficient option.</span></div>
+<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">return</span> dmlc::GetEnv(<span class="stringliteral">"MXNET_GPU_WORKER_NTHREADS"</span>, 2);</div>
+<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> }</div>
+<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div>
+<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="comment">// heuristic to get number of matching colors.</span></div>
+<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="comment">// this decides how much parallelism we can get in each GPU.</span></div>
+<div class="line"><a name="l00035"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common.html#a56555edcc52c5b25bed90bd05e00b5fa"> 35</a></span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="namespacemxnet_1_1common.html#a56555edcc52c5b25bed90bd05e00b5fa">GetExecNumMatchColor</a>() {</div>
+<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="comment">// This is resource efficient option.</span></div>
+<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordtype">int</span> num_match_color = dmlc::GetEnv(<span class="stringliteral">"MXNET_EXEC_NUM_TEMP"</span>, 1);</div>
+<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">return</span> std::min(num_match_color, <a class="code" href="namespacemxnet_1_1common.html#abc7580a95e3de5cf38d5bc2617c6d541">GetNumThreadPerGPU</a>());</div>
+<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> }</div>
+<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div>
+<div class="line"><a name="l00044"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common.html#aceba8f2d6de65796966d7f1d341dcf33"> 44</a></span> <span class="keyword">typedef</span> std::mt19937 <a class="code" href="namespacemxnet_1_1common.html#aceba8f2d6de65796966d7f1d341dcf33">RANDOM_ENGINE</a>;</div>
+<div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div>
+<div class="line"><a name="l00049"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common_1_1helper.html"> 49</a></span> <span class="keyword">namespace </span>helper {</div>
+<div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div>
+<div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="keyword">template</span> <<span class="keyword">class</span> T></div>
+<div class="line"><a name="l00055"></a><span class="lineno"><a class="line" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html"> 55</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html">UniqueIf</a> {</div>
+<div class="line"><a name="l00059"></a><span class="lineno"><a class="line" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html#a98153aaeb43a71efa85b5cf753505cac"> 59</a></span>  <span class="keyword">using</span> <a class="code" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html#a98153aaeb43a71efa85b5cf753505cac">SingleObject</a> = std::unique_ptr<T>;</div>
+<div class="line"><a name="l00060"></a><span class="lineno"> 60</span> };</div>
+<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div>
+<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="keyword">template</span> <<span class="keyword">class</span> T></div>
+<div class="line"><a name="l00066"></a><span class="lineno"><a class="line" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html"> 66</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html">UniqueIf</a><T[]> {</div>
+<div class="line"><a name="l00070"></a><span class="lineno"><a class="line" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html#a0cf05a5a2f3e1ac49ebdef497701055c"> 70</a></span>  <span class="keyword">using</span> <a class="code" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html#a0cf05a5a2f3e1ac49ebdef497701055c">UnknownBound</a> = std::unique_ptr<T[]>;</div>
+<div class="line"><a name="l00071"></a><span class="lineno"> 71</span> };</div>
+<div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div>
+<div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="keyword">template</span> <<span class="keyword">class</span> T, <span class="keywordtype">size_t</span> kSize></div>
+<div class="line"><a name="l00077"></a><span class="lineno"><a class="line" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html"> 77</a></span> <span class="keyword">struct </span><a class="code" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html">UniqueIf</a><T[kSize]> {</div>
+<div class="line"><a name="l00081"></a><span class="lineno"><a class="line" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html#af31c1728c4b497d304680d69d4e61421"> 81</a></span>  <span class="keyword">using</span> <a class="code" href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html#af31c1728c4b497d304680d69d4e61421">KnownBound</a> = void;</div>
+<div class="line"><a name="l00082"></a><span class="lineno"> 82</span> };</div>
+<div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div>
+<div class="line"><a name="l00084"></a><span class="lineno"> 84</span> } <span class="comment">// namespace helper</span></div>
+<div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div>
+<div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="keyword">template</span> <<span class="keyword">class </span>T, <span class="keyword">class</span>... Args></div>
+<div class="line"><a name="l00098"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common.html#a6ca270747bb7ab942c61d364f14f3042"> 98</a></span> <span class="keyword">typename</span> <a class="code" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html#a98153aaeb43a71efa85b5cf753505cac">helper::UniqueIf<T>::SingleObject</a> <a class="code" href="namespacemxnet_1_1common.html#a9d9abe40c84b9924e51d5d57b3f0b7d6">MakeUnique</a>(Args&&... args) {</div>
+<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">return</span> std::unique_ptr<T>(<span class="keyword">new</span> T(std::forward<Args>(args)...));</div>
+<div class="line"><a name="l00100"></a><span class="lineno"> 100</span> }</div>
+<div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div>
+<div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="keyword">template</span> <<span class="keyword">class</span> T></div>
+<div class="line"><a name="l00112"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common.html#a39b3453302aa3aa56230fbc88005861d"> 112</a></span> <span class="keyword">typename</span> <a class="code" href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html">helper::UniqueIf<T>::UnknownBound</a> <a class="code" href="namespacemxnet_1_1common.html#a9d9abe40c84b9924e51d5d57b3f0b7d6">MakeUnique</a>(<span class="keywordtype">size_t</span> n) {</div>
+<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keyword">using</span> U = <span class="keyword">typename</span> std::remove_extent<T>::type;</div>
+<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordflow">return</span> std::unique_ptr<T>(<span class="keyword">new</span> U[n]{});</div>
+<div class="line"><a name="l00115"></a><span class="lineno"> 115</span> }</div>
+<div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div>
+<div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="keyword">template</span> <<span class="keyword">class </span>T, <span class="keyword">class</span>... Args></div>
+<div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="keyword">typename</span> helper::UniqueIf<T>::KnownBound <a class="code" href="namespacemxnet_1_1common.html#a9d9abe40c84b9924e51d5d57b3f0b7d6">MakeUnique</a>(Args&&... args) = <span class="keyword">delete</span>;</div>
+<div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div>
+<div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="keyword">template</span><<span class="keyword">typename</span> FCompType></div>
+<div class="line"><a name="l00129"></a><span class="lineno"><a class="line" href="namespacemxnet_1_1common.html#a1abcb76ccd9022ebacdacd69c821cdc2"> 129</a></span> FCompType <a class="code" href="namespacemxnet_1_1common.html#a1abcb76ccd9022ebacdacd69c821cdc2">GetFCompute</a>(<span class="keyword">const</span> <a class="code" href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52">nnvm::Op</a>* op, <span class="keyword">const</span> std::string& name,</div>
+<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keyword">const</span> <a class="code" href="structmxnet_1_1Context.html">Context</a>& ctx) {</div>
+<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keyword">static</span> <span class="keyword">auto</span>& fcompute_cpu = nnvm::Op::GetAttr<FCompType>(name + <span class="stringliteral">"<cpu>"</span>);</div>
+<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keyword">static</span> <span class="keyword">auto</span>& fcompute_gpu = nnvm::Op::GetAttr<FCompType>(name + <span class="stringliteral">"<gpu>"</span>);</div>
+<div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div>
+<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">if</span> (ctx.<a class="code" href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe">dev_mask</a>() == cpu::kDevMask) {</div>
+<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">return</span> fcompute_cpu.get(op, <span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (ctx.<a class="code" href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe">dev_mask</a>() == gpu::kDevMask) {</div>
+<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">return</span> fcompute_gpu.get(op, <span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  } <span class="keywordflow">else</span> {</div>
+<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  LOG(FATAL) << <span class="stringliteral">"Unknown device mask"</span>;</div>
+<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div>
+<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  }</div>
+<div class="line"><a name="l00142"></a><span class="lineno"> 142</span> }</div>
+<div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div>
+<div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="preprocessor">#endif // DMLC_USE_CXX11</span></div>
+<div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="preprocessor"></span></div>
+<div class="line"><a name="l00146"></a><span class="lineno"> 146</span> } <span class="comment">// namespace common</span></div>
+<div class="line"><a name="l00147"></a><span class="lineno"> 147</span> } <span class="comment">// namespace mxnet</span></div>
+<div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="preprocessor">#endif // MXNET_COMMON_UTILS_H_</span></div>
+<div class="ttc" id="engine_8h_html"><div class="ttname"><a href="engine_8h.html">engine.h</a></div><div class="ttdoc">Engine that schedules all the operations according to dependency. </div></div>
<div class="ttc" id="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4_html_af31c1728c4b497d304680d69d4e61421"><div class="ttname"><a href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[kSize]_4.html#af31c1728c4b497d304680d69d4e61421">mxnet::common::helper::UniqueIf< T[kSize]>::KnownBound</a></div><div class="ttdeci">void KnownBound</div><div class="ttdoc">Type of T. </div><div class="ttdef"><b>Definition:</b> utils.h:81</div></div>
<div class="ttc" id="namespacemxnet_1_1common_html_a1abcb76ccd9022ebacdacd69c821cdc2"><div class="ttname"><a href="namespacemxnet_1_1common.html#a1abcb76ccd9022ebacdacd69c821cdc2">mxnet::common::GetFCompute</a></div><div class="ttdeci">FCompType GetFCompute(const nnvm::Op *op, const std::string &name, const Context &ctx)</div><div class="ttdef"><b>Definition:</b> utils.h:129</div></div>
<div class="ttc" id="namespacemxnet_1_1common_html_abc7580a95e3de5cf38d5bc2617c6d541"><div class="ttname"><a href="namespacemxnet_1_1common.html#abc7580a95e3de5cf38d5bc2617c6d541">mxnet::common::GetNumThreadPerGPU</a></div><div class="ttdeci">int GetNumThreadPerGPU()</div><div class="ttdef"><b>Definition:</b> utils.h:28</div></div>
-<div class="ttc" id="structmxnet_1_1Context_html_a4182c1a2f910064d058832c57489d8f5"><div class="ttname"><a href="structmxnet_1_1Context.html#a4182c1a2f910064d058832c57489d8f5">mxnet::Context::dev_mask</a></div><div class="ttdeci">int dev_mask() const</div><div class="ttdoc">Get corresponding device mask. </div><div class="ttdef"><b>Definition:</b> base.h:133</div></div>
+<div class="ttc" id="structmxnet_1_1Context_html_a4f6fd8608a4abc9673cb6da4654f84fe"><div class="ttname"><a href="structmxnet_1_1Context.html#a4f6fd8608a4abc9673cb6da4654f84fe">mxnet::Context::dev_mask</a></div><div class="ttdeci">int dev_mask() const </div><div class="ttdoc">Get corresponding device mask. </div><div class="ttdef"><b>Definition:</b> base.h:133</div></div>
<div class="ttc" id="namespacemxnet_1_1common_html_aceba8f2d6de65796966d7f1d341dcf33"><div class="ttname"><a href="namespacemxnet_1_1common.html#aceba8f2d6de65796966d7f1d341dcf33">mxnet::common::RANDOM_ENGINE</a></div><div class="ttdeci">std::mt19937 RANDOM_ENGINE</div><div class="ttdoc">Random Engine. </div><div class="ttdef"><b>Definition:</b> utils.h:44</div></div>
<div class="ttc" id="structmxnet_1_1common_1_1helper_1_1UniqueIf_html"><div class="ttname"><a href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html">mxnet::common::helper::UniqueIf</a></div><div class="ttdoc">Helper for non-array type T. </div><div class="ttdef"><b>Definition:</b> utils.h:55</div></div>
-<div class="ttc" id="namespacemxnet_1_1common_html_ad56ff4f856437bba25d38027aecfe766"><div class="ttname"><a href="namespacemxnet_1_1common.html#ad56ff4f856437bba25d38027aecfe766">mxnet::common::MakeUnique</a></div><div class="ttdeci">helper::UniqueIf< T >::SingleObject MakeUnique(Args &&... args)</div><div class="ttdoc">Constructs an object of type T and wraps it in a std::unique_ptr. </div><div class="ttdef"><b>Definition:</b> utils.h:98</div></div>
<div class="ttc" id="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4_html_a0cf05a5a2f3e1ac49ebdef497701055c"><div class="ttname"><a href="structmxnet_1_1common_1_1helper_1_1UniqueIf_3_01T[]_4.html#a0cf05a5a2f3e1ac49ebdef497701055c">mxnet::common::helper::UniqueIf< T[]>::UnknownBound</a></div><div class="ttdeci">std::unique_ptr< T[]> UnknownBound</div><div class="ttdoc">Type of T. </div><div class="ttdef"><b>Definition:</b> utils.h:70</div></div>
<div class="ttc" id="namespacemxnet_html_ac2fb286fd7ddfbc7af1456f6c7a7ee52"><div class="ttname"><a href="namespacemxnet.html#ac2fb286fd7ddfbc7af1456f6c7a7ee52">mxnet::Op</a></div><div class="ttdeci">nnvm::Op Op</div><div class="ttdoc">operator structure from NNVM </div><div class="ttdef"><b>Definition:</b> base.h:113</div></div>
<div class="ttc" id="structmxnet_1_1common_1_1helper_1_1UniqueIf_html_a98153aaeb43a71efa85b5cf753505cac"><div class="ttname"><a href="structmxnet_1_1common_1_1helper_1_1UniqueIf.html#a98153aaeb43a71efa85b5cf753505cac">mxnet::common::helper::UniqueIf::SingleObject</a></div><div class="ttdeci">std::unique_ptr< T > SingleObject</div><div class="ttdoc">Type of T. </div><div class="ttdef"><b>Definition:</b> utils.h:59</div></div>
<div class="ttc" id="namespacemxnet_1_1common_html_a56555edcc52c5b25bed90bd05e00b5fa"><div class="ttname"><a href="namespacemxnet_1_1common.html#a56555edcc52c5b25bed90bd05e00b5fa">mxnet::common::GetExecNumMatchColor</a></div><div class="ttdeci">int GetExecNumMatchColor()</div><div class="ttdef"><b>Definition:</b> utils.h:35</div></div>
+<div class="ttc" id="namespacemxnet_1_1common_html_a9d9abe40c84b9924e51d5d57b3f0b7d6"><div class="ttname"><a href="namespacemxnet_1_1common.html#a9d9abe40c84b9924e51d5d57b3f0b7d6">mxnet::common::MakeUnique</a></div><div class="ttdeci">helper::UniqueIf< T >::SingleObject MakeUnique(Args &&...args)</div><div class="ttdoc">Constructs an object of type T and wraps it in a std::unique_ptr. </div><div class="ttdef"><b>Definition:</b> utils.h:98</div></div>
<div class="ttc" id="structmxnet_1_1Context_html"><div class="ttname"><a href="structmxnet_1_1Context.html">mxnet::Context</a></div><div class="ttdoc">Context information about the execution environment. </div><div class="ttdef"><b>Definition:</b> base.h:116</div></div>
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<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
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+Generated on Mon Aug 7 2017 07:09:27 for mxnet by  <a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
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+</a> 1.8.6
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diff --git a/versions/master/genindex.html b/versions/master/genindex.html
index 2fd3064..5924b9c 100644
--- a/versions/master/genindex.html
+++ b/versions/master/genindex.html
@@ -3,15 +3,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Index — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="_static/basic.css" rel="stylesheet" type="text/css">
+<link href="_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: './',
@@ -19,7 +19,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -49,10 +49,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="#" rel="index" title="Index"/>
-<link href="search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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<div class="container">
@@ -152,7 +150,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="./architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="./community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "./" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -179,7 +177,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="./architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -189,11 +187,11 @@ Previous Navbar Layout End -->
<form action="search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -253,6 +251,7 @@ Previous Navbar Layout End -->
| <a href="#N"><strong>N</strong></a>
| <a href="#O"><strong>O</strong></a>
| <a href="#P"><strong>P</strong></a>
+ | <a href="#Q"><strong>Q</strong></a>
| <a href="#R"><strong>R</strong></a>
| <a href="#S"><strong>S</strong></a>
| <a href="#T"><strong>T</strong></a>
@@ -264,2283 +263,2892 @@ Previous Navbar Layout End -->
</div>
<h2 id="_">_</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.__call__">__call__() (mxnet.gluon.rnn.RecurrentCell method)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.__call__">(mxnet.rnn.BaseRNNCell method)</a>
-</li>
-</ul></li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__add__">__add__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__add__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/callback.html#mxnet.callback.ProgressBar.__call__">__call__() (mxnet.callback.ProgressBar method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/callback.html#mxnet.callback.Speedometer.__call__">(mxnet.callback.Speedometer method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.__call__">(mxnet.gluon.Block method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.Augmenter.__call__">(mxnet.image.Augmenter method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.DetAugmenter.__call__">(mxnet.image.DetAugmenter method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.initializer.Initializer.__call__">(mxnet.initializer.Initializer method)</a>, <a href="api/python/optimization.html#mxnet.initializer.Initializer.__call__">[1]</a>
+</dt>
+<dt><a href="api/python/optimization.html#mxnet.lr_scheduler.LRScheduler.__call__">(mxnet.lr_scheduler.LRScheduler method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Updater.__call__">(mxnet.optimizer.Updater method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Updater.__call__">[1]</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.__call__">(mxnet.rnn.BaseRNNCell method)</a>, <a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.__call__">[1]</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__call__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__deepcopy__">__deepcopy__() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__div__">__div__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__div__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__eq__">__eq__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__eq__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__ge__">__ge__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__ge__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__getitem__">__getitem__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__getitem__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__gt__">__gt__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__gt__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__iadd__">__iadd__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__idiv__">__idiv__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__imod__">__imod__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__imul__">__imul__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__isub__">__isub__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__iter__">__iter__() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__le__">__le__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__le__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__len__">__len__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__lt__">__lt__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__lt__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__mod__">__mod__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__mod__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__mul__">__mul__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__mul__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__ne__">__ne__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__ne__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__neg__">__neg__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__neg__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__pow__">__pow__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__pow__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__rdiv__">__rdiv__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__rdiv__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__repr__">__repr__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__repr__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__rmod__">__rmod__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__rmod__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__rpow__">__rpow__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__rsub__">__rsub__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__rsub__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.__setattr__">__setattr__() (mxnet.gluon.Block method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.HybridBlock.__setattr__">(mxnet.gluon.HybridBlock method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__setitem__">__setitem__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.__sub__">__sub__() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.__sub__">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+</dl></td>
</tr></table>
<h2 id="A">A</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.abs">abs() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.abs">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/metric.html#mxnet.metric.Accuracy">Accuracy (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Accuracy">[1]</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Activation">Activation (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Activation">Activation() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Activation">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.optimizer.AdaDelta">AdaDelta (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.AdaDelta">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.AdaGrad">AdaGrad (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.AdaGrad">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Adam">Adam (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Adam">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.adam_update">adam_update() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.adam_update">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.optimizer.Adamax">Adamax (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Adamax">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.add">add() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.HybridSequential.add">(mxnet.gluon.nn.HybridSequential method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Sequential.add">(mxnet.gluon.nn.Sequential method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.SequentialRNNCell.add">(mxnet.gluon.rnn.SequentialRNNCell method)</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.add">(mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.add">[1]</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.add">(mxnet.module.SequentialModule method)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.SequentialRNNCell.add">(mxnet.rnn.SequentialRNNCell method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.add_n">add_n() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.add_n">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.arange">arange() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.arange">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.arccos">arccos() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.arccos">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.arccosh">arccosh() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.arccosh">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.arcsin">arcsin() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.arcsin">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.arcsinh">arcsinh() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.arcsinh">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.arctan">arctan() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.arctan">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.arctanh">arctanh() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.arctanh">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.argmax">argmax() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.argmax">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.argmax_channel">argmax_channel() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.argmax_channel">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.argmin">argmin() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.argmin">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.argsort">argsort() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.argsort">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.array">array() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.as_in_context">as_in_context() (mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.asnumpy">asnumpy() (mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.asscalar">asscalar() (mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.astype">astype() (mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.attach_grad">attach_grad() (mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.attr">attr() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.attr_dict">attr_dict() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ImageDetIter.augmentation_transform">augmentation_transform() (mxnet.image.ImageDetIter method)</a>
-<ul>
-<li><a href="api/python/image.html#mxnet.image.ImageIter.augmentation_transform">(mxnet.image.ImageIter method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.Augmenter">Augmenter (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.AvgPool1D">AvgPool1D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.AvgPool2D">AvgPool2D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.AvgPool3D">AvgPool3D (class in mxnet.gluon.nn)</a>
-</li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.abs">abs() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.abs">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/metric.html#mxnet.metric.Accuracy">Accuracy (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Accuracy">[1]</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Activation">Activation (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Activation">Activation() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Activation">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.optimizer.AdaDelta">AdaDelta (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.AdaDelta">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.AdaGrad">AdaGrad (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.AdaGrad">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Adam">Adam (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Adam">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.adam_update">adam_update() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.adam_update">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.optimizer.Adamax">Adamax (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Adamax">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.add">add() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.HybridSequential.add">(mxnet.gluon.nn.HybridSequential method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Sequential.add">(mxnet.gluon.nn.Sequential method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.SequentialRNNCell.add">(mxnet.gluon.rnn.SequentialRNNCell method)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.add">(mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.add">[1]</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.add">(mxnet.module.SequentialModule method)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.SequentialRNNCell.add">(mxnet.rnn.SequentialRNNCell method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.add_n">add_n() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.add_n">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.AlexNet">AlexNet (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.alexnet">alexnet() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.arange">arange() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.arange">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.arccos">arccos() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.arccos">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.arccosh">arccosh() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.arccosh">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.arcsin">arcsin() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.arcsin">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.arcsinh">arcsinh() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.arcsinh">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.arctan">arctan() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.arctan">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.arctanh">arctanh() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.arctanh">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.argmax">argmax() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.argmax">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.argmax_channel">argmax_channel() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.argmax_channel">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.argmin">argmin() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.argmin">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.argsort">argsort() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.argsort">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.array">array() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.ArrayDataset">ArrayDataset (class in mxnet.gluon.data)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.as_in_context">as_in_context() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.asnumpy">asnumpy() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.asscalar">asscalar() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.astype">astype() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.attach_grad">attach_grad() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.attr">attr() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.attr_dict">attr_dict() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ImageDetIter.augmentation_transform">augmentation_transform() (mxnet.image.ImageDetIter method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter.augmentation_transform">(mxnet.image.ImageIter method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.Augmenter">Augmenter (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.AvgPool1D">AvgPool1D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.AvgPool2D">AvgPool2D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.AvgPool3D">AvgPool3D (class in mxnet.gluon.nn)</a>
+</dt>
+</dl></td>
</tr></table>
<h2 id="B">B</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.backward">backward() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.backward">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.backward">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonLossModule.backward">(mxnet.module.PythonLossModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.backward">(mxnet.module.SequentialModule method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.backward">(mxnet.ndarray.NDArray method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule">BaseModule (class in mxnet.module)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell">BaseRNNCell (class in mxnet.rnn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.batch_dot">batch_dot() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.batch_dot">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.batch_take">batch_take() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.batch_take">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.model.BatchEndParam">BatchEndParam (in module mxnet.model)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.BatchNorm">BatchNorm (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.BatchNorm">BatchNorm() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.BatchNorm">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.BatchNorm_v1">BatchNorm_v1() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.BatchNorm_v1">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.begin_state">begin_state() (mxnet.gluon.rnn.RecurrentCell method)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.begin_state">(mxnet.rnn.BaseRNNCell method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.BidirectionalCell">BidirectionalCell (class in mxnet.gluon.rnn)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.BidirectionalCell">(class in mxnet.rnn)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.initializer.Bilinear">Bilinear (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Bilinear">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.BilinearSampler">BilinearSampler() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.BilinearSampler">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.bind">bind() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.bind">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.bind">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.bind">(mxnet.module.PythonModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.bind">(mxnet.module.SequentialModule method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.bind">(mxnet.symbol.Symbol method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block">Block (class in mxnet.gluon)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.BlockGrad">BlockGrad() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.BlockGrad">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.Module.borrow_optimizer">borrow_optimizer() (mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.BrightnessJitterAug">BrightnessJitterAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_add">broadcast_add() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_add">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_axes">broadcast_axes() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_axes">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_axis">broadcast_axis() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_axis">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_div">broadcast_div() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_div">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_equal">broadcast_equal() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_equal">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_greater">broadcast_greater() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_greater">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_greater_equal">broadcast_greater_equal() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_greater_equal">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_hypot">broadcast_hypot() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_hypot">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_lesser">broadcast_lesser() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_lesser">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_lesser_equal">broadcast_lesser_equal() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_lesser_equal">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_maximum">broadcast_maximum() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_maximum">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_minimum">broadcast_minimum() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_minimum">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_minus">broadcast_minus() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_minus">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_mod">broadcast_mod() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_mod">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_mul">broadcast_mul() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_mul">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_not_equal">broadcast_not_equal() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_not_equal">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_plus">broadcast_plus() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_plus">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_power">broadcast_power() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_power">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_sub">broadcast_sub() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_sub">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_to">broadcast_to() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.broadcast_to">(in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.broadcast_to">(mxnet.ndarray.NDArray method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule">BucketingModule (class in mxnet.module)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.BucketSentenceIter">BucketSentenceIter (class in mxnet.rnn)</a>
-</li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.backward">backward() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.backward">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.backward">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonLossModule.backward">(mxnet.module.PythonLossModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.backward">(mxnet.module.SequentialModule method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.backward">(mxnet.ndarray.NDArray method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule">BaseModule (class in mxnet.module)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell">BaseRNNCell (class in mxnet.rnn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.BasicBlockV1">BasicBlockV1 (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.BasicBlockV2">BasicBlockV2 (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.batch_dot">batch_dot() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.batch_dot">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.batch_take">batch_take() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.batch_take">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.model.BatchEndParam">BatchEndParam (in module mxnet.model)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.BatchNorm">BatchNorm (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.BatchNorm">BatchNorm() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.BatchNorm">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.BatchNorm_v1">BatchNorm_v1() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.BatchNorm_v1">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.BatchSampler">BatchSampler (class in mxnet.gluon.data)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.begin_state">begin_state() (mxnet.gluon.rnn.RecurrentCell method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.begin_state">(mxnet.rnn.BaseRNNCell method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.BidirectionalCell">BidirectionalCell (class in mxnet.gluon.rnn)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BidirectionalCell">(class in mxnet.rnn)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.initializer.Bilinear">Bilinear (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Bilinear">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.BilinearSampler">BilinearSampler() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.BilinearSampler">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.bind">bind() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.bind">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.bind">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.bind">(mxnet.module.PythonModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.bind">(mxnet.module.SequentialModule method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.bind">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block">Block (class in mxnet.gluon)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.BlockGrad">BlockGrad() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.BlockGrad">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.Module.borrow_optimizer">borrow_optimizer() (mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.BottleneckV1">BottleneckV1 (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.BottleneckV2">BottleneckV2 (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.BrightnessJitterAug">BrightnessJitterAug (class in mxnet.image)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_add">broadcast_add() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_add">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_axes">broadcast_axes() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_axes">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_axis">broadcast_axis() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_axis">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_div">broadcast_div() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_div">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_equal">broadcast_equal() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_equal">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_greater">broadcast_greater() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_greater">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_greater_equal">broadcast_greater_equal() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_greater_equal">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_hypot">broadcast_hypot() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_hypot">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_lesser">broadcast_lesser() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_lesser">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_lesser_equal">broadcast_lesser_equal() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_lesser_equal">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_maximum">broadcast_maximum() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_maximum">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_minimum">broadcast_minimum() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_minimum">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_minus">broadcast_minus() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_minus">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_mod">broadcast_mod() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_mod">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_mul">broadcast_mul() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_mul">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_not_equal">broadcast_not_equal() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_not_equal">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_plus">broadcast_plus() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_plus">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_power">broadcast_power() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_power">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_sub">broadcast_sub() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_sub">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.broadcast_to">broadcast_to() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.broadcast_to">(in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.broadcast_to">(mxnet.ndarray.NDArray method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule">BucketingModule (class in mxnet.module)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BucketSentenceIter">BucketSentenceIter (class in mxnet.rnn)</a>
+</dt>
+</dl></td>
</tr></table>
<h2 id="C">C</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/metric.html#mxnet.metric.Caffe">Caffe (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Caffe">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Cast">Cast() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.cast">cast() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Cast">Cast() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.cast">cast() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.CastAug">CastAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.ccSGD">ccSGD (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.ccSGD">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.ceil">ceil() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.ceil">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.center_crop">center_crop() (mxnet.image method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.CenterCropAug">CenterCropAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ImageIter.check_data_shape">check_data_shape() (mxnet.image.ImageIter method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ImageDetIter.check_label_shape">check_label_shape() (mxnet.image.ImageDetIter method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ImageIter.check_valid_image">check_valid_image() (mxnet.image.ImageIter method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.choose_element_0index">choose_element_0index() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.choose_element_0index">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.clip">clip() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.clip">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.utils.clip_global_norm">clip_global_norm() (mxnet.gluon.utils method)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.close">close() (mxnet.recordio.MXIndexedRecordIO method)</a>
-<ul>
-<li><a href="api/python/io.html#mxnet.recordio.MXRecordIO.close">(mxnet.recordio.MXRecordIO method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.collect_params">collect_params() (mxnet.gluon.Block method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.color_normalize">color_normalize() (mxnet.image method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ColorJitterAug">ColorJitterAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ColorNormalizeAug">ColorNormalizeAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric">CompositeEvalMetric (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Concat">Concat() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.concat">concat() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Concat">Concat() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.concat">concat() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.concatenate">concatenate() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.initializer.Constant">Constant (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Constant">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.context">context (mxnet.ndarray.NDArray attribute)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ContrastJitterAug">ContrastJitterAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Conv1D">Conv1D (class in mxnet.gluon.nn)</a>
-</li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Conv1DTranspose">Conv1DTranspose (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Conv2D">Conv2D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Conv2DTranspose">Conv2DTranspose (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Conv3D">Conv3D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Conv3DTranspose">Conv3DTranspose (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Convolution">Convolution() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Convolution">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Convolution_v1">Convolution_v1() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Convolution_v1">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.copy">copy() (mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.copyto">copyto() (mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Correlation">Correlation() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Correlation">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.cos">cos() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.cos">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.cosh">cosh() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.cosh">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.create">create() (in module mxnet.kvstore)</a>
-<ul>
-<li><a href="api/python/metric.html#mxnet.metric.create">(in module mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.create">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.create">(in module mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.create">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.model.FeedForward.create">(mxnet.model.FeedForward static method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.optimizer.Optimizer.create_optimizer">create_optimizer() (mxnet.optimizer.Optimizer static method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.create_optimizer">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Optimizer.create_state">create_state() (mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.create_state">[1]</a>
-<ul>
-<li><a href="api/python/model.html#mxnet.optimizer.Test.create_state">(mxnet.optimizer.Test method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Test.create_state">[1]</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.CreateAugmenter">CreateAugmenter() (mxnet.image method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.CreateDetAugmenter">CreateDetAugmenter() (mxnet.image method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Crop">Crop() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.crop">crop() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Crop">Crop() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.crop">crop() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.CrossEntropy">CrossEntropy (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.CrossEntropy">[1]</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.CSVIter">CSVIter() (in module mxnet.io)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Custom">Custom() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Custom">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/metric.html#mxnet.metric.CustomMetric">CustomMetric (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.CustomMetric">[1]</a>
-</li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/metric.html#mxnet.metric.Caffe">Caffe (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Caffe">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Cast">Cast() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.cast">cast() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Cast">Cast() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.cast">cast() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.CastAug">CastAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.ccSGD">ccSGD (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.ccSGD">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.ceil">ceil() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.ceil">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.center_crop">center_crop() (mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.CenterCropAug">CenterCropAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter.check_data_shape">check_data_shape() (mxnet.image.ImageIter method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ImageDetIter.check_label_shape">check_label_shape() (mxnet.image.ImageDetIter method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter.check_valid_image">check_valid_image() (mxnet.image.ImageIter method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.choose_element_0index">choose_element_0index() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.choose_element_0index">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.vision.CIFAR10">CIFAR10 (class in mxnet.gluon.data.vision)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.clip">clip() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.clip">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.utils.clip_global_norm">clip_global_norm() (mxnet.gluon.utils method)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.close">close() (mxnet.recordio.MXIndexedRecordIO method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/io.html#mxnet.recordio.MXRecordIO.close">(mxnet.recordio.MXRecordIO method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.collect_params">collect_params() (mxnet.gluon.Block method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.color_normalize">color_normalize() (mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ColorJitterAug">ColorJitterAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ColorNormalizeAug">ColorNormalizeAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric">CompositeEvalMetric (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Concat">Concat() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.concat">concat() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Concat">Concat() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.concat">concat() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.concatenate">concatenate() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.initializer.Constant">Constant (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Constant">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.context">context (mxnet.ndarray.NDArray attribute)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/image.html#mxnet.image.ContrastJitterAug">ContrastJitterAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Conv1D">Conv1D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Conv1DTranspose">Conv1DTranspose (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Conv2D">Conv2D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Conv2DTranspose">Conv2DTranspose (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Conv3D">Conv3D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Conv3DTranspose">Conv3DTranspose (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Convolution">Convolution() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Convolution">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Convolution_v1">Convolution_v1() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Convolution_v1">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.copy">copy() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.copyto">copyto() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Correlation">Correlation() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Correlation">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.cos">cos() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.cos">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.cosh">cosh() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.cosh">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.count_sketch">count_sketch() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.count_sketch">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.create">create() (in module mxnet.kvstore)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/metric.html#mxnet.metric.create">(in module mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.create">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.create">(in module mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.create">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.model.FeedForward.create">(mxnet.model.FeedForward static method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.optimizer.Optimizer.create_optimizer">create_optimizer() (mxnet.optimizer.Optimizer static method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.create_optimizer">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Optimizer.create_state">create_state() (mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.create_state">[1]</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/model.html#mxnet.optimizer.Test.create_state">(mxnet.optimizer.Test method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Test.create_state">[1]</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.CreateAugmenter">CreateAugmenter() (mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.CreateDetAugmenter">CreateDetAugmenter() (mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Crop">Crop() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.crop">crop() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Crop">Crop() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.crop">crop() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.CrossEntropy">CrossEntropy (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.CrossEntropy">[1]</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.CSVIter">CSVIter() (in module mxnet.io)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.ctc_loss">ctc_loss() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.ctc_loss">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.CTCLoss">CTCLoss() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.CTCLoss">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Custom">Custom() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Custom">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/metric.html#mxnet.metric.CustomMetric">CustomMetric (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.CustomMetric">[1]</a>
+</dt>
+</dl></td>
</tr></table>
<h2 id="D">D</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter.data">data() (mxnet.gluon.Parameter method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.data_names">data_names (mxnet.module.BaseModule attribute)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.data_names">(mxnet.module.BucketingModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.data_names">(mxnet.module.Module attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.data_names">(mxnet.module.PythonModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.data_names">(mxnet.module.SequentialModule attribute)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.data_shapes">data_shapes (mxnet.module.BaseModule attribute)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.data_shapes">(mxnet.module.BucketingModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.data_shapes">(mxnet.module.Module attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.data_shapes">(mxnet.module.PythonModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.data_shapes">(mxnet.module.SequentialModule attribute)</a>
-</li>
-</ul></li>
-<li><a href="api/python/io.html#mxnet.io.DataBatch">DataBatch (class in mxnet.io)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataDesc">DataDesc (class in mxnet.io)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataIter">DataIter (class in mxnet.io)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.DCASGD">DCASGD (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.DCASGD">[1]</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.debug_str">debug_str() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Deconvolution">Deconvolution() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Deconvolution">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.degrees">degrees() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.degrees">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Dense">Dense (class in mxnet.gluon.nn)</a>
-</li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.detach">detach() (mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.DetAugmenter">DetAugmenter (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.DetBorrowAug">DetBorrowAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.DetHorizontalFlipAug">DetHorizontalFlipAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.DetRandomCropAug">DetRandomCropAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.DetRandomPadAug">DetRandomPadAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.DetRandomSelectAug">DetRandomSelectAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.divide">divide() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/callback.html#mxnet.callback.do_checkpoint">do_checkpoint() (in module mxnet.callback)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.do_rnn_checkpoint">do_rnn_checkpoint() (mxnet.rnn method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.dot">dot() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.dot">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.ImageDetIter.draw_next">draw_next() (mxnet.image.ImageDetIter method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Dropout">Dropout (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Dropout">Dropout() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Dropout">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.DropoutCell">DropoutCell (class in mxnet.gluon.rnn)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.DropoutCell">(class in mxnet.rnn)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.dtype">dtype (mxnet.ndarray.NDArray attribute)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.Augmenter.dumps">dumps() (mxnet.image.Augmenter method)</a>
-<ul>
-<li><a href="api/python/image.html#mxnet.image.DetAugmenter.dumps">(mxnet.image.DetAugmenter method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.initializer.Initializer.dumps">(mxnet.initializer.Initializer method)</a>, <a href="api/python/optimization.html#mxnet.initializer.Initializer.dumps">[1]</a>
-</li>
-</ul></li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.data">data() (mxnet.gluon.Parameter method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.data_names">data_names (mxnet.module.BaseModule attribute)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.data_names">(mxnet.module.BucketingModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.data_names">(mxnet.module.Module attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.data_names">(mxnet.module.PythonModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.data_names">(mxnet.module.SequentialModule attribute)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.data_shapes">data_shapes (mxnet.module.BaseModule attribute)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.data_shapes">(mxnet.module.BucketingModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.data_shapes">(mxnet.module.Module attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.data_shapes">(mxnet.module.PythonModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.data_shapes">(mxnet.module.SequentialModule attribute)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/io.html#mxnet.io.DataBatch">DataBatch (class in mxnet.io)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.DataDesc">DataDesc (class in mxnet.io)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.DataIter">DataIter (class in mxnet.io)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.DataLoader">DataLoader (class in mxnet.gluon.data)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.Dataset">Dataset (class in mxnet.gluon.data)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.DCASGD">DCASGD (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.DCASGD">[1]</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.debug_str">debug_str() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Deconvolution">Deconvolution() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Deconvolution">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.DeformableConvolution">DeformableConvolution() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.DeformableConvolution">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.DeformablePSROIPooling">DeformablePSROIPooling() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.DeformablePSROIPooling">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.degrees">degrees() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.degrees">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Dense">Dense (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.DenseNet">DenseNet (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.densenet121">densenet121() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.densenet161">densenet161() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.densenet169">densenet169() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.densenet201">densenet201() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.dequantize">dequantize() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.dequantize">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.detach">detach() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.DetAugmenter">DetAugmenter (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.DetBorrowAug">DetBorrowAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.DetHorizontalFlipAug">DetHorizontalFlipAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.DetRandomCropAug">DetRandomCropAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.DetRandomPadAug">DetRandomPadAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.DetRandomSelectAug">DetRandomSelectAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.divide">divide() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/callback.html#mxnet.callback.do_checkpoint">do_checkpoint() (in module mxnet.callback)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.do_rnn_checkpoint">do_rnn_checkpoint() (mxnet.rnn method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.dot">dot() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.dot">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.ImageDetIter.draw_next">draw_next() (mxnet.image.ImageDetIter method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Dropout">Dropout (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Dropout">Dropout() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Dropout">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.DropoutCell">DropoutCell (class in mxnet.gluon.rnn)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.DropoutCell">(class in mxnet.rnn)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.dtype">dtype (mxnet.ndarray.NDArray attribute)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.Augmenter.dumps">dumps() (mxnet.image.Augmenter method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/image.html#mxnet.image.DetAugmenter.dumps">(mxnet.image.DetAugmenter method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.initializer.Initializer.dumps">(mxnet.initializer.Initializer method)</a>, <a href="api/python/optimization.html#mxnet.initializer.Initializer.dumps">[1]</a>
+</dt>
+</dl></dd>
+</dl></td>
</tr></table>
<h2 id="E">E</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.ElementWiseSum">ElementWiseSum() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.ElementWiseSum">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.elemwise_add">elemwise_add() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.elemwise_add">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Embedding">Embedding (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Embedding">Embedding() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Embedding">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.empty">empty() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.encode_sentences">encode_sentences() (mxnet.rnn method)</a>
-</li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.equal">equal() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.eval">eval() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.EvalMetric">EvalMetric (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.exp">exp() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.exp">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.expand_dims">expand_dims() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.expand_dims">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.expm1">expm1() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.expm1">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-</tr></table>
-<h2 id="F">F</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/metric.html#mxnet.metric.F1">F1 (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.F1">[1]</a>
-</li>
-<li><a href="api/python/optimization.html#mxnet.lr_scheduler.FactorScheduler">FactorScheduler (class in mxnet.lr_scheduler)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.model.FeedForward">FeedForward (class in mxnet.model)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.fill_element_0index">fill_element_0index() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.fill_element_0index">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.model.FeedForward.fit">fit() (mxnet.model.FeedForward method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.fit">(mxnet.module.BaseModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.fix">fix() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.fix">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.fixed_crop">fixed_crop() (mxnet.image method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Flatten">Flatten() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.flatten">flatten() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Flatten">Flatten() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.flatten">flatten() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.flip">flip() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.flip">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.floor">floor() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.floor">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/image.html#mxnet.image.ForceResizeAug">ForceResizeAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.forward">forward() (mxnet.gluon.Block method)</a>, <a href="api/python/gluon.html#mxnet.gluon.Block.forward">[1]</a>
-<ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.HybridBlock.forward">(mxnet.gluon.HybridBlock method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.forward">(mxnet.gluon.rnn.RecurrentCell method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.forward">(mxnet.module.BaseModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.forward">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.forward">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonLossModule.forward">(mxnet.module.PythonLossModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.forward">(mxnet.module.SequentialModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.forward_backward">forward_backward() (mxnet.module.BaseModule method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Ftrl">Ftrl (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Ftrl">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.full">full() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.FullyConnected">FullyConnected() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.FullyConnected">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.initializer.FusedRNN">FusedRNN (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.FusedRNN">[1]</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.FusedRNNCell">FusedRNNCell (class in mxnet.rnn)</a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="G">G</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.gamma">gamma() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.gamma">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.gammaln">gammaln() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.gammaln">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.get">get() (mxnet.gluon.ParameterDict method)</a>
-<ul>
-<li><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.get">(mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.get">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.EvalMetric.get">(mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.get">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.Perplexity.get">(mxnet.metric.Perplexity method)</a>, <a href="api/python/model.html#mxnet.metric.Perplexity.get">[1]</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.RNNParams.get">(mxnet.rnn.RNNParams method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/io.html#mxnet.io.DataDesc.get_batch_axis">get_batch_axis() (mxnet.io.DataDesc static method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.get_children">get_children() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.EvalMetric.get_config">get_config() (mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.get_config">[1]</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.get_input_grads">get_input_grads() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.get_input_grads">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.get_input_grads">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonLossModule.get_input_grads">(mxnet.module.PythonLossModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.get_input_grads">(mxnet.module.SequentialModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.get_internals">get_internals() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataDesc.get_list">get_list() (mxnet.io.DataDesc static method)</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.get_metric">get_metric() (mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.get_metric">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.EvalMetric.get_name_value">get_name_value() (mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.get_name_value">[1]</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.get_outputs">get_outputs() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.get_outputs">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.get_outputs">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonLossModule.get_outputs">(mxnet.module.PythonLossModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.get_outputs">(mxnet.module.SequentialModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.get_params">get_params() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.get_params">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.get_params">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.get_params">(mxnet.module.PythonModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.get_params">(mxnet.module.SequentialModule method)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.get_states">get_states() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.get_states">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.get_states">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Updater.get_states">(mxnet.optimizer.Updater method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Updater.get_states">[1]</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.optimizer.get_updater">get_updater() (in module mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.get_updater">[1]</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataIter.getdata">getdata() (mxnet.io.DataIter method)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataIter.getindex">getindex() (mxnet.io.DataIter method)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataIter.getlabel">getlabel() (mxnet.io.DataIter method)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataIter.getpad">getpad() (mxnet.io.DataIter method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalAvgPool1D">GlobalAvgPool1D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalAvgPool2D">GlobalAvgPool2D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalAvgPool3D">GlobalAvgPool3D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalMaxPool1D">GlobalMaxPool1D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalMaxPool2D">GlobalMaxPool2D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalMaxPool3D">GlobalMaxPool3D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.grad">grad (mxnet.ndarray.NDArray attribute)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter.grad">grad() (mxnet.gluon.Parameter method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.gradient">gradient() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.greater">greater() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.greater_equal">greater_equal() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.GridGenerator">GridGenerator() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.GridGenerator">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Group">Group() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.GRU">GRU (class in mxnet.gluon.rnn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.GRUCell">GRUCell (class in mxnet.gluon.rnn)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.GRUCell">(class in mxnet.rnn)</a>
-</li>
-</ul></li>
-</ul></td>
-</tr></table>
-<h2 id="H">H</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/io.html#mxnet.io.NDArrayIter.hard_reset">hard_reset() (mxnet.io.NDArrayIter method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.HorizontalFlipAug">HorizontalFlipAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.HueJitterAug">HueJitterAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward">hybrid_forward() (mxnet.gluon.HybridBlock method)</a>, <a href="api/python/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward">[1]</a>
-</li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.HybridBlock">HybridBlock (class in mxnet.gluon)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.hybridize">hybridize() (mxnet.gluon.Block method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.HybridSequential">HybridSequential (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.hypot">hypot() (in module mxnet.symbol)</a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="I">I</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.identity">identity() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.identity">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.IdentityAttachKLSparseReg">IdentityAttachKLSparseReg() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.IdentityAttachKLSparseReg">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.ImageDetIter">ImageDetIter (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.ImageDetRecordIter">ImageDetRecordIter() (in module mxnet.io)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ImageIter">ImageIter (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.ImageRecordIter">ImageRecordIter() (in module mxnet.io)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.ImageRecordIter_v1">ImageRecordIter_v1() (in module mxnet.io)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.ImageRecordUInt8Iter">ImageRecordUInt8Iter() (in module mxnet.io)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.ImageRecordUInt8Iter_v1">ImageRecordUInt8Iter_v1() (in module mxnet.io)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.imdecode">imdecode() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/image.html#mxnet.image.imdecode">(mxnet.image method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ImageIter.imdecode">(mxnet.image.ImageIter method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.HybridBlock.infer_shape">infer_shape() (mxnet.gluon.HybridBlock method)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.infer_shape">(mxnet.symbol.Symbol method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.infer_shape_partial">infer_shape_partial() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.infer_type">infer_type() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.init">init() (mxnet.kvstore.KVStore method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.init_optimizer">init_optimizer() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.init_optimizer">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.init_optimizer">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.init_optimizer">(mxnet.module.PythonModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.init_optimizer">(mxnet.module.SequentialModule method)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.init_params">init_params() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.init_params">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.init_params">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.init_params">(mxnet.module.PythonModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.init_params">(mxnet.module.SequentialModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.initializer.InitDesc">InitDesc (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.InitDesc">[1]</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.initialize">initialize() (mxnet.gluon.Block method)</a>
-<ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter.initialize">(mxnet.gluon.Parameter method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.initialize">(mxnet.gluon.ParameterDict method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.initializer.Initializer">Initializer (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Initializer">[1]</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.install_monitor">install_monitor() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.install_monitor">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.install_monitor">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonLossModule.install_monitor">(mxnet.module.PythonLossModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.install_monitor">(mxnet.module.SequentialModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.InstanceNorm">InstanceNorm() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.InstanceNorm">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/io.html#mxnet.recordio.IRHeader">IRHeader (in module mxnet.recordio)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataIter.iter_next">iter_next() (mxnet.io.DataIter method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.iter_predict">iter_predict() (mxnet.module.BaseModule method)</a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="K">K</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.loss.KLDivLoss">KLDivLoss (class in mxnet.gluon.loss)</a>
-</li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore">KVStore (class in mxnet.kvstore)</a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="L">L</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.loss.L1Loss">L1Loss (class in mxnet.gluon.loss)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.loss.L2Loss">L2Loss (class in mxnet.gluon.loss)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.L2Normalization">L2Normalization() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.L2Normalization">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.Module.label_names">label_names (mxnet.module.Module attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.label_shapes">label_shapes (mxnet.module.BaseModule attribute)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.label_shapes">(mxnet.module.BucketingModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.label_shapes">(mxnet.module.Module attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.label_shapes">(mxnet.module.PythonModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.label_shapes">(mxnet.module.SequentialModule attribute)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.LeakyReLU">LeakyReLU (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.LeakyReLU">LeakyReLU() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.LeakyReLU">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.lesser">lesser() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.lesser_equal">lesser_equal() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.LightingAug">LightingAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.linalg_gemm">linalg_gemm() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.linalg_gemm">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.linalg_gemm2">linalg_gemm2() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.linalg_gemm2">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.linalg_potrf">linalg_potrf() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.linalg_potrf">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.linalg_potri">linalg_potri() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.linalg_potri">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.linalg_sumlogdiag">linalg_sumlogdiag() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.linalg_sumlogdiag">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.linalg_trmm">linalg_trmm() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.linalg_trmm">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.linalg_trsm">linalg_trsm() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.linalg_trsm">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.LinearRegressionOutput">LinearRegressionOutput() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.LinearRegressionOutput">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_arguments">list_arguments() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_attr">list_attr() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_auxiliary_states">list_auxiliary_states() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter.list_ctx">list_ctx() (mxnet.gluon.Parameter method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter.list_data">list_data() (mxnet.gluon.Parameter method)</a>
-</li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter.list_grad">list_grad() (mxnet.gluon.Parameter method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_inputs">list_inputs() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_outputs">list_outputs() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.initializer.Load">Load (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Load">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.load">load() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.load">(in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.load">(mxnet.gluon.ParameterDict method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.model.FeedForward.load">(mxnet.model.FeedForward static method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.load">(mxnet.module.Module static method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.model.load_checkpoint">load_checkpoint() (in module mxnet.model)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.load_json">load_json() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.load_optimizer_states">load_optimizer_states() (mxnet.kvstore.KVStore method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.Module.load_optimizer_states">(mxnet.module.Module method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.load_params">load_params() (mxnet.gluon.Block method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.load_params">(mxnet.module.BaseModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/rnn.html#mxnet.rnn.load_rnn_checkpoint">load_rnn_checkpoint() (mxnet.rnn method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.log">log() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.log">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.log10">log10() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.log10">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.log1p">log1p() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.log1p">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.log2">log2() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.log2">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.log_softmax">log_softmax() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.log_softmax">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/callback.html#mxnet.callback.log_train_metric">log_train_metric() (in module mxnet.callback)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.LogisticRegressionOutput">LogisticRegressionOutput() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.LogisticRegressionOutput">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/callback.html#mxnet.callback.LogValidationMetricsCallback">LogValidationMetricsCallback (class in mxnet.callback)</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.Loss">Loss (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Loss">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.LRN">LRN() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.LRN">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/optimization.html#mxnet.lr_scheduler.LRScheduler">LRScheduler (class in mxnet.lr_scheduler)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.LSTM">LSTM (class in mxnet.gluon.rnn)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.initializer.LSTMBias">LSTMBias (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.LSTMBias">[1]</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.LSTMCell">LSTMCell (class in mxnet.gluon.rnn)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.LSTMCell">(class in mxnet.rnn)</a>
-</li>
-</ul></li>
-</ul></td>
-</tr></table>
-<h2 id="M">M</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/metric.html#mxnet.metric.MAE">MAE (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.MAE">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.MAERegressionOutput">MAERegressionOutput() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.MAERegressionOutput">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.make_loss">make_loss() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.make_loss">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.MakeLoss">MakeLoss() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.MakeLoss">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.max">max() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.max">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.max_axis">max_axis() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.max_axis">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.maximum">maximum() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.maximum">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.MaxPool1D">MaxPool1D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.MaxPool2D">MaxPool2D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.MaxPool3D">MaxPool3D (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.mean">mean() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.mean">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.min">min() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.min">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.min_axis">min_axis() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.min_axis">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.minimum">minimum() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.minimum">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.initializer.Mixed">Mixed (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Mixed">[1]</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.MNISTIter">MNISTIter() (in module mxnet.io)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module">Module (class in mxnet.module)</a>
-</li>
-<li><a href="api/python/callback.html#mxnet.callback.module_checkpoint">module_checkpoint() (in module mxnet.callback)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.modulo">modulo() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.moveaxis">moveaxis() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.mp_sgd_mom_update">mp_sgd_mom_update() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.mp_sgd_mom_update">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.mp_sgd_update">mp_sgd_update() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.mp_sgd_update">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/metric.html#mxnet.metric.MSE">MSE (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.MSE">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.initializer.MSRAPrelu">MSRAPrelu (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.MSRAPrelu">[1]</a>
-</li>
-<li><a href="api/python/optimization.html#mxnet.lr_scheduler.MultiFactorScheduler">MultiFactorScheduler (class in mxnet.lr_scheduler)</a>
-</li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.multiply">multiply() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.MXDataIter">MXDataIter (class in mxnet.io)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO">MXIndexedRecordIO (class in mxnet.recordio)</a>
-</li>
-<li><a href="api/python/callback.html#module-mxnet.callback">mxnet.callback (module)</a>
-</li>
-<li><a href="api/python/image.html#module-mxnet.image">mxnet.image (module)</a>
-</li>
-<li><a href="api/python/model.html#module-mxnet.initializer">mxnet.initializer (module)</a>, <a href="api/python/optimization.html#module-mxnet.initializer">[1]</a>
-</li>
-<li><a href="api/python/io.html#module-mxnet.io">mxnet.io (module)</a>
-</li>
-<li><a href="api/python/kvstore.html#module-mxnet.kvstore">mxnet.kvstore (module)</a>
-</li>
-<li><a href="api/python/optimization.html#module-mxnet.lr_scheduler">mxnet.lr_scheduler (module)</a>
-</li>
-<li><a href="api/python/metric.html#module-mxnet.metric">mxnet.metric (module)</a>, <a href="api/python/model.html#module-mxnet.metric">[1]</a>
-</li>
-<li><a href="api/python/model.html#module-mxnet.model">mxnet.model (module)</a>
-</li>
-<li><a href="api/python/ndarray.html#module-mxnet.ndarray">mxnet.ndarray (module)</a>
-</li>
-<li><a href="api/python/model.html#module-mxnet.optimizer">mxnet.optimizer (module)</a>, <a href="api/python/optimization.html#module-mxnet.optimizer">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#module-mxnet.random">mxnet.random (module)</a>
-</li>
-<li><a href="api/python/io.html#module-mxnet.recordio">mxnet.recordio (module)</a>
-</li>
-<li><a href="api/python/symbol.html#module-mxnet.symbol">mxnet.symbol (module)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6EngineE">mxnet::Engine (C++ class)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine13_GetSharedRefEv">mxnet::Engine::_GetSharedRef (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine7AsyncFnE">mxnet::Engine::AsyncFn (C++ type)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine18CallbackOnCompleteE">mxnet::Engine::CallbackOnComplete (C++ type)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine14CreateCallbackEPFvP6EnginePvEPv">mxnet::Engine::CreateCallback (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine14DeleteOperatorE9OprHandle">mxnet::Engine::DeleteOperator (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine14DeleteVariableE6SyncFn7Context9VarHandle">mxnet::Engine::DeleteVariable (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine3GetEv">mxnet::Engine::Get (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine11NewOperatorE7AsyncFnRNSt6vectorI9VarHandleEERNSt6vectorI9VarHandleEE10FnPropertyPKc">mxnet::Engine::NewOperator (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine11NewVariableEv">mxnet::Engine::NewVariable (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine14NotifyShutdownEv">mxnet::Engine::NotifyShutdown (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine9OprHandleE">mxnet::Engine::OprHandle (C++ type)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine4PushE9OprHandle7Contextib">mxnet::Engine::Push (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine9PushAsyncE7AsyncFn7ContextRNSt6vectorI9VarHandleEERNSt6vectorI9VarHandleEE10FnPropertyiPKc">mxnet::Engine::PushAsync (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine8PushSyncE6SyncFn7ContextRNSt6vectorI9VarHandleEERNSt6vectorI9VarHandleEE10FnPropertyiPKc">mxnet::Engine::PushSync (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine6SyncFnE">mxnet::Engine::SyncFn (C++ type)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine9VarHandleE">mxnet::Engine::VarHandle (C++ type)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine10WaitForAllEv">mxnet::Engine::WaitForAll (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6Engine10WaitForVarE9VarHandle">mxnet::Engine::WaitForVar (C++ function)</a>
-</li>
-<li><a href="architecture/overview.html#_CPPv2N5mxnet6EngineD0Ev">mxnet::Engine::~Engine (C++ function)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.MXRecordIO">MXRecordIO (class in mxnet.recordio)</a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="N">N</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/model.html#mxnet.optimizer.Nadam">Nadam (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Nadam">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.NAG">NAG (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.NAG">[1]</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.name">name (mxnet.gluon.Block attribute)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.name">(mxnet.symbol.Symbol attribute)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.name_scope">name_scope() (mxnet.gluon.Block method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.nanprod">nanprod() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.nanprod">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.nansum">nansum() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.nansum">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray">NDArray (class in mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.NDArrayIter">NDArrayIter (class in mxnet.io)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.ndim">ndim (mxnet.ndarray.NDArray attribute)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.negative">negative() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.negative">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/image.html#mxnet.image.ImageDetIter.next">next() (mxnet.image.ImageDetIter method)</a>
-<ul>
-<li><a href="api/python/image.html#mxnet.image.ImageIter.next">(mxnet.image.ImageIter method)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataIter.next">(mxnet.io.DataIter method)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.BucketSentenceIter.next">(mxnet.rnn.BucketSentenceIter method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.ImageIter.next_sample">next_sample() (mxnet.image.ImageIter method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.norm">norm() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.norm">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.initializer.Normal">Normal (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Normal">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.normal">normal() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.normal">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.not_equal">not_equal() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.np">np() (in module mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.np">[1]</a>
-</li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.num_workers">num_workers (mxnet.kvstore.KVStore attribute)</a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="O">O</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/model.html#mxnet.initializer.One">One (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.One">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.one_hot">one_hot() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.one_hot">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.onehot_encode">onehot_encode() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.ones">ones() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.ones">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.ones_like">ones_like() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.ones_like">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/io.html#mxnet.recordio.MXRecordIO.open">open() (mxnet.recordio.MXRecordIO method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Optimizer">Optimizer (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer">[1]</a>
-</li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/model.html#mxnet.initializer.Orthogonal">Orthogonal (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Orthogonal">[1]</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.output_names">output_names (mxnet.module.BaseModule attribute)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.output_names">(mxnet.module.BucketingModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.output_names">(mxnet.module.Module attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.output_names">(mxnet.module.PythonModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.output_names">(mxnet.module.SequentialModule attribute)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.output_shapes">output_shapes (mxnet.module.BaseModule attribute)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.output_shapes">(mxnet.module.BucketingModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.output_shapes">(mxnet.module.Module attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.output_shapes">(mxnet.module.PythonModule attribute)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.output_shapes">(mxnet.module.SequentialModule attribute)</a>
-</li>
-</ul></li>
-</ul></td>
-</tr></table>
-<h2 id="P">P</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/io.html#mxnet.recordio.pack">pack() (in module mxnet.recordio)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.pack_img">pack_img() (in module mxnet.recordio)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.pack_weights">pack_weights() (mxnet.rnn.BaseRNNCell method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Pad">Pad() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.pad">pad() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Pad">Pad() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.pad">pad() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter">Parameter (class in mxnet.gluon)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.ParameterDict">ParameterDict (class in mxnet.gluon)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.params">params (mxnet.gluon.Block attribute)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.params">(mxnet.rnn.BaseRNNCell attribute)</a>
-</li>
-</ul></li>
-<li><a href="api/python/metric.html#mxnet.metric.PearsonCorrelation">PearsonCorrelation (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.PearsonCorrelation">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.Perplexity">Perplexity (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Perplexity">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.pick">pick() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.pick">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Pooling">Pooling() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Pooling">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Pooling_v1">Pooling_v1() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Pooling_v1">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/image.html#mxnet.image.ImageIter.postprocess_data">postprocess_data() (mxnet.image.ImageIter method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.pow">pow() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.power">power() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.model.FeedForward.predict">predict() (mxnet.model.FeedForward method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.predict">(mxnet.module.BaseModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/io.html#mxnet.io.PrefetchingIter">PrefetchingIter (class in mxnet.io)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.prefix">prefix (mxnet.gluon.Block attribute)</a>
-<ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.prefix">(mxnet.gluon.ParameterDict attribute)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.prepare">prepare() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.prepare">(mxnet.module.BucketingModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.prod">prod() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.prod">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/callback.html#mxnet.callback.ProgressBar">ProgressBar (class in mxnet.callback)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.NDArrayIter.provide_data">provide_data (mxnet.io.NDArrayIter attribute)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.NDArrayIter.provide_label">provide_label (mxnet.io.NDArrayIter attribute)</a>
-</li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.pull">pull() (mxnet.kvstore.KVStore method)</a>
-</li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.push">push() (mxnet.kvstore.KVStore method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonLossModule">PythonLossModule (class in mxnet.module)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule">PythonModule (class in mxnet.module)</a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="R">R</h2>
-<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.radians">radians() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.radians">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.random_crop">random_crop() (mxnet.image method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.random_exponential">random_exponential() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.random_exponential">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.random_gamma">random_gamma() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.random_gamma">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.random_generalized_negative_binomial">random_generalized_negative_binomial() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.random_generalized_negative_binomial">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.random_negative_binomial">random_negative_binomial() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.random_negative_binomial">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.random_normal">random_normal() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.random_normal">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.random_poisson">random_poisson() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.random_poisson">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.random_size_crop">random_size_crop() (mxnet.image method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.random_uniform">random_uniform() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.random_uniform">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.RandomCropAug">RandomCropAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.RandomGrayAug">RandomGrayAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.RandomOrderAug">RandomOrderAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.RandomSizedCropAug">RandomSizedCropAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.rank">rank (mxnet.kvstore.KVStore attribute)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.MXRecordIO.read">read() (mxnet.recordio.MXRecordIO method)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.read_idx">read_idx() (mxnet.recordio.MXIndexedRecordIO method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ImageIter.read_image">read_image() (mxnet.image.ImageIter method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.reciprocal">reciprocal() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.reciprocal">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell">RecurrentCell (class in mxnet.gluon.rnn)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.initializer.register">register() (in module mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.register">[1]</a>
-<ul>
-<li><a href="api/python/model.html#mxnet.optimizer.register">(in module mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.register">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Optimizer.register">(mxnet.optimizer.Optimizer static method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.register">[1]</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.register_child">register_child() (mxnet.gluon.Block method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.relu">relu() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.relu">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.repeat">repeat() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.repeat">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.reset">reset() (mxnet.gluon.rnn.RecurrentCell method)</a>
-<ul>
-<li><a href="api/python/image.html#mxnet.image.ImageIter.reset">(mxnet.image.ImageIter method)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.DataIter.reset">(mxnet.io.DataIter method)</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.reset">(mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.reset">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.EvalMetric.reset">(mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.reset">[1]</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.MXRecordIO.reset">(mxnet.recordio.MXRecordIO method)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.reset">(mxnet.rnn.BaseRNNCell method)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.BucketSentenceIter.reset">(mxnet.rnn.BucketSentenceIter method)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Reshape">Reshape() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.reshape">reshape() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Reshape">Reshape() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.reshape">reshape() (in module mxnet.symbol)</a>
-<ul>
-<li><a href="api/python/image.html#mxnet.image.ImageDetIter.reshape">(mxnet.image.ImageDetIter method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.reshape">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.reshape">(mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.reshape">(mxnet.symbol.Symbol method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.ResidualCell">ResidualCell (class in mxnet.gluon.rnn)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.ResidualCell">(class in mxnet.rnn)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.resize_short">resize_short() (mxnet.image method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.ResizeAug">ResizeAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.io.ResizeIter">ResizeIter (class in mxnet.io)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.reverse">reverse() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.reverse">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.rint">rint() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.rint">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/metric.html#mxnet.metric.RMSE">RMSE (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.RMSE">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.RMSProp">RMSProp (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.RMSProp">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.rmsprop_update">rmsprop_update() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.rmsprop_update">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.rmspropalex_update">rmspropalex_update() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.rmspropalex_update">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.RNN">RNN (class in mxnet.gluon.rnn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.RNN">RNN() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.RNN">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.RNNCell">RNNCell (class in mxnet.gluon.rnn)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.RNNCell">(class in mxnet.rnn)</a>
-</li>
-</ul></li>
-<li><a href="api/python/rnn.html#mxnet.rnn.RNNParams">RNNParams (class in mxnet.rnn)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.ROIPooling">ROIPooling() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.ROIPooling">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.round">round() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.round">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.rsqrt">rsqrt() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.rsqrt">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.ElementWiseSum">ElementWiseSum() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.ElementWiseSum">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.elemwise_add">elemwise_add() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.elemwise_add">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Embedding">Embedding (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Embedding">Embedding() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Embedding">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.empty">empty() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.encode_sentences">encode_sentences() (mxnet.rnn method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.equal">equal() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.eval">eval() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.EvalMetric">EvalMetric (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.exp">exp() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.exp">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.expand_dims">expand_dims() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.expand_dims">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.expm1">expm1() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.expm1">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+</dl></td>
+</tr></table>
+<h2 id="F">F</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/metric.html#mxnet.metric.F1">F1 (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.F1">[1]</a>
+</dt>
+<dt><a href="api/python/optimization.html#mxnet.lr_scheduler.FactorScheduler">FactorScheduler (class in mxnet.lr_scheduler)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.model.FeedForward">FeedForward (class in mxnet.model)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.fft">fft() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.fft">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.fill_element_0index">fill_element_0index() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.fill_element_0index">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.model.FeedForward.fit">fit() (mxnet.model.FeedForward method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.fit">(mxnet.module.BaseModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.fix">fix() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.fix">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.fixed_crop">fixed_crop() (mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Flatten">Flatten() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.flatten">flatten() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Flatten">Flatten() (in module mxnet.symbol)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.flatten">flatten() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.flip">flip() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.flip">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.floor">floor() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.floor">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.ForceResizeAug">ForceResizeAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.forward">forward() (mxnet.gluon.Block method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.HybridBlock.forward">(mxnet.gluon.HybridBlock method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.forward">(mxnet.gluon.rnn.RecurrentCell method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.forward">(mxnet.module.BaseModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.forward">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.forward">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonLossModule.forward">(mxnet.module.PythonLossModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.forward">(mxnet.module.SequentialModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.forward_backward">forward_backward() (mxnet.module.BaseModule method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Ftrl">Ftrl (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Ftrl">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.full">full() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.full">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.FullyConnected">FullyConnected() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.FullyConnected">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.initializer.FusedRNN">FusedRNN (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.FusedRNN">[1]</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.FusedRNNCell">FusedRNNCell (class in mxnet.rnn)</a>
+</dt>
+</dl></td>
+</tr></table>
+<h2 id="G">G</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.gamma">gamma() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.gamma">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.gammaln">gammaln() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.gammaln">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.get">get() (mxnet.gluon.ParameterDict method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.get">(mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.get">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.EvalMetric.get">(mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.get">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.Perplexity.get">(mxnet.metric.Perplexity method)</a>, <a href="api/python/model.html#mxnet.metric.Perplexity.get">[1]</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.RNNParams.get">(mxnet.rnn.RNNParams method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/io.html#mxnet.io.DataDesc.get_batch_axis">get_batch_axis() (mxnet.io.DataDesc static method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.get_children">get_children() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.EvalMetric.get_config">get_config() (mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.get_config">[1]</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.get_input_grads">get_input_grads() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.get_input_grads">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.get_input_grads">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonLossModule.get_input_grads">(mxnet.module.PythonLossModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.get_input_grads">(mxnet.module.SequentialModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.get_internals">get_internals() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.DataDesc.get_list">get_list() (mxnet.io.DataDesc static method)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.get_metric">get_metric() (mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.get_metric">[1]</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.get_model">get_model() (in module mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.EvalMetric.get_name_value">get_name_value() (mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.get_name_value">[1]</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.get_outputs">get_outputs() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.get_outputs">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.get_outputs">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonLossModule.get_outputs">(mxnet.module.PythonLossModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.get_outputs">(mxnet.module.SequentialModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.get_params">get_params() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.get_params">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.get_params">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.get_params">(mxnet.module.PythonModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.get_params">(mxnet.module.SequentialModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.get_resnet">get_resnet() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.get_states">get_states() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.get_states">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.get_states">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Updater.get_states">(mxnet.optimizer.Updater method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Updater.get_states">[1]</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.optimizer.get_updater">get_updater() (in module mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.get_updater">[1]</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.get_vgg">get_vgg() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.DataIter.getdata">getdata() (mxnet.io.DataIter method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/io.html#mxnet.io.DataIter.getindex">getindex() (mxnet.io.DataIter method)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.DataIter.getlabel">getlabel() (mxnet.io.DataIter method)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.DataIter.getpad">getpad() (mxnet.io.DataIter method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalAvgPool1D">GlobalAvgPool1D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalAvgPool2D">GlobalAvgPool2D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalAvgPool3D">GlobalAvgPool3D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalMaxPool1D">GlobalMaxPool1D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalMaxPool2D">GlobalMaxPool2D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.GlobalMaxPool3D">GlobalMaxPool3D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.grad">grad (mxnet.ndarray.NDArray attribute)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.grad">grad() (mxnet.gluon.Parameter method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.gradient">gradient() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.greater">greater() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.greater_equal">greater_equal() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.GridGenerator">GridGenerator() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.GridGenerator">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Group">Group() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.GRU">GRU (class in mxnet.gluon.rnn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.GRUCell">GRUCell (class in mxnet.gluon.rnn)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.GRUCell">(class in mxnet.rnn)</a>
+</dt>
+</dl></dd>
+</dl></td>
+</tr></table>
+<h2 id="H">H</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/io.html#mxnet.io.NDArrayIter.hard_reset">hard_reset() (mxnet.io.NDArrayIter method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.HorizontalFlipAug">HorizontalFlipAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.HueJitterAug">HueJitterAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.HybridBlock.hybrid_forward">hybrid_forward() (mxnet.gluon.HybridBlock method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.HybridBlock">HybridBlock (class in mxnet.gluon)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.hybridize">hybridize() (mxnet.gluon.Block method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.HybridSequential">HybridSequential (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.hypot">hypot() (in module mxnet.symbol)</a>
+</dt>
+</dl></td>
+</tr></table>
+<h2 id="I">I</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.identity">identity() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.identity">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.IdentityAttachKLSparseReg">IdentityAttachKLSparseReg() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.IdentityAttachKLSparseReg">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.ifft">ifft() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.ifft">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.ImageDetIter">ImageDetIter (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.ImageDetRecordIter">ImageDetRecordIter() (in module mxnet.io)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter">ImageIter (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.ImageRecordDataset">ImageRecordDataset (class in mxnet.gluon.data)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.ImageRecordIter">ImageRecordIter() (in module mxnet.io)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.ImageRecordIter_v1">ImageRecordIter_v1() (in module mxnet.io)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.ImageRecordUInt8Iter">ImageRecordUInt8Iter() (in module mxnet.io)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.ImageRecordUInt8Iter_v1">ImageRecordUInt8Iter_v1() (in module mxnet.io)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.imdecode">imdecode() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/image.html#mxnet.image.imdecode">(mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter.imdecode">(mxnet.image.ImageIter method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.Inception3">Inception3 (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.inception_v3">inception_v3() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.HybridBlock.infer_shape">infer_shape() (mxnet.gluon.HybridBlock method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.infer_shape">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.infer_shape_partial">infer_shape_partial() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.infer_type">infer_type() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.init">init() (mxnet.kvstore.KVStore method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.init_optimizer">init_optimizer() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.init_optimizer">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.init_optimizer">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.init_optimizer">(mxnet.module.PythonModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.init_optimizer">(mxnet.module.SequentialModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.init_params">init_params() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.init_params">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.init_params">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.init_params">(mxnet.module.PythonModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.init_params">(mxnet.module.SequentialModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.initializer.InitDesc">InitDesc (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.InitDesc">[1]</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.initialize">initialize() (mxnet.gluon.Block method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.initialize">(mxnet.gluon.Parameter method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.initialize">(mxnet.gluon.ParameterDict method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.initializer.Initializer">Initializer (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Initializer">[1]</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.install_monitor">install_monitor() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.install_monitor">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.install_monitor">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonLossModule.install_monitor">(mxnet.module.PythonLossModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.install_monitor">(mxnet.module.SequentialModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.InstanceNorm">InstanceNorm() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.InstanceNorm">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/io.html#mxnet.recordio.IRHeader">IRHeader (in module mxnet.recordio)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.DataIter.iter_next">iter_next() (mxnet.io.DataIter method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.iter_predict">iter_predict() (mxnet.module.BaseModule method)</a>
+</dt>
+</dl></td>
+</tr></table>
+<h2 id="K">K</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.loss.KLDivLoss">KLDivLoss (class in mxnet.gluon.loss)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore">KVStore (class in mxnet.kvstore)</a>
+</dt>
+</dl></td>
+</tr></table>
+<h2 id="L">L</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.loss.L1Loss">L1Loss (class in mxnet.gluon.loss)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.loss.L2Loss">L2Loss (class in mxnet.gluon.loss)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.L2Normalization">L2Normalization() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.L2Normalization">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.Module.label_names">label_names (mxnet.module.Module attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.label_shapes">label_shapes (mxnet.module.BaseModule attribute)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.label_shapes">(mxnet.module.BucketingModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.label_shapes">(mxnet.module.Module attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.label_shapes">(mxnet.module.PythonModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.label_shapes">(mxnet.module.SequentialModule attribute)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.LeakyReLU">LeakyReLU (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.LeakyReLU">LeakyReLU() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.LeakyReLU">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.lesser">lesser() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.lesser_equal">lesser_equal() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.LightingAug">LightingAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.linalg_gemm">linalg_gemm() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.linalg_gemm">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.linalg_gemm2">linalg_gemm2() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.linalg_gemm2">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.linalg_potrf">linalg_potrf() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.linalg_potrf">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.linalg_potri">linalg_potri() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.linalg_potri">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.linalg_sumlogdiag">linalg_sumlogdiag() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.linalg_sumlogdiag">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.linalg_trmm">linalg_trmm() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.linalg_trmm">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.linalg_trsm">linalg_trsm() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.linalg_trsm">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.LinearRegressionOutput">LinearRegressionOutput() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.LinearRegressionOutput">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_arguments">list_arguments() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_attr">list_attr() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_auxiliary_states">list_auxiliary_states() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.list_ctx">list_ctx() (mxnet.gluon.Parameter method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.list_data">list_data() (mxnet.gluon.Parameter method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.list_grad">list_grad() (mxnet.gluon.Parameter method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_inputs">list_inputs() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.list_outputs">list_outputs() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.initializer.Load">Load (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Load">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.load">load() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.load">(in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.load">(mxnet.gluon.ParameterDict method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.model.FeedForward.load">(mxnet.model.FeedForward static method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.load">(mxnet.module.Module static method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.model.load_checkpoint">load_checkpoint() (in module mxnet.model)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.load_json">load_json() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.load_optimizer_states">load_optimizer_states() (mxnet.kvstore.KVStore method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.Module.load_optimizer_states">(mxnet.module.Module method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.load_params">load_params() (mxnet.gluon.Block method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.load_params">(mxnet.module.BaseModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/rnn.html#mxnet.rnn.load_rnn_checkpoint">load_rnn_checkpoint() (mxnet.rnn method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.log">log() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.log">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.log10">log10() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.log10">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.log1p">log1p() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.log1p">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.log2">log2() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.log2">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.log_softmax">log_softmax() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.log_softmax">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/callback.html#mxnet.callback.log_train_metric">log_train_metric() (in module mxnet.callback)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.LogisticRegressionOutput">LogisticRegressionOutput() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.LogisticRegressionOutput">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/callback.html#mxnet.callback.LogValidationMetricsCallback">LogValidationMetricsCallback (class in mxnet.callback)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.Loss">Loss (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Loss">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.LRN">LRN() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.LRN">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/optimization.html#mxnet.lr_scheduler.LRScheduler">LRScheduler (class in mxnet.lr_scheduler)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.LSTM">LSTM (class in mxnet.gluon.rnn)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.initializer.LSTMBias">LSTMBias (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.LSTMBias">[1]</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.LSTMCell">LSTMCell (class in mxnet.gluon.rnn)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.LSTMCell">(class in mxnet.rnn)</a>
+</dt>
+</dl></dd>
+</dl></td>
+</tr></table>
+<h2 id="M">M</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/metric.html#mxnet.metric.MAE">MAE (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.MAE">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.MAERegressionOutput">MAERegressionOutput() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.MAERegressionOutput">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.make_loss">make_loss() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.make_loss">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.MakeLoss">MakeLoss() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.MakeLoss">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.max">max() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.max">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.max_axis">max_axis() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.max_axis">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.maximum">maximum() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.maximum">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.MaxPool1D">MaxPool1D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.MaxPool2D">MaxPool2D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.MaxPool3D">MaxPool3D (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.mean">mean() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.mean">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.min">min() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.min">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.min_axis">min_axis() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.min_axis">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.minimum">minimum() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.minimum">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.initializer.Mixed">Mixed (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Mixed">[1]</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.vision.MNIST">MNIST (class in mxnet.gluon.data.vision)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.MNISTIter">MNISTIter() (in module mxnet.io)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module">Module (class in mxnet.module)</a>
+</dt>
+<dt><a href="api/python/callback.html#mxnet.callback.module_checkpoint">module_checkpoint() (in module mxnet.callback)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.modulo">modulo() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.moveaxis">moveaxis() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.mp_sgd_mom_update">mp_sgd_mom_update() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.mp_sgd_mom_update">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.mp_sgd_update">mp_sgd_update() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.mp_sgd_update">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/metric.html#mxnet.metric.MSE">MSE (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.MSE">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.initializer.MSRAPrelu">MSRAPrelu (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.MSRAPrelu">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.MultiBoxDetection">MultiBoxDetection() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.MultiBoxDetection">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.MultiBoxPrior">MultiBoxPrior() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.MultiBoxPrior">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.MultiBoxTarget">MultiBoxTarget() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.MultiBoxTarget">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/optimization.html#mxnet.lr_scheduler.MultiFactorScheduler">MultiFactorScheduler (class in mxnet.lr_scheduler)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.multiply">multiply() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.MultiProposal">MultiProposal() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.MultiProposal">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/io.html#mxnet.io.MXDataIter">MXDataIter (class in mxnet.io)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO">MXIndexedRecordIO (class in mxnet.recordio)</a>
+</dt>
+<dt><a href="api/python/callback.html#module-mxnet.callback">mxnet.callback (module)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#module-mxnet.contrib.ndarray">mxnet.contrib.ndarray (module)</a>
+</dt>
+<dt><a href="api/python/symbol.html#module-mxnet.contrib.symbol">mxnet.contrib.symbol (module)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/gluon.html#module-mxnet.gluon.data.vision">mxnet.gluon.data.vision (module)</a>
+</dt>
+<dt><a href="api/python/gluon.html#module-mxnet.gluon.model_zoo.vision">mxnet.gluon.model_zoo.vision (module)</a>
+</dt>
+<dt><a href="api/python/image.html#module-mxnet.image">mxnet.image (module)</a>
+</dt>
+<dt><a href="api/python/model.html#module-mxnet.initializer">mxnet.initializer (module)</a>, <a href="api/python/optimization.html#module-mxnet.initializer">[1]</a>
+</dt>
+<dt><a href="api/python/io.html#module-mxnet.io">mxnet.io (module)</a>
+</dt>
+<dt><a href="api/python/kvstore.html#module-mxnet.kvstore">mxnet.kvstore (module)</a>
+</dt>
+<dt><a href="api/python/optimization.html#module-mxnet.lr_scheduler">mxnet.lr_scheduler (module)</a>
+</dt>
+<dt><a href="api/python/metric.html#module-mxnet.metric">mxnet.metric (module)</a>, <a href="api/python/model.html#module-mxnet.metric">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#module-mxnet.model">mxnet.model (module)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#module-mxnet.ndarray">mxnet.ndarray (module)</a>
+</dt>
+<dt><a href="api/python/model.html#module-mxnet.optimizer">mxnet.optimizer (module)</a>, <a href="api/python/optimization.html#module-mxnet.optimizer">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#module-mxnet.random">mxnet.random (module)</a>
+</dt>
+<dt><a href="api/python/io.html#module-mxnet.recordio">mxnet.recordio (module)</a>
+</dt>
+<dt><a href="api/python/symbol.html#module-mxnet.symbol">mxnet.symbol (module)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6EngineE">mxnet::Engine (C++ class)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine13_GetSharedRefEv">mxnet::Engine::_GetSharedRef (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine7AsyncFnE">mxnet::Engine::AsyncFn (C++ type)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine18CallbackOnCompleteE">mxnet::Engine::CallbackOnComplete (C++ type)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine14CreateCallbackEPFvP6EnginePvEPv">mxnet::Engine::CreateCallback (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine14DeleteOperatorE9OprHandle">mxnet::Engine::DeleteOperator (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine14DeleteVariableE6SyncFn7Context9VarHandle">mxnet::Engine::DeleteVariable (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine3GetEv">mxnet::Engine::Get (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine11NewOperatorE7AsyncFnRNSt6vectorI9VarHandleEERNSt6vectorI9VarHandleEE10FnPropertyPKc">mxnet::Engine::NewOperator (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine11NewVariableEv">mxnet::Engine::NewVariable (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine14NotifyShutdownEv">mxnet::Engine::NotifyShutdown (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine9OprHandleE">mxnet::Engine::OprHandle (C++ type)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine4PushE9OprHandle7Contextib">mxnet::Engine::Push (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine9PushAsyncE7AsyncFn7ContextRNSt6vectorI9VarHandleEERNSt6vectorI9VarHandleEE10FnPropertyiPKc">mxnet::Engine::PushAsync (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine8PushSyncE6SyncFn7ContextRNSt6vectorI9VarHandleEERNSt6vectorI9VarHandleEE10FnPropertyiPKc">mxnet::Engine::PushSync (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine6SyncFnE">mxnet::Engine::SyncFn (C++ type)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine9VarHandleE">mxnet::Engine::VarHandle (C++ type)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine10WaitForAllEv">mxnet::Engine::WaitForAll (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6Engine10WaitForVarE9VarHandle">mxnet::Engine::WaitForVar (C++ function)</a>
+</dt>
+<dt><a href="architecture/overview.html#_CPPv2N5mxnet6EngineD0Ev">mxnet::Engine::~Engine (C++ function)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.MXRecordIO">MXRecordIO (class in mxnet.recordio)</a>
+</dt>
+</dl></td>
+</tr></table>
+<h2 id="N">N</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/model.html#mxnet.optimizer.Nadam">Nadam (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Nadam">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.NAG">NAG (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.NAG">[1]</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.name">name (mxnet.gluon.Block attribute)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.name">(mxnet.symbol.Symbol attribute)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.name_scope">name_scope() (mxnet.gluon.Block method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.nanprod">nanprod() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.nanprod">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.nansum">nansum() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.nansum">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray">NDArray (class in mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.NDArrayIter">NDArrayIter (class in mxnet.io)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.ndim">ndim (mxnet.ndarray.NDArray attribute)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.negative">negative() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.negative">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.ImageDetIter.next">next() (mxnet.image.ImageDetIter method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter.next">(mxnet.image.ImageIter method)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.DataIter.next">(mxnet.io.DataIter method)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BucketSentenceIter.next">(mxnet.rnn.BucketSentenceIter method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter.next_sample">next_sample() (mxnet.image.ImageIter method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.norm">norm() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.norm">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.initializer.Normal">Normal (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Normal">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.normal">normal() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.normal">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.not_equal">not_equal() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.np">np() (in module mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.np">[1]</a>
+</dt>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.num_workers">num_workers (mxnet.kvstore.KVStore attribute)</a>
+</dt>
+</dl></td>
+</tr></table>
+<h2 id="O">O</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/model.html#mxnet.initializer.One">One (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.One">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.one_hot">one_hot() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.one_hot">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.onehot_encode">onehot_encode() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.ones">ones() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.ones">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.ones_like">ones_like() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.ones_like">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/io.html#mxnet.recordio.MXRecordIO.open">open() (mxnet.recordio.MXRecordIO method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Optimizer">Optimizer (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.initializer.Orthogonal">Orthogonal (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Orthogonal">[1]</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.output_names">output_names (mxnet.module.BaseModule attribute)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.output_names">(mxnet.module.BucketingModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.output_names">(mxnet.module.Module attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.output_names">(mxnet.module.PythonModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.output_names">(mxnet.module.SequentialModule attribute)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.output_shapes">output_shapes (mxnet.module.BaseModule attribute)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.output_shapes">(mxnet.module.BucketingModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.output_shapes">(mxnet.module.Module attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.output_shapes">(mxnet.module.PythonModule attribute)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.output_shapes">(mxnet.module.SequentialModule attribute)</a>
+</dt>
+</dl></dd>
+</dl></td>
+</tr></table>
+<h2 id="P">P</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/io.html#mxnet.recordio.pack">pack() (in module mxnet.recordio)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.pack_img">pack_img() (in module mxnet.recordio)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.pack_weights">pack_weights() (mxnet.rnn.BaseRNNCell method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Pad">Pad() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.pad">pad() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Pad">Pad() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.pad">pad() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter">Parameter (class in mxnet.gluon)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.ParameterDict">ParameterDict (class in mxnet.gluon)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.params">params (mxnet.gluon.Block attribute)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.params">(mxnet.rnn.BaseRNNCell attribute)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/metric.html#mxnet.metric.PearsonCorrelation">PearsonCorrelation (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.PearsonCorrelation">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.Perplexity">Perplexity (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Perplexity">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.pick">pick() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.pick">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Pooling">Pooling() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Pooling">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Pooling_v1">Pooling_v1() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Pooling_v1">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter.postprocess_data">postprocess_data() (mxnet.image.ImageIter method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.pow">pow() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.power">power() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.model.FeedForward.predict">predict() (mxnet.model.FeedForward method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.predict">(mxnet.module.BaseModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/io.html#mxnet.io.PrefetchingIter">PrefetchingIter (class in mxnet.io)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.prefix">prefix (mxnet.gluon.Block attribute)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.prefix">(mxnet.gluon.ParameterDict attribute)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.prepare">prepare() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.prepare">(mxnet.module.BucketingModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.prod">prod() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.prod">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/callback.html#mxnet.callback.ProgressBar">ProgressBar (class in mxnet.callback)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.Proposal">Proposal() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.Proposal">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/io.html#mxnet.io.NDArrayIter.provide_data">provide_data (mxnet.io.NDArrayIter attribute)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.NDArrayIter.provide_label">provide_label (mxnet.io.NDArrayIter attribute)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.PSROIPooling">PSROIPooling() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.PSROIPooling">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.pull">pull() (mxnet.kvstore.KVStore method)</a>
+</dt>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.push">push() (mxnet.kvstore.KVStore method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonLossModule">PythonLossModule (class in mxnet.module)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule">PythonModule (class in mxnet.module)</a>
+</dt>
+</dl></td>
+</tr></table>
+<h2 id="Q">Q</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.contrib.ndarray.quantize">quantize() (in module mxnet.contrib.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.contrib.symbol.quantize">(in module mxnet.contrib.symbol)</a>
+</dt>
+</dl></dd>
+</dl></td>
+</tr></table>
+<h2 id="R">R</h2>
+<table class="indextable genindextable" style="width: 100%"><tr>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.radians">radians() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.radians">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.random_crop">random_crop() (mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.random_exponential">random_exponential() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.random_exponential">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.random_gamma">random_gamma() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.random_gamma">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.random_generalized_negative_binomial">random_generalized_negative_binomial() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.random_generalized_negative_binomial">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.random_negative_binomial">random_negative_binomial() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.random_negative_binomial">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.random_normal">random_normal() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.random_normal">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.random_poisson">random_poisson() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.random_poisson">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.random_size_crop">random_size_crop() (mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.random_uniform">random_uniform() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.random_uniform">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.RandomCropAug">RandomCropAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.RandomGrayAug">RandomGrayAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.RandomOrderAug">RandomOrderAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.RandomSampler">RandomSampler (class in mxnet.gluon.data)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.RandomSizedCropAug">RandomSizedCropAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.rank">rank (mxnet.kvstore.KVStore attribute)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.MXRecordIO.read">read() (mxnet.recordio.MXRecordIO method)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.read_idx">read_idx() (mxnet.recordio.MXIndexedRecordIO method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter.read_image">read_image() (mxnet.image.ImageIter method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.reciprocal">reciprocal() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.reciprocal">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.RecordFileDataset">RecordFileDataset (class in mxnet.gluon.data)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell">RecurrentCell (class in mxnet.gluon.rnn)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.initializer.register">register() (in module mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.register">[1]</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/model.html#mxnet.optimizer.register">(in module mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.register">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Optimizer.register">(mxnet.optimizer.Optimizer static method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.register">[1]</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.register_child">register_child() (mxnet.gluon.Block method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.relu">relu() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.relu">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.repeat">repeat() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.repeat">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.reset">reset() (mxnet.gluon.rnn.RecurrentCell method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/image.html#mxnet.image.ImageIter.reset">(mxnet.image.ImageIter method)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.DataIter.reset">(mxnet.io.DataIter method)</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.reset">(mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.reset">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.EvalMetric.reset">(mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.reset">[1]</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.MXRecordIO.reset">(mxnet.recordio.MXRecordIO method)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.reset">(mxnet.rnn.BaseRNNCell method)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BucketSentenceIter.reset">(mxnet.rnn.BucketSentenceIter method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.reset_ctx">reset_ctx() (mxnet.gluon.Parameter method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.reset_ctx">(mxnet.gluon.ParameterDict method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Reshape">Reshape() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.reshape">reshape() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Reshape">Reshape() (in module mxnet.symbol)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.reshape">reshape() (in module mxnet.symbol)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/image.html#mxnet.image.ImageDetIter.reshape">(mxnet.image.ImageDetIter method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.reshape">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.reshape">(mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.reshape">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.ResidualCell">ResidualCell (class in mxnet.gluon.rnn)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.ResidualCell">(class in mxnet.rnn)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/image.html#mxnet.image.resize_short">resize_short() (mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ResizeAug">ResizeAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.io.ResizeIter">ResizeIter (class in mxnet.io)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet101_v1">resnet101_v1() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet101_v2">resnet101_v2() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet152_v1">resnet152_v1() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet152_v2">resnet152_v2() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet18_v1">resnet18_v1() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet18_v2">resnet18_v2() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet34_v1">resnet34_v1() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet34_v2">resnet34_v2() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet50_v1">resnet50_v1() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.resnet50_v2">resnet50_v2() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.ResNetV1">ResNetV1 (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.ResNetV2">ResNetV2 (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.reverse">reverse() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.reverse">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.rint">rint() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.rint">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/metric.html#mxnet.metric.RMSE">RMSE (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.RMSE">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.RMSProp">RMSProp (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.RMSProp">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.rmsprop_update">rmsprop_update() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.rmsprop_update">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.rmspropalex_update">rmspropalex_update() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.rmspropalex_update">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.RNN">RNN (class in mxnet.gluon.rnn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.RNN">RNN() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.RNN">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.RNNCell">RNNCell (class in mxnet.gluon.rnn)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.RNNCell">(class in mxnet.rnn)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/rnn.html#mxnet.rnn.RNNParams">RNNParams (class in mxnet.rnn)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.ROIPooling">ROIPooling() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.ROIPooling">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.round">round() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.round">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.rsqrt">rsqrt() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.rsqrt">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+</dl></td>
</tr></table>
<h2 id="S">S</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sample_exponential">sample_exponential() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sample_exponential">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sample_gamma">sample_gamma() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sample_gamma">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sample_generalized_negative_binomial">sample_generalized_negative_binomial() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sample_generalized_negative_binomial">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sample_multinomial">sample_multinomial() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sample_multinomial">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sample_negative_binomial">sample_negative_binomial() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sample_negative_binomial">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sample_normal">sample_normal() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sample_normal">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sample_poisson">sample_poisson() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sample_poisson">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sample_uniform">sample_uniform() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sample_uniform">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.SaturationJitterAug">SaturationJitterAug (class in mxnet.image)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.save">save() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.save">(mxnet.gluon.ParameterDict method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.model.FeedForward.save">(mxnet.model.FeedForward method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.save">(mxnet.symbol.Symbol method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.model.save_checkpoint">save_checkpoint() (in module mxnet.model)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.Module.save_checkpoint">(mxnet.module.Module method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.save_optimizer_states">save_optimizer_states() (mxnet.kvstore.KVStore method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.Module.save_optimizer_states">(mxnet.module.Module method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Block.save_params">save_params() (mxnet.gluon.Block method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.save_params">(mxnet.module.BaseModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/rnn.html#mxnet.rnn.save_rnn_checkpoint">save_rnn_checkpoint() (mxnet.rnn method)</a>
-</li>
-<li><a href="api/python/image.html#mxnet.image.scale_down">scale_down() (mxnet.image method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.model.FeedForward.score">score() (mxnet.model.FeedForward method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.score">(mxnet.module.BaseModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.random.seed">seed() (in module mxnet.random)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.seek">seek() (mxnet.recordio.MXIndexedRecordIO method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.SequenceLast">SequenceLast() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.SequenceLast">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.SequenceMask">SequenceMask() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.SequenceMask">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.SequenceReverse">SequenceReverse() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.SequenceReverse">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.nn.Sequential">Sequential (class in mxnet.gluon.nn)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule">SequentialModule (class in mxnet.module)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.SequentialRNNCell">SequentialRNNCell (class in mxnet.gluon.rnn)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.SequentialRNNCell">(class in mxnet.rnn)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter.set_data">set_data() (mxnet.gluon.Parameter method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Optimizer.set_lr_mult">set_lr_mult() (mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.set_lr_mult">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Optimizer.set_lr_scale">set_lr_scale() (mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.set_lr_scale">[1]</a>
-</li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.set_optimizer">set_optimizer() (mxnet.kvstore.KVStore method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.set_params">set_params() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.set_params">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.set_params">(mxnet.module.Module method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.set_states">set_states() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.set_states">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.set_states">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Updater.set_states">(mxnet.optimizer.Updater method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Updater.set_states">[1]</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.initializer.Initializer.set_verbosity">set_verbosity() (mxnet.initializer.Initializer method)</a>, <a href="api/python/optimization.html#mxnet.initializer.Initializer.set_verbosity">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Optimizer.set_wd_mult">set_wd_mult() (mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.set_wd_mult">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.SGD">SGD (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.SGD">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sgd_mom_update">sgd_mom_update() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sgd_mom_update">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sgd_update">sgd_update() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sgd_update">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.optimizer.SGLD">SGLD (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.SGLD">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.shape">shape (mxnet.ndarray.NDArray attribute)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sigmoid">sigmoid() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sigmoid">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sign">sign() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sign">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.simple_bind">simple_bind() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sin">sin() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sin">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sinh">sinh() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sinh">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.size">size (mxnet.ndarray.NDArray attribute)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.slice">slice() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.slice">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.slice_axis">slice_axis() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.slice_axis">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.SliceChannel">SliceChannel() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.SliceChannel">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.smooth_l1">smooth_l1() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.smooth_l1">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.Softmax">Softmax() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.softmax">softmax() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Softmax">Softmax() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.softmax">softmax() (in module mxnet.symbol)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.softmax_cross_entropy">softmax_cross_entropy() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.softmax_cross_entropy">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.SoftmaxActivation">SoftmaxActivation() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.SoftmaxActivation">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.loss.SoftmaxCrossEntropyLoss">SoftmaxCrossEntropyLoss (class in mxnet.gluon.loss)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.SoftmaxOutput">SoftmaxOutput() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.SoftmaxOutput">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sort">sort() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sort">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.SpatialTransformer">SpatialTransformer() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.SpatialTransformer">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/callback.html#mxnet.callback.Speedometer">Speedometer (class in mxnet.callback)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.split">split() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.split">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.utils.split_and_load">split_and_load() (mxnet.gluon.utils method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.utils.split_data">split_data() (mxnet.gluon.utils method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sqrt">sqrt() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sqrt">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.square">square() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.square">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.stack">stack() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.stack">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.state_info">state_info (mxnet.rnn.BaseRNNCell attribute)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.state_info">state_info() (mxnet.gluon.rnn.RecurrentCell method)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.state_shape">state_shape (mxnet.rnn.BaseRNNCell attribute)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Trainer.step">step() (mxnet.gluon.Trainer method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.stop_gradient">stop_gradient() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.stop_gradient">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.subtract">subtract() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sum">sum() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sum">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.sum_axis">sum_axis() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.sum_axis">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.SVMOutput">SVMOutput() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.SVMOutput">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.swapaxes">swapaxes() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.swapaxes">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.SwapAxis">SwapAxis() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.SwapAxis">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.switch_bucket">switch_bucket() (mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol">Symbol (class in mxnet.symbol)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.symbol">symbol (mxnet.module.BaseModule attribute)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.symbol">(mxnet.module.BucketingModule attribute)</a>
-</li>
-</ul></li>
-<li><a href="api/python/image.html#mxnet.image.ImageDetIter.sync_label_shape">sync_label_shape() (mxnet.image.ImageDetIter method)</a>
-</li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sample_exponential">sample_exponential() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sample_exponential">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sample_gamma">sample_gamma() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sample_gamma">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sample_generalized_negative_binomial">sample_generalized_negative_binomial() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sample_generalized_negative_binomial">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sample_multinomial">sample_multinomial() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sample_multinomial">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sample_negative_binomial">sample_negative_binomial() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sample_negative_binomial">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sample_normal">sample_normal() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sample_normal">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sample_poisson">sample_poisson() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sample_poisson">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sample_uniform">sample_uniform() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sample_uniform">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.Sampler">Sampler (class in mxnet.gluon.data)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.SaturationJitterAug">SaturationJitterAug (class in mxnet.image)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.save">save() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.save">(mxnet.gluon.ParameterDict method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.model.FeedForward.save">(mxnet.model.FeedForward method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.save">(mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.model.save_checkpoint">save_checkpoint() (in module mxnet.model)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.Module.save_checkpoint">(mxnet.module.Module method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.save_optimizer_states">save_optimizer_states() (mxnet.kvstore.KVStore method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.Module.save_optimizer_states">(mxnet.module.Module method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Block.save_params">save_params() (mxnet.gluon.Block method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.save_params">(mxnet.module.BaseModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/rnn.html#mxnet.rnn.save_rnn_checkpoint">save_rnn_checkpoint() (mxnet.rnn method)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.scale_down">scale_down() (mxnet.image method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.model.FeedForward.score">score() (mxnet.model.FeedForward method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.score">(mxnet.module.BaseModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.random.seed">seed() (in module mxnet.random)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.seek">seek() (mxnet.recordio.MXIndexedRecordIO method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.SequenceLast">SequenceLast() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.SequenceLast">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.SequenceMask">SequenceMask() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.SequenceMask">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.SequenceReverse">SequenceReverse() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.SequenceReverse">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.nn.Sequential">Sequential (class in mxnet.gluon.nn)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule">SequentialModule (class in mxnet.module)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.SequentialRNNCell">SequentialRNNCell (class in mxnet.gluon.rnn)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.SequentialRNNCell">(class in mxnet.rnn)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.data.SequentialSampler">SequentialSampler (class in mxnet.gluon.data)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.set_data">set_data() (mxnet.gluon.Parameter method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Optimizer.set_lr_mult">set_lr_mult() (mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.set_lr_mult">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Optimizer.set_lr_scale">set_lr_scale() (mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.set_lr_scale">[1]</a>
+</dt>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.set_optimizer">set_optimizer() (mxnet.kvstore.KVStore method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.set_params">set_params() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.set_params">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.set_params">(mxnet.module.Module method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.set_states">set_states() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.set_states">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.set_states">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Updater.set_states">(mxnet.optimizer.Updater method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Updater.set_states">[1]</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.initializer.Initializer.set_verbosity">set_verbosity() (mxnet.initializer.Initializer method)</a>, <a href="api/python/optimization.html#mxnet.initializer.Initializer.set_verbosity">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Optimizer.set_wd_mult">set_wd_mult() (mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.set_wd_mult">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.SGD">SGD (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.SGD">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sgd_mom_update">sgd_mom_update() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sgd_mom_update">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sgd_update">sgd_update() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sgd_update">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.optimizer.SGLD">SGLD (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.SGLD">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.shape">shape (mxnet.ndarray.NDArray attribute)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sigmoid">sigmoid() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sigmoid">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sign">sign() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sign">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.simple_bind">simple_bind() (mxnet.symbol.Symbol method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sin">sin() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sin">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sinh">sinh() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sinh">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.size">size (mxnet.ndarray.NDArray attribute)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.slice">slice() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.slice">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.slice_axis">slice_axis() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.slice_axis">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.SliceChannel">SliceChannel() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.SliceChannel">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.smooth_l1">smooth_l1() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.smooth_l1">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.Softmax">Softmax() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.softmax">softmax() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Softmax">Softmax() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.softmax">softmax() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.softmax_cross_entropy">softmax_cross_entropy() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.softmax_cross_entropy">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.SoftmaxActivation">SoftmaxActivation() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.SoftmaxActivation">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.loss.SoftmaxCrossEntropyLoss">SoftmaxCrossEntropyLoss (class in mxnet.gluon.loss)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.SoftmaxOutput">SoftmaxOutput() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.SoftmaxOutput">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sort">sort() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sort">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.SpatialTransformer">SpatialTransformer() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.SpatialTransformer">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/callback.html#mxnet.callback.Speedometer">Speedometer (class in mxnet.callback)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.split">split() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.split">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.utils.split_and_load">split_and_load() (mxnet.gluon.utils method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.utils.split_data">split_data() (mxnet.gluon.utils method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sqrt">sqrt() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sqrt">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.square">square() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.square">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.SqueezeNet">SqueezeNet (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.squeezenet1_0">squeezenet1_0() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.squeezenet1_1">squeezenet1_1() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.stack">stack() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.stack">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.state_info">state_info (mxnet.rnn.BaseRNNCell attribute)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.state_info">state_info() (mxnet.gluon.rnn.RecurrentCell method)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.state_shape">state_shape (mxnet.rnn.BaseRNNCell attribute)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Trainer.step">step() (mxnet.gluon.Trainer method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.stop_gradient">stop_gradient() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.stop_gradient">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.subtract">subtract() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sum">sum() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sum">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.sum_axis">sum_axis() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.sum_axis">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.SVMOutput">SVMOutput() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.SVMOutput">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.swapaxes">swapaxes() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.swapaxes">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.SwapAxis">SwapAxis() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.SwapAxis">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.switch_bucket">switch_bucket() (mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol">Symbol (class in mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.symbol">symbol (mxnet.module.BaseModule attribute)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.symbol">(mxnet.module.BucketingModule attribute)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.SymbolBlock">SymbolBlock (class in mxnet.gluon)</a>
+</dt>
+<dt><a href="api/python/image.html#mxnet.image.ImageDetIter.sync_label_shape">sync_label_shape() (mxnet.image.ImageDetIter method)</a>
+</dt>
+</dl></td>
</tr></table>
<h2 id="T">T</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.T">T (mxnet.ndarray.NDArray attribute)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.take">take() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.take">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.tan">tan() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.tan">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.tanh">tanh() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.tanh">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.tell">tell() (mxnet.recordio.MXIndexedRecordIO method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Test">Test (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Test">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.tile">tile() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.tile">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Symbol.tojson">tojson() (mxnet.symbol.Symbol method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.topk">topk() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.topk">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/metric.html#mxnet.metric.TopKAccuracy">TopKAccuracy (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.TopKAccuracy">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.Torch">Torch (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Torch">[1]</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Trainer">Trainer (class in mxnet.gluon)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.transpose">transpose() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.transpose">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.true_divide">true_divide() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.trunc">trunc() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.trunc">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.type">type (mxnet.kvstore.KVStore attribute)</a>
-</li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.T">T (mxnet.ndarray.NDArray attribute)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.take">take() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.take">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.tan">tan() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.tan">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.tanh">tanh() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.tanh">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.tell">tell() (mxnet.recordio.MXIndexedRecordIO method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Test">Test (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Test">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.tile">tile() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.tile">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Symbol.tojson">tojson() (mxnet.symbol.Symbol method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.topk">topk() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.topk">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/metric.html#mxnet.metric.TopKAccuracy">TopKAccuracy (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.TopKAccuracy">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.Torch">Torch (class in mxnet.metric)</a>, <a href="api/python/model.html#mxnet.metric.Torch">[1]</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Trainer">Trainer (class in mxnet.gluon)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.transpose">transpose() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.transpose">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.true_divide">true_divide() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.trunc">trunc() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.trunc">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/kvstore.html#mxnet.kvstore.KVStore.type">type (mxnet.kvstore.KVStore attribute)</a>
+</dt>
+</dl></td>
</tr></table>
<h2 id="U">U</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.FusedRNNCell.unfuse">unfuse() (mxnet.rnn.FusedRNNCell method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.initializer.Uniform">Uniform (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Uniform">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.uniform">uniform() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.uniform">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/io.html#mxnet.recordio.unpack">unpack() (in module mxnet.recordio)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.unpack_img">unpack_img() (in module mxnet.recordio)</a>
-</li>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.unpack_weights">unpack_weights() (mxnet.rnn.BaseRNNCell method)</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.unroll">unroll() (mxnet.gluon.rnn.RecurrentCell method)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.unroll">(mxnet.rnn.BaseRNNCell method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.update">update() (mxnet.gluon.ParameterDict method)</a>
-<ul>
-<li><a href="api/python/metric.html#mxnet.metric.Accuracy.update">(mxnet.metric.Accuracy method)</a>, <a href="api/python/model.html#mxnet.metric.Accuracy.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.update">(mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.CrossEntropy.update">(mxnet.metric.CrossEntropy method)</a>, <a href="api/python/model.html#mxnet.metric.CrossEntropy.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.CustomMetric.update">(mxnet.metric.CustomMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CustomMetric.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.EvalMetric.update">(mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.F1.update">(mxnet.metric.F1 method)</a>, <a href="api/python/model.html#mxnet.metric.F1.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.MAE.update">(mxnet.metric.MAE method)</a>, <a href="api/python/model.html#mxnet.metric.MAE.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.MSE.update">(mxnet.metric.MSE method)</a>, <a href="api/python/model.html#mxnet.metric.MSE.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.PearsonCorrelation.update">(mxnet.metric.PearsonCorrelation method)</a>, <a href="api/python/model.html#mxnet.metric.PearsonCorrelation.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.Perplexity.update">(mxnet.metric.Perplexity method)</a>, <a href="api/python/model.html#mxnet.metric.Perplexity.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.RMSE.update">(mxnet.metric.RMSE method)</a>, <a href="api/python/model.html#mxnet.metric.RMSE.update">[1]</a>
-</li>
-<li><a href="api/python/metric.html#mxnet.metric.TopKAccuracy.update">(mxnet.metric.TopKAccuracy method)</a>, <a href="api/python/model.html#mxnet.metric.TopKAccuracy.update">[1]</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.update">(mxnet.module.BaseModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.update">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.update">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.update">(mxnet.module.PythonModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.update">(mxnet.module.SequentialModule method)</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Optimizer.update">(mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.update">[1]</a>
-</li>
-<li><a href="api/python/model.html#mxnet.optimizer.Test.update">(mxnet.optimizer.Test method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Test.update">[1]</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/metric.html#mxnet.metric.EvalMetric.update_dict">update_dict() (mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.update_dict">[1]</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.BaseModule.update_metric">update_metric() (mxnet.module.BaseModule method)</a>
-<ul>
-<li><a href="api/python/module.html#mxnet.module.BucketingModule.update_metric">(mxnet.module.BucketingModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.Module.update_metric">(mxnet.module.Module method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.PythonModule.update_metric">(mxnet.module.PythonModule method)</a>
-</li>
-<li><a href="api/python/module.html#mxnet.module.SequentialModule.update_metric">(mxnet.module.SequentialModule method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/model.html#mxnet.optimizer.Updater">Updater (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Updater">[1]</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.UpSampling">UpSampling() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.UpSampling">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.FusedRNNCell.unfuse">unfuse() (mxnet.rnn.FusedRNNCell method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.initializer.Uniform">Uniform (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Uniform">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.uniform">uniform() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.uniform">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/io.html#mxnet.recordio.unpack">unpack() (in module mxnet.recordio)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.unpack_img">unpack_img() (in module mxnet.recordio)</a>
+</dt>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.unpack_weights">unpack_weights() (mxnet.rnn.BaseRNNCell method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.RecurrentCell.unroll">unroll() (mxnet.gluon.rnn.RecurrentCell method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.BaseRNNCell.unroll">(mxnet.rnn.BaseRNNCell method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.update">update() (mxnet.gluon.ParameterDict method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/metric.html#mxnet.metric.Accuracy.update">(mxnet.metric.Accuracy method)</a>, <a href="api/python/model.html#mxnet.metric.Accuracy.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.CompositeEvalMetric.update">(mxnet.metric.CompositeEvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CompositeEvalMetric.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.CrossEntropy.update">(mxnet.metric.CrossEntropy method)</a>, <a href="api/python/model.html#mxnet.metric.CrossEntropy.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.CustomMetric.update">(mxnet.metric.CustomMetric method)</a>, <a href="api/python/model.html#mxnet.metric.CustomMetric.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.EvalMetric.update">(mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.F1.update">(mxnet.metric.F1 method)</a>, <a href="api/python/model.html#mxnet.metric.F1.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.MAE.update">(mxnet.metric.MAE method)</a>, <a href="api/python/model.html#mxnet.metric.MAE.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.MSE.update">(mxnet.metric.MSE method)</a>, <a href="api/python/model.html#mxnet.metric.MSE.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.PearsonCorrelation.update">(mxnet.metric.PearsonCorrelation method)</a>, <a href="api/python/model.html#mxnet.metric.PearsonCorrelation.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.Perplexity.update">(mxnet.metric.Perplexity method)</a>, <a href="api/python/model.html#mxnet.metric.Perplexity.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.RMSE.update">(mxnet.metric.RMSE method)</a>, <a href="api/python/model.html#mxnet.metric.RMSE.update">[1]</a>
+</dt>
+<dt><a href="api/python/metric.html#mxnet.metric.TopKAccuracy.update">(mxnet.metric.TopKAccuracy method)</a>, <a href="api/python/model.html#mxnet.metric.TopKAccuracy.update">[1]</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.update">(mxnet.module.BaseModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.update">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.update">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.update">(mxnet.module.PythonModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.update">(mxnet.module.SequentialModule method)</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Optimizer.update">(mxnet.optimizer.Optimizer method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Optimizer.update">[1]</a>
+</dt>
+<dt><a href="api/python/model.html#mxnet.optimizer.Test.update">(mxnet.optimizer.Test method)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Test.update">[1]</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/metric.html#mxnet.metric.EvalMetric.update_dict">update_dict() (mxnet.metric.EvalMetric method)</a>, <a href="api/python/model.html#mxnet.metric.EvalMetric.update_dict">[1]</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.BaseModule.update_metric">update_metric() (mxnet.module.BaseModule method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/module.html#mxnet.module.BucketingModule.update_metric">(mxnet.module.BucketingModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.Module.update_metric">(mxnet.module.Module method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.PythonModule.update_metric">(mxnet.module.PythonModule method)</a>
+</dt>
+<dt><a href="api/python/module.html#mxnet.module.SequentialModule.update_metric">(mxnet.module.SequentialModule method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/model.html#mxnet.optimizer.Updater">Updater (class in mxnet.optimizer)</a>, <a href="api/python/optimization.html#mxnet.optimizer.Updater">[1]</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.UpSampling">UpSampling() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.UpSampling">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+</dl></td>
</tr></table>
<h2 id="V">V</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.var">var() (in module mxnet.symbol)</a>
-<ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter.var">(mxnet.gluon.Parameter method)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.Variable">Variable() (in module mxnet.symbol)</a>
-</li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.var">var() (in module mxnet.symbol)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.var">(mxnet.gluon.Parameter method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/symbol.html#mxnet.symbol.Variable">Variable() (in module mxnet.symbol)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.VGG">VGG (class in mxnet.gluon.model_zoo.vision)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.vgg11">vgg11() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.vgg11_bn">vgg11_bn() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.vgg13">vgg13() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.vgg13_bn">vgg13_bn() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.vgg16">vgg16() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.vgg16_bn">vgg16_bn() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.vgg19">vgg19() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.model_zoo.vision.vgg19_bn">vgg19_bn() (mxnet.gluon.model_zoo.vision method)</a>
+</dt>
+</dl></td>
</tr></table>
<h2 id="W">W</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.wait_to_read">wait_to_read() (mxnet.ndarray.NDArray method)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.waitall">waitall() (in module mxnet.ndarray)</a>
-</li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.where">where() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.where">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/io.html#mxnet.recordio.MXRecordIO.write">write() (mxnet.recordio.MXRecordIO method)</a>
-</li>
-<li><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.write_idx">write_idx() (mxnet.recordio.MXIndexedRecordIO method)</a>
-</li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.NDArray.wait_to_read">wait_to_read() (mxnet.ndarray.NDArray method)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.waitall">waitall() (in module mxnet.ndarray)</a>
+</dt>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.where">where() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.where">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/io.html#mxnet.recordio.MXRecordIO.write">write() (mxnet.recordio.MXRecordIO method)</a>
+</dt>
+<dt><a href="api/python/io.html#mxnet.recordio.MXIndexedRecordIO.write_idx">write_idx() (mxnet.recordio.MXIndexedRecordIO method)</a>
+</dt>
+</dl></td>
</tr></table>
<h2 id="X">X</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/model.html#mxnet.initializer.Xavier">Xavier (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Xavier">[1]</a>
-</li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/model.html#mxnet.initializer.Xavier">Xavier (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Xavier">[1]</a>
+</dt>
+</dl></td>
</tr></table>
<h2 id="Z">Z</h2>
<table class="indextable genindextable" style="width: 100%"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/model.html#mxnet.initializer.Zero">Zero (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Zero">[1]</a>
-</li>
-<li><a href="api/python/gluon.html#mxnet.gluon.Parameter.zero_grad">zero_grad() (mxnet.gluon.Parameter method)</a>
-<ul>
-<li><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.zero_grad">(mxnet.gluon.ParameterDict method)</a>
-</li>
-</ul></li>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.zeros">zeros() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.zeros">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-</ul></td>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="api/python/ndarray.html#mxnet.ndarray.zeros_like">zeros_like() (in module mxnet.ndarray)</a>
-<ul>
-<li><a href="api/python/symbol.html#mxnet.symbol.zeros_like">(in module mxnet.symbol)</a>
-</li>
-</ul></li>
-<li><a href="api/python/gluon.html#mxnet.gluon.rnn.ZoneoutCell">ZoneoutCell (class in mxnet.gluon.rnn)</a>
-<ul>
-<li><a href="api/python/rnn.html#mxnet.rnn.ZoneoutCell">(class in mxnet.rnn)</a>
-</li>
-</ul></li>
-</ul></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/model.html#mxnet.initializer.Zero">Zero (class in mxnet.initializer)</a>, <a href="api/python/optimization.html#mxnet.initializer.Zero">[1]</a>
+</dt>
+<dt><a href="api/python/gluon.html#mxnet.gluon.Parameter.zero_grad">zero_grad() (mxnet.gluon.Parameter method)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/gluon.html#mxnet.gluon.ParameterDict.zero_grad">(mxnet.gluon.ParameterDict method)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.zeros">zeros() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.zeros">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+</dl></td>
+<td style="width: 33%" valign="top"><dl>
+<dt><a href="api/python/ndarray.html#mxnet.ndarray.zeros_like">zeros_like() (in module mxnet.ndarray)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/symbol.html#mxnet.symbol.zeros_like">(in module mxnet.symbol)</a>
+</dt>
+</dl></dd>
+<dt><a href="api/python/gluon.html#mxnet.gluon.rnn.ZoneoutCell">ZoneoutCell (class in mxnet.gluon.rnn)</a>
+</dt>
+<dd><dl>
+<dt><a href="api/python/rnn.html#mxnet.rnn.ZoneoutCell">(class in mxnet.rnn)</a>
+</dt>
+</dl></dd>
+</dl></td>
</tr></table>
<div class="container">
<div class="footer">
diff --git a/versions/master/get_started/amazonlinux_setup.html b/versions/master/get_started/amazonlinux_setup.html
index 30aa7a0..ea44cfe 100644
--- a/versions/master/get_started/amazonlinux_setup.html
+++ b/versions/master/get_started/amazonlinux_setup.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
-<title><no title=""> — mxnet documentation</no></title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
+<title><no title> — mxnet documentation</title>
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -236,7 +234,7 @@ Previous Navbar Layout End -->
<div class="content">
<!-- This page should be deleted after sometime (Allowing search engines
to update links) -->
-<meta content="3; url=http://mxnet.io/get_started/install.html" http-equiv="refresh"/>
+<meta content="3; url=http://mxnet.io/get_started/install.html" http-equiv="refresh">
<!-- Just in case redirection does not work -->
<p>
<a href="http://mxnet.io/get_started/install.html">
@@ -247,7 +245,7 @@ to update links) -->
<p> © 2015-2017 DMLC. All rights reserved. </p>
</div>
</div>
-</div>
+</meta></div>
<div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation">
<div class="sphinxsidebarwrapper">
</div>
diff --git a/versions/master/get_started/build_from_source.html b/versions/master/get_started/build_from_source.html
index d0eed45..febc302 100644
--- a/versions/master/get_started/build_from_source.html
+++ b/versions/master/get_started/build_from_source.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Build MXNet from Source — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/get_started/centos_setup.html b/versions/master/get_started/centos_setup.html
index 30aa7a0..ea44cfe 100644
--- a/versions/master/get_started/centos_setup.html
+++ b/versions/master/get_started/centos_setup.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
-<title><no title=""> — mxnet documentation</no></title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
+<title><no title> — mxnet documentation</title>
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -236,7 +234,7 @@ Previous Navbar Layout End -->
<div class="content">
<!-- This page should be deleted after sometime (Allowing search engines
to update links) -->
-<meta content="3; url=http://mxnet.io/get_started/install.html" http-equiv="refresh"/>
+<meta content="3; url=http://mxnet.io/get_started/install.html" http-equiv="refresh">
<!-- Just in case redirection does not work -->
<p>
<a href="http://mxnet.io/get_started/install.html">
@@ -247,7 +245,7 @@ to update links) -->
<p> © 2015-2017 DMLC. All rights reserved. </p>
</div>
</div>
-</div>
+</meta></div>
<div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation">
<div class="sphinxsidebarwrapper">
</div>
diff --git a/versions/master/get_started/index.html b/versions/master/get_started/index.html
index 9d6ecc4..ace43e4 100644
--- a/versions/master/get_started/index.html
+++ b/versions/master/get_started/index.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
-<title><no title=""> — mxnet documentation</no></title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
+<title><no title> — mxnet documentation</title>
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="../architecture/index.html" rel="next" title="MXNet Architecture"/>
+<link href="../architecture/index.html" rel="next" title="MXNet Architecture">
<link href="../how_to/s3_integration.html" rel="prev" title="Use data from S3 for training"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -153,7 +151,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -180,7 +178,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -190,11 +188,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -238,10 +236,10 @@ Previous Navbar Layout End -->
<div class="content">
<p><html lang="en-US">
<head>
-<meta charset="utf-8">
-<meta content="0; url=http://mxnet.io/get_started/why_mxnet.html" http-equiv="refresh">
+<meta charset="utf-8"/>
+<meta content="0; url=http://mxnet.io/get_started/why_mxnet.html" http-equiv="refresh"/>
<title>Page Redirection</title>
-</meta></meta></head>
+</head>
</html></p>
<div class="container">
<div class="footer">
diff --git a/versions/master/get_started/install.html b/versions/master/get_started/install.html
index 44c001e..6c89582 100644
--- a/versions/master/get_started/install.html
+++ b/versions/master/get_started/install.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Installing MXNet — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -795,22 +793,22 @@ cran<span class="p">[</span><span class="s">"dmlc"</span><span class="p">]</span
install.packages<span class="p">(</span><span class="s">"mxnet"</span><span class="p">)</span>
</pre></div>
</div>
-<p>Alternatively, You can also follow the installation instructions <a class="reference internal" href="windows_setup.html"><span class="doc">in this guide</span></a> to build MXNet from source.</p>
+<p>Alternatively, You can also follow the installation instructions <a class="reference internal" href="windows_setup.html"><em>in this guide</em></a> to build MXNet from source.</p>
</div>
</div>
</div><!-- END - Windows R GPU Installation Instructions --><div class="linux">
<div class="scala julia perl">
-<div class="cpu gpu"><p>Follow the installation instructions <a class="reference internal" href="ubuntu_setup.html"><span class="doc">in this guide</span></a> to set up MXNet.</p>
+<div class="cpu gpu"><p>Follow the installation instructions <a class="reference internal" href="ubuntu_setup.html"><em>in this guide</em></a> to set up MXNet.</p>
</div>
</div>
</div><div class="macos">
<div class="scala julia perl">
-<div class="cpu gpu"><p>Follow the installation instructions <a class="reference internal" href="osx_setup.html"><span class="doc">in this guide</span></a> to set up MXNet.</p>
+<div class="cpu gpu"><p>Follow the installation instructions <a class="reference internal" href="osx_setup.html"><em>in this guide</em></a> to set up MXNet.</p>
</div>
</div>
</div><div class="windows">
<div class="python scala julia perl">
-<div class="gpu"><p>Follow the installation instructions <a class="reference internal" href="windows_setup.html"><span class="doc">in this guide</span></a> to set up MXNet.</p>
+<div class="gpu"><p>Follow the installation instructions <a class="reference internal" href="windows_setup.html"><em>in this guide</em></a> to set up MXNet.</p>
</div>
</div>
</div><div class="devices">
diff --git a/versions/master/get_started/osx_setup.html b/versions/master/get_started/osx_setup.html
index bf6cad2..a6155b1 100644
--- a/versions/master/get_started/osx_setup.html
+++ b/versions/master/get_started/osx_setup.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Installing MXNet froum source on OS X (Mac) — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -372,8 +370,8 @@ Previous Navbar Layout End -->
<div class="section" id="install-the-mxnet-package-for-julia">
<span id="install-the-mxnet-package-for-julia"></span><h2>Install the MXNet Package for Julia<a class="headerlink" href="#install-the-mxnet-package-for-julia" title="Permalink to this headline">¶</a></h2>
<p>The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on <a class="reference external" href="https://github.com/dmlc/MXNet.jl">GitHub</a>. To use Julia binding it with an existing libmxnet installation, set the <code class="docutils literal"><span class="pre">MXNET_HOME</span></code> environment variable by running the following command:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/<path to="">/libmxnet
-</path></pre></div>
+<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/<path to>/libmxnet
+</pre></div>
</div>
<p>The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the <code class="docutils literal"><span class="pre">libmxnet.so</span></code> file at <code class="docutils literal"><span class="pre">$MXNET_HOME/lib</span></code>. For example, if the root directory of libmxnet is <code class="docutils literal"><span class="pre">~</span></code>, you would run the following command:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/~/libmxnet
diff --git a/versions/master/get_started/raspbian_setup.html b/versions/master/get_started/raspbian_setup.html
index 30aa7a0..ea44cfe 100644
--- a/versions/master/get_started/raspbian_setup.html
+++ b/versions/master/get_started/raspbian_setup.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
-<title><no title=""> — mxnet documentation</no></title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
+<title><no title> — mxnet documentation</title>
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -236,7 +234,7 @@ Previous Navbar Layout End -->
<div class="content">
<!-- This page should be deleted after sometime (Allowing search engines
to update links) -->
-<meta content="3; url=http://mxnet.io/get_started/install.html" http-equiv="refresh"/>
+<meta content="3; url=http://mxnet.io/get_started/install.html" http-equiv="refresh">
<!-- Just in case redirection does not work -->
<p>
<a href="http://mxnet.io/get_started/install.html">
@@ -247,7 +245,7 @@ to update links) -->
<p> © 2015-2017 DMLC. All rights reserved. </p>
</div>
</div>
-</div>
+</meta></div>
<div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation">
<div class="sphinxsidebarwrapper">
</div>
diff --git a/versions/master/get_started/tx2_setup.html b/versions/master/get_started/tx2_setup.html
index 30aa7a0..ea44cfe 100644
--- a/versions/master/get_started/tx2_setup.html
+++ b/versions/master/get_started/tx2_setup.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
-<title><no title=""> — mxnet documentation</no></title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
+<title><no title> — mxnet documentation</title>
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -236,7 +234,7 @@ Previous Navbar Layout End -->
<div class="content">
<!-- This page should be deleted after sometime (Allowing search engines
to update links) -->
-<meta content="3; url=http://mxnet.io/get_started/install.html" http-equiv="refresh"/>
+<meta content="3; url=http://mxnet.io/get_started/install.html" http-equiv="refresh">
<!-- Just in case redirection does not work -->
<p>
<a href="http://mxnet.io/get_started/install.html">
@@ -247,7 +245,7 @@ to update links) -->
<p> © 2015-2017 DMLC. All rights reserved. </p>
</div>
</div>
-</div>
+</meta></div>
<div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation">
<div class="sphinxsidebarwrapper">
</div>
diff --git a/versions/master/get_started/ubuntu_setup.html b/versions/master/get_started/ubuntu_setup.html
index 976b4f0..c7ccf4a 100644
--- a/versions/master/get_started/ubuntu_setup.html
+++ b/versions/master/get_started/ubuntu_setup.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Installing MXNet on Ubuntu — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -366,8 +364,8 @@ Previous Navbar Layout End -->
<div class="section" id="install-the-mxnet-package-for-julia">
<span id="install-the-mxnet-package-for-julia"></span><h3>Install the MXNet Package for Julia<a class="headerlink" href="#install-the-mxnet-package-for-julia" title="Permalink to this headline">¶</a></h3>
<p>The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on <a class="reference external" href="https://github.com/dmlc/MXNet.jl">GitHub</a>. To use Julia binding it with an existing libmxnet installation, set the <code class="docutils literal"><span class="pre">MXNET_HOME</span></code> environment variable by running the following command:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/<path to="">/libmxnet
-</path></pre></div>
+<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/<path to>/libmxnet
+</pre></div>
</div>
<p>The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the <code class="docutils literal"><span class="pre">libmxnet.so</span></code> file at <code class="docutils literal"><span class="pre">$MXNET_HOME/lib</span></code>. For example, if the root directory of libmxnet is <code class="docutils literal"><span class="pre">~</span></code>, you would run the following command:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/~/libmxnet
@@ -390,27 +388,27 @@ Previous Navbar Layout End -->
<span id="use-the-prebuilt-binary-package"></span><h4>Use the Prebuilt Binary Package<a class="headerlink" href="#use-the-prebuilt-binary-package" title="Permalink to this headline">¶</a></h4>
<p>For Linux users, MXNet provides prebuilt binary packages that support computers with either GPU or CPU processors. To download and build these packages using <code class="docutils literal"><span class="pre">Maven</span></code>, change the <code class="docutils literal"><span class="pre">artifactId</span></code> in the following Maven dependency to match your architecture:</p>
<div class="highlight-HTML"><div class="highlight"><pre><span></span><span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
- <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"><!--</span--><span class="nt">groupId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_<span class="p"><</span><span class="nt">system</span> <span class="na">architecture</span><span class="p">><!--</span--><span class="nt">artifactId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"><!--</span--><span class="nt">version</span><span class="p">></span>
-<span class="p"><!--</span--><span class="nt">dependency</span><span class="p">></span>
-</span></span></span></span></pre></div>
+ <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_<span class="p"><</span><span class="nt">system</span> <span class="na">architecture</span><span class="p">></</span><span class="nt">artifactId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
+<span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
+</pre></div>
</div>
<p>For example, to download and build the 64-bit CPU-only version for Linux, use:</p>
<div class="highlight-HTML"><div class="highlight"><pre><span></span><span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
- <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"><!--</span--><span class="nt">groupId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_2.10-linux-x86_64-cpu<span class="p"><!--</span--><span class="nt">artifactId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"><!--</span--><span class="nt">version</span><span class="p">></span>
-<span class="p"><!--</span--><span class="nt">dependency</span><span class="p">></span>
-</span></span></span></span></pre></div>
+ <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_2.10-linux-x86_64-cpu<span class="p"></</span><span class="nt">artifactId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
+<span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
+</pre></div>
</div>
<p>If your native environment differs slightly from the assembly package, for example, if you use the openblas package instead of the atlas package, it’s better to use the mxnet-core package and put the compiled Java native library in your load path:</p>
<div class="highlight-HTML"><div class="highlight"><pre><span></span><span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
- <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"><!--</span--><span class="nt">groupId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-core_2.10<span class="p"><!--</span--><span class="nt">artifactId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"><!--</span--><span class="nt">version</span><span class="p">></span>
-<span class="p"><!--</span--><span class="nt">dependency</span><span class="p">></span>
-</span></span></span></span></pre></div>
+ <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-core_2.10<span class="p"></</span><span class="nt">artifactId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
+<span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
+</pre></div>
</div>
</div>
<div class="section" id="build-the-library-from-source-code">
diff --git a/versions/master/get_started/why_mxnet.html b/versions/master/get_started/why_mxnet.html
index dc3f497..4ed0a4a 100644
--- a/versions/master/get_started/why_mxnet.html
+++ b/versions/master/get_started/why_mxnet.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Why MXNet? — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -307,7 +305,7 @@ However you plan to use MXNet, neural networks make for a powerful motivating ex
In the case of image classification, <code class="docutils literal"><span class="pre">X</span></code> might represent the pixel values of an image, and <code class="docutils literal"><span class="pre">Y</span></code> might represent the corresponding probabilities that the image belongs to each of <code class="docutils literal"><span class="pre">10</span></code> classes.
For language translation, <code class="docutils literal"><span class="pre">X</span></code> and <code class="docutils literal"><span class="pre">Y</span></code> both might denote sequences of words. We’ll revisit the way you might represent sequences in subsequent tutorials - so for now it’s safe to think of <code class="docutils literal"><span class="pre">X</span></code> and <code class="docutils literal"><span class="pre">Y</span></code> as fixed length vectors.</p>
<p>To perform this mapping, neural networks stack <em>layers</em> of computation. Each layer consists of a linear function followed by a nonlinear transformation. In <em>MXNet</em> we might express this as:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">hidden_linear</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">hidden_linear</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
<span class="n">hidden_activation</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">hidden_linear</span><span class="p">)</span>
</pre></div>
</div>
@@ -333,7 +331,7 @@ In either case, the dataset typically must be managed by the CPU.</p>
<p><img alt="alt text" src="https://raw.githubusercontent.com/kevinthesun/web-data/master/mxnet/get-started/architecture.png"/></p>
<p>To compute the transformation of a neural network quickly, we need both the parameters and data points to make it into GPU memory. For any example <em>X</em>, the parameters <em>W</em> are the same. Moreover the size of the model tends to dwarf the size of an individual example. So we might arrive at the natural insight that parameters should always live on the GPU, even if the dataset itself must live on the CPU or stream in. This prevents IO from becoming the bottleneck during train [...]
<p>Fortunately, <em>MXNet</em> makes this kind of assignment easy.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet.ndarray</span> <span class="k">as</span> <span class="nn">nd</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet.ndarray</span> <span class="kn">as</span> <span class="nn">nd</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">10000</span><span class="p">,</span> <span class="mi">40000</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> <span class="c1">#Allocate an array to store 1000 datapoints (of 40k d [...]
<span class="n">W1</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">40000</span><span class="p">,</span> <span class="mi">1024</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> < [...]
@@ -341,7 +339,7 @@ In either case, the dataset typically must be managed by the CPU.</p>
</pre></div>
</div>
<!-- * __Talk about how mxnet also makes it easy to assign a context (on which device the computation happens__ -->
-Similarly, _MXNet_ makes it easy to specify the computing device<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">Context</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">()):</span> <span class="c1"># Absent this statement, by default, MXNet will execute on CPU</span>
+Similarly, _MXNet_ makes it easy to specify the computing device<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">Context</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">()):</span> <span class="c1"># Absent this statement, by default, MXNet will execute on CPU</span>
<span class="n">h</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W1</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">h1</span><span class="p">,</span> <span class="n">W2</span><span class="p">))</span>
</pre></div>
@@ -354,7 +352,7 @@ Similarly, _MXNet_ makes it easy to specify the computing device<div class="high
<div class="section" id="imperative-programming-with-ndarray">
<span id="imperative-programming-with-ndarray"></span><h3>Imperative Programming with <em>NDArray</em><a class="headerlink" href="#imperative-programming-with-ndarray" title="Permalink to this headline">¶</a></h3>
<p>If you’re familiar with NumPy, then the mechanics of <em>NDArray</em> should be old hat. Like the corresponding <code class="docutils literal"><span class="pre">numpy.ndarray</span></code>, <code class="docutils literal"><span class="pre">mxnet.ndarray</span></code> (<code class="docutils literal"><span class="pre">mxnet.nd</span></code> for short) allows us to represent and manipulate multi-dimensional, homogenous arrays of fixed-size components. Converting between the two is effortl [...]
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Create a numpy array from an mxnet NDArray</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Create a numpy array from an mxnet NDArray</span>
<span class="n">A_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p" [...]
<span class="n">A_nd</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
@@ -365,7 +363,7 @@ Similarly, _MXNet_ makes it easy to specify the computing device<div class="high
<p>Other deep learning libraries tend to rely on NumPy exclusively for imperative programming and the syntax.
So you might reasonably wonder, why do we need to bother with <em>NDArray</em>?
Put simply, other libraries only reap the advantages of GPU computing when executing symbolic functions. By using <em>NDArray</em>, <em>MXNet</em> users can specify device context and run on GPUs. In other words, <em>MXNet</em> gives you access to the high-speed computation for imperative operations that Tensorflow and Theano only give for symbolic operations.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]])</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]])</span>
<span class="n">Y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">]])</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">X</span> <span class="o">+</span> <span class="n">Y</span>
</pre></div>
@@ -376,7 +374,7 @@ Put simply, other libraries only reap the advantages of GPU computing when execu
<p>In addition to providing fast math operations through NDArray, <em>MXNet</em> provides an interface for defining operations abstractly via a computation graph.
With <code class="docutils literal"><span class="pre">mxnet.symbol</span></code>, we define operations abstractly in terms of place holders. For example, in the following code <code class="docutils literal"><span class="pre">a</span></code> and <code class="docutils literal"><span class="pre">b</span></code> stand in for real values that will be supplied at run time.
When we call <code class="docutils literal"><span class="pre">c</span> <span class="pre">=</span> <span class="pre">a+b</span></code>, no numerical computation is performed. This operation simply builds a graph that defines the relationship between <code class="docutils literal"><span class="pre">a</span></code>, <code class="docutils literal"><span class="pre">b</span></code> and <code class="docutils literal"><span class="pre">c</span></code>. In order to perform a real calculation, we [...]
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">executor</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">X</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">:</span> <span class="n">Y</span><span class="p">})</span>
@@ -395,7 +393,7 @@ This higher-level interface has several benefits.</p>
<li>Predefined layers allow you to express large models concisely. Repetitive work, like allocating parameters and inferring their dimensions are eliminated.</li>
<li>Layers are written directly in C++, giving even faster performance than equivalent layers implemented manually in Python.</li>
</ul>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="n">fc1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc1'</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span cla [...]
<span class="n">act1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">fc1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'relu1'</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s [...]
<span class="n">fc2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">act1</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s1">'fc2'</span><span class="p">,</span> <span class="n">num_hidden</span> <span class="o">=</span> <span [...]
diff --git a/versions/master/get_started/windows_setup.html b/versions/master/get_started/windows_setup.html
index 542d703..3ac0247 100644
--- a/versions/master/get_started/windows_setup.html
+++ b/versions/master/get_started/windows_setup.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Installing MXNet in Windows — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -394,24 +392,24 @@ You can install the package directly in the R console.</p>
<li>Create a folder called <code class="docutils literal"><span class="pre">R-package/inst/libs/x64</span></code>. MXNet supports only 64-bit operating systems, so you need the x64 folder.</li>
<li>Copy the following shared libraries (.dll files) into the <code class="docutils literal"><span class="pre">R-package/inst/libs/x64</span></code> folder:</li>
</ol>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">cublas64_80</span><span class="o">.</span><span class="n">dll</span>
-<span class="n">cudart64_80</span><span class="o">.</span><span class="n">dll</span>
-<span class="n">cudnn64_5</span><span class="o">.</span><span class="n">dll</span>
-<span class="n">curand64_80</span><span class="o">.</span><span class="n">dll</span>
-<span class="n">libgcc_s_seh</span><span class="o">-</span><span class="mf">1.</span><span class="n">dll</span>
-<span class="n">libgfortran</span><span class="o">-</span><span class="mf">3.</span><span class="n">dll</span>
-<span class="n">libmxnet</span><span class="o">.</span><span class="n">dll</span>
-<span class="n">libmxnet</span><span class="o">.</span><span class="n">lib</span>
-<span class="n">libopenblas</span><span class="o">.</span><span class="n">dll</span>
-<span class="n">libquadmath</span><span class="o">-</span><span class="mf">0.</span><span class="n">dll</span>
-<span class="n">nvrtc64_80</span><span class="o">.</span><span class="n">dll</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>cublas64_80.dll
+cudart64_80.dll
+cudnn64_5.dll
+curand64_80.dll
+libgcc_s_seh-1.dll
+libgfortran-3.dll
+libmxnet.dll
+libmxnet.lib
+libopenblas.dll
+libquadmath-0.dll
+nvrtc64_80.dll
</pre></div>
</div>
<p>These dlls can be found in <code class="docutils literal"><span class="pre">prebuildbase_win10_x64_vc14/3rdparty/cudart</span></code>, <code class="docutils literal"><span class="pre">prebuildbase_win10_x64_vc14/3rdparty/openblas/bin</span></code>, <code class="docutils literal"><span class="pre">mxnet_x64_vc14_gpu/build</span></code>, <code class="docutils literal"><span class="pre">mxnet_x64_vc14_gpu/lib</span></code> and the <code class="docutils literal"><span class="pre">cuDNN</s [...]
<ol class="simple">
<li>Copy the header files from <code class="docutils literal"><span class="pre">dmlc</span></code>, <code class="docutils literal"><span class="pre">mxnet</span></code> and <code class="docutils literal"><span class="pre">nnvm</span></code> into <code class="docutils literal"><span class="pre">./R-package/inst/include</span></code>. It should look like:</li>
</ol>
-<div class="highlight-default"><div class="highlight"><pre><span></span>./R-package/inst
+<div class="highlight-python"><div class="highlight"><pre><span></span>./R-package/inst
└── include
├── dmlc
├── mxnet
@@ -446,8 +444,8 @@ R CMD INSTALL --build --no-multiarch R-package
<div class="section" id="install-the-mxnet-package-for-julia">
<span id="install-the-mxnet-package-for-julia"></span><h2>Install the MXNet Package for Julia<a class="headerlink" href="#install-the-mxnet-package-for-julia" title="Permalink to this headline">¶</a></h2>
<p>The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on <a class="reference external" href="https://github.com/dmlc/MXNet.jl">GitHub</a>. To use Julia binding it with an existing libmxnet installation, set the <code class="docutils literal"><span class="pre">MXNET_HOME</span></code> environment variable by running the following command:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/<path to="">/libmxnet
-</path></pre></div>
+<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/<path to>/libmxnet
+</pre></div>
</div>
<p>The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the <code class="docutils literal"><span class="pre">libmxnet.so</span></code> file at <code class="docutils literal"><span class="pre">$MXNET_HOME/lib</span></code>. For example, if the root directory of libmxnet is <code class="docutils literal"><span class="pre">~</span></code>, you would run the following command:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/~/libmxnet
@@ -470,27 +468,27 @@ R CMD INSTALL --build --no-multiarch R-package
<span id="use-the-prebuilt-binary-package"></span><h3>Use the Prebuilt Binary Package<a class="headerlink" href="#use-the-prebuilt-binary-package" title="Permalink to this headline">¶</a></h3>
<p>For Linux and OS X (Mac) users, MXNet provides prebuilt binary packages that support computers with either GPU or CPU processors. To download and build these packages using <code class="docutils literal"><span class="pre">Maven</span></code>, change the <code class="docutils literal"><span class="pre">artifactId</span></code> in the following Maven dependency to match your architecture:</p>
<div class="highlight-HTML"><div class="highlight"><pre><span></span><span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
- <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"><!--</span--><span class="nt">groupId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_<span class="p"><</span><span class="nt">system</span> <span class="na">architecture</span><span class="p">><!--</span--><span class="nt">artifactId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"><!--</span--><span class="nt">version</span><span class="p">></span>
-<span class="p"><!--</span--><span class="nt">dependency</span><span class="p">></span>
-</span></span></span></span></pre></div>
+ <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_<span class="p"><</span><span class="nt">system</span> <span class="na">architecture</span><span class="p">></</span><span class="nt">artifactId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
+<span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
+</pre></div>
</div>
<p>For example, to download and build the 64-bit CPU-only version for Linux, use:</p>
<div class="highlight-HTML"><div class="highlight"><pre><span></span><span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
- <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"><!--</span--><span class="nt">groupId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_2.10-linux-x86_64-cpu<span class="p"><!--</span--><span class="nt">artifactId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"><!--</span--><span class="nt">version</span><span class="p">></span>
-<span class="p"><!--</span--><span class="nt">dependency</span><span class="p">></span>
-</span></span></span></span></pre></div>
+ <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_2.10-linux-x86_64-cpu<span class="p"></</span><span class="nt">artifactId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
+<span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
+</pre></div>
</div>
<p>If your native environment differs slightly from the assembly package, for example, if you use the openblas package instead of the atlas package, it’s better to use the mxnet-core package and put the compiled Java native library in your load path:</p>
<div class="highlight-HTML"><div class="highlight"><pre><span></span><span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
- <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"><!--</span--><span class="nt">groupId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-core_2.10<span class="p"><!--</span--><span class="nt">artifactId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"><!--</span--><span class="nt">version</span><span class="p">></span>
-<span class="p"><!--</span--><span class="nt">dependency</span><span class="p">></span>
-</span></span></span></span></pre></div>
+ <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-core_2.10<span class="p"></</span><span class="nt">artifactId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
+<span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
+</pre></div>
</div>
</div>
<div class="section" id="build-the-library-from-source-code">
diff --git a/versions/master/how_to/bucketing.html b/versions/master/how_to/bucketing.html
index 6a20a19..24d0fe2 100644
--- a/versions/master/how_to/bucketing.html
+++ b/versions/master/how_to/bucketing.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Bucketing in MXNet — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/how_to/caffe.html b/versions/master/how_to/caffe.html
index 5c52621..2b81bcb 100644
--- a/versions/master/how_to/caffe.html
+++ b/versions/master/how_to/caffe.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>How to | Convert from Caffe to MXNet — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/how_to/cloud.html b/versions/master/how_to/cloud.html
index b8c824e..541d44f 100644
--- a/versions/master/how_to/cloud.html
+++ b/versions/master/how_to/cloud.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet on the Cloud — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -301,8 +299,8 @@ on how to connect to a Jupyter notebook running on an EC2 instance.</p>
on how to install CUDA 7.0 on Ubuntu 14.04.</p>
<p><strong><em>Note:</em></strong> We tried CUDA 7.5 on Nov 7, 2015, but found it problematic.</p>
<p>You can install the rest using the package manager. For example, on Ubuntu:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">sudo</span> <span class="n">apt</span><span class="o">-</span><span class="n">get</span> <span class="n">update</span>
-<span class="n">sudo</span> <span class="n">apt</span><span class="o">-</span><span class="n">get</span> <span class="n">install</span> <span class="o">-</span><span class="n">y</span> <span class="n">build</span><span class="o">-</span><span class="n">essential</span> <span class="n">git</span> <span class="n">libcurl4</span><span class="o">-</span><span class="n">openssl</span><span class="o">-</span><span class="n">dev</span> <span class="n">libatlas</span><span class="o">-</span><spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>sudo apt-get update
+sudo apt-get install -y build-essential git libcurl4-openssl-dev libatlas-base-dev libopencv-dev python-numpy
</pre></div>
</div>
<p>The Amazon Machine Image (AMI) <a class="reference external" href="https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-12fd8178">ami-12fd8178</a> has the packages listed above installed.</p>
diff --git a/versions/master/how_to/develop_and_hack.html b/versions/master/how_to/develop_and_hack.html
index a397fc5..8f542e4 100644
--- a/versions/master/how_to/develop_and_hack.html
+++ b/versions/master/how_to/develop_and_hack.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Develop and Hack MXNet — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/how_to/env_var.html b/versions/master/how_to/env_var.html
index ffb5823..0354286 100644
--- a/versions/master/how_to/env_var.html
+++ b/versions/master/how_to/env_var.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Environment Variables — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -239,7 +237,7 @@ Previous Navbar Layout End -->
<p>MXNet has several settings that you can change with environment variables.
Typically, you wouldn’t need to change these settings, but they are listed here for reference.</p>
<p>For example, you can set these environment variables in Linux or macOS as follows:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">MXNET_GPU_WORKER_NTHREADS</span><span class="o">=</span><span class="mi">3</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>export MXNET_GPU_WORKER_NTHREADS=3
</pre></div>
</div>
<div class="section" id="set-the-number-of-threads">
diff --git a/versions/master/how_to/faq.html b/versions/master/how_to/faq.html
index 6566177..e0875d5 100644
--- a/versions/master/how_to/faq.html
+++ b/versions/master/how_to/faq.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Frequently Asked Questions — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/how_to/finetune.html b/versions/master/how_to/finetune.html
index 32cac91..463ecec 100644
--- a/versions/master/how_to/finetune.html
+++ b/versions/master/how_to/finetune.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Fine-tune with Pretrained Models — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -266,8 +264,8 @@ identification.</p>
even outperform state-of-the-art results on caltech-256.</p>
<table border="1" class="docutils">
<colgroup>
-<col width="50%"></col>
-<col width="50%"></col>
+<col width="50%"/>
+<col width="50%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Network</th>
@@ -289,6 +287,8 @@ even outperform state-of-the-art results on caltech-256.</p>
training set, and the rest for the validation set. We resize images into 256x256
size and pack them into the rec file. The scripts to prepare the data is as
following.</p>
+<blockquote>
+<div>In order to successfully run the following bash script on Windows please use https://cygwin.com/install.html .</div></blockquote>
<div class="highlight-sh"><div class="highlight"><pre><span></span>wget http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tar
tar -xf 256_ObjectCategories.tar
diff --git a/versions/master/how_to/finetune.ipynb b/versions/master/how_to/finetune.ipynb
index 5be815a..da2f717 100644
--- a/versions/master/how_to/finetune.ipynb
+++ b/versions/master/how_to/finetune.ipynb
@@ -1 +1 @@
-{"nbformat": 4, "cells": [{"source": "# Fine-tune with Pretrained Models\n\nMany of the exciting deep learning algorithms for computer vision require\nmassive datasets for training. The most popular benchmark dataset,\n[ImageNet](http://www.image-net.org/), for example, contains one million images\nfrom one thousand categories. But for any practical problem, we typically have\naccess to comparatively small datasets. In these cases, if we were to train a\nneural network's weights from scr [...]
\ No newline at end of file
+{"nbformat": 4, "cells": [{"source": "# Fine-tune with Pretrained Models\n\nMany of the exciting deep learning algorithms for computer vision require\nmassive datasets for training. The most popular benchmark dataset,\n[ImageNet](http://www.image-net.org/), for example, contains one million images\nfrom one thousand categories. But for any practical problem, we typically have\naccess to comparatively small datasets. In these cases, if we were to train a\nneural network's weights from scr [...]
\ No newline at end of file
diff --git a/versions/master/how_to/index.html b/versions/master/how_to/index.html
index 319cb9b..18d9c45 100644
--- a/versions/master/how_to/index.html
+++ b/versions/master/how_to/index.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet How To — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="s3_integration.html" rel="next" title="Use data from S3 for training"/>
+<link href="s3_integration.html" rel="next" title="Use data from S3 for training">
<link href="../api/perl/index.html" rel="prev" title="MXNet - Perl API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -153,7 +151,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -180,7 +178,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -190,11 +188,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -229,7 +227,7 @@ Previous Navbar Layout End -->
<li class="toctree-l1"><a class="reference internal" href="../api/c++/index.html">C++ Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../api/scala/index.html">Scala Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../api/perl/index.html">Perl Documents</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">HowTo Documents</a><ul>
+<li class="toctree-l1 current"><a class="current reference internal" href="">HowTo Documents</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#modeling">Modeling</a><ul>
<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/how_to/finetune.html">How do I fine-tune pre-trained models to a new dataset?</a></li>
<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/how_to/bucketing.html">How do I work with variable-length input in MXNet (bucketing)?</a></li>
@@ -249,6 +247,7 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference external" href="http://docs.aws.amazon.com/mxnet/latest/dg/mxnet-on-ec2-instance.html">How to setup MXNet on AWS?</a></li>
<li class="toctree-l3"><a class="reference external" href="http://docs.aws.amazon.com/mxnet/latest/dg/mxnet-on-ec2-cluster.html">How to do distributed training using MXNet on AWS?</a></li>
<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/tutorials/embedded/wine_detector.html">How do I run MXNet on a Raspberry Pi for computer vision?</a></li>
+<li class="toctree-l3"><a class="reference external" href="https://github.com/dmlc/keras/wiki/Installation">How do I run Keras 1.2.2 with mxnet backend?</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#extend-and-contribute-to-mxnet">Extend and Contribute to MXNet</a><ul>
@@ -275,7 +274,7 @@ Previous Navbar Layout End -->
<p>This section addresses common questions about how to use <em>MXNet</em>. These include performance issues, e.g., how to train with multiple GPUs.
They also include workflow questions, e.g., how to visualize a neural network computation graph.
These answers are fairly focused. For more didactic, self-contained introductions to neural networks
-and full working examples, visit the <a class="reference internal" href="../tutorials/index.html"><span class="doc">tutorials section</span></a>.</p>
+and full working examples, visit the <a class="reference internal" href="../tutorials/index.html"><em>tutorials section</em></a>.</p>
<div class="section" id="modeling">
<span id="modeling"></span><h2>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">¶</a></h2>
<div class="toctree-wrapper compound">
@@ -306,6 +305,7 @@ and full working examples, visit the <a class="reference internal" href="../tuto
<li class="toctree-l1"><a class="reference external" href="http://docs.aws.amazon.com/mxnet/latest/dg/mxnet-on-ec2-instance.html">How to setup MXNet on AWS?</a></li>
<li class="toctree-l1"><a class="reference external" href="http://docs.aws.amazon.com/mxnet/latest/dg/mxnet-on-ec2-cluster.html">How to do distributed training using MXNet on AWS?</a></li>
<li class="toctree-l1"><a class="reference external" href="http://mxnet.io/tutorials/embedded/wine_detector.html">How do I run MXNet on a Raspberry Pi for computer vision?</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/dmlc/keras/wiki/Installation">How do I run Keras 1.2.2 with mxnet backend?</a></li>
</ul>
</div>
</div>
diff --git a/versions/master/how_to/model_parallel_lstm.html b/versions/master/how_to/model_parallel_lstm.html
index 071a003..527c737 100644
--- a/versions/master/how_to/model_parallel_lstm.html
+++ b/versions/master/how_to/model_parallel_lstm.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Training with Multiple GPUs Using Model Parallelism — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -247,7 +245,7 @@ to multiple machines and multiple GPUs.
One key strength of <em>MXNet</em> is its ability to leverage
powerful heterogeneous hardware environments to achieve significant speedups.</p>
<p>There are two primary ways that we can spread a workload across multiple devices.
-In a previous document, <a class="reference internal" href="multi_devices.html"><span class="doc">we addressed data parallelism</span></a>,
+In a previous document, <a class="reference internal" href="multi_devices.html"><em>we addressed data parallelism</em></a>,
an approach in which examples within a batch are divvied among the available devices.
With data parallelism, each device stores a complete copy of the model.
Here, we explore <em>model parallelism</em>, a different approach.
diff --git a/versions/master/how_to/multi_devices.html b/versions/master/how_to/multi_devices.html
index d8005c4..db23785 100644
--- a/versions/master/how_to/multi_devices.html
+++ b/versions/master/how_to/multi_devices.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Run MXNet on Multiple CPU/GPUs with Data Parallelism — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
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+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
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+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
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+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
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<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -249,7 +247,7 @@ updated model are communicated across these devices.</p>
<p>MXNet also supports model parallelism.
In this approach, each device holds onto only part of the model.
This proves useful when the model is too large to fit onto a single device.
-As an example, see the following <a class="reference internal" href="model_parallel_lstm.html"><span class="doc">tutorial</span></a>
+As an example, see the following <a class="reference internal" href="model_parallel_lstm.html"><em>tutorial</em></a>
which shows how to use model parallelism for training a multi-layer LSTM model.
In this tutorial, we’ll focus on data parallelism.</p>
</div>
@@ -396,14 +394,14 @@ But for machines that have multiple interfaces,
we can specify which network interface to use for data
communication by the environment variable <code class="docutils literal"><span class="pre">DMLC_INTERFACE</span></code>.
For example, to use the interface <code class="docutils literal"><span class="pre">eth0</span></code>, we can</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">DMLC_INTERFACE</span><span class="o">=</span><span class="n">eth0</span><span class="p">;</span> <span class="n">python</span> <span class="o">../../</span><span class="n">tools</span><span class="o">/</span><span class="n">launch</span><span class="o">.</span><span class="n">py</span> <span class="o">...</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>export DMLC_INTERFACE=eth0; python ../../tools/launch.py ...
</pre></div>
</div>
</div>
<div class="section" id="debug-connection">
<span id="debug-connection"></span><h3>Debug Connection<a class="headerlink" href="#debug-connection" title="Permalink to this headline">¶</a></h3>
<p>Set<code class="docutils literal"><span class="pre">PS_VERBOSE=1</span></code> to see the debug logging, e.g</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">PS_VERBOSE</span><span class="o">=</span><span class="mi">1</span><span class="p">;</span> <span class="n">python</span> <span class="o">../../</span><span class="n">tools</span><span class="o">/</span><span class="n">launch</span><span class="o">.</span><span class="n">py</span> <span class="o">...</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>export PS_VERBOSE=1; python ../../tools/launch.py ...
</pre></div>
</div>
</div>
diff --git a/versions/master/how_to/new_op.html b/versions/master/how_to/new_op.html
index 20917ad..bef434b 100644
--- a/versions/master/how_to/new_op.html
+++ b/versions/master/how_to/new_op.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
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+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>How to Create New Operators (Layers) — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -362,7 +360,7 @@ We can register any attribute to any operator, with the syntax</p>
</pre></div>
</div>
<p>These attributes can be retrieved later for various purposes.
-For example, <code class="docutils literal"><span class="pre">FInferShape</span></code> is used for shape inference, <code class="docutils literal"><span class="pre">FCompute<cpu></cpu></span></code> is used for carrying out actual computation on CPU.</p>
+For example, <code class="docutils literal"><span class="pre">FInferShape</span></code> is used for shape inference, <code class="docutils literal"><span class="pre">FCompute<cpu></span></code> is used for carrying out actual computation on CPU.</p>
<p>As long as all attributes registered with the same key have the same type,
we can register any attributes to operators.
The more attribute an operator provides,
@@ -394,12 +392,12 @@ You can find the definition for them in the following two files:</p>
</pre></div>
</div>
<p>The parsed arguments can then be accessed in other attribute functions with</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">double</span> <span class="n">alpha</span> <span class="o">=</span> <span class="n">nnvm</span><span class="p">::</span><span class="n">get</span><span class="o"><</span><span class="n">double</span><span class="o">></span><span class="p">(</span><span class="n">attrs</span><span class="o">.</span><span class="n">parsed</span><span class="p">);</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>double alpha = nnvm::get<double>(attrs.parsed);
</pre></div>
</div>
<p>More complex ops can use <code class="docutils literal"><span class="pre">dmlc::Parameters</span></code> and <code class="docutils literal"><span class="pre">ParamParser</span></code> (defined in operator_common.h) for parsing:</p>
-<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf"><dmlc parameter.h=""></dmlc></span><span class="cp"></span>
-<span class="cp">#include</span> <span class="cpf"><operator_common.h></operator_common.h></span><span class="cp"></span>
+<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf"><dmlc/parameter.h></span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf"><operator_common.h></span><span class="cp"></span>
<span class="k">struct</span> <span class="nl">ActivationParam</span> <span class="p">:</span> <span class="k">public</span> <span class="n">dmlc</span><span class="o">::</span><span class="n">Parameter</span><span class="o"><</span><span class="n">ActivationParam</span><span class="o">></span> <span class="p">{</span>
<span class="c1">// use int for enumeration</span>
<span class="kt">int</span> <span class="n">act_type</span><span class="p">;</span>
@@ -415,7 +413,7 @@ You can find the definition for them in the following two files:</p>
<span class="n">NNVM_REGISTER_OP</span><span class="p">(</span><span class="n">Activation</span><span class="p">)</span>
<span class="p">.</span><span class="n">set_attr_parser</span><span class="p">(</span><span class="n">ParamParser</span><span class="o"><</span><span class="n">ActivationParam</span><span class="o">></span><span class="p">);</span>
<span class="c1">// access with:</span>
-<span class="c1">// const ActivationParam& param = nnvm::get<activationparam>(attrs.parsed);</activationparam></span>
+<span class="c1">// const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);</span>
</pre></div>
</div>
</div>
@@ -428,7 +426,7 @@ you can set <code class="docutils literal"><span class="pre">n_in</span></code>/
that return the number of inputs/outputs based on parsed arguments.</p>
<p>Outputs can be made invisible to other operators by registering <code class="docutils literal"><span class="pre">FNumVisibleOutputs</span></code>
and returning an integer smaller than <code class="docutils literal"><span class="pre">n_out</span></code>.</p>
-<p>Inputs/outputs can be named by registering <code class="docutils literal"><span class="pre">FListInputNames</span></code> and <code class="docutils literal"><span class="pre">FListOutputNames</span></code> with prototype <code class="docutils literal"><span class="pre">std::vector<std::string>(const</std::string></span> <span class="pre">NodeAttrs&</span> <span class="pre">attrs)</span></code>.</p>
+<p>Inputs/outputs can be named by registering <code class="docutils literal"><span class="pre">FListInputNames</span></code> and <code class="docutils literal"><span class="pre">FListOutputNames</span></code> with prototype <code class="docutils literal"><span class="pre">std::vector<std::string>(const</span> <span class="pre">NodeAttrs&</span> <span class="pre">attrs)</span></code>.</p>
</div>
<div class="section" id="argument-descriptions">
<span id="argument-descriptions"></span><h4>Argument Descriptions<a class="headerlink" href="#argument-descriptions" title="Permalink to this headline">¶</a></h4>
@@ -443,17 +441,17 @@ can add argument descriptions in bulk with <code class="docutils literal"><span
</div>
<div class="section" id="finfershape-or-tisbackward-for-backward-only-ops">
<span id="finfershape-or-tisbackward-for-backward-only-ops"></span><h4>FInferShape or TIsBackward (for Backward Only Ops)<a class="headerlink" href="#finfershape-or-tisbackward-for-backward-only-ops" title="Permalink to this headline">¶</a></h4>
-<p>Normally operators need to have <code class="docutils literal"><span class="pre">FInferShape</span></code> with prototype <code class="docutils literal"><span class="pre">bool(const</span> <span class="pre">nnvm::NodeAttrs&</span> <span class="pre">attrs,</span> <span class="pre">std::vector<tshape></tshape></span> <span class="pre">*in_attrs,</span> <span class="pre">std::vector<tshape></tshape></span> <span class="pre">*out_attrs)</span></code>. <code class="docutils literal"><s [...]
-<p>Operators that are only used for a backward pass can instead register <code class="docutils literal"><span class="pre">.set_attr<nnvm::tisbackward>("TIsBackward",</nnvm::tisbackward></span> <span class="pre">true)</span></code>
+<p>Normally operators need to have <code class="docutils literal"><span class="pre">FInferShape</span></code> with prototype <code class="docutils literal"><span class="pre">bool(const</span> <span class="pre">nnvm::NodeAttrs&</span> <span class="pre">attrs,</span> <span class="pre">std::vector<TShape></span> <span class="pre">*in_attrs,</span> <span class="pre">std::vector<TShape></span> <span class="pre">*out_attrs)</span></code>. <code class="docutils literal"><span class="pre">FI [...]
+<p>Operators that are only used for a backward pass can instead register <code class="docutils literal"><span class="pre">.set_attr<nnvm::TIsBackward>("TIsBackward",</span> <span class="pre">true)</span></code>
and their shapes with be copied from the corresponding forward operators.</p>
</div>
<div class="section" id="finfertype">
<span id="finfertype"></span><h4>FInferType<a class="headerlink" href="#finfertype" title="Permalink to this headline">¶</a></h4>
-<p>Similar to <code class="docutils literal"><span class="pre">FInferShape</span></code>, <code class="docutils literal"><span class="pre">FInferType</span></code> fills unknown types (-1) based on known types. Use <code class="docutils literal"><span class="pre">ElemwiseType<n_in,< span=""> <span class="pre">n_out></span></n_in,<></span></code> for simple operators with uniform types. Operators that registered <code class="docutils literal"><span class="pre">TIsBackward</span></code> do [...]
+<p>Similar to <code class="docutils literal"><span class="pre">FInferShape</span></code>, <code class="docutils literal"><span class="pre">FInferType</span></code> fills unknown types (-1) based on known types. Use <code class="docutils literal"><span class="pre">ElemwiseType<n_in,</span> <span class="pre">n_out></span></code> for simple operators with uniform types. Operators that registered <code class="docutils literal"><span class="pre">TIsBackward</span></code> don’t need to registe [...]
</div>
<div class="section" id="finplaceoption-optional">
<span id="finplaceoption-optional"></span><h4>FInplaceOption (Optional)<a class="headerlink" href="#finplaceoption-optional" title="Permalink to this headline">¶</a></h4>
-<p><code class="docutils literal"><span class="pre">FInplaceOption</span></code> with prototype <code class="docutils literal"><span class="pre">std::vector<std::pair<int,< span=""> <span class="pre">int></span> <span class="pre">>(const</span> <span class="pre">NodeAttrs&</span> <span class="pre">attrs)</span></std::pair<int,<></span></code>
+<p><code class="docutils literal"><span class="pre">FInplaceOption</span></code> with prototype <code class="docutils literal"><span class="pre">std::vector<std::pair<int,</span> <span class="pre">int></span> <span class="pre">>(const</span> <span class="pre">NodeAttrs&</span> <span class="pre">attrs)</span></code>
specifies which input/output pairs can be computed in-place
and share memory with each other.
Each pair (i, j) in the returned list means
@@ -472,8 +470,8 @@ output or nothing to calculating gradient.</p>
where heads are input entries to the backward op, composed from ograds and n->inputs.</p>
</div>
<div class="section" id="fcompute-xpu">
-<span id="fcompute-xpu"></span><h4>FCompute<xpu><a class="headerlink" href="#fcompute-xpu" title="Permalink to this headline">¶</a></xpu></h4>
-<p>Simple operators can register FCompute<xpu> with <code class="docutils literal"><span class="pre">.set_attr<fcompute>("FCompute<cpu>",</cpu></fcompute></span> <span class="pre">...)</span></code> and <code class="docutils literal"><span class="pre">.set_attr<fcompute>("FCompute<gpu>",</gpu></fcompute></span> <span class="pre">...)</span></code> for both CPU and (optionally) GPU computation.</xpu></p>
+<span id="fcompute-xpu"></span><h4>FCompute<xpu><a class="headerlink" href="#fcompute-xpu" title="Permalink to this headline">¶</a></h4>
+<p>Simple operators can register FCompute<xpu> with <code class="docutils literal"><span class="pre">.set_attr<FCompute>("FCompute<cpu>",</span> <span class="pre">...)</span></code> and <code class="docutils literal"><span class="pre">.set_attr<FCompute>("FCompute<gpu>",</span> <span class="pre">...)</span></code> for both CPU and (optionally) GPU computation.</xpu></p>
<p>FCompute has prototype</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="kt">void</span><span class="p">(</span><span class="k">const</span> <span class="n">nnvm</span><span class="o">::</span><span class="n">NodeAttrs</span><span class="o">&</span> <span class="n">attrs</span><span class="p">,</span>
<span class="k">const</span> <span class="n">OpContext</span><span class="o">&</span> <span class="n">ctx</span><span class="p">,</span>
@@ -517,7 +515,7 @@ indicating that a <code class="docutils literal"><span class="pre">+=</span></co
<span class="p">[](</span><span class="k">const</span> <span class="n">NodeAttrs</span><span class="o">&</span> <span class="n">attrs</span><span class="p">){</span>
<span class="k">return</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">pair</span><span class="o"><</span><span class="kt">int</span><span class="p">,</span> <span class="kt">int</span><span class="o">></span> <span class="o">></span><span class="p">{{</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">}};</span>
<span class="p">})</span>
-<span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">FCompute</span><span class="o">></span><span class="p">(</span><span class="s">"FCompute<cpu>"</cpu></span><span class="p">,</span> <span class="n">UnaryCompute</span><span class="o"><</span><span class="n">cpu</span><span class="p">,</span> <span class="n">mshadow_op</span><span class="o">::</span><span class="n">abs</span><span class="o">></span><span class="p">)</span>
+<span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">FCompute</span><span class="o">></span><span class="p">(</span><span class="s">"FCompute<cpu>"</span><span class="p">,</span> <span class="n">UnaryCompute</span><span class="o"><</span><span class="n">cpu</span><span class="p">,</span> <span class="n">mshadow_op</span><span class="o">::</span><span class="n">abs</span><span class="o">></span><span class="p">)</span>
<span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">FGradient</span><span class="o">></span><span class="p">(</span><span class="s">"FGradient"</span><span class="p">,</span> <span class="n">ElemwiseGradUseIn</span><span class="p">{</span><span class="s">"_backward_abs"</span><span class="p">});</span>
<span class="p">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s">"data"</span><span class="p">,</span> <span class="s">"NDArray"</span><span class="p">,</span> <span class="s">"Source input"</span><span class="p">)</span>
@@ -530,7 +528,7 @@ indicating that a <code class="docutils literal"><span class="pre">+=</span></co
<span class="p">[](</span><span class="k">const</span> <span class="n">NodeAttrs</span><span class="o">&</span> <span class="n">attrs</span><span class="p">){</span>
<span class="k">return</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">pair</span><span class="o"><</span><span class="kt">int</span><span class="p">,</span> <span class="kt">int</span><span class="o">></span> <span class="o">></span><span class="p">{{</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">},</span> [...]
<span class="p">})</span>
-<span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">FCompute</span><span class="o">></span><span class="p">(</span><span class="s">"FCompute<cpu>"</cpu></span><span class="p">,</span> <span class="n">BinaryCompute</span><span class="o"><</span><span class="n">cpu</span><span class="p">,</span> <span class="n">unary_bwd</span><span class="o"><</span><span class="n">mshadow_op</span><span class="o">::</span><span class="n">sign</span><span class=" [...]
+<span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">FCompute</span><span class="o">></span><span class="p">(</span><span class="s">"FCompute<cpu>"</span><span class="p">,</span> <span class="n">BinaryCompute</span><span class="o"><</span><span class="n">cpu</span><span class="p">,</span> <span class="n">unary_bwd</span><span class="o"><</span><span class="n">mshadow_op</span><span class="o">::</span><span class="n">sign</span><span class="o">></ [...]
</pre></div>
</div>
</div>
@@ -569,7 +567,7 @@ indicating that a <code class="docutils literal"><span class="pre">+=</span></co
<li><a class="reference internal" href="#finfertype">FInferType</a></li>
<li><a class="reference internal" href="#finplaceoption-optional">FInplaceOption (Optional)</a></li>
<li><a class="reference internal" href="#fgradient-optional-for-imperative-use-required-for-symbolic-use">FGradient (Optional for imperative use, required for symbolic use)</a></li>
-<li><a class="reference internal" href="#fcompute-xpu">FCompute<xpu></xpu></a></li>
+<li><a class="reference internal" href="#fcompute-xpu">FCompute<xpu></a></li>
</ul>
</li>
<li><a class="reference internal" href="#example-abs-operator">Example: abs operator</a></li>
diff --git a/versions/master/how_to/nnpack.html b/versions/master/how_to/nnpack.html
index a4c00b8..861bf75 100644
--- a/versions/master/how_to/nnpack.html
+++ b/versions/master/how_to/nnpack.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>NNPACK for Multi-Core CPU Support in MXNet — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
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<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
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<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -255,8 +253,8 @@ When favorable conditions for running NNPACKS are not met,
The following table explains under which conditions NNPACK will work.</p>
<table border="1" class="docutils">
<colgroup>
-<col width="50%"></col>
-<col width="50%"></col>
+<col width="50%"/>
+<col width="50%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">operation</th>
@@ -327,53 +325,53 @@ $ <span class="nb">cd</span> ~
for some popular models it provides significant speedups. These include the most popular image recognition networks: Alexnet, VGG, and Inception-bn.</p>
<p>To benchmark NNPACK, we use <code class="docutils literal"><span class="pre">example/image-classification/benchmark_score.py</span></code>(changed with more range of batch-size). We use CPU e5-2670, MXNET_CPU_NNPACK_NTHREADS=4.</p>
<p>build MXNet without NNPACK, the log is:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">network</span><span class="p">:</span> <span class="n">alexnet</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">device</span><span class="p">:</span> <span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">1</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">6.389429</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">2</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">7.961457</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">4</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">8.950112</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">8</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">9.578176</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">16</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">9.701248</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">32</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">9.839940</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">64</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">10.075369</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">128</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">10.053556</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">256</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">9.972228</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">network</span><span class="p">:</span> <span class="n">vgg</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">device</span><span class="p">:</span> <span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">1</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">1.223822</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">2</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">1.322814</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">4</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">1.383586</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">8</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">1.402376</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">16</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">1.415972</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">32</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">1.428377</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">64</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">1.443987</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">128</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">1.427531</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">256</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">1.435279</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>INFO:root:network: alexnet
+INFO:root:device: cpu(0)
+INFO:root:batch size 1, image/sec: 6.389429
+INFO:root:batch size 2, image/sec: 7.961457
+INFO:root:batch size 4, image/sec: 8.950112
+INFO:root:batch size 8, image/sec: 9.578176
+INFO:root:batch size 16, image/sec: 9.701248
+INFO:root:batch size 32, image/sec: 9.839940
+INFO:root:batch size 64, image/sec: 10.075369
+INFO:root:batch size 128, image/sec: 10.053556
+INFO:root:batch size 256, image/sec: 9.972228
+INFO:root:network: vgg
+INFO:root:device: cpu(0)
+INFO:root:batch size 1, image/sec: 1.223822
+INFO:root:batch size 2, image/sec: 1.322814
+INFO:root:batch size 4, image/sec: 1.383586
+INFO:root:batch size 8, image/sec: 1.402376
+INFO:root:batch size 16, image/sec: 1.415972
+INFO:root:batch size 32, image/sec: 1.428377
+INFO:root:batch size 64, image/sec: 1.443987
+INFO:root:batch size 128, image/sec: 1.427531
+INFO:root:batch size 256, image/sec: 1.435279
</pre></div>
</div>
<p>build MXNet with NNPACK, log is:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">network</span><span class="p">:</span> <span class="n">alexnet</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">device</span><span class="p">:</span> <span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">1</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">19.027215</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">2</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">12.879975</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">4</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">17.424076</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">8</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">21.283966</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">16</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">24.469325</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">32</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">25.910348</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">64</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">27.441672</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">128</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">28.009156</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">256</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">28.918950</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">network</span><span class="p">:</span> <span class="n">vgg</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">device</span><span class="p">:</span> <span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">1</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">3.980907</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">2</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">2.392069</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">4</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">3.610553</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">8</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">4.994450</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">16</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">6.396612</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">32</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">7.614288</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">64</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">8.826084</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">128</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">9.193653</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">batch</span> <span class="n">size</span> <span class="mi">256</span><span class="p">,</span> <span class="n">image</span><span class="o">/</span><span class="n">sec</span><span class="p">:</span> <span class="mf">9.991472</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>INFO:root:network: alexnet
+INFO:root:device: cpu(0)
+INFO:root:batch size 1, image/sec: 19.027215
+INFO:root:batch size 2, image/sec: 12.879975
+INFO:root:batch size 4, image/sec: 17.424076
+INFO:root:batch size 8, image/sec: 21.283966
+INFO:root:batch size 16, image/sec: 24.469325
+INFO:root:batch size 32, image/sec: 25.910348
+INFO:root:batch size 64, image/sec: 27.441672
+INFO:root:batch size 128, image/sec: 28.009156
+INFO:root:batch size 256, image/sec: 28.918950
+INFO:root:network: vgg
+INFO:root:device: cpu(0)
+INFO:root:batch size 1, image/sec: 3.980907
+INFO:root:batch size 2, image/sec: 2.392069
+INFO:root:batch size 4, image/sec: 3.610553
+INFO:root:batch size 8, image/sec: 4.994450
+INFO:root:batch size 16, image/sec: 6.396612
+INFO:root:batch size 32, image/sec: 7.614288
+INFO:root:batch size 64, image/sec: 8.826084
+INFO:root:batch size 128, image/sec: 9.193653
+INFO:root:batch size 256, image/sec: 9.991472
</pre></div>
</div>
<p>The results show that NNPACK can confer a speedup of about 2X~7X as compared to the original <em>MXNet</em> CPU implementation.</p>
diff --git a/versions/master/how_to/perf.html b/versions/master/how_to/perf.html
index 7da1ca8..00e7013 100644
--- a/versions/master/how_to/perf.html
+++ b/versions/master/how_to/perf.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Some Tips for Improving MXNet Performance — mxnet documentation</title>
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-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -283,13 +281,13 @@ to measure the performance on different AWS EC2 machines.</p>
<p>AWS EC2 C4.8xlarge:</p>
<table border="1" class="docutils">
<colgroup>
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+<col width="14%"/>
+<col width="14%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -355,13 +353,13 @@ to measure the performance on different AWS EC2 machines.</p>
<p>AWS EC2 C4.4xlarge:</p>
<table border="1" class="docutils">
<colgroup>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
+<col width="14%"/>
+<col width="14%"/>
+<col width="14%"/>
+<col width="14%"/>
+<col width="14%"/>
+<col width="14%"/>
+<col width="14%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -427,13 +425,13 @@ to measure the performance on different AWS EC2 machines.</p>
<p>AWS EC2 C4.2xlarge:</p>
<table border="1" class="docutils">
<colgroup>
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-<col width="14%"></col>
-<col width="14%"></col>
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+<col width="14%"/>
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+<col width="14%"/>
+<col width="14%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -499,13 +497,13 @@ to measure the performance on different AWS EC2 machines.</p>
<p>AWS EC2 C4.xlarge:</p>
<table border="1" class="docutils">
<colgroup>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
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+<col width="14%"/>
+<col width="14%"/>
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+<col width="14%"/>
+<col width="14%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -571,13 +569,13 @@ to measure the performance on different AWS EC2 machines.</p>
<p>AWS EC2 C4.large:</p>
<table border="1" class="docutils">
<colgroup>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
-<col width="14%"></col>
+<col width="14%"/>
+<col width="14%"/>
+<col width="14%"/>
+<col width="14%"/>
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+<col width="14%"/>
+<col width="14%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -644,7 +642,7 @@ to measure the performance on different AWS EC2 machines.</p>
</div>
<div class="section" id="other-cpu">
<span id="other-cpu"></span><h2>Other CPU<a class="headerlink" href="#other-cpu" title="Permalink to this headline">¶</a></h2>
-<p>If using CPUs (not just Intel CPUs – ARMs also), NNPACK can improve the running performance with 2x~7x, please check <a class="reference internal" href="nnpack.html"><span class="doc">nnpack.md</span></a> for details.</p>
+<p>If using CPUs (not just Intel CPUs – ARMs also), NNPACK can improve the running performance with 2x~7x, please check <a class="reference internal" href="nnpack.html"><em>nnpack.md</em></a> for details.</p>
</div>
<div class="section" id="nvidia-gpu">
<span id="nvidia-gpu"></span><h2>Nvidia GPU<a class="headerlink" href="#nvidia-gpu" title="Permalink to this headline">¶</a></h2>
@@ -664,13 +662,13 @@ and MXNet commit <code class="docutils literal"><span class="pre">0a03417</span>
</ul>
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<colgroup>
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+<col width="14%"/>
+<col width="14%"/>
+<col width="14%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -738,13 +736,13 @@ and MXNet commit <code class="docutils literal"><span class="pre">0a03417</span>
</ul>
<table border="1" class="docutils">
<colgroup>
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-<col width="14%"></col>
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+<col width="14%"/>
+<col width="14%"/>
+<col width="14%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -812,13 +810,13 @@ and MXNet commit <code class="docutils literal"><span class="pre">0a03417</span>
</ul>
<table border="1" class="docutils">
<colgroup>
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+<col width="14%"/>
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<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -894,10 +892,10 @@ where the batch size for Alexnet is increased by 8x.</p>
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+<col width="25%"/>
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<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -944,10 +942,10 @@ where the batch size for Alexnet is increased by 8x.</p>
</ul>
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+<col width="25%"/>
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<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -994,10 +992,10 @@ where the batch size for Alexnet is increased by 8x.</p>
</ul>
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<colgroup>
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-<col width="25%"></col>
-<col width="25%"></col>
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+<col width="25%"/>
+<col width="25%"/>
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<thead valign="bottom">
<tr class="row-odd"><th class="head">Batch</th>
@@ -1082,7 +1080,7 @@ by summarizing at the operator level, instead of a function, kernel, or instruct
for an entire program run, or programmatically for just part of a run.
See <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example/profiler">example/profiler</a>
for complete examples of how to use the profiler in code, but briefly, the Python code looks like:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">mx</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profiler_set_config</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">'all'</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">'profile_output.json'</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="n">mx</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profiler_set_config</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">'all'</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">'profile_output.json'</span><span class="p">)</span>
<span class="n">mx</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profiler_set_state</span><span class="p">(</span><span class="s1">'run'</span><span class="p">)</span>
<span class="c1"># Code to be profiled goes here...</span>
diff --git a/versions/master/how_to/recordio.html b/versions/master/how_to/recordio.html
index 47cbbca..e2d2ba9 100644
--- a/versions/master/how_to/recordio.html
+++ b/versions/master/how_to/recordio.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Create a Dataset Using RecordIO — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
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-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
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<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
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<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
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+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -250,7 +248,7 @@ Previous Navbar Layout End -->
<div class="section" id="step-1-make-an-image-list-file">
<span id="step-1-make-an-image-list-file"></span><h2>Step 1. Make an Image List File<a class="headerlink" href="#step-1-make-an-image-list-file" title="Permalink to this headline">¶</a></h2>
<p>After you download the data, you need to make an image list file. The format is:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">integer_image_index</span> \<span class="n">t</span> <span class="n">label_index</span> \<span class="n">t</span> <span class="n">path_to_image</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>integer_image_index \t label_index \t path_to_image
</pre></div>
</div>
<p>Typically, the program takes the list of names of all of the images, shuffles them, then separates them into two lists: a training filename list and a testing filename list. Write the list in the right format.</p>
@@ -284,7 +282,7 @@ For example, if you have four labels for a single image, you can use the followi
<ol class="simple">
<li>Write the image list files as follows:</li>
</ol>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">integer_image_index</span> \<span class="n">t</span> <span class="n">label_1</span> \<span class="n">t</span> <span class="n">label_2</span> \<span class="n">t</span> <span class="n">label_3</span> \<span class="n">t</span> <span class="n">label_4</span> \<span class="n">t</span> <span class="n">path_to_image</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>integer_image_index \t label_1 \t label_2 \t label_3 \t label_4 \t path_to_image
</pre></div>
</div>
<ol class="simple">
@@ -294,7 +292,7 @@ For example, if you have four labels for a single image, you can use the followi
</pre></div>
</div>
<ol class="simple">
-<li>In the iterator generation code, set <code class="docutils literal"><span class="pre">label_width=4</span></code> and <code class="docutils literal"><span class="pre">path_imglist=<<the< span=""> <span class="pre">PATH</span> <span class="pre">TO</span> <span class="pre">YOUR</span> <span class="pre">image.lst>></span></the<></span></code>, for example:</li>
+<li>In the iterator generation code, set <code class="docutils literal"><span class="pre">label_width=4</span></code> and <code class="docutils literal"><span class="pre">path_imglist=<<The</span> <span class="pre">PATH</span> <span class="pre">TO</span> <span class="pre">YOUR</span> <span class="pre">image.lst>></span></code>, for example:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ImageRecordIter</span><span class="p">(</span>
<span class="n">path_imgrec</span><span class="o">=</span><span class="s2">"data/cifar/train.rec"</span><span class="p">,</span>
diff --git a/versions/master/how_to/s3_integration.html b/versions/master/how_to/s3_integration.html
index 8f3c73a..20766ae 100644
--- a/versions/master/how_to/s3_integration.html
+++ b/versions/master/how_to/s3_integration.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
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+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Use data from S3 for training — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="index.html" rel="up" title="MXNet How To"/>
+<link href="index.html" rel="up" title="MXNet How To">
<link href="../get_started/index.html" rel="next" title="<no title>"/>
<link href="index.html" rel="prev" title="MXNet How To"/>
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
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@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
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@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
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@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
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@@ -235,7 +233,7 @@ Previous Navbar Layout End -->
<li class="toctree-l2"><a class="reference internal" href="index.html#speed">Speed</a></li>
<li class="toctree-l2 current"><a class="reference internal" href="index.html#deployment-environments">Deployment Environments</a><ul class="current">
<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/how_to/smart_device.html">Can I run MXNet on smart or mobile devices?</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">How to use data from S3 for training?</a><ul>
+<li class="toctree-l3 current"><a class="current reference internal" href="">How to use data from S3 for training?</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#step-1-build-mxnet-with-s3-integration-enabled">Step 1: Build MXNet with S3 integration enabled</a></li>
<li class="toctree-l4"><a class="reference internal" href="#step-2-configure-s3-authentication-tokens">Step 2: Configure S3 authentication tokens</a></li>
<li class="toctree-l4"><a class="reference internal" href="#step-3-upload-data-to-s3">Step 3: Upload data to S3</a></li>
@@ -245,6 +243,7 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference external" href="http://docs.aws.amazon.com/mxnet/latest/dg/mxnet-on-ec2-instance.html">How to setup MXNet on AWS?</a></li>
<li class="toctree-l3"><a class="reference external" href="http://docs.aws.amazon.com/mxnet/latest/dg/mxnet-on-ec2-cluster.html">How to do distributed training using MXNet on AWS?</a></li>
<li class="toctree-l3"><a class="reference external" href="http://mxnet.io/tutorials/embedded/wine_detector.html">How do I run MXNet on a Raspberry Pi for computer vision?</a></li>
+<li class="toctree-l3"><a class="reference external" href="https://github.com/dmlc/keras/wiki/Installation">How do I run Keras 1.2.2 with mxnet backend?</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="index.html#extend-and-contribute-to-mxnet">Extend and Contribute to MXNet</a></li>
@@ -263,7 +262,7 @@ Previous Navbar Layout End -->
<span id="use-data-from-s3-for-training"></span><h1>Use data from S3 for training<a class="headerlink" href="#use-data-from-s3-for-training" title="Permalink to this headline">¶</a></h1>
<p>AWS S3 is a cloud-based object storage service that allows storage and retrieval of large amounts of data at a very low cost. This makes it an attractive option to store large training datasets. MXNet is deeply integrated with S3 for this purpose.</p>
<p>An S3 protocol URL (like <code class="docutils literal"><span class="pre">s3://bucket-name/training-data</span></code>) can be provided as a parameter for any data iterator that takes a file path as input. For example,</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">data_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ImageRecordIter</span><span class="p">(</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ImageRecordIter</span><span class="p">(</span>
<span class="n">path_imgrec</span><span class="o">=</span><span class="s2">"s3://bucket-name/training-data/caltech_train.rec"</span><span class="p">,</span>
<span class="n">data_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">227</span><span class="p">,</span> <span class="mi">227</span><span class="p">),</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
@@ -278,7 +277,7 @@ Previous Navbar Layout End -->
<li><p class="first">Install <code class="docutils literal"><span class="pre">libcurl4-openssl-dev</span></code> and <code class="docutils literal"><span class="pre">libssl-dev</span></code> before building MXNet. These packages are required to read/write from AWS S3.</p>
</li>
<li><p class="first">Append <code class="docutils literal"><span class="pre">USE_S3=1</span></code> to <code class="docutils literal"><span class="pre">config.mk</span></code> before building MXNet.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">echo</span> <span class="s2">"USE_S3=1"</span> <span class="o">>></span> <span class="n">config</span><span class="o">.</span><span class="n">mk</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>echo "USE_S3=1" >> config.mk
</pre></div>
</div>
</li>
@@ -287,15 +286,15 @@ Previous Navbar Layout End -->
<div class="section" id="step-2-configure-s3-authentication-tokens">
<span id="step-2-configure-s3-authentication-tokens"></span><h2>Step 2: Configure S3 authentication tokens<a class="headerlink" href="#step-2-configure-s3-authentication-tokens" title="Permalink to this headline">¶</a></h2>
<p>MXNet requires the S3 environment variables <code class="docutils literal"><span class="pre">AWS_ACCESS_KEY_ID</span></code> and <code class="docutils literal"><span class="pre">AWS_SECRET_ACCESS_KEY</span></code> to be set. <a class="reference external" href="https://aws.amazon.com/blogs/security/wheres-my-secret-access-key/">Here</a> are instructions to get the access keys from AWS console.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">AWS_ACCESS_KEY_ID</span><span class="o">=<</span><span class="n">your</span><span class="o">-</span><span class="n">access</span><span class="o">-</span><span class="n">key</span><span class="o">-</span><span class="nb">id</span><span class="o">></span>
-<span class="n">AWS_SECRET_ACCESS_KEY</span><span class="o">=<</span><span class="n">your</span><span class="o">-</span><span class="n">secret</span><span class="o">-</span><span class="n">access</span><span class="o">-</span><span class="n">key</span><span class="o">></span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>export AWS_ACCESS_KEY_ID=<your-access-key-id>
+AWS_SECRET_ACCESS_KEY=<your-secret-access-key>
</pre></div>
</div>
</div>
<div class="section" id="step-3-upload-data-to-s3">
<span id="step-3-upload-data-to-s3"></span><h2>Step 3: Upload data to S3<a class="headerlink" href="#step-3-upload-data-to-s3" title="Permalink to this headline">¶</a></h2>
<p>There are several ways to upload data to S3. One easy way is to use the AWS command line utility. For example, the following <code class="docutils literal"><span class="pre">sync</span></code> command will recursively copy contents from a local directory to a directory in S3.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">aws</span> <span class="n">s3</span> <span class="n">sync</span> <span class="o">./</span><span class="n">training</span><span class="o">-</span><span class="n">data</span> <span class="n">s3</span><span class="p">:</span><span class="o">//</span><span class="n">bucket</span><span class="o">-</span><span class="n">name</span><span class="o">/</span><span class="n">training</span><span class="o">-</spa [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>aws s3 sync ./training-data s3://bucket-name/training-data
</pre></div>
</div>
</div>
@@ -303,7 +302,7 @@ Previous Navbar Layout End -->
<span id="step-4-train-with-data-from-s3"></span><h2>Step 4: Train with data from S3<a class="headerlink" href="#step-4-train-with-data-from-s3" title="Permalink to this headline">¶</a></h2>
<p>Once the data is in S3, it is very straightforward to use it from MXNet. Any data iterator that can read/write data from a local drive can also read/write data from S3.</p>
<p>Let’s modify an existing example code in MXNet repository to read data from S3 instead of local disk. <a class="reference external" href="https://github.com/dmlc/mxnet/blob/master/tests/python/train/test_conv.py"><code class="docutils literal"><span class="pre">mxnet/tests/python/train/test_conv.py</span></code></a> trains a convolutional network using MNIST data from local disk. We’ll do the following change to read the data from S3 instead.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>~/mxnet$ sed -i -- 's/data\//s3:\/\/bucket-name\/training-data\//g' ./tests/python/train/test_conv.py
+<div class="highlight-python"><div class="highlight"><pre><span></span>~/mxnet$ sed -i -- 's/data\//s3:\/\/bucket-name\/training-data\//g' ./tests/python/train/test_conv.py
~/mxnet$ git diff ./tests/python/train/test_conv.py
diff --git a/tests/python/train/test_conv.py b/tests/python/train/test_conv.py
@@ -332,24 +331,24 @@ index 039790e..66a60ce 100644
</pre></div>
</div>
<p>After the above change <code class="docutils literal"><span class="pre">test_conv.py</span></code> will fetch data from S3 instead of the local disk.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">./</span><span class="n">tests</span><span class="o">/</span><span class="n">python</span><span class="o">/</span><span class="n">train</span><span class="o">/</span><span class="n">test_conv</span><span class="o">.</span><span class="n">py</span>
-<span class="p">[</span><span class="mi">21</span><span class="p">:</span><span class="mi">59</span><span class="p">:</span><span class="mi">19</span><span class="p">]</span> <span class="n">src</span><span class="o">/</span><span class="n">io</span><span class="o">/</span><span class="n">s3_filesys</span><span class="o">.</span><span class="n">cc</span><span class="p">:</span><span class="mi">878</span><span class="p">:</span> <span class="n">No</span> <span class="n">AWS</span> <span c [...]
-<span class="p">[</span><span class="mi">21</span><span class="p">:</span><span class="mi">59</span><span class="p">:</span><span class="mi">21</span><span class="p">]</span> <span class="n">src</span><span class="o">/</span><span class="n">io</span><span class="o">/</span><span class="n">iter_mnist</span><span class="o">.</span><span class="n">cc</span><span class="p">:</span><span class="mi">94</span><span class="p">:</span> <span class="n">MNISTIter</span><span class="p">:</span> <spa [...]
-<span class="p">[</span><span class="mi">21</span><span class="p">:</span><span class="mi">59</span><span class="p">:</span><span class="mi">21</span><span class="p">]</span> <span class="n">src</span><span class="o">/</span><span class="n">io</span><span class="o">/</span><span class="n">iter_mnist</span><span class="o">.</span><span class="n">cc</span><span class="p">:</span><span class="mi">94</span><span class="p">:</span> <span class="n">MNISTIter</span><span class="p">:</span> <spa [...]
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">Start</span> <span class="n">training</span> <span class="k">with</span> <span class="p">[</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)]</span>
-<span class="n">Start</span> <span class="n">training</span> <span class="k">with</span> <span class="p">[</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)]</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">Epoch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">Resetting</span> <span class="n">Data</span> <span class="n">Iterator</span>
-<span class="n">Epoch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">Resetting</span> <span class="n">Data</span> <span class="n">Iterator</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">Epoch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">Time</span> <span class="n">cost</span><span class="o">=</span><span class="mf">11.277</span>
-<span class="n">Epoch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">Time</span> <span class="n">cost</span><span class="o">=</span><span class="mf">11.277</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">Epoch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">Validation</span><span class="o">-</span><span class="n">accuracy</span><span class="o">=</span><span class="mf">0.955100</span>
-<span class="n">Epoch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">Validation</span><span class="o">-</span><span class="n">accuracy</span><span class="o">=</span><span class="mf">0.955100</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">Finish</span> <span class="n">fit</span><span class="o">...</span>
-<span class="n">Finish</span> <span class="n">fit</span><span class="o">...</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">Finish</span> <span class="n">predict</span><span class="o">...</span>
-<span class="n">Finish</span> <span class="n">predict</span><span class="o">...</span>
-<span class="n">INFO</span><span class="p">:</span><span class="n">root</span><span class="p">:</span><span class="n">final</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.955100</span>
-<span class="n">final</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.955100</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>python ./tests/python/train/test_conv.py
+[21:59:19] src/io/s3_filesys.cc:878: No AWS Region set, using default region us-east-1
+[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 60000 images, shuffle=1, shape=(100,1,28,28)
+[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 10000 images, shuffle=1, shape=(100,1,28,28)
+INFO:root:Start training with [cpu(0)]
+Start training with [cpu(0)]
+INFO:root:Epoch[0] Resetting Data Iterator
+Epoch[0] Resetting Data Iterator
+INFO:root:Epoch[0] Time cost=11.277
+Epoch[0] Time cost=11.277
+INFO:root:Epoch[0] Validation-accuracy=0.955100
+Epoch[0] Validation-accuracy=0.955100
+INFO:root:Finish fit...
+Finish fit...
+INFO:root:Finish predict...
+Finish predict...
+INFO:root:final accuracy = 0.955100
+final accuracy = 0.955100
</pre></div>
</div>
</div>
diff --git a/versions/master/how_to/smart_device.html b/versions/master/how_to/smart_device.html
index dbfec5c..80da54c 100644
--- a/versions/master/how_to/smart_device.html
+++ b/versions/master/how_to/smart_device.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Deep Learning in a Single File for Smart Devices — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -294,11 +292,11 @@ After compiling the library for smart platforms, the last thing we must do is to
<p>We tested our model on a Nexus 5:</p>
<table border="1" class="docutils">
<colgroup>
-<col width="20%"></col>
-<col width="20%"></col>
-<col width="20%"></col>
-<col width="20%"></col>
-<col width="20%"></col>
+<col width="20%"/>
+<col width="20%"/>
+<col width="20%"/>
+<col width="20%"/>
+<col width="20%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"> </th>
diff --git a/versions/master/how_to/torch.html b/versions/master/how_to/torch.html
index 9c84d4b..85a2176 100644
--- a/versions/master/how_to/torch.html
+++ b/versions/master/how_to/torch.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>How to Use MXNet As an (Almost) Full-function Torch Front End — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
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<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
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@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
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<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
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<form action="../search.html" autocomplete="off" class="" method="get" role="search">
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
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diff --git a/versions/master/how_to/visualize_graph.html b/versions/master/how_to/visualize_graph.html
index 287375f..e7ab8be 100644
--- a/versions/master/how_to/visualize_graph.html
+++ b/versions/master/how_to/visualize_graph.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
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+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>How to visualize Neural Networks as computation graph — mxnet documentation</title>
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-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
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@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/index.html b/versions/master/index.html
index 373b219..72bfc75 100644
--- a/versions/master/index.html
+++ b/versions/master/index.html
@@ -2,13 +2,13 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet: A Scalable Deep Learning Framework</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: './',
@@ -16,7 +16,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -46,11 +46,9 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="genindex.html" rel="index" title="Index"/>
-<link href="search.html" rel="search" title="Search"/>
-<link href="api/python/index.html" rel="next" title="MXNet - Python API"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="api/python/index.html" rel="next" title="MXNet - Python API">
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -150,7 +148,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="./architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="./community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "./" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -177,7 +175,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="./architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -187,11 +185,11 @@ Previous Navbar Layout End -->
<form action="search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -234,6 +232,12 @@ Previous Navbar Layout End -->
<div class="container">
<div class="row">
<div class="col-lg-4 col-sm-12">
+<h3>Introducing - Gluon</h3>
+<p>We’re happy to introduce a new elegant, easy to use, imperative interface for MXNet.
+ </p>
+<a href="http://gluon.mxnet.io">Learn More</a>
+</div>
+<div class="col-lg-4 col-sm-12">
<h3>MXNet 0.10.0 Released</h3>
<p>We're excited to announce the release of MXNet 0.10.0! Check out the release notes for latest updates.</p>
<a href="https://github.com/dmlc/mxnet/releases">Learn More</a>
@@ -244,12 +248,6 @@ Previous Navbar Layout End -->
</p>
<a href="http://incubator.apache.org/projects/mxnet.html">Learn More</a>
</div>
-<div class="col-lg-4 col-sm-12">
-<h3>MXNet in AWS re:Invent 2016</h3>
-<p> Learn how to use MXNet to build neural network models for recommendation systems.
- </p>
-<a href="https://www.youtube.com/watch?v=cftJAuwKWkA">Watch Video</a>
-</div>
</div>
</div>
</div>
@@ -284,47 +282,47 @@ Previous Navbar Layout End -->
MXNet and sponsoring its major developers (alphabetical order).
</p>
<div class="col-lg-4 col-sm-6">
-<img height="60px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/aws-logo.png">
-</img></div>
+<img height="60px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/aws-logo.png"/>
+</div>
<div class="col-lg-4 col-sm-6">
-<img height="55px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/baidu-logo.jpg">
-</img></div>
+<img height="55px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/baidu-logo.jpg"/>
+</div>
<div class="col-lg-4 col-sm-6 smallClear">
-<img height="70px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/cmu-logo.png">
-</img></div>
+<img height="70px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/cmu-logo.png"/>
+</div>
<div class="col-lg-4 col-sm-6">
-<img height="70px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/dato-logo.png">
-</img></div>
+<img height="70px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/dato-logo.png"/>
+</div>
<div class="col-lg-4 col-sm-6">
-<img height="70px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/intel-logo.png">
-</img></div>
+<img height="70px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/intel-logo.png"/>
+</div>
<div class="col-lg-4 col-sm-6">
-<img height="55px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/nyu-logo.jpg">
-</img></div>
+<img height="55px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/nyu-logo.jpg"/>
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-<img height="40px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/nvidia-logo.png">
-</img></div>
+<img height="40px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/nvidia-logo.png"/>
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-<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/mit-logo.png">
-</img></div>
+<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/mit-logo.png"/>
+</div>
<div class="col-lg-4 col-sm-6 smallClear">
-<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/ms-logo.png">
-</img></div>
+<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/ms-logo.png"/>
+</div>
<div class="col-lg-4 col-sm-6 clear smallClear">
-<img height="55px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/hkust-logo.png">
-</img></div>
+<img height="55px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/hkust-logo.png"/>
+</div>
<div class="col-lg-4 col-sm-6">
-<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/tusimple-logo.png">
-</img></div>
+<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/tusimple-logo.png"/>
+</div>
<div class="col-lg-4 col-sm-6">
-<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/university-alberta-logo.png">
-</img></div>
+<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/university-alberta-logo.png"/>
+</div>
<div class="col-lg-4 col-sm-6 clear smallClear">
-<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/university-washington-logo.jpg">
-</img></div>
+<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/university-washington-logo.jpg"/>
+</div>
<div class="col-lg-4 col-sm-6">
-<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/wolfram-logo.png">
-</img></div>
+<img height="45px" src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo/wolfram-logo.png"/>
+</div>
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</div>
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diff --git a/versions/master/model_zoo/index.html b/versions/master/model_zoo/index.html
index 71b3437..e8a76f5 100644
--- a/versions/master/model_zoo/index.html
+++ b/versions/master/model_zoo/index.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>MXNet Model Zoo — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
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@@ -419,11 +417,11 @@ network that tries to distinguish between generated content and supplied “real
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<head>
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@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -238,7 +236,7 @@ Previous Navbar Layout End -->
<li class="toctree-l4"><a class="reference internal" href="ndarray.html">NDArray - Imperative tensor operations on CPU/GPU</a></li>
<li class="toctree-l4"><a class="reference internal" href="symbol.html">Symbol - Neural network graphs and auto-differentiation</a></li>
<li class="toctree-l4"><a class="reference internal" href="module.html">Module - Neural network training and inference</a></li>
-<li class="toctree-l4 current"><a class="current reference internal" href="#">Iterators - Loading data</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="">Iterators - Loading data</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../index.html#training-and-inference">Training and Inference</a></li>
@@ -264,13 +262,13 @@ Here we discuss the API conventions and several provided iterators.</p>
<li>MXNet. See the instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a>.</li>
<li><a class="reference external" href="http://opencv.org/opencv-3-2.html">OpenCV Python library</a>, <a class="reference external" href="http://docs.python-requests.org/en/master/">Python Requests</a>, <a class="reference external" href="https://matplotlib.org/">Matplotlib</a> and <a class="reference external" href="http://jupyter.org/index.html">Jupyter Notebook</a>.</li>
</ul>
-<div class="highlight-default"><div class="highlight"><pre><span></span>$ pip install opencv-python requests matplotlib jupyter
+<div class="highlight-python"><div class="highlight"><pre><span></span>$ pip install opencv-python requests matplotlib jupyter
</pre></div>
</div>
<ul class="simple">
<li>Set the environment variable <code class="docutils literal"><span class="pre">MXNET_HOME</span></code> to the root of the MXNet source folder.</li>
</ul>
-<div class="highlight-default"><div class="highlight"><pre><span></span>$ git clone https://github.com/dmlc/mxnet ~/mxnet
+<div class="highlight-python"><div class="highlight"><pre><span></span>$ git clone https://github.com/dmlc/mxnet ~/mxnet
$ MXNET_HOME = '~/mxnet'
</pre></div>
</div>
diff --git a/versions/master/tutorials/basic/image_io.html b/versions/master/tutorials/basic/image_io.html
index e63dea2..cf6d3ab 100644
--- a/versions/master/tutorials/basic/image_io.html
+++ b/versions/master/tutorials/basic/image_io.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Image IO - Loading and pre-processing images — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/basic/module.html b/versions/master/tutorials/basic/module.html
index 665a0d2..a01b0ac 100644
--- a/versions/master/tutorials/basic/module.html
+++ b/versions/master/tutorials/basic/module.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Module - Neural network training and inference — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../index.html" rel="up" title="Tutorials"/>
+<link href="../index.html" rel="up" title="Tutorials">
<link href="data.html" rel="next" title="Iterators - Loading data"/>
<link href="symbol.html" rel="prev" title="Symbol - Neural network graphs and auto-differentiation"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -237,7 +235,7 @@ Previous Navbar Layout End -->
<li class="toctree-l3 current"><a class="reference internal" href="../index.html#basic">Basic</a><ul class="current">
<li class="toctree-l4"><a class="reference internal" href="ndarray.html">NDArray - Imperative tensor operations on CPU/GPU</a></li>
<li class="toctree-l4"><a class="reference internal" href="symbol.html">Symbol - Neural network graphs and auto-differentiation</a></li>
-<li class="toctree-l4 current"><a class="current reference internal" href="#">Module - Neural network training and inference</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="">Module - Neural network training and inference</a></li>
<li class="toctree-l4"><a class="reference internal" href="data.html">Iterators - Loading data</a></li>
</ul>
</li>
@@ -271,7 +269,7 @@ this tutorial.</p>
<li>MXNet. See the instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a>.</li>
<li><a class="reference external" href="http://jupyter.org/index.html">Jupyter Notebook</a> and <a class="reference external" href="http://docs.python-requests.org/en/master/">Python Requests</a> packages.</li>
</ul>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">jupyter</span> <span class="n">requests</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>pip install jupyter requests
</pre></div>
</div>
</div>
@@ -408,8 +406,8 @@ return all the prediction results.</p>
set, we can call the <code class="docutils literal"><span class="pre">score()</span></code> function. It runs prediction in the input validation
dataset and evaluates the performance according to the given input metric.</p>
<p>It can be used as follows:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">score</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">val_iter</span><span class="p">,</span> <span class="p">[</span><span class="s1">'mse'</span><span class="p">,</span> <span class="s1">'acc'</span><span class="p">])</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">"Accuracy score is </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">score</span><span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">score</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">val_iter</span><span class="p">,</span> <span class="p">[</span><span class="s1">'acc'</span><span class="p">])</span>
+<span class="k">print</span><span class="p">(</span><span class="s2">"Accuracy score is </span><span class="si">%f</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">score</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
</pre></div>
</div>
<p>Some of the other metrics which can be used are <code class="docutils literal"><span class="pre">top_k_acc</span></code>(top-k-accuracy),
diff --git a/versions/master/tutorials/basic/module.ipynb b/versions/master/tutorials/basic/module.ipynb
index cc41736..d6701b4 100644
--- a/versions/master/tutorials/basic/module.ipynb
+++ b/versions/master/tutorials/basic/module.ipynb
@@ -1 +1 @@
-{"nbformat": 4, "cells": [{"source": "# Module - Neural network training and inference\n\nTraining a neural network involves quite a few steps. One need to specify how\nto feed input training data, initialize model parameters, perform forward and\nbackward passes through the network, update weights based on computed gradients, do\nmodel checkpoints, etc. During prediction, one ends up repeating most of these\nsteps. All this can be quite daunting to both newcomers as well as experienced\ [...]
\ No newline at end of file
+{"nbformat": 4, "cells": [{"source": "# Module - Neural network training and inference\n\nTraining a neural network involves quite a few steps. One need to specify how\nto feed input training data, initialize model parameters, perform forward and\nbackward passes through the network, update weights based on computed gradients, do\nmodel checkpoints, etc. During prediction, one ends up repeating most of these\nsteps. All this can be quite daunting to both newcomers as well as experienced\ [...]
\ No newline at end of file
diff --git a/versions/master/tutorials/basic/ndarray.html b/versions/master/tutorials/basic/ndarray.html
index b9918d4..06b495e 100644
--- a/versions/master/tutorials/basic/ndarray.html
+++ b/versions/master/tutorials/basic/ndarray.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>NDArray - Imperative tensor operations on CPU/GPU — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../index.html" rel="up" title="Tutorials"/>
+<link href="../index.html" rel="up" title="Tutorials">
<link href="symbol.html" rel="next" title="Symbol - Neural network graphs and auto-differentiation"/>
<link href="../index.html" rel="prev" title="Tutorials"/>
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
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@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
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@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
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-</input></div>
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+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -235,7 +233,7 @@ Previous Navbar Layout End -->
<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Tutorials</a><ul class="current">
<li class="toctree-l2 current"><a class="reference internal" href="../index.html#python">Python</a><ul class="current">
<li class="toctree-l3 current"><a class="reference internal" href="../index.html#basic">Basic</a><ul class="current">
-<li class="toctree-l4 current"><a class="current reference internal" href="#">NDArray - Imperative tensor operations on CPU/GPU</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="">NDArray - Imperative tensor operations on CPU/GPU</a></li>
<li class="toctree-l4"><a class="reference internal" href="symbol.html">Symbol - Neural network graphs and auto-differentiation</a></li>
<li class="toctree-l4"><a class="reference internal" href="module.html">Module - Neural network training and inference</a></li>
<li class="toctree-l4"><a class="reference internal" href="data.html">Iterators - Loading data</a></li>
@@ -270,7 +268,7 @@ could represent the coordinates of a point in 3D space, e.g. <code class="docuti
array with shape (3). Similarly, we could represent a 2D array. Below, we
present an array with length 2 along the first axis and length 3 along the
second axis.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
</pre></div>
</div>
@@ -295,7 +293,7 @@ product of the components of its <code class="docutils literal"><span class="pre
<li><p class="first">MXNet. See the instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a></p>
</li>
<li><p class="first"><a class="reference external" href="http://jupyter.org/">Jupyter</a></p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">jupyter</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>pip install jupyter
</pre></div>
</div>
</li>
@@ -592,18 +590,18 @@ the data in Python:</li>
</pre></div>
</div>
<p>we can later load it from R:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o"><-</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"temp.ndarray"</span><span class="p">)</span>
-<span class="k">as</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">[[</span><span class="mi">1</span><span class="p">]])</span>
-<span class="c1">## [,1] [,2] [,3]</span>
-<span class="c1">## [1,] 1 1 1</span>
-<span class="c1">## [2,] 1 1 1</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>a <- mx.nd.load("temp.ndarray")
+as.array(a[[1]])
+## [,1] [,2] [,3]
+## [1,] 1 1 1
+## [2,] 1 1 1
</pre></div>
</div>
<ol class="simple">
<li>When a distributed filesystem such as Amazon S3 or Hadoop HDFS is set up, we
can directly save to and load from it.</li>
</ol>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'s3://mybucket/mydata.ndarray'</span><span class="p">,</span> <span class="p">[</span><span class="n">a</span><span class="p">,])</span> <span class="c1"># if compiled with USE_S3=1</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'s3://mybucket/mydata.ndarray'</span><span class="p">,</span> <span class="p">[</span><span class="n">a</span><span class="p">,])</span> <span class="c1"># if compiled with USE_S3=1</span>
<span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'hdfs///users/myname/mydata.bin'</span><span class="p">,</span> <span class="p">[</span><span class="n">a</span><span class="p">,])</span> <span class="c1"># if compiled with USE_HDFS=1</span>
</pre></div>
</div>
diff --git a/versions/master/tutorials/basic/record_io.html b/versions/master/tutorials/basic/record_io.html
index 709166d..ba52906 100644
--- a/versions/master/tutorials/basic/record_io.html
+++ b/versions/master/tutorials/basic/record_io.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Record IO - Pack free-format data in binary files — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
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<div class="navbar navbar-default navbar-fixed-top">
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
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<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/basic/symbol.html b/versions/master/tutorials/basic/symbol.html
index f1d27a0..3132704 100644
--- a/versions/master/tutorials/basic/symbol.html
+++ b/versions/master/tutorials/basic/symbol.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Symbol - Neural network graphs and auto-differentiation — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../index.html" rel="up" title="Tutorials"/>
+<link href="../index.html" rel="up" title="Tutorials">
<link href="module.html" rel="next" title="Module - Neural network training and inference"/>
<link href="ndarray.html" rel="prev" title="NDArray - Imperative tensor operations on CPU/GPU"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -236,7 +234,7 @@ Previous Navbar Layout End -->
<li class="toctree-l2 current"><a class="reference internal" href="../index.html#python">Python</a><ul class="current">
<li class="toctree-l3 current"><a class="reference internal" href="../index.html#basic">Basic</a><ul class="current">
<li class="toctree-l4"><a class="reference internal" href="ndarray.html">NDArray - Imperative tensor operations on CPU/GPU</a></li>
-<li class="toctree-l4 current"><a class="current reference internal" href="#">Symbol - Neural network graphs and auto-differentiation</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="">Symbol - Neural network graphs and auto-differentiation</a></li>
<li class="toctree-l4"><a class="reference internal" href="module.html">Module - Neural network training and inference</a></li>
<li class="toctree-l4"><a class="reference internal" href="data.html">Iterators - Loading data</a></li>
</ul>
@@ -299,7 +297,7 @@ There are a few different ways to compose a <code class="docutils literal"><span
<li><p class="first">MXNet. See the instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a></p>
</li>
<li><p class="first"><a class="reference external" href="http://jupyter.org/">Jupyter</a></p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">jupyter</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>pip install jupyter
</pre></div>
</div>
</li>
diff --git a/versions/master/tutorials/c++/basics.html b/versions/master/tutorials/c++/basics.html
index cc856c5..60eb641 100644
--- a/versions/master/tutorials/c++/basics.html
+++ b/versions/master/tutorials/c++/basics.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Basics — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/embedded/wine_detector.html b/versions/master/tutorials/embedded/wine_detector.html
index 35c1647..4af4c05 100644
--- a/versions/master/tutorials/embedded/wine_detector.html
+++ b/versions/master/tutorials/embedded/wine_detector.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Real-time Object Detection with MXNet On The Raspberry Pi — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -445,10 +443,10 @@ mv Inception_BN-0039.params Inception_BN-0000.params
<span class="n">usageInfo</span> <span class="o">=</span> <span class="s2">"""Usage:</span>
<span class="s2"> </span>
<span class="s2">Use certificate based mutual authentication:</span>
-<span class="s2">python wine_alerter.py -e <endpoint> -r <rootcafilepath> -c <certfilepath> -k <privatekeyfilepath></privatekeyfilepath></certfilepath></rootcafilepath></endpoint></span>
+<span class="s2">python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -c <certFilePath> -k <privateKeyFilePath></span>
<span class="s2"> </span>
<span class="s2">Use MQTT over WebSocket:</span>
-<span class="s2">python wine_alerter.py -e <endpoint> -r <rootcafilepath> -w</rootcafilepath></endpoint></span>
+<span class="s2">python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -w</span>
<span class="s2"> </span>
<span class="s2">Type "python wine_alerter.py -h" for available options.</span>
<span class="s2">"""</span>
@@ -570,8 +568,8 @@ mv Inception_BN-0039.params Inception_BN-0000.params
</pre></div>
</div>
<p>You can then run this file by entering the following command</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>python wine_alerter.py -e <endpointurl> -r <rootcafilepath> -c <certfilepath> -k <privatekeyfilepath>
-</privatekeyfilepath></certfilepath></rootcafilepath></endpointurl></pre></div>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>python wine_alerter.py -e <endpointURL> -r <rootCAFilePath> -c <certFilePath> -k <privateKeyFilePath>
+</pre></div>
</div>
<p>If this is working you should see the same kind of image preview you did with camera_test.py every few seconds, however the console will only print a message now when a wine bottle is detected in the shot (you can edit the bottom lines in the wine_alerter.py code to make this alert for any object label from the <a class="reference external" href="http://image-net.org/index">ImageNet-11k dataset</a> that you specify).</p>
<p>You can open up the activity tab for the thing that you registered on the AWS IoT Dashboard and see the corresponding messages pushed to the server whenever a wine bottle is detected in a camera shot. Even if network connectivity periodically fails, AWS IoT will push updates out to the server when possible, allowing this system to reliably let you know when there is wine around.</p>
diff --git a/versions/master/tutorials/general_ml/recommendation_systems.html b/versions/master/tutorials/general_ml/recommendation_systems.html
index 408e3b9..e3a8186 100644
--- a/versions/master/tutorials/general_ml/recommendation_systems.html
+++ b/versions/master/tutorials/general_ml/recommendation_systems.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Recommendation Systems — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/gluon/autograd.html b/versions/master/tutorials/gluon/autograd.html
index 55b32f6..07da526 100644
--- a/versions/master/tutorials/gluon/autograd.html
+++ b/versions/master/tutorials/gluon/autograd.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Automatic differentiation — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
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<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
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<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
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<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
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+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
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</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
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<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
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<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/gluon/customop.html b/versions/master/tutorials/gluon/customop.html
index 690c50d..78fa98e 100644
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<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Creating custom operators with numpy — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
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-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
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COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
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<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
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<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../search.html" rel="search" title="Search"/>
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
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<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
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<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
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</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/gluon/gluon.html b/versions/master/tutorials/gluon/gluon.html
index a1e923c..22b1831 100644
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@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Gluon - Neural network building blocks — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
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-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
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<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
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<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/gluon/hybrid.html b/versions/master/tutorials/gluon/hybrid.html
index 890595a..cb73a43 100644
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<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Hybrid - Faster training and easy deployment — mxnet documentation</title>
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-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
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COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
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<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
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+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -236,6 +234,7 @@ Previous Navbar Layout End -->
<div class="content">
<div class="section" id="hybrid-faster-training-and-easy-deployment">
<span id="hybrid-faster-training-and-easy-deployment"></span><h1>Hybrid - Faster training and easy deployment<a class="headerlink" href="#hybrid-faster-training-and-easy-deployment" title="Permalink to this headline">¶</a></h1>
+<p><em>Note: a newer version is available <a class="reference external" href="http://gluon.mxnet.io/P14-C05-hybridize.html">here</a>.</em></p>
<p>Deep learning frameworks can be roughly divided into two categories: declarative
and imperative. With declarative frameworks (including Tensorflow, Theano, etc)
users first declare a fixed computation graph and then execute it end-to-end.
diff --git a/versions/master/tutorials/gluon/hybrid.ipynb b/versions/master/tutorials/gluon/hybrid.ipynb
index 1a460c5..66fb31e 100644
--- a/versions/master/tutorials/gluon/hybrid.ipynb
+++ b/versions/master/tutorials/gluon/hybrid.ipynb
@@ -1 +1 @@
-{"nbformat": 4, "cells": [{"source": "# Hybrid - Faster training and easy deployment\n\nDeep learning frameworks can be roughly divided into two categories: declarative\nand imperative. With declarative frameworks (including Tensorflow, Theano, etc)\nusers first declare a fixed computation graph and then execute it end-to-end.\nThe benefit of fixed computation graph is it's portable and runs more\nefficiently. However, it's less flexible because any logic must be encoded\ninto the graph [...]
\ No newline at end of file
+{"nbformat": 4, "cells": [{"source": "# Hybrid - Faster training and easy deployment\n\n*Note: a newer version is available [here](http://gluon.mxnet.io/P14-C05-hybridize.html).*\n\nDeep learning frameworks can be roughly divided into two categories: declarative\nand imperative. With declarative frameworks (including Tensorflow, Theano, etc)\nusers first declare a fixed computation graph and then execute it end-to-end.\nThe benefit of fixed computation graph is it's portable and runs mor [...]
\ No newline at end of file
diff --git a/versions/master/tutorials/gluon/mnist.html b/versions/master/tutorials/gluon/mnist.html
index cd595c5..0736d55 100644
--- a/versions/master/tutorials/gluon/mnist.html
+++ b/versions/master/tutorials/gluon/mnist.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Handwritten Digit Recognition — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -250,7 +248,7 @@ imperative fashion.</p>
<li>MXNet. See the instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a>.</li>
<li><a class="reference external" href="http://docs.python-requests.org/en/master/">Python Requests</a> and <a class="reference external" href="http://jupyter.org/index.html">Jupyter Notebook</a>.</li>
</ul>
-<div class="highlight-default"><div class="highlight"><pre><span></span>$ pip install requests jupyter
+<div class="highlight-python"><div class="highlight"><pre><span></span>$ pip install requests jupyter
</pre></div>
</div>
</div>
diff --git a/versions/master/tutorials/gluon/ndarray.html b/versions/master/tutorials/gluon/ndarray.html
index e75ea0b..60479c6 100644
--- a/versions/master/tutorials/gluon/ndarray.html
+++ b/versions/master/tutorials/gluon/ndarray.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>NDArray - Scientific computing on CPU and GPU — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -349,7 +347,7 @@ with python syntax:</p>
<span class="k">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
-<p>See the <a class="reference internal" href="../basic/ndarray.html"><span class="doc">Advanced NDArray tutorial</span></a> for a more detailed
+<p>See the <a class="reference internal" href="../basic/ndarray.html"><em>Advanced NDArray tutorial</em></a> for a more detailed
introduction to NDArray API.</p>
<div class="btn-group" role="group">
<div class="download_btn"><a download="ndarray.ipynb" href="ndarray.ipynb"><span class="glyphicon glyphicon-download-alt"></span> ndarray.ipynb</a></div></div></div>
diff --git a/versions/master/tutorials/index.html b/versions/master/tutorials/index.html
index e5b2a7a..48e4e09 100644
--- a/versions/master/tutorials/index.html
+++ b/versions/master/tutorials/index.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Tutorials — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../genindex.html" rel="index" title="Index"/>
-<link href="../search.html" rel="search" title="Search"/>
-<link href="basic/ndarray.html" rel="next" title="NDArray - Imperative tensor operations on CPU/GPU"/>
+<link href="basic/ndarray.html" rel="next" title="NDArray - Imperative tensor operations on CPU/GPU">
<link href="../architecture/index.html" rel="prev" title="MXNet Architecture"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -153,7 +151,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -180,7 +178,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -190,11 +188,11 @@ Previous Navbar Layout End -->
<form action="../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -231,7 +229,7 @@ Previous Navbar Layout End -->
<li class="toctree-l1"><a class="reference internal" href="../api/perl/index.html">Perl Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/index.html">HowTo Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architecture/index.html">System Documents</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorials</a><ul>
+<li class="toctree-l1 current"><a class="current reference internal" href="">Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#python">Python</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#basic">Basic</a><ul>
<li class="toctree-l4"><a class="reference internal" href="basic/ndarray.html">NDArray - Imperative tensor operations on CPU/GPU</a></li>
@@ -283,8 +281,8 @@ Previous Navbar Layout End -->
<li class="toctree-l1"><a class="reference internal" href="vision/large_scale_classification.html">Large Scale Image Classification</a></li>
</ul>
</div>
-<p><br>
-More tutorials and examples are available in the GitHub <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example">repository</a>.</br></p>
+<p><br/>
+More tutorials and examples are available in the GitHub <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example">repository</a>.</p>
</div>
</div>
<div class="section" id="contributing-tutorials">
diff --git a/versions/master/tutorials/nlp/cnn.html b/versions/master/tutorials/nlp/cnn.html
index 41689bb..f794b1f 100644
--- a/versions/master/tutorials/nlp/cnn.html
+++ b/versions/master/tutorials/nlp/cnn.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Text Classification Using a Convolutional Neural Network on MXNet — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
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@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
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<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -292,38 +290,43 @@ Previous Navbar Layout End -->
<span class="k">return</span> <span class="p">[</span><span class="n">x_text</span><span class="p">,</span> <span class="n">y</span><span class="p">]</span>
-<span class="k">def</span> <span class="nf">pad_sentences</span><span class="p">(</span><span class="n">sentences</span><span class="p">,</span> <span class="n">padding_word</span><span class="o">=</span><span class="s2">"</span></pre></div></div></div></div></div></div></body></html>"<span class="p">):</span>
-<span class="sd">"""</span>
+<span class="k">def</span> <span class="nf">pad_sentences</span><span class="p">(</span><span class="n">sentences</span><span class="p">,</span> <span class="n">padding_word</span><span class="o">=</span><span class="s2">"</s>"</span><span class="p">):</span>
+ <span class="sd">"""</span>
<span class="sd"> Pads all sentences to the same length. The length is defined by the longest sentence.</span>
<span class="sd"> Returns padded sentences.</span>
<span class="sd"> """</span>
-<span class="n">sequence_length</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">)</span>
-<span class="n">padded_sentences</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sentences</span><span class="p">)):</span>
-<span class="n">sentence</span> <span class="o">=</span> <span class="n">sentences</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-<span class="n">num_padding</span> <span class="o">=</span> <span class="n">sequence_length</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">sentence</span><span class="p">)</span>
-<span class="n">new_sentence</span> <span class="o">=</span> <span class="n">sentence</span> <span class="o">+</span> <span class="p">[</span><span class="n">padding_word</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_padding</span>
-<span class="n">padded_sentences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_sentence</span><span class="p">)</span>
-<span class="k">return</span> <span class="n">padded_sentences</span>
+ <span class="n">sequence_length</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">)</span>
+ <span class="n">padded_sentences</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sentences</span><span class="p">)):</span>
+ <span class="n">sentence</span> <span class="o">=</span> <span class="n">sentences</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+ <span class="n">num_padding</span> <span class="o">=</span> <span class="n">sequence_length</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">sentence</span><span class="p">)</span>
+ <span class="n">new_sentence</span> <span class="o">=</span> <span class="n">sentence</span> <span class="o">+</span> <span class="p">[</span><span class="n">padding_word</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_padding</span>
+ <span class="n">padded_sentences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_sentence</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">padded_sentences</span>
+
+
<span class="k">def</span> <span class="nf">build_vocab</span><span class="p">(</span><span class="n">sentences</span><span class="p">):</span>
-<span class="sd">"""</span>
+ <span class="sd">"""</span>
<span class="sd"> Builds a vocabulary mapping from word to index based on the sentences.</span>
<span class="sd"> Returns vocabulary mapping and inverse vocabulary mapping.</span>
<span class="sd"> """</span>
-<span class="c1"># Build vocabulary</span>
-<span class="n">word_counts</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="p">(</span><span class="o">*</span><span class="n">sentences</span><span class="p">))</span>
-<span class="c1"># Mapping from index to word</span>
-<span class="n">vocabulary_inv</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">word_counts</span><span class="o">.</span><span class="n">most_common</span><span class="p">()]</span>
-<span class="c1"># Mapping from word to index</span>
-<span class="n">vocabulary</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">vocabulary_inv</span><span class="p">)}</span>
-<span class="k">return</span> <span class="p">[</span><span class="n">vocabulary</span><span class="p">,</span> <span class="n">vocabulary_inv</span><span class="p">]</span>
+ <span class="c1"># Build vocabulary</span>
+ <span class="n">word_counts</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="p">(</span><span class="o">*</span><span class="n">sentences</span><span class="p">))</span>
+ <span class="c1"># Mapping from index to word</span>
+ <span class="n">vocabulary_inv</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">word_counts</span><span class="o">.</span><span class="n">most_common</span><span class="p">()]</span>
+ <span class="c1"># Mapping from word to index</span>
+ <span class="n">vocabulary</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">vocabulary_inv</span><span class="p">)}</span>
+ <span class="k">return</span> <span class="p">[</span><span class="n">vocabulary</span><span class="p">,</span> <span class="n">vocabulary_inv</span><span class="p">]</span>
+
+
<span class="k">def</span> <span class="nf">build_input_data</span><span class="p">(</span><span class="n">sentences</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">vocabulary</span><span class="p">):</span>
-<span class="sd">"""</span>
+ <span class="sd">"""</span>
<span class="sd"> Maps sentences and labels to vectors based on a vocabulary.</span>
<span class="sd"> """</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">vocabulary</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">sentence</span><span class="p">]</span> <span class="k">for</span> <span class="n">sentence</span> <span class="ow">in</span> <span [...]
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
-<span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">]</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">vocabulary</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">sentence</span><span class="p">]</span> <span class="k">for</span> <span class="n">sentence</span> <span class="ow">in</span> <s [...]
+ <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
+ <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">]</span>
+
<span class="sd">"""</span>
<span class="sd">Loads and preprocessed data for the MR dataset.</span>
<span class="sd">Returns input vectors, labels, vocabulary, and inverse vocabulary.</span>
@@ -333,29 +336,34 @@ Previous Navbar Layout End -->
<span class="n">sentences_padded</span> <span class="o">=</span> <span class="n">pad_sentences</span><span class="p">(</span><span class="n">sentences</span><span class="p">)</span>
<span class="n">vocabulary</span><span class="p">,</span> <span class="n">vocabulary_inv</span> <span class="o">=</span> <span class="n">build_vocab</span><span class="p">(</span><span class="n">sentences_padded</span><span class="p">)</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">build_input_data</span><span class="p">(</span><span class="n">sentences_padded</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">vocabulary</span><span class="p">)</span>
+
<span class="n">vocab_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">vocabulary</span><span class="p">)</span>
+
<span class="c1"># randomly shuffle data</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">shuffle_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)))</span>
<span class="n">x_shuffled</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">shuffle_indices</span><span class="p">]</span>
<span class="n">y_shuffled</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">shuffle_indices</span><span class="p">]</span>
+
<span class="c1"># split train/dev set</span>
<span class="c1"># there are a total of 10662 labeled examples to train on</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_dev</span> <span class="o">=</span> <span class="n">x_shuffled</span><span class="p">[:</span><span class="o">-</span><span class="mi">1000</span><span class="p">],</span> <span class="n">x_shuffled</span><span class="p">[</span><span class="o">-</span><span class="mi">1000</span><span class="p">:]</span>
<span class="n">y_train</span><span class="p">,</span> <span class="n">y_dev</span> <span class="o">=</span> <span class="n">y_shuffled</span><span class="p">[:</span><span class="o">-</span><span class="mi">1000</span><span class="p">],</span> <span class="n">y_shuffled</span><span class="p">[</span><span class="o">-</span><span class="mi">1000</span><span class="p">:]</span>
+
<span class="n">sentence_size</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
<span class="k">print</span> <span class="s1">'Train/Dev split: </span><span class="si">%d</span><span class="s1">/</span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_dev</span><span class="p">))</span>
<span class="k">print</span> <span class="s1">'train shape:'</span><span class="p">,</span> <span class="n">x_train</span><span class="o">.</span><span class="n">shape</span>
<span class="k">print</span> <span class="s1">'dev shape:'</span><span class="p">,</span> <span class="n">x_dev</span><span class="o">.</span><span class="n">shape</span>
<span class="k">print</span> <span class="s1">'vocab_size'</span><span class="p">,</span> <span class="n">vocab_size</span>
<span class="k">print</span> <span class="s1">'sentence max words'</span><span class="p">,</span> <span class="n">sentence_size</span>
-
-
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">Train</span><span class="o">/</span><span class="n">Dev</span> <span class="n">split</span><span class="p">:</span> <span class="mi">9662</span><span class="o">/</span><span class="mi">1000</span>
-<span class="n">train</span> <span class="n">shape</span><span class="p">:</span> <span class="p">(</span><span class="mi">9662</span><span class="p">,</span> <span class="mi">56</span><span class="p">)</span>
-<span class="n">dev</span> <span class="n">shape</span><span class="p">:</span> <span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">56</span><span class="p">)</span>
-<span class="n">vocab_size</span> <span class="mi">18766</span>
-<span class="n">sentence</span> <span class="nb">max</span> <span class="n">words</span> <span class="mi">56</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span>Train/Dev split: 9662/1000
+train shape: (9662, 56)
+dev shape: (1000, 56)
+vocab_size 18766
+sentence max words 56
</pre></div>
</div>
<p>Now that we prepared the training and test data by loading, vectorizing and shuffling it we can go on to defining the network architecture we want to train with the data.</p>
@@ -388,8 +396,8 @@ Previous Navbar Layout End -->
<span class="n">conv_input</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Reshape</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">embed_layer</span><span class="p">,</span> <span class="n">target_shape</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><spa [...]
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">batch</span> <span class="n">size</span> <span class="mi">50</span>
-<span class="n">embedding</span> <span class="n">dimensions</span> <span class="mi">300</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>batch size 50
+embedding dimensions 300
</pre></div>
</div>
<p>The next layer in the network performs convolutions over the ordered embedded word vectors in a sentence using multiple filter sizes, sliding over 3, 4 or 5 words at a time. This is the equivalent of looking at all 3-grams, 4-grams and 5-grams in a sentence and will allow us to understand how words contribute to sentiment in the context of those around them.</p>
@@ -415,7 +423,7 @@ Previous Navbar Layout End -->
<span class="n">h_pool</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Reshape</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">concat</span><span class="p">,</span> <span class="n">target_shape</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">total_filters</span><s [...]
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">convolution</span> <span class="n">filters</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>convolution filters [3, 4, 5]
</pre></div>
</div>
<p>Next, we add dropout regularization, which will randomly disable a fraction of neurons in the layer (set to 50% here) to ensure that that model does not overfit. This works by preventing neurons from co-adapting and forcing them to learn individually useful features.</p>
@@ -430,7 +438,7 @@ Previous Navbar Layout End -->
<span class="n">h_drop</span> <span class="o">=</span> <span class="n">h_pool</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">dropout</span> <span class="n">probability</span> <span class="mf">0.5</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>dropout probability 0.5
</pre></div>
</div>
<p>Finally, we add a fully connected layer to add non-linearity to the model. We then classify the resulting output of this layer using a softmax function, yielding a result between 0 (negative sentiment) and 1 (positive).</p>
@@ -625,13 +633,13 @@ Previous Navbar Layout End -->
</ul>
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@@ -644,7 +652,7 @@ Previous Navbar Layout End -->
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- <!-- pagename != index -->
+</div> <!-- pagename != index -->
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diff --git a/versions/master/tutorials/nlp/nce_loss.html b/versions/master/tutorials/nlp/nce_loss.html
index 43c2538..20c81a5 100644
--- a/versions/master/tutorials/nlp/nce_loss.html
+++ b/versions/master/tutorials/nlp/nce_loss.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
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+<meta content="width=device-width, initial-scale=1" name="viewport"/>
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@@ -48,10 +48,8 @@
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-</form>
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@@ -250,8 +248,8 @@ Previous Navbar Layout End -->
<li>word2vec.py. A CBOW word2vec example using nce loss</li>
</ul>
<p>Run word2vec.py with the following command:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="o">./</span><span class="n">get_text8</span><span class="o">.</span><span class="n">sh</span>
- <span class="n">python</span> <span class="n">word2vec</span><span class="o">.</span><span class="n">py</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> ./get_text8.sh
+ python word2vec.py
</pre></div>
</div>
</div>
@@ -261,8 +259,8 @@ Previous Navbar Layout End -->
<li>lstm_word.py. An LSTM example using nce loss</li>
</ul>
<p>Run lstm_word.py with the following command:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="o">./</span><span class="n">get_text8</span><span class="o">.</span><span class="n">sh</span>
- <span class="n">python</span> <span class="n">lstm_word</span><span class="o">.</span><span class="n">py</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> ./get_text8.sh
+ python lstm_word.py
</pre></div>
</div>
</div>
diff --git a/versions/master/tutorials/nlp/rnn.html b/versions/master/tutorials/nlp/rnn.html
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+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
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diff --git a/versions/master/tutorials/python/kvstore.html b/versions/master/tutorials/python/kvstore.html
index b2d0c73..6436e57 100644
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-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/python/linear-regression.html b/versions/master/tutorials/python/linear-regression.html
index ee17c99..32f5f6c 100644
--- a/versions/master/tutorials/python/linear-regression.html
+++ b/versions/master/tutorials/python/linear-regression.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Linear Regression — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../index.html" rel="up" title="Tutorials"/>
+<link href="../index.html" rel="up" title="Tutorials">
<link href="mnist.html" rel="next" title="Handwritten Digit Recognition"/>
<link href="../basic/data.html" rel="prev" title="Iterators - Loading data"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -236,7 +234,7 @@ Previous Navbar Layout End -->
<li class="toctree-l2 current"><a class="reference internal" href="../index.html#python">Python</a><ul class="current">
<li class="toctree-l3"><a class="reference internal" href="../index.html#basic">Basic</a></li>
<li class="toctree-l3 current"><a class="reference internal" href="../index.html#training-and-inference">Training and Inference</a><ul class="current">
-<li class="toctree-l4 current"><a class="current reference internal" href="#">Linear Regression</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="">Linear Regression</a></li>
<li class="toctree-l4"><a class="reference internal" href="mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l4"><a class="reference internal" href="predict_image.html">Predict with pre-trained models</a></li>
<li class="toctree-l4"><a class="reference internal" href="../vision/large_scale_classification.html">Large Scale Image Classification</a></li>
@@ -262,7 +260,7 @@ Previous Navbar Layout End -->
<li>MXNet. See the instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a>.</li>
<li><a class="reference external" href="http://jupyter.org/index.html">Jupyter Notebook</a>.</li>
</ul>
-<div class="highlight-default"><div class="highlight"><pre><span></span>$ pip install jupyter
+<div class="highlight-python"><div class="highlight"><pre><span></span>$ pip install jupyter
</pre></div>
</div>
<p>To begin, the following code imports the necessary packages we’ll need for this exercise.</p>
diff --git a/versions/master/tutorials/python/matrix_factorization.html b/versions/master/tutorials/python/matrix_factorization.html
index 449bd78..d238e3c 100644
--- a/versions/master/tutorials/python/matrix_factorization.html
+++ b/versions/master/tutorials/python/matrix_factorization.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Matrix Factorization — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/python/mnist.html b/versions/master/tutorials/python/mnist.html
index 342afbc..c6b9358 100644
--- a/versions/master/tutorials/python/mnist.html
+++ b/versions/master/tutorials/python/mnist.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Handwritten Digit Recognition — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../index.html" rel="up" title="Tutorials"/>
+<link href="../index.html" rel="up" title="Tutorials">
<link href="predict_image.html" rel="next" title="Predict with pre-trained models"/>
<link href="linear-regression.html" rel="prev" title="Linear Regression"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -237,7 +235,7 @@ Previous Navbar Layout End -->
<li class="toctree-l3"><a class="reference internal" href="../index.html#basic">Basic</a></li>
<li class="toctree-l3 current"><a class="reference internal" href="../index.html#training-and-inference">Training and Inference</a><ul class="current">
<li class="toctree-l4"><a class="reference internal" href="linear-regression.html">Linear Regression</a></li>
-<li class="toctree-l4 current"><a class="current reference internal" href="#">Handwritten Digit Recognition</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="">Handwritten Digit Recognition</a></li>
<li class="toctree-l4"><a class="reference internal" href="predict_image.html">Predict with pre-trained models</a></li>
<li class="toctree-l4"><a class="reference internal" href="../vision/large_scale_classification.html">Large Scale Image Classification</a></li>
</ul>
@@ -264,7 +262,7 @@ Previous Navbar Layout End -->
<li>MXNet version 0.10 or later. See the installation instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a>.</li>
<li><a class="reference external" href="http://docs.python-requests.org/en/master/">Python Requests</a> and <a class="reference external" href="http://jupyter.org/index.html">Jupyter Notebook</a>.</li>
</ul>
-<div class="highlight-default"><div class="highlight"><pre><span></span>$ pip install requests jupyter
+<div class="highlight-python"><div class="highlight"><pre><span></span>$ pip install requests jupyter
</pre></div>
</div>
</div>
diff --git a/versions/master/tutorials/python/predict_image.html b/versions/master/tutorials/python/predict_image.html
index 52251ad..9d6bf5e 100644
--- a/versions/master/tutorials/python/predict_image.html
+++ b/versions/master/tutorials/python/predict_image.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Predict with pre-trained models — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,13 +48,11 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../index.html" rel="up" title="Tutorials"/>
+<link href="../index.html" rel="up" title="Tutorials">
<link href="../vision/large_scale_classification.html" rel="next" title="Large Scale Image Classification"/>
<link href="mnist.html" rel="prev" title="Handwritten Digit Recognition"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -154,7 +152,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -181,7 +179,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -191,11 +189,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -238,7 +236,7 @@ Previous Navbar Layout End -->
<li class="toctree-l3 current"><a class="reference internal" href="../index.html#training-and-inference">Training and Inference</a><ul class="current">
<li class="toctree-l4"><a class="reference internal" href="linear-regression.html">Linear Regression</a></li>
<li class="toctree-l4"><a class="reference internal" href="mnist.html">Handwritten Digit Recognition</a></li>
-<li class="toctree-l4 current"><a class="current reference internal" href="#">Predict with pre-trained models</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="">Predict with pre-trained models</a></li>
<li class="toctree-l4"><a class="reference internal" href="../vision/large_scale_classification.html">Large Scale Image Classification</a></li>
</ul>
</li>
@@ -262,7 +260,7 @@ pre-trained model, and how to perform feature extraction.</p>
<li>MXNet. See the instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a></li>
<li><a class="reference external" href="http://docs.python-requests.org/en/master/">Python Requests</a>, <a class="reference external" href="https://matplotlib.org/">Matplotlib</a> and <a class="reference external" href="http://jupyter.org/index.html">Jupyter Notebook</a>.</li>
</ul>
-<div class="highlight-default"><div class="highlight"><pre><span></span>$ pip install requests matplotlib jupyter
+<div class="highlight-python"><div class="highlight"><pre><span></span>$ pip install requests matplotlib jupyter
</pre></div>
</div>
</div>
diff --git a/versions/master/tutorials/r/CallbackFunctionTutorial.html b/versions/master/tutorials/r/CallbackFunctionTutorial.html
index 84f3d3f..cddaaeb 100644
--- a/versions/master/tutorials/r/CallbackFunctionTutorial.html
+++ b/versions/master/tutorials/r/CallbackFunctionTutorial.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Callback Function — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -259,7 +257,7 @@ which can very useful in model training.</p>
learning.rate<span class="o">=</span><span class="m">2e-6</span><span class="p">,</span> momentum<span class="o">=</span><span class="m">0.9</span><span class="p">,</span> eval.metric<span class="o">=</span>mx.metric.rmse<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
<span class="c1">## Start training with 1 devices</span>
<span class="c1">## [1] Train-rmse=16.063282524034</span>
<span class="c1">## [1] Validation-rmse=10.1766446093622</span>
@@ -299,7 +297,7 @@ which can very useful in model training.</p>
epoch.end.callback <span class="o">=</span> mx.callback.save.checkpoint<span class="p">(</span><span class="s">"boston"</span><span class="p">))</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
<span class="c1">## Start training with 1 devices</span>
<span class="c1">## [1] Train-rmse=19.1621424021617</span>
<span class="c1">## [1] Validation-rmse=20.721515592165</span>
@@ -321,7 +319,7 @@ which can very useful in model training.</p>
batch.end.callback <span class="o">=</span> mx.callback.log.train.metric<span class="p">(</span><span class="m">5</span><span class="p">))</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
<span class="c1">## Start training with 1 devices</span>
<span class="c1">## Batch [5] Train-rmse=17.6514558545416</span>
<span class="c1">## [1] Train-rmse=15.2879610219001</span>
@@ -342,7 +340,7 @@ which can very useful in model training.</p>
epoch.end.callback <span class="o">=</span> mx.callback.log.train.metric<span class="p">(</span><span class="m">5</span><span class="p">,</span> logger<span class="p">))</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
<span class="c1">## Start training with 1 devices</span>
<span class="c1">## [1] Train-rmse=19.1083228733256</span>
<span class="c1">## [1] Validation-rmse=12.7150687428974</span>
@@ -354,13 +352,13 @@ which can very useful in model training.</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kp">head</span><span class="p">(</span>logger<span class="o">$</span>train<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## [1] 19.108323 15.768438 13.531470 11.386050 9.555477 9.351324</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] 19.108323 15.768438 13.531470 11.386050 9.555477 9.351324</span>
</pre></div>
</div>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kp">head</span><span class="p">(</span>logger<span class="o">$</span><span class="kp">eval</span><span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## [1] 12.715069 14.810532 15.840361 10.898733 9.349706 9.363087</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] 12.715069 14.810532 15.840361 10.898733 9.349706 9.363087</span>
</pre></div>
</div>
</div>
@@ -436,7 +434,7 @@ process:</p>
epoch.end.callback <span class="o">=</span> mx.callback.early.stop<span class="p">(</span><span class="m">10</span><span class="p">))</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use row major..</span>
<span class="c1">## Start training with 1 devices</span>
<span class="c1">## [1] Train-rmse=18.5897984387033</span>
<span class="c1">## [1] Validation-rmse=13.5555213820571</span>
diff --git a/versions/master/tutorials/r/CustomIteratorTutorial.html b/versions/master/tutorials/r/CustomIteratorTutorial.html
index 015ea91..c3d16dd 100644
--- a/versions/master/tutorials/r/CustomIteratorTutorial.html
+++ b/versions/master/tutorials/r/CustomIteratorTutorial.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Custom Iterator Tutorial — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
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diff --git a/versions/master/tutorials/r/CustomLossFunction.html b/versions/master/tutorials/r/CustomLossFunction.html
index 901ee60..b9103f0 100644
--- a/versions/master/tutorials/r/CustomLossFunction.html
+++ b/versions/master/tutorials/r/CustomLossFunction.html
@@ -2,15 +2,15 @@
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@@ -18,7 +18,7 @@
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@@ -48,10 +48,8 @@
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<div class="section" id="model-training-example">
<span id="model-training-example"></span><h2>Model Training Example<a class="headerlink" href="#model-training-example" title="Permalink to this headline">¶</a></h2>
<p>Let’s begin with a small regression example. We can build and train a regression model with the following code:</p>
-<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">library</span><span class="p">(</span>mxnet<span class="p">)</span>
- data<span class="p">(</span>BostonHousing<span class="p">,</span> package<span class="o">=</span><span class="s">"mlbench"</span><span class="p">)</span>
- train.ind <span class="o">=</span> <span class="kp">seq</span><span class="p">(</span><span class="m">1</span><span class="p">,</span> <span class="m">506</span><span class="p">,</span> <span class="m">3</span><span class="p">)</span>
- train.x <span class="o">=</span> <span class="kp">data.matrix</span><span class="p">(</span>BostonHousing<span class="p">[</span>train.ind<span class="p">,</span> <span class="m">-14</span><span class="p">])</span>
- train.y <span class="o">=</span> BostonHousing<span class="p">[</span>train.ind<span class="p">,</span> <span class="m">14</span><span class="p">]</span>
- test.x <span class="o">=</span> <span class="kp">data.matrix</span><span class="p">(</span>BostonHousing<span class="p">[</span><span class="o">-</span>train.ind<span class="p">,</span> <span class="m">-14</span><span class="p">])</span>
- test.y <span class="o">=</span> BostonHousing<span class="p">[</span><span class="o">-</span>train.ind<span class="p">,</span> <span class="m">14</span><span class="p">]</span>
- data <span class="o"><-</span> mx.symbol.Variable<span class="p">(</span><span class="s">"data"</span><span class="p">)</span>
- fc1 <span class="o"><-</span> mx.symbol.FullyConnected<span class="p">(</span>data<span class="p">,</span> num_hidden<span class="o">=</span><span class="m">1</span><span class="p">)</span>
- lro <span class="o"><-</span> mx.symbol.LinearRegressionOutput<span class="p">(</span>fc1<span class="p">)</span>
- mx.set.seed<span class="p">(</span><span class="m">0</span><span class="p">)</span>
- model <span class="o"><-</span> mx.model.FeedForward.create<span class="p">(</span>
- lro<span class="p">,</span> X<span class="o">=</span>train.x<span class="p">,</span> y<span class="o">=</span>train.y<span class="p">,</span>
- eval.data<span class="o">=</span><span class="kt">list</span><span class="p">(</span>data<span class="o">=</span>test.x<span class="p">,</span> label<span class="o">=</span>test.y<span class="p">),</span>
- ctx<span class="o">=</span>mx.cpu<span class="p">(),</span> num.round<span class="o">=</span><span class="m">10</span><span class="p">,</span> array.batch.size<span class="o">=</span><span class="m">20</span><span class="p">,</span>
- learning.rate<span class="o">=</span><span class="m">2e-6</span><span class="p">,</span> momentum<span class="o">=</span><span class="m">0.9</span><span class="p">,</span> eval.metric<span class="o">=</span>mx.metric.rmse<span class="p">)</span>
+<div class="highlight-r"><div class="highlight"><pre><span></span>data<span class="p">(</span>BostonHousing<span class="p">,</span> package <span class="o">=</span> <span class="s">"mlbench"</span><span class="p">)</span>
+BostonHousing<span class="p">[,</span> <span class="kp">sapply</span><span class="p">(</span>BostonHousing<span class="p">,</span> <span class="kp">is.factor</span><span class="p">)]</span> <span class="o"><-</span>
+ <span class="kp">as.numeric</span><span class="p">(</span><span class="kp">as.character</span><span class="p">(</span>BostonHousing<span class="p">[,</span> <span class="kp">sapply</span><span class="p">(</span>BostonHousing<span class="p">,</span> <span class="kp">is.factor</span><span class="p">)]))</span>
+BostonHousing <span class="o"><-</span> <span class="kt">data.frame</span><span class="p">(</span><span class="kp">scale</span><span class="p">(</span>BostonHousing<span class="p">))</span>
+
+test.ind <span class="o">=</span> <span class="kp">seq</span><span class="p">(</span><span class="m">1</span><span class="p">,</span> <span class="m">506</span><span class="p">,</span> <span class="m">5</span><span class="p">)</span> <span class="c1"># 1 pt in 5 used for testing</span>
+train.x <span class="o">=</span> <span class="kp">data.matrix</span><span class="p">(</span>BostonHousing<span class="p">[</span><span class="o">-</span>test.ind<span class="p">,</span><span class="m">-14</span><span class="p">])</span>
+train.y <span class="o">=</span> BostonHousing<span class="p">[</span><span class="o">-</span>test.ind<span class="p">,</span> <span class="m">14</span><span class="p">]</span>
+test.x <span class="o">=</span> <span class="kp">data.matrix</span><span class="p">(</span>BostonHousing<span class="p">[</span><span class="o">--</span>test.ind<span class="p">,</span><span class="m">-14</span><span class="p">])</span>
+test.y <span class="o">=</span> BostonHousing<span class="p">[</span><span class="o">--</span>test.ind<span class="p">,</span> <span class="m">14</span><span class="p">]</span>
+
+<span class="kn">require</span><span class="p">(</span>mxnet<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Loading required package: mxnet</span>
</pre></div>
</div>
-<p>Besides the <code class="docutils literal"><span class="pre">LinearRegressionOutput</span></code>, we also provide <code class="docutils literal"><span class="pre">LogisticRegressionOutput</span></code> and <code class="docutils literal"><span class="pre">MAERegressionOutput</span></code>.
-However, this might not be enough for real-world models. You can provide your own loss function
-by using <code class="docutils literal"><span class="pre">mx.symbol.MakeLoss</span></code> when constructing the network.</p>
+<div class="highlight-r"><div class="highlight"><pre><span></span>data <span class="o"><-</span> mx.symbol.Variable<span class="p">(</span><span class="s">"data"</span><span class="p">)</span>
+label <span class="o"><-</span> mx.symbol.Variable<span class="p">(</span><span class="s">"label"</span><span class="p">)</span>
+fc1 <span class="o"><-</span> mx.symbol.FullyConnected<span class="p">(</span>data<span class="p">,</span> num_hidden <span class="o">=</span> <span class="m">14</span><span class="p">,</span> name <span class="o">=</span> <span class="s">"fc1"</span><span class="p">)</span>
+tanh1 <span class="o"><-</span> mx.symbol.Activation<span class="p">(</span>fc1<span class="p">,</span> act_type <span class="o">=</span> <span class="s">"tanh"</span><span class="p">,</span> name <span class="o">=</span> <span class="s">"tanh1"</span><span class="p">)</span>
+fc2 <span class="o"><-</span> mx.symbol.FullyConnected<span class="p">(</span>tanh1<span class="p">,</span> num_hidden <span class="o">=</span> <span class="m">1</span><span class="p">,</span> name <span class="o">=</span> <span class="s">"fc2"</span><span class="p">)</span>
+lro <span class="o"><-</span> mx.symbol.LinearRegressionOutput<span class="p">(</span>fc2<span class="p">,</span> name <span class="o">=</span> <span class="s">"lro"</span><span class="p">)</span>
+
+mx.set.seed<span class="p">(</span><span class="m">0</span><span class="p">)</span>
+model <span class="o"><-</span> mx.model.FeedForward.create<span class="p">(</span>lro<span class="p">,</span> X <span class="o">=</span> train.x<span class="p">,</span> y <span class="o">=</span> train.y<span class="p">,</span>
+ ctx <span class="o">=</span> mx.cpu<span class="p">(),</span>
+ num.round <span class="o">=</span> <span class="m">5</span><span class="p">,</span>
+ array.batch.size <span class="o">=</span> <span class="m">60</span><span class="p">,</span>
+ optimizer <span class="o">=</span> <span class="s">"rmsprop"</span><span class="p">,</span>
+ verbose <span class="o">=</span> <span class="kc">TRUE</span><span class="p">,</span>
+ array.layout <span class="o">=</span> <span class="s">"rowmajor"</span><span class="p">,</span>
+ batch.end.callback <span class="o">=</span> <span class="kc">NULL</span><span class="p">,</span>
+ epoch.end.callback <span class="o">=</span> <span class="kc">NULL</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Start training with 1 devices</span>
+</pre></div>
+</div>
+<div class="highlight-r"><div class="highlight"><pre><span></span>pred <span class="o"><-</span> predict<span class="p">(</span>model<span class="p">,</span> test.x<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..</span>
+</pre></div>
+</div>
+<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kp">sum</span><span class="p">((</span>test.y <span class="o">-</span> pred<span class="p">[</span><span class="m">1</span><span class="p">,])</span><span class="o">^</span><span class="m">2</span><span class="p">)</span> <span class="o">/</span> <span class="kp">length</span><span class="p">(</span>test.y<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] 0.2485236</span>
+</pre></div>
+</div>
+<p>Besides the <code class="docutils literal"><span class="pre">LinearRegressionOutput</span></code>, we also provide <code class="docutils literal"><span class="pre">LogisticRegressionOutput</span></code> and <code class="docutils literal"><span class="pre">MAERegressionOutput</span></code>. However, this might not be enough for real-world models. You can provide your own loss function by using <code class="docutils literal"><span class="pre">mx.symbol.MakeLoss</span></code> when constr [...]
</div>
<div class="section" id="how-to-use-your-own-loss-function">
<span id="how-to-use-your-own-loss-function"></span><h2>How to Use Your Own Loss Function<a class="headerlink" href="#how-to-use-your-own-loss-function" title="Permalink to this headline">¶</a></h2>
-<p>We still use our previous example.</p>
-<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">library</span><span class="p">(</span>mxnet<span class="p">)</span>
- data <span class="o"><-</span> mx.symbol.Variable<span class="p">(</span><span class="s">"data"</span><span class="p">)</span>
- fc1 <span class="o"><-</span> mx.symbol.FullyConnected<span class="p">(</span>data<span class="p">,</span> num_hidden<span class="o">=</span><span class="m">1</span><span class="p">)</span>
- lro <span class="o"><-</span> mx.symbol.MakeLoss<span class="p">(</span>mx.symbol.square<span class="p">(</span>mx.symbol.Reshape<span class="p">(</span>fc1<span class="p">,</span> shape <span class="o">=</span> <span class="m">0</span><span class="p">)</span> <span class="o">-</span> label<span class="p">))</span>
+<p>We still use our previous example, but this time we use <code class="docutils literal"><span class="pre">mx.symbol.MakeLoss</span></code> to minimize the <code class="docutils literal"><span class="pre">(pred-label)^2</span></code></p>
+<div class="highlight-r"><div class="highlight"><pre><span></span>data <span class="o"><-</span> mx.symbol.Variable<span class="p">(</span><span class="s">"data"</span><span class="p">)</span>
+label <span class="o"><-</span> mx.symbol.Variable<span class="p">(</span><span class="s">"label"</span><span class="p">)</span>
+fc1 <span class="o"><-</span> mx.symbol.FullyConnected<span class="p">(</span>data<span class="p">,</span> num_hidden <span class="o">=</span> <span class="m">14</span><span class="p">,</span> name <span class="o">=</span> <span class="s">"fc1"</span><span class="p">)</span>
+tanh1 <span class="o"><-</span> mx.symbol.Activation<span class="p">(</span>fc1<span class="p">,</span> act_type <span class="o">=</span> <span class="s">"tanh"</span><span class="p">,</span> name <span class="o">=</span> <span class="s">"tanh1"</span><span class="p">)</span>
+fc2 <span class="o"><-</span> mx.symbol.FullyConnected<span class="p">(</span>tanh1<span class="p">,</span> num_hidden <span class="o">=</span> <span class="m">1</span><span class="p">,</span> name <span class="o">=</span> <span class="s">"fc2"</span><span class="p">)</span>
+lro2 <span class="o"><-</span> mx.symbol.MakeLoss<span class="p">(</span>mx.symbol.square<span class="p">(</span>mx.symbol.Reshape<span class="p">(</span>fc2<span class="p">,</span> shape <span class="o">=</span> <span class="m">0</span><span class="p">)</span> <span class="o">-</span> label<span class="p">),</span> name<span class="o">=</span><span class="s">"lro2"</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Then we can train the network just as usual.</p>
+<div class="highlight-r"><div class="highlight"><pre><span></span>mx.set.seed<span class="p">(</span><span class="m">0</span><span class="p">)</span>
+model2 <span class="o"><-</span> mx.model.FeedForward.create<span class="p">(</span>lro2<span class="p">,</span> X <span class="o">=</span> train.x<span class="p">,</span> y <span class="o">=</span> train.y<span class="p">,</span>
+ ctx <span class="o">=</span> mx.cpu<span class="p">(),</span>
+ num.round <span class="o">=</span> <span class="m">5</span><span class="p">,</span>
+ array.batch.size <span class="o">=</span> <span class="m">60</span><span class="p">,</span>
+ optimizer <span class="o">=</span> <span class="s">"rmsprop"</span><span class="p">,</span>
+ verbose <span class="o">=</span> <span class="kc">TRUE</span><span class="p">,</span>
+ array.layout <span class="o">=</span> <span class="s">"rowmajor"</span><span class="p">,</span>
+ batch.end.callback <span class="o">=</span> <span class="kc">NULL</span><span class="p">,</span>
+ epoch.end.callback <span class="o">=</span> <span class="kc">NULL</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Start training with 1 devices</span>
+</pre></div>
+</div>
+<p>We should get very similar results because we are actually minimizing the same loss function. However, the result is quite different.</p>
+<div class="highlight-r"><div class="highlight"><pre><span></span>pred2 <span class="o"><-</span> predict<span class="p">(</span>model2<span class="p">,</span> test.x<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..</span>
+</pre></div>
+</div>
+<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kp">sum</span><span class="p">((</span>test.y <span class="o">-</span> pred2<span class="p">)</span><span class="o">^</span><span class="m">2</span><span class="p">)</span> <span class="o">/</span> <span class="kp">length</span><span class="p">(</span>test.y<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] 1.234584</span>
+</pre></div>
+</div>
+<p>This is because output of <code class="docutils literal"><span class="pre">mx.symbol.MakeLoss</span></code> is the gradient of loss with respect to the input data. We can get the real prediction as below.</p>
+<div class="highlight-r"><div class="highlight"><pre><span></span>internals <span class="o">=</span> internals<span class="p">(</span>model2<span class="o">$</span>symbol<span class="p">)</span>
+fc_symbol <span class="o">=</span> internals<span class="p">[[</span><span class="kp">match</span><span class="p">(</span><span class="s">"fc2_output"</span><span class="p">,</span> outputs<span class="p">(</span>internals<span class="p">))]]</span>
+
+model3 <span class="o"><-</span> <span class="kt">list</span><span class="p">(</span>symbol <span class="o">=</span> fc_symbol<span class="p">,</span>
+ arg.params <span class="o">=</span> model2<span class="o">$</span>arg.params<span class="p">,</span>
+ aux.params <span class="o">=</span> model2<span class="o">$</span>aux.params<span class="p">)</span>
+
+<span class="kp">class</span><span class="p">(</span>model3<span class="p">)</span> <span class="o"><-</span> <span class="s">"MXFeedForwardModel"</span>
+
+pred3 <span class="o"><-</span> predict<span class="p">(</span>model3<span class="p">,</span> test.x<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..</span>
+</pre></div>
+</div>
+<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kp">sum</span><span class="p">((</span>test.y <span class="o">-</span> pred3<span class="p">[</span><span class="m">1</span><span class="p">,])</span><span class="o">^</span><span class="m">2</span><span class="p">)</span> <span class="o">/</span> <span class="kp">length</span><span class="p">(</span>test.y<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] 0.248294</span>
+</pre></div>
+</div>
+<p>We have provided many operations on the symbols. An example of <code class="docutils literal"><span class="pre">|pred-label|</span></code> can be found below.</p>
+<div class="highlight-r"><div class="highlight"><pre><span></span>lro_abs <span class="o"><-</span> mx.symbol.MakeLoss<span class="p">(</span>mx.symbol.abs<span class="p">(</span>mx.symbol.Reshape<span class="p">(</span>fc2<span class="p">,</span> shape <span class="o">=</span> <span class="m">0</span><span class="p">)</span> <span class="o">-</span> label<span class="p">))</span>
+mx.set.seed<span class="p">(</span><span class="m">0</span><span class="p">)</span>
+model4 <span class="o"><-</span> mx.model.FeedForward.create<span class="p">(</span>lro_abs<span class="p">,</span> X <span class="o">=</span> train.x<span class="p">,</span> y <span class="o">=</span> train.y<span class="p">,</span>
+ ctx <span class="o">=</span> mx.cpu<span class="p">(),</span>
+ num.round <span class="o">=</span> <span class="m">20</span><span class="p">,</span>
+ array.batch.size <span class="o">=</span> <span class="m">60</span><span class="p">,</span>
+ optimizer <span class="o">=</span> <span class="s">"sgd"</span><span class="p">,</span>
+ learning.rate <span class="o">=</span> <span class="m">0.001</span><span class="p">,</span>
+ verbose <span class="o">=</span> <span class="kc">TRUE</span><span class="p">,</span>
+ array.layout <span class="o">=</span> <span class="s">"rowmajor"</span><span class="p">,</span>
+ batch.end.callback <span class="o">=</span> <span class="kc">NULL</span><span class="p">,</span>
+ epoch.end.callback <span class="o">=</span> <span class="kc">NULL</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Start training with 1 devices</span>
+</pre></div>
+</div>
+<div class="highlight-r"><div class="highlight"><pre><span></span>internals <span class="o">=</span> internals<span class="p">(</span>model4<span class="o">$</span>symbol<span class="p">)</span>
+fc_symbol <span class="o">=</span> internals<span class="p">[[</span><span class="kp">match</span><span class="p">(</span><span class="s">"fc2_output"</span><span class="p">,</span> outputs<span class="p">(</span>internals<span class="p">))]]</span>
+
+model5 <span class="o"><-</span> <span class="kt">list</span><span class="p">(</span>symbol <span class="o">=</span> fc_symbol<span class="p">,</span>
+ arg.params <span class="o">=</span> model4<span class="o">$</span>arg.params<span class="p">,</span>
+ aux.params <span class="o">=</span> model4<span class="o">$</span>aux.params<span class="p">)</span>
+
+<span class="kp">class</span><span class="p">(</span>model5<span class="p">)</span> <span class="o"><-</span> <span class="s">"MXFeedForwardModel"</span>
+
+pred5 <span class="o"><-</span> predict<span class="p">(</span>model5<span class="p">,</span> test.x<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..</span>
+</pre></div>
+</div>
+<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kp">sum</span><span class="p">(</span><span class="kp">abs</span><span class="p">(</span>test.y <span class="o">-</span> pred5<span class="p">[</span><span class="m">1</span><span class="p">,]))</span> <span class="o">/</span> <span class="kp">length</span><span class="p">(</span>test.y<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] 0.7056902</span>
+</pre></div>
+</div>
+<div class="highlight-r"><div class="highlight"><pre><span></span>lro_mae <span class="o"><-</span> mx.symbol.MAERegressionOutput<span class="p">(</span>fc2<span class="p">,</span> name <span class="o">=</span> <span class="s">"lro"</span><span class="p">)</span>
+mx.set.seed<span class="p">(</span><span class="m">0</span><span class="p">)</span>
+model6 <span class="o"><-</span> mx.model.FeedForward.create<span class="p">(</span>lro_mae<span class="p">,</span> X <span class="o">=</span> train.x<span class="p">,</span> y <span class="o">=</span> train.y<span class="p">,</span>
+ ctx <span class="o">=</span> mx.cpu<span class="p">(),</span>
+ num.round <span class="o">=</span> <span class="m">20</span><span class="p">,</span>
+ array.batch.size <span class="o">=</span> <span class="m">60</span><span class="p">,</span>
+ optimizer <span class="o">=</span> <span class="s">"sgd"</span><span class="p">,</span>
+ learning.rate <span class="o">=</span> <span class="m">0.001</span><span class="p">,</span>
+ verbose <span class="o">=</span> <span class="kc">TRUE</span><span class="p">,</span>
+ array.layout <span class="o">=</span> <span class="s">"rowmajor"</span><span class="p">,</span>
+ batch.end.callback <span class="o">=</span> <span class="kc">NULL</span><span class="p">,</span>
+ epoch.end.callback <span class="o">=</span> <span class="kc">NULL</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Start training with 1 devices</span>
+</pre></div>
+</div>
+<div class="highlight-r"><div class="highlight"><pre><span></span>pred6 <span class="o"><-</span> predict<span class="p">(</span>model6<span class="p">,</span> test.x<span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor..</span>
+</pre></div>
+</div>
+<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kp">sum</span><span class="p">(</span><span class="kp">abs</span><span class="p">(</span>test.y <span class="o">-</span> pred6<span class="p">[</span><span class="m">1</span><span class="p">,]))</span> <span class="o">/</span> <span class="kp">length</span><span class="p">(</span>test.y<span class="p">)</span>
</pre></div>
</div>
-<p>In the last line of network definition, we do not use the predefined loss function. We define the loss
-by ourselves, which is <code class="docutils literal"><span class="pre">(pred-label)^2</span></code>.</p>
-<p>We have provided many operations on the symbols, so you can also define <code class="docutils literal"><span class="pre">|pred-label|</span></code> using the line below.</p>
-<div class="highlight-r"><div class="highlight"><pre><span></span> lro <span class="o"><-</span> mx.symbol.MakeLoss<span class="p">(</span>mx.symbol.abs<span class="p">(</span>mx.symbol.Reshape<span class="p">(</span>fc1<span class="p">,</span> shape <span class="o">=</span> <span class="m">0</span><span class="p">)</span> <span class="o">-</span> label<span class="p">))</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] 0.7056902</span>
</pre></div>
</div>
</div>
diff --git a/versions/master/tutorials/r/charRnnModel.html b/versions/master/tutorials/r/charRnnModel.html
index e25f027..8be3b4d 100644
--- a/versions/master/tutorials/r/charRnnModel.html
+++ b/versions/master/tutorials/r/charRnnModel.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Char RNN Example — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
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-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
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@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
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<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -243,10 +241,10 @@ Previous Navbar Layout End -->
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">require</span><span class="p">(</span>mxnet<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: methods</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: methods</span>
</pre></div>
</div>
<p>Set the basic network parameters:</p>
@@ -350,7 +348,7 @@ Previous Navbar Layout End -->
ret <span class="o"><-</span> make.data<span class="p">(</span><span class="s">"./data/input.txt"</span><span class="p">,</span> seq.len<span class="o">=</span>seq.len<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Total unique char: 65</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Total unique char: 65</span>
</pre></div>
</div>
<div class="highlight-r"><div class="highlight"><pre><span></span> X <span class="o"><-</span> ret<span class="o">$</span>data
@@ -396,7 +394,7 @@ Previous Navbar Layout End -->
clip_gradient<span class="o">=</span>clip_gradient<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Epoch [31] Train: NLL=3.53787130224343, Perp=34.3936275728271</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Epoch [31] Train: NLL=3.53787130224343, Perp=34.3936275728271</span>
<span class="c1">## Epoch [62] Train: NLL=3.43087958036949, Perp=30.903813186055</span>
<span class="c1">## Epoch [93] Train: NLL=3.39771238228587, Perp=29.8956319855751</span>
<span class="c1">## Epoch [124] Train: NLL=3.37581711716687, Perp=29.2481732041015</span>
@@ -510,8 +508,8 @@ Previous Navbar Layout End -->
</pre></div>
</div>
<p>The result:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">ah</span> <span class="ow">not</span> <span class="n">a</span> <span class="n">drobl</span> <span class="n">greens</span>
- <span class="n">Settled</span> <span class="n">asing</span> <span class="n">lately</span> <span class="n">sistering</span> <span class="n">sounted</span> <span class="n">to</span> <span class="n">their</span> <span class="n">hight</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> ah not a drobl greens
+ Settled asing lately sistering sounted to their hight
</pre></div>
</div>
</div>
diff --git a/versions/master/tutorials/r/classifyRealImageWithPretrainedModel.html b/versions/master/tutorials/r/classifyRealImageWithPretrainedModel.html
index b9f439e..00177f9 100644
--- a/versions/master/tutorials/r/classifyRealImageWithPretrainedModel.html
+++ b/versions/master/tutorials/r/classifyRealImageWithPretrainedModel.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Classify Images with a PreTrained Model — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
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-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
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+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
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+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
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@@ -18,7 +18,7 @@
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- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
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@@ -48,10 +48,8 @@
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+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -248,7 +246,7 @@ This model gives the recent state-of-art prediction accuracy on image net datase
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">require</span><span class="p">(</span>mxnet<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span>
<span class="c1">## Loading required package: methods</span>
</pre></div>
</div>
@@ -256,7 +254,7 @@ This model gives the recent state-of-art prediction accuracy on image net datase
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">require</span><span class="p">(</span>imager<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: imager</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: imager</span>
<span class="c1">## Loading required package: plyr</span>
<span class="c1">## Loading required package: magrittr</span>
<span class="c1">## Loading required package: stringr</span>
@@ -346,7 +344,7 @@ to get the probability over classes:</p>
<span class="kp">dim</span><span class="p">(</span>prob<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## [1] 1000 1</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] 1000 1</span>
</pre></div>
</div>
<p>As you can see, <code class="docutils literal"><span class="pre">prob</span></code> is a 1 times 1000 array, which gives the probability
@@ -356,7 +354,7 @@ over the 1000 image classes of the input.</p>
max.idx
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## [1] 89</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] 89</span>
</pre></div>
</div>
<p>The index doesn’t make much sense, so let’s see what it really means.
@@ -368,7 +366,7 @@ Read the names of the classes from the following file:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kp">print</span><span class="p">(</span><span class="kp">paste0</span><span class="p">(</span><span class="s">"Predicted Top-class: "</span><span class="p">,</span> synsets <span class="p">[[</span>max.idx<span class="p">]]))</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## [1] "Predicted Top-class: n01818515 macaw"</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] "Predicted Top-class: n01818515 macaw"</span>
</pre></div>
</div>
<p>It’s a macaw!</p>
diff --git a/versions/master/tutorials/r/fiveMinutesNeuralNetwork.html b/versions/master/tutorials/r/fiveMinutesNeuralNetwork.html
index e45592b..713f707 100644
--- a/versions/master/tutorials/r/fiveMinutesNeuralNetwork.html
+++ b/versions/master/tutorials/r/fiveMinutesNeuralNetwork.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Develop a Neural Network with MXNet in Five Minutes — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -242,13 +240,13 @@ Previous Navbar Layout End -->
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">require</span><span class="p">(</span>mlbench<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mlbench</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mlbench</span>
</pre></div>
</div>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">require</span><span class="p">(</span>mxnet<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span>
<span class="c1">## Loading required package: methods</span>
</pre></div>
</div>
@@ -280,7 +278,7 @@ Previous Navbar Layout End -->
eval.metric<span class="o">=</span>mx.metric.accuracy<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor..</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor..</span>
<span class="c1">## Start training with 1 devices</span>
<span class="c1">## [1] Train-accuracy=0.488888888888889</span>
<span class="c1">## [2] Train-accuracy=0.514285714285714</span>
@@ -313,14 +311,14 @@ Previous Navbar Layout End -->
<div class="highlight-r"><div class="highlight"><pre><span></span> preds <span class="o">=</span> predict<span class="p">(</span>model<span class="p">,</span> test.x<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor.</span>
</pre></div>
</div>
<div class="highlight-r"><div class="highlight"><pre><span></span> pred.label <span class="o">=</span> <span class="kp">max.col</span><span class="p">(</span><span class="kp">t</span><span class="p">(</span>preds<span class="p">))</span><span class="m">-1</span>
<span class="kp">table</span><span class="p">(</span>pred.label<span class="p">,</span> test.y<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## test.y</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## test.y</span>
<span class="c1">## pred.label 0 1</span>
<span class="c1">## 0 24 14</span>
<span class="c1">## 1 36 33</span>
@@ -360,7 +358,7 @@ Previous Navbar Layout End -->
learning.rate<span class="o">=</span><span class="m">2e-6</span><span class="p">,</span> momentum<span class="o">=</span><span class="m">0.9</span><span class="p">,</span> eval.metric<span class="o">=</span>mx.metric.rmse<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor.</span>
<span class="c1">## Start training with 1 devices</span>
<span class="c1">## [1] Train-rmse=16.063282524034</span>
<span class="c1">## [2] Train-rmse=12.2792375712573</span>
@@ -418,13 +416,13 @@ Previous Navbar Layout End -->
<div class="highlight-r"><div class="highlight"><pre><span></span> preds <span class="o">=</span> predict<span class="p">(</span>model<span class="p">,</span> test.x<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor..</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor..</span>
</pre></div>
</div>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kp">sqrt</span><span class="p">(</span><span class="kp">mean</span><span class="p">((</span>preds<span class="o">-</span>test.y<span class="p">)</span><span class="o">^</span><span class="m">2</span><span class="p">))</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## [1] 7.800502</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] 7.800502</span>
</pre></div>
</div>
<p>Currently, we have four predefined metrics: “accuracy”, “rmse”, “mae”, and “rmsle”. MXNet provides the interface for defining your own metrics:</p>
@@ -441,7 +439,7 @@ Previous Navbar Layout End -->
learning.rate<span class="o">=</span><span class="m">2e-6</span><span class="p">,</span> momentum<span class="o">=</span><span class="m">0.9</span><span class="p">,</span> eval.metric<span class="o">=</span>demo.metric.mae<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Auto detect layout of input matrix, use rowmajor.</span>
<span class="c1">## Start training with 1 devices</span>
<span class="c1">## [1] Train-mae=13.1889538083225</span>
<span class="c1">## [2] Train-mae=9.81431959337658</span>
diff --git a/versions/master/tutorials/r/mnistCompetition.html b/versions/master/tutorials/r/mnistCompetition.html
index 48f3337..e7ce759 100644
--- a/versions/master/tutorials/r/mnistCompetition.html
+++ b/versions/master/tutorials/r/mnistCompetition.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Handwritten Digits Classification Competition — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -245,7 +243,7 @@ This tutorial shows how to use <a class="reference external" href="https://githu
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">require</span><span class="p">(</span>mxnet<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span>
<span class="c1">## Loading required package: methods</span>
</pre></div>
</div>
@@ -268,7 +266,7 @@ This tutorial shows how to use <a class="reference external" href="https://githu
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kp">table</span><span class="p">(</span>train.y<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## train.y</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## train.y</span>
<span class="c1">## 0 1 2 3 4 5 6 7 8 9</span>
<span class="c1">## 4132 4684 4177 4351 4072 3795 4137 4401 4063 4188</span>
</pre></div>
@@ -311,7 +309,7 @@ This tutorial shows how to use <a class="reference external" href="https://githu
epoch.end.callback<span class="o">=</span>mx.callback.log.train.metric<span class="p">(</span><span class="m">100</span><span class="p">))</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Start training with 1 devices</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Start training with 1 devices</span>
<span class="c1">## Batch [100] Train-accuracy=0.6563</span>
<span class="c1">## Batch [200] Train-accuracy=0.777999999999999</span>
<span class="c1">## Batch [300] Train-accuracy=0.827466666666665</span>
@@ -372,7 +370,7 @@ This tutorial shows how to use <a class="reference external" href="https://githu
<span class="kp">dim</span><span class="p">(</span>preds<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## [1] 10 28000</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] 10 28000</span>
</pre></div>
</div>
<p>It is a matrix with 28000 rows and 10 cols, containing the desired classification probabilities from the output layer. To extract the maximum label for each row, use <code class="docutils literal"><span class="pre">max.col</span></code>:</p>
@@ -380,7 +378,7 @@ This tutorial shows how to use <a class="reference external" href="https://githu
<span class="kp">table</span><span class="p">(</span>pred.label<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## pred.label</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## pred.label</span>
<span class="c1">## 0 1 2 3 4 5 6 7 8 9</span>
<span class="c1">## 2818 3195 2744 2767 2683 2596 2798 2790 2784 2825</span>
</pre></div>
@@ -444,7 +442,7 @@ but because internal computation of CPUs is already multi-threaded, there is les
epoch.end.callback<span class="o">=</span>mx.callback.log.train.metric<span class="p">(</span><span class="m">100</span><span class="p">))</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Start training with 1 devices</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Start training with 1 devices</span>
<span class="c1">## Batch [100] Train-accuracy=0.1066</span>
<span class="c1">## Batch [200] Train-accuracy=0.16495</span>
<span class="c1">## Batch [300] Train-accuracy=0.401766666666667</span>
@@ -455,7 +453,7 @@ but because internal computation of CPUs is already multi-threaded, there is les
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kp">print</span><span class="p">(</span><span class="kp">proc.time</span><span class="p">()</span> <span class="o">-</span> tic<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## user system elapsed</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## user system elapsed</span>
<span class="c1">## 130.030 204.976 83.821</span>
</pre></div>
</div>
@@ -469,7 +467,7 @@ but because internal computation of CPUs is already multi-threaded, there is les
epoch.end.callback<span class="o">=</span>mx.callback.log.train.metric<span class="p">(</span><span class="m">100</span><span class="p">))</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Start training with 1 devices</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Start training with 1 devices</span>
<span class="c1">## Batch [100] Train-accuracy=0.1066</span>
<span class="c1">## Batch [200] Train-accuracy=0.1596</span>
<span class="c1">## Batch [300] Train-accuracy=0.3983</span>
@@ -500,7 +498,7 @@ but because internal computation of CPUs is already multi-threaded, there is les
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kp">print</span><span class="p">(</span><span class="kp">proc.time</span><span class="p">()</span> <span class="o">-</span> tic<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## user system elapsed</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## user system elapsed</span>
<span class="c1">## 9.288 1.680 6.889</span>
</pre></div>
</div>
diff --git a/versions/master/tutorials/r/ndarray.html b/versions/master/tutorials/r/ndarray.html
index 6ff5681..9474d95 100644
--- a/versions/master/tutorials/r/ndarray.html
+++ b/versions/master/tutorials/r/ndarray.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>NDArray: Vectorized Tensor Computations on CPUs and GPUs — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
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-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -252,7 +250,7 @@ parallel with each other.</li>
<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kn">require</span><span class="p">(</span>mxnet<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## Loading required package: mxnet</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## Loading required package: mxnet</span>
<span class="c1">## Loading required package: methods</span>
</pre></div>
</div>
@@ -275,13 +273,13 @@ b <span class="o"><-</span> <span class="kp">as.array</span><span class="p">(</s
<span class="kp">class</span><span class="p">(</span>b<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [1] "matrix"</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] "matrix"</span>
</pre></div>
</div>
<div class="highlight-r"><div class="highlight"><pre><span></span>b
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3]</span>
<span class="c1">## [1,] 1 1 1</span>
<span class="c1">## [2,] 1 1 1</span>
</pre></div>
@@ -297,7 +295,7 @@ b <span class="o"><-</span> mx.nd.ones<span class="p">(</span><span class="kt">c
<span class="kp">as.array</span><span class="p">(</span>a<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3] [,4]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3] [,4]</span>
<span class="c1">## [1,] 2 2 2 2</span>
<span class="c1">## [2,] 2 2 2 2</span>
</pre></div>
@@ -305,7 +303,7 @@ b <span class="o"><-</span> mx.nd.ones<span class="p">(</span><span class="kt">c
<div class="highlight-r"><div class="highlight"><pre><span></span><span class="kp">as.array</span><span class="p">(</span>b<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3] [,4]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3] [,4]</span>
<span class="c1">## [1,] 0.125 0.125 0.125 0.125</span>
<span class="c1">## [2,] 0.125 0.125 0.125 0.125</span>
</pre></div>
@@ -314,7 +312,7 @@ b <span class="o"><-</span> mx.nd.ones<span class="p">(</span><span class="kt">c
<span class="kp">as.array</span><span class="p">(</span><span class="kt">c</span><span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3] [,4]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3] [,4]</span>
<span class="c1">## [1,] 2.125 2.125 2.125 2.125</span>
<span class="c1">## [2,] 2.125 2.125 2.125 2.125</span>
</pre></div>
@@ -323,7 +321,7 @@ b <span class="o"><-</span> mx.nd.ones<span class="p">(</span><span class="kt">c
<span class="kp">as.array</span><span class="p">(</span>d<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3] [,4]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3] [,4]</span>
<span class="c1">## [1,] -3.9375 -3.9375 -3.9375 -3.9375</span>
<span class="c1">## [2,] -3.9375 -3.9375 -3.9375 -3.9375</span>
</pre></div>
@@ -348,7 +346,7 @@ mx.nd.save<span class="p">(</span><span class="kt">list</span><span class="p">(<
<span class="kp">as.array</span><span class="p">(</span>a<span class="p">[[</span><span class="m">1</span><span class="p">]])</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3]</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [,1] [,2] [,3]</span>
<span class="c1">## [1,] 1 1 1</span>
<span class="c1">## [2,] 1 1 1</span>
</pre></div>
diff --git a/versions/master/tutorials/r/symbol.html b/versions/master/tutorials/r/symbol.html
index cf7ee86..fd0432d 100644
--- a/versions/master/tutorials/r/symbol.html
+++ b/versions/master/tutorials/r/symbol.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Symbol and Automatic Differentiation — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
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var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
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-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
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@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
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<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -249,7 +247,7 @@ net <span class="o"><-</span> mx.symbol.Softmax<span class="p">(</span>data<span
<span class="kp">class</span><span class="p">(</span>net<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [1] "Rcpp_MXSymbol"</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] "Rcpp_MXSymbol"</span>
<span class="c1">## attr(,"package")</span>
<span class="c1">## [1] "mxnet"</span>
</pre></div>
@@ -263,7 +261,7 @@ names are automatically generated and can be retrieved with the following comman
<div class="highlight-r"><div class="highlight"><pre><span></span>arguments<span class="p">(</span>net<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [1] "data" "fc1_weight" "fc1_bias" "fc2_weight" "fc2_bias"</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] "data" "fc1_weight" "fc1_bias" "fc2_weight" "fc2_bias"</span>
<span class="c1">## [6] "out_label"</span>
</pre></div>
</div>
@@ -281,7 +279,7 @@ net <span class="o"><-</span> mx.symbol.FullyConnected<span class="p">(</span>da
arguments<span class="p">(</span>net<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [1] "data" "myweight" "fc1_bias"</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] "data" "myweight" "fc1_bias"</span>
</pre></div>
</div>
</div>
@@ -297,7 +295,7 @@ net <span class="o"><-</span> mx.symbol.FullyConnected<span class="p">(</span>da
arguments<span class="p">(</span>net<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [1] "data1" "data2" "fc1_weight" "fc1_bias"</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] "data1" "data2" "fc1_weight" "fc1_bias"</span>
</pre></div>
</div>
<p>We can construct a symbol more flexibly than by using the single
@@ -310,7 +308,7 @@ composed.net <span class="o"><-</span> mx.apply<span class="p">(</span>net<span
arguments<span class="p">(</span>composed.net<span class="p">)</span>
</pre></div>
</div>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">## [1] "data2" "net2_weight" "net2_bias" "fc1_weight" "fc1_bias"</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">## [1] "data2" "net2_weight" "net2_bias" "fc1_weight" "fc1_bias"</span>
</pre></div>
</div>
<p>In the example, <em>net</em> is used as a function to apply to an existing symbol
diff --git a/versions/master/tutorials/scala/char_lstm.html b/versions/master/tutorials/scala/char_lstm.html
index f430bb8..2f3c484 100644
--- a/versions/master/tutorials/scala/char_lstm.html
+++ b/versions/master/tutorials/scala/char_lstm.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Developing a Character-level Language model — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -257,21 +255,21 @@ Previous Navbar Layout End -->
<li>Build and train the model with the <a class="reference external" href="https://github.com/dmlc/mxnet/blob/master/scala-package/examples/scripts/rnn/run_train_charrnn.sh">run_train_charrnn.sh script</a>. Edit the script as follows:</li>
</ul>
<p>Edit the CLASS_PATH variable in the script to include your operating system-specific folder (e.g., linux-x86_64-cpu/linux-x86_64-gpu/osx-x86_64-cpu) in the path. Run the script with the following command:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span> bash run_train_charrnn.sh <which card="" class="p" gpu="" to="" use<span="">;</which></pre></div></div></div></div></div></div></div></body></html> -1 means CPU> <input data="" path=""> <location model="" save="" the="" to="">
+<div class="highlight-bash"><div class="highlight"><pre><span></span> bash run_train_charrnn.sh <which GPU card to use<span class="p">;</span> -1 means CPU> <input data path> <location to save the model>
e.g.,
bash run_train_charrnn.sh -1 ./datas/obama.txt ./models/obama
-</location></input>
-
+</pre></div>
+</div>
<ul class="simple">
<li>Run inference with the <a class="reference external" href="https://github.com/dmlc/mxnet/blob/master/scala-package/examples/scripts/rnn/run_test_charrnn.sh">run_test_charrnn.sh script</a>. Edit the script as follows:</li>
</ul>
<p>Edit the CLASS_PATH variable in the script to include your operating system-specific folder (e.g., linux-x86_64-cpu/linux-x86_64-gpu/osx-x86_64-cpu) in the path. Run the script with the following command:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span> bash run_test_charrnn.sh <input data="" path=""> <trained from="" model="" previous="" script="">
+<div class="highlight-bash"><div class="highlight"><pre><span></span> bash run_test_charrnn.sh <input data path> <trained model from previous script>
e.g.,
bash run_test_charrnn.sh ./datas/obama.txt ./models/obama
-</trained></input></pre></div>
+</pre></div>
</div>
<p>In this tutorial, you will accomplish the following:</p>
<ul class="simple">
@@ -281,7 +279,7 @@ Previous Navbar Layout End -->
<li>Fit the model.</li>
<li>Build the inference model.</li>
</ul>
-
+</div>
<div class="section" id="prerequisites">
<span id="prerequisites"></span><h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2>
<p>To complete this tutorial, you need:</p>
@@ -314,16 +312,16 @@ Previous Navbar Layout End -->
</pre></div>
</div>
<p>Output:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">Call</span> <span class="n">to</span> <span class="n">Renewal</span> <span class="n">Keynote</span> <span class="n">Address</span> <span class="n">Call</span> <span class="n">to</span> <span class="n">Renewal</span> <span class="n">Pt</span> <span class="mi">1</span><span class="n">Call</span> <span class="n">to</span> <span class="n">Renewal</span> <span class="n">Part</span> <span class="mi" [...]
- <span class="mi">28</span><span class="p">,</span> <span class="mi">2006</span> <span class="n">Call</span> <span class="n">to</span> <span class="n">Renewal</span><span class="s1">' Keynote Address Complete Text Good morning. I appreciate the opportunity to speak here at the Call to R</span>
- <span class="n">enewal</span><span class="s1">'s Building a Covenant for a New America conference. I'</span><span class="n">ve</span> <span class="n">had</span> <span class="n">the</span> <span class="n">opportunity</span> <span class="n">to</span> <span class="n">take</span> <span class="n">a</span> <span class="n">look</span> <span class="n">at</span> <span class="n">your</span> <span class="n">Covenant</span> <span class="k">for</span> <span class="n">a</span> <span class="n"> [...]
- <span class="n">rica</span><span class="o">.</span> <span class="n">It</span> <span class="ow">is</span> <span class="n">filled</span> <span class="k">with</span> <span class="n">outstanding</span> <span class="n">policies</span> <span class="ow">and</span> <span class="n">prescriptions</span> <span class="k">for</span> <span class="n">much</span> <span class="n">of</span> <span class="n">what</span> <span class="n">ails</span> <span class="n">this</span> <span class="n">country< [...]
- <span class="n">u</span> <span class="nb">all</span> <span class="n">on</span> <span class="n">the</span> <span class="n">thoughtful</span> <span class="n">presentations</span> <span class="n">you</span><span class="s1">'ve given so far about poverty and justice in America, and for putting fire under the fe</span>
- <span class="n">et</span> <span class="n">of</span> <span class="n">the</span> <span class="n">political</span> <span class="n">leadership</span> <span class="n">here</span> <span class="ow">in</span> <span class="n">Washington</span><span class="o">.</span><span class="n">But</span> <span class="n">today</span> <span class="n">I</span><span class="s1">'d like to talk about the connection between religion and politics a</span>
- <span class="n">nd</span> <span class="n">perhaps</span> <span class="n">offer</span> <span class="n">some</span> <span class="n">thoughts</span> <span class="n">about</span> <span class="n">how</span> <span class="n">we</span> <span class="n">can</span> <span class="n">sort</span> <span class="n">through</span> <span class="n">some</span> <span class="n">of</span> <span class="n">the</span> <span class="n">often</span> <span class="n">bitter</span> <span class="n">arguments</spa [...]
- <span class="n">ast</span> <span class="n">several</span> <span class="n">years</span><span class="o">.</span><span class="n">I</span> <span class="n">do</span> <span class="n">so</span> <span class="n">because</span><span class="p">,</span> <span class="k">as</span> <span class="n">you</span> <span class="nb">all</span> <span class="n">know</span><span class="p">,</span> <span class="n">we</span> <span class="n">can</span> <span class="n">affirm</span> <span class="n">the</span> [...]
- <span class="k">pass</span> <span class="n">out</span> <span class="n">this</span> <span class="n">Covenant</span> <span class="k">for</span> <span class="n">a</span> <span class="n">New</span> <span class="n">America</span><span class="o">.</span> <span class="n">We</span> <span class="n">can</span> <span class="n">talk</span> <span class="n">to</span> <span class="n">the</span> <span class="n">press</span><span class="p">,</span> <span class="ow">and</span> <span class="n">we< [...]
- <span class="n">environmental</span> <span class="n">stewardship</span> <span class="nb">all</span> <span class="n">we</span> <span class="n">want</span><span class="p">,</span> <span class="n">but</span> <span class="n">it</span> <span class="n">won</span><span class="s1">'t have an impact unless we tackle head-on the mutual suspicion that sometimes</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> Call to Renewal Keynote Address Call to Renewal Pt 1Call to Renewal Part 2 TOPIC: Our Past, Our Future & Vision for America June
+ 28, 2006 Call to Renewal' Keynote Address Complete Text Good morning. I appreciate the opportunity to speak here at the Call to R
+ enewal's Building a Covenant for a New America conference. I've had the opportunity to take a look at your Covenant for a New Ame
+ rica. It is filled with outstanding policies and prescriptions for much of what ails this country. So I'd like to congratulate yo
+ u all on the thoughtful presentations you've given so far about poverty and justice in America, and for putting fire under the fe
+ et of the political leadership here in Washington.But today I'd like to talk about the connection between religion and politics a
+ nd perhaps offer some thoughts about how we can sort through some of the often bitter arguments that we've been seeing over the l
+ ast several years.I do so because, as you all know, we can affirm the importance of poverty in the Bible; and we can raise up and
+ pass out this Covenant for a New America. We can talk to the press, and we can discuss the religious call to address poverty and
+ environmental stewardship all we want, but it won't have an impact unless we tackle head-on the mutual suspicion that sometimes
</pre></div>
</div>
</div>
@@ -730,13 +728,13 @@ Note: The BucketSentenceIter data iterator supports various length examples; how
</ul>
</div>
</div>
-
+</div>
<div class="container">
<div class="footer">
<p> © 2015-2017 DMLC. All rights reserved. </p>
</div>
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-
+</div>
<div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation">
<div class="sphinxsidebarwrapper">
<h3><a href="../../index.html">Table Of Contents</a></h3>
@@ -754,7 +752,7 @@ Note: The BucketSentenceIter data iterator supports various length examples; how
</ul>
</div>
</div>
- <!-- pagename != index -->
+</div> <!-- pagename != index -->
<script crossorigin="anonymous" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js"></script>
<script src="../../_static/js/sidebar.js" type="text/javascript"></script>
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@@ -766,4 +764,5 @@ Note: The BucketSentenceIter data iterator supports various length examples; how
$('body').css('visibility', 'visible');
});
</script>
-
+</div></body>
+</html>
\ No newline at end of file
diff --git a/versions/master/tutorials/scala/mnist.html b/versions/master/tutorials/scala/mnist.html
index a8d1f34..b18a627 100644
--- a/versions/master/tutorials/scala/mnist.html
+++ b/versions/master/tutorials/scala/mnist.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Handwritten Digit Recognition — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/scala/mxnet_scala_on_intellij.html b/versions/master/tutorials/scala/mxnet_scala_on_intellij.html
index 3a5bba6..6311b61 100644
--- a/versions/master/tutorials/scala/mxnet_scala_on_intellij.html
+++ b/versions/master/tutorials/scala/mxnet_scala_on_intellij.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Run MXNet Scala Examples Using the IntelliJ IDE — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -254,26 +252,26 @@ Previous Navbar Layout End -->
<li>Install the plugin for IntelliJ IDE by following these steps:
On <strong>Menu</strong>, choose <strong>Preferences</strong>, choose <strong>Plugins</strong>, type <strong>Scala</strong>, and then choose <strong>Install</strong>.</li>
<li>Follow the instructions for <a class="reference external" href="https://www.jetbrains.com/help/idea/2016.3/scala.html">Scala plugin setup for IDE</a>.</li>
-<li>When you build the MXNet package with Scala, a JAR file called <code class="docutils literal"><span class="pre">mxnet-full_${scala.binary.version}-${platform}</span></code> is generated in <code class="docutils literal"><span class="pre">native/<your-architecture>/target</your-architecture></span></code> directory. You need this file to create an example package that has a dependency on MXNet.</li>
+<li>When you build the MXNet package with Scala, a JAR file called <code class="docutils literal"><span class="pre">mxnet-full_${scala.binary.version}-${platform}</span></code> is generated in <code class="docutils literal"><span class="pre">native/<your-architecture>/target</span></code> directory. You need this file to create an example package that has a dependency on MXNet.</li>
<li>Specify project dependencies in pom.xml:</li>
</ul>
<div class="highlight-HTML"><div class="highlight"><pre><span></span> <span class="p"><</span><span class="nt">dependencies</span><span class="p">></span>
<span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
- <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"><!--</span--><span class="nt">groupId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_${scala.binary.version}-${platform}<span class="p"><!--</span--><span class="nt">artifactId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"><!--</span--><span class="nt">version</span><span class="p">></span>
- <span class="p"><</span><span class="nt">scope</span><span class="p">></span>system<span class="p"><!--</span--><span class="nt">scope</span><span class="p">></span>
- <span class="p"><</span><span class="nt">systemPath</span><span class="p">></span>`MXNet-Scala-jar-path`<span class="p"><!--</span--><span class="nt">systemPath</span><span class="p">></span>
- <span class="p"><!--</span--><span class="nt">dependency</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_${scala.binary.version}-${platform}<span class="p"></</span><span class="nt">artifactId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">scope</span><span class="p">></span>system<span class="p"></</span><span class="nt">scope</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">systemPath</span><span class="p">></span>`MXNet-Scala-jar-path`<span class="p"></</span><span class="nt">systemPath</span><span class="p">></span>
+ <span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
<span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
- <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>args4j<span class="p"><!--</span--><span class="nt">groupId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>args4j<span class="p"><!--</span--><span class="nt">artifactId</span><span class="p">></span>
- <span class="p"><</span><span class="nt">version</span><span class="p">></span>2.0.29<span class="p"><!--</span--><span class="nt">version</span><span class="p">></span>
- <span class="p"><!--</span--><span class="nt">dependency</span><span class="p">></span>
- <span class="p"><!--</span--><span class="nt">dependencies</span><span class="p">></span>
-</span></span></span></span></span></span></span></span></span></span></span></pre></div>
+ <span class="p"><</span><span class="nt">groupId</span><span class="p">></span>args4j<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>args4j<span class="p"></</span><span class="nt">artifactId</span><span class="p">></span>
+ <span class="p"><</span><span class="nt">version</span><span class="p">></span>2.0.29<span class="p"></</span><span class="nt">version</span><span class="p">></span>
+ <span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
+ <span class="p"></</span><span class="nt">dependencies</span><span class="p">></span>
+</pre></div>
</div>
-<p>Be sure to change the system path of MXNet-Scala-jar, which is in the <code class="docutils literal"><span class="pre">native/<your-architecture>/target</your-architecture></span></code> directory.</p>
+<p>Be sure to change the system path of MXNet-Scala-jar, which is in the <code class="docutils literal"><span class="pre">native/<your-architecture>/target</span></code> directory.</p>
<ul class="simple">
<li>Choose the example project, choose Maven, and then reimport. These steps add all of the dependencies in pom.xml as external libraries in your project.</li>
<li>To build the project, choose Menu, choose Build, and then choose Rebuild Project. If errors are reported in the IDE, address them.</li>
diff --git a/versions/master/tutorials/speech_recognition/baidu_warp_ctc.html b/versions/master/tutorials/speech_recognition/baidu_warp_ctc.html
index b09482c..54857ea 100644
--- a/versions/master/tutorials/speech_recognition/baidu_warp_ctc.html
+++ b/versions/master/tutorials/speech_recognition/baidu_warp_ctc.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Using Baidu Warp-CTC with MXNet — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -240,20 +238,20 @@ Previous Navbar Layout End -->
<p>You can get the source code for the example on <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example/warpctc">GitHub</a>.</p>
<div class="section" id="install-baidu-warp-ctc">
<span id="install-baidu-warp-ctc"></span><h2>Install Baidu Warp-CTC<a class="headerlink" href="#install-baidu-warp-ctc" title="Permalink to this headline">¶</a></h2>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">cd</span> <span class="o">~/</span>
- <span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">baidu</span><span class="o">-</span><span class="n">research</span><span class="o">/</span><span class="n">warp</span><span class="o">-</span><span class="n">ctc</span>
- <span class="n">cd</span> <span class="n">warp</span><span class="o">-</span><span class="n">ctc</span>
- <span class="n">mkdir</span> <span class="n">build</span>
- <span class="n">cd</span> <span class="n">build</span>
- <span class="n">cmake</span> <span class="o">..</span>
- <span class="n">make</span>
- <span class="n">sudo</span> <span class="n">make</span> <span class="n">install</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> cd ~/
+ git clone https://github.com/baidu-research/warp-ctc
+ cd warp-ctc
+ mkdir build
+ cd build
+ cmake ..
+ make
+ sudo make install
</pre></div>
</div>
</div>
<div class="section" id="enable-warp-ctc-in-mxnet">
<span id="enable-warp-ctc-in-mxnet"></span><h2>Enable Warp-CTC in MXNet<a class="headerlink" href="#enable-warp-ctc-in-mxnet" title="Permalink to this headline">¶</a></h2>
-<div class="highlight-default"><div class="highlight"><pre><span></span> comment out following lines in make/config.mk
+<div class="highlight-python"><div class="highlight"><pre><span></span> comment out following lines in make/config.mk
WARPCTC_PATH = $(HOME)/warp-ctc
MXNET_PLUGINS += plugin/warpctc/warpctc.mk
@@ -265,8 +263,8 @@ Previous Navbar Layout End -->
<div class="section" id="run-examples">
<span id="run-examples"></span><h2>Run Examples<a class="headerlink" href="#run-examples" title="Permalink to this headline">¶</a></h2>
<p>There are two examples. One is a toy example that validates CTC integration. The second is an OCR example with LSTM and CTC. You can run it by typing the following code:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">cd</span> <span class="n">examples</span><span class="o">/</span><span class="n">warpctc</span>
- <span class="n">python</span> <span class="n">lstm_ocr</span><span class="o">.</span><span class="n">py</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> cd examples/warpctc
+ python lstm_ocr.py
</pre></div>
</div>
<p>The OCR example is constructed as follows:</p>
@@ -276,7 +274,7 @@ Previous Navbar Layout End -->
<li>The output layer use CTC loss.</li>
</ol>
<p>The following code shows the detailed construction of the net:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="k">def</span> <span class="nf">lstm_unroll</span><span class="p">(</span><span class="n">num_lstm_layer</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">,</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="k">def</span> <span class="nf">lstm_unroll</span><span class="p">(</span><span class="n">num_lstm_layer</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">,</span>
<span class="n">num_hidden</span><span class="p">,</span> <span class="n">num_label</span><span class="p">):</span>
<span class="n">param_cells</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">last_states</span> <span class="o">=</span> <span class="p">[]</span>
diff --git a/versions/master/tutorials/speech_recognition/speech_lstm.html b/versions/master/tutorials/speech_recognition/speech_lstm.html
index 2a9ec32..d70918c 100644
--- a/versions/master/tutorials/speech_recognition/speech_lstm.html
+++ b/versions/master/tutorials/speech_recognition/speech_lstm.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Speech LSTM — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -273,8 +271,8 @@ make
<li>Copy or link the attached <code class="docutils literal"><span class="pre">python_wrap</span></code> folder to <code class="docutils literal"><span class="pre">kaldi/src</span></code>.</li>
<li>Compile python_wrap/.</li>
</ol>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">cd</span> <span class="n">kaldi</span><span class="o">/</span><span class="n">src</span><span class="o">/</span><span class="n">python_wrap</span><span class="o">/</span>
-<span class="n">make</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>cd kaldi/src/python_wrap/
+make
</pre></div>
</div>
</div>
@@ -315,14 +313,14 @@ make
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">apply-cmvn</span></code> provides mean-variance normalization. The default setup was applied per speaker. It’s more common to perform mean-variance normalization for the whole corpus, and then feed the results to the neural networks:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">compute</span><span class="o">-</span><span class="n">cmvn</span><span class="o">-</span><span class="n">stats</span> <span class="n">scp</span><span class="p">:</span><span class="n">data</span><span class="o">/</span><span class="n">sdm1</span><span class="o">/</span><span class="n">train_fbank</span><span class="o">/</span><span class="n">feats</span><span class="o">.</span><span class="n">scp</sp [...]
- <span class="n">apply</span><span class="o">-</span><span class="n">cmvn</span> <span class="o">--</span><span class="n">norm</span><span class="o">-</span><span class="nb">vars</span><span class="o">=</span><span class="n">true</span> <span class="n">data</span><span class="o">/</span><span class="n">sdm1</span><span class="o">/</span><span class="n">train_fbank</span><span class="o">/</span><span class="n">cmvn_g</span><span class="o">.</span><span class="n">ark</span> <span class="n" [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span> compute-cmvn-stats scp:data/sdm1/train_fbank/feats.scp data/sdm1/train_fbank/cmvn_g.ark
+ apply-cmvn --norm-vars=true data/sdm1/train_fbank/cmvn_g.ark scp:data/sdm1/train_fbank/feats.scp ark,scp:data/sdm1/train_fbank_gcmvn/feats.ark,data/sdm1/train_fbank_gcmvn/feats.scp
</pre></div>
</div>
<p>Note that Kaldi always tries to find features in <code class="docutils literal"><span class="pre">feats.scp</span></code>. Ensure that the normalized features are organized as Kaldi expects them during decoding.</p>
<p>Finally, put the features and labels together in a file so that MXNet can find them. More specifically, for each data set (train, dev, eval), you will need to create a file similar to <code class="docutils literal"><span class="pre">train_mxnet.feats</span></code>, with the following contents:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">TRANSFORM</span> <span class="n">scp</span><span class="p">:</span><span class="n">feat</span><span class="o">.</span><span class="n">scp</span>
-<span class="n">scp</span><span class="p">:</span><span class="n">label</span><span class="o">.</span><span class="n">scp</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>TRANSFORM scp:feat.scp
+scp:label.scp
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">TRANSFORM</span></code> is the transformation you want to apply to the features. By default, we use <code class="docutils literal"><span class="pre">NO_FEATURE_TRANSFORM</span></code>. The <code class="docutils literal"><span class="pre">scp:</span></code> syntax is from Kaldi. <code class="docutils literal"><span class="pre">feat.scp</span></code> is typically the file from <code class="docutils literal"><span class="pre">data/sdm1/tra [...]
@@ -334,34 +332,34 @@ make
<li>Run <code class="docutils literal"><span class="pre">python</span> <span class="pre">train_lstm.py</span> <span class="pre">--configfile=your-config.cfg</span></code>. For help, use <code class="docutils literal"><span class="pre">python</span> <span class="pre">train_lstm.py</span> <span class="pre">--help</span></code>. You can set all of the configuration parameters in <code class="docutils literal"><span class="pre">default.cfg</span></code>, the customized config file, and throu [...]
</ol>
<p>Here are some example outputs from training on the TIMIT dataset:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">Example</span> <span class="n">output</span> <span class="k">for</span> <span class="n">TIMIT</span><span class="p">:</span>
-<span class="n">Summary</span> <span class="n">of</span> <span class="n">dataset</span> <span class="o">==================</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">100</span> <span class="p">:</span> <span class="mi">3</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">200</span> <span class="p">:</span> <span class="mi">346</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">300</span> <span class="p">:</span> <span class="mi">1496</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">400</span> <span class="p">:</span> <span class="mi">974</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">500</span> <span class="p">:</span> <span class="mi">420</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">600</span> <span class="p">:</span> <span class="mi">90</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">700</span> <span class="p">:</span> <span class="mi">11</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">800</span> <span class="p">:</span> <span class="mi">2</span> <span class="n">samples</span>
-<span class="n">Summary</span> <span class="n">of</span> <span class="n">dataset</span> <span class="o">==================</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">100</span> <span class="p">:</span> <span class="mi">0</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">200</span> <span class="p">:</span> <span class="mi">28</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">300</span> <span class="p">:</span> <span class="mi">169</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">400</span> <span class="p">:</span> <span class="mi">107</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">500</span> <span class="p">:</span> <span class="mi">41</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">600</span> <span class="p">:</span> <span class="mi">6</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">700</span> <span class="p">:</span> <span class="mi">3</span> <span class="n">samples</span>
-<span class="n">bucket</span> <span class="n">of</span> <span class="nb">len</span> <span class="mi">800</span> <span class="p">:</span> <span class="mi">0</span> <span class="n">samples</span>
-<span class="mi">2016</span><span class="o">-</span><span class="mi">04</span><span class="o">-</span><span class="mi">21</span> <span class="mi">20</span><span class="p">:</span><span class="mi">02</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span><span class="mi">904</span> <span class="n">Epoch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">Train</span><span class="o">-</span><span class="n">Acc_exlude_padd [...]
-<span class="mi">2016</span><span class="o">-</span><span class="mi">04</span><span class="o">-</span><span class="mi">21</span> <span class="mi">20</span><span class="p">:</span><span class="mi">02</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span><span class="mi">904</span> <span class="n">Epoch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">Time</span> <span class="n">cost</span><span class="o">=</span><spa [...]
-<span class="mi">2016</span><span class="o">-</span><span class="mi">04</span><span class="o">-</span><span class="mi">21</span> <span class="mi">20</span><span class="p">:</span><span class="mi">02</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span><span class="mi">419</span> <span class="n">Epoch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">Validation</span><span class="o">-</span><span class="n">Acc_exlude [...]
-<span class="mi">2016</span><span class="o">-</span><span class="mi">04</span><span class="o">-</span><span class="mi">21</span> <span class="mi">20</span><span class="p">:</span><span class="mi">04</span><span class="p">:</span><span class="mi">17</span><span class="p">,</span><span class="mi">290</span> <span class="n">Epoch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="n">Train</span><span class="o">-</span><span class="n">Acc_exlude_padd [...]
-<span class="mi">2016</span><span class="o">-</span><span class="mi">04</span><span class="o">-</span><span class="mi">21</span> <span class="mi">20</span><span class="p">:</span><span class="mi">04</span><span class="p">:</span><span class="mi">17</span><span class="p">,</span><span class="mi">290</span> <span class="n">Epoch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="n">Time</span> <span class="n">cost</span><span class="o">=</span><spa [...]
-<span class="mi">2016</span><span class="o">-</span><span class="mi">04</span><span class="o">-</span><span class="mi">21</span> <span class="mi">20</span><span class="p">:</span><span class="mi">04</span><span class="p">:</span><span class="mi">20</span><span class="p">,</span><span class="mi">738</span> <span class="n">Epoch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="n">Validation</span><span class="o">-</span><span class="n">Acc_exlude [...]
-<span class="mi">2016</span><span class="o">-</span><span class="mi">04</span><span class="o">-</span><span class="mi">21</span> <span class="mi">20</span><span class="p">:</span><span class="mi">05</span><span class="p">:</span><span class="mi">53</span><span class="p">,</span><span class="mi">127</span> <span class="n">Epoch</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="n">Train</span><span class="o">-</span><span class="n">Acc_exlude_padd [...]
-<span class="mi">2016</span><span class="o">-</span><span class="mi">04</span><span class="o">-</span><span class="mi">21</span> <span class="mi">20</span><span class="p">:</span><span class="mi">05</span><span class="p">:</span><span class="mi">53</span><span class="p">,</span><span class="mi">128</span> <span class="n">Epoch</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="n">Time</span> <span class="n">cost</span><span class="o">=</span><spa [...]
-<span class="mi">2016</span><span class="o">-</span><span class="mi">04</span><span class="o">-</span><span class="mi">21</span> <span class="mi">20</span><span class="p">:</span><span class="mi">05</span><span class="p">:</span><span class="mi">56</span><span class="p">,</span><span class="mi">568</span> <span class="n">Epoch</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="n">Validation</span><span class="o">-</span><span class="n">Acc_exlude [...]
+<div class="highlight-python"><div class="highlight"><pre><span></span>Example output for TIMIT:
+Summary of dataset ==================
+bucket of len 100 : 3 samples
+bucket of len 200 : 346 samples
+bucket of len 300 : 1496 samples
+bucket of len 400 : 974 samples
+bucket of len 500 : 420 samples
+bucket of len 600 : 90 samples
+bucket of len 700 : 11 samples
+bucket of len 800 : 2 samples
+Summary of dataset ==================
+bucket of len 100 : 0 samples
+bucket of len 200 : 28 samples
+bucket of len 300 : 169 samples
+bucket of len 400 : 107 samples
+bucket of len 500 : 41 samples
+bucket of len 600 : 6 samples
+bucket of len 700 : 3 samples
+bucket of len 800 : 0 samples
+2016-04-21 20:02:40,904 Epoch[0] Train-Acc_exlude_padding=0.154763
+2016-04-21 20:02:40,904 Epoch[0] Time cost=91.574
+2016-04-21 20:02:44,419 Epoch[0] Validation-Acc_exlude_padding=0.353552
+2016-04-21 20:04:17,290 Epoch[1] Train-Acc_exlude_padding=0.447318
+2016-04-21 20:04:17,290 Epoch[1] Time cost=92.870
+2016-04-21 20:04:20,738 Epoch[1] Validation-Acc_exlude_padding=0.506458
+2016-04-21 20:05:53,127 Epoch[2] Train-Acc_exlude_padding=0.557543
+2016-04-21 20:05:53,128 Epoch[2] Time cost=92.390
+2016-04-21 20:05:56,568 Epoch[2] Validation-Acc_exlude_padding=0.548100
</pre></div>
</div>
<p>The final frame accuracy was approximately 62%.</p>
@@ -375,8 +373,8 @@ make
<p>Here are the results for the TIMIT and AMI test sets (using the default setup, three-layer LSTM with projection layers):</p>
<table border="1" class="docutils">
<colgroup>
-<col width="50%"></col>
-<col width="50%"></col>
+<col width="50%"/>
+<col width="50%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Corpus</th>
diff --git a/versions/master/tutorials/unsupervised_learning/auto_encoders.html b/versions/master/tutorials/unsupervised_learning/auto_encoders.html
index 54b32ac..bfac4e6 100644
--- a/versions/master/tutorials/unsupervised_learning/auto_encoders.html
+++ b/versions/master/tutorials/unsupervised_learning/auto_encoders.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Autoencoders — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
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-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/unsupervised_learning/gan.html b/versions/master/tutorials/unsupervised_learning/gan.html
index d80f4f5..496aa44 100644
--- a/versions/master/tutorials/unsupervised_learning/gan.html
+++ b/versions/master/tutorials/unsupervised_learning/gan.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Generative Adversarial Network — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,10 +48,8 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -151,7 +149,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
@@ -178,7 +176,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -188,11 +186,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
diff --git a/versions/master/tutorials/vision/large_scale_classification.html b/versions/master/tutorials/vision/large_scale_classification.html
index ac05269..781fb73 100644
--- a/versions/master/tutorials/vision/large_scale_classification.html
+++ b/versions/master/tutorials/vision/large_scale_classification.html
@@ -2,15 +2,15 @@
<html lang="en">
<head>
-<meta charset="utf-8">
-<meta content="IE=edge" http-equiv="X-UA-Compatible">
-<meta content="width=device-width, initial-scale=1" name="viewport">
+<meta charset="utf-8"/>
+<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
+<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>Large Scale Image Classification — mxnet documentation</title>
-<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet">
-<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
-<link href="../../_static/basic.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/pygments.css" rel="stylesheet" type="text/css"/>
-<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css">
+<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
+<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
+<link href="../../_static/basic.css" rel="stylesheet" type="text/css">
+<link href="../../_static/pygments.css" rel="stylesheet" type="text/css">
+<link href="../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
@@ -18,7 +18,7 @@
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt'
+ SOURCELINK_SUFFIX: ''
};
</script>
<script src="../../_static/jquery-1.11.1.js" type="text/javascript"></script>
@@ -48,12 +48,10 @@
<!-- -->
<!-- <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
-<link href="../../genindex.html" rel="index" title="Index"/>
-<link href="../../search.html" rel="search" title="Search"/>
-<link href="../index.html" rel="up" title="Tutorials"/>
+<link href="../index.html" rel="up" title="Tutorials">
<link href="../python/predict_image.html" rel="prev" title="Predict with pre-trained models"/>
-<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png">
-</link></link></link></link></meta></meta></meta></head>
+<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
+</link></link></link></head>
<body role="document"><!-- Previous Navbar Layout
<div class="navbar navbar-default navbar-fixed-top">
<div class="container">
@@ -153,7 +151,7 @@ Previous Navbar Layout End -->
<a class="main-nav-link" href="../../architecture/index.html">Architecture</a>
<!-- <a class="main-nav-link" href="../../community/index.html">Community</a> -->
<a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a>
-<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://localhost:8008/>0.10</a></li><li><a class="main-nav-link" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></span></nav>
+<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Versions(master)<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a class="main-nav-link" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a> [...]
<script> function getRootPath(){ return "../../" } </script>
<div class="burgerIcon dropdown">
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@@ -180,7 +178,7 @@ Previous Navbar Layout End -->
</li>
<li><a href="../../architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://github.com/dmlc/mxnet">Github</a></li>
-<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://localhost:8008/>0.10</a></li><li><a tabindex="-1" href=http://localhost:8008/versions/master/index.html>master</a></li></ul></li></ul>
+<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">Versions(master)</a><ul class="dropdown-menu"><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/>0.10</a></li><li><a tabindex="-1" href=http://mxnet.incubator.apache.org/versions/master/index.html>master</a></li></ul></li></ul>
</div>
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<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
@@ -190,11 +188,11 @@ Previous Navbar Layout End -->
<form action="../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
-<input class="form-control" name="q" placeholder="Search" type="text">
-</input></div>
-<input name="check_keywords" type="hidden" value="yes"/>
+<input class="form-control" name="q" placeholder="Search" type="text"/>
+</div>
+<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
-</form>
+</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
@@ -238,7 +236,7 @@ Previous Navbar Layout End -->
<li class="toctree-l4"><a class="reference internal" href="../python/linear-regression.html">Linear Regression</a></li>
<li class="toctree-l4"><a class="reference internal" href="../python/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l4"><a class="reference internal" href="../python/predict_image.html">Predict with pre-trained models</a></li>
-<li class="toctree-l4 current"><a class="current reference internal" href="#">Large Scale Image Classification</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="">Large Scale Image Classification</a></li>
</ul>
</li>
</ul>
@@ -259,7 +257,7 @@ Previous Navbar Layout End -->
<li>MXNet. See the instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a>.</li>
<li><a class="reference external" href="http://opencv.org/opencv-3-2.html">OpenCV Python library</a></li>
</ul>
-<div class="highlight-default"><div class="highlight"><pre><span></span>$ pip install opencv-python
+<div class="highlight-python"><div class="highlight"><pre><span></span>$ pip install opencv-python
</pre></div>
</div>
</div>
@@ -269,7 +267,7 @@ Previous Navbar Layout End -->
<span id="disk-space"></span><h3>Disk space<a class="headerlink" href="#disk-space" title="Permalink to this headline">¶</a></h3>
<p>The first step in training with large data is downloading the data and preprocessing it. For this tutorial, we will be using the full ImageNet dataset. Note that, at least 2 TB of disk space is required to download and preprocess this data. It is strongly recommended to use SSD instead of HDD. SSD is much better at dealing with a large number of small image files. After the preprocessing completes and images are packed into recordIO files, HDD should be fine for training.</p>
<p>In this tutorial, we will use an AWS storage instance for data preprocessing. The storage instance <code class="docutils literal"><span class="pre">i3.4xlarge</span></code> has 3.8 TB of disk space across two NVMe SSD disks. We will use software RAID to combine them into one disk and mount it at <code class="docutils literal"><span class="pre">~/data</span></code>.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>sudo mdadm --create --verbose /dev/md0 --level=stripe --raid-devices=2 \
+<div class="highlight-python"><div class="highlight"><pre><span></span>sudo mdadm --create --verbose /dev/md0 --level=stripe --raid-devices=2 \
/dev/nvme0n1 /dev/nvme1n1
sudo mkfs /dev/md0
sudo mkdir ~/data
@@ -283,13 +281,13 @@ sudo chown ${whoami} ~/data
<span id="download-imagenet"></span><h3>Download ImageNet<a class="headerlink" href="#download-imagenet" title="Permalink to this headline">¶</a></h3>
<p>In this tutorial, we will be using the full ImageNet dataset which can be downloaded from http://www.image-net.org/download-images. <code class="docutils literal"><span class="pre">fall11_whole.tar</span></code> contains all the images. This file is 1.2 TB in size and could take a long time to download.</p>
<p>After downloading, untar the file.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>export ROOT=full
+<div class="highlight-python"><div class="highlight"><pre><span></span>export ROOT=full
mkdir $ROOT
tar -xvf fall11_whole.tar -C $ROOT
</pre></div>
</div>
<p>That should give you a collection of tar files. Each tar file represents a category and contains all images belonging to that category. We can unzip each tar file and copy the images into a folder named after the name of the tar file.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>for i in $ROOT/*.tar; do j=${i%.*}; echo $j; mkdir -p $j; tar -xf $i -C $j; done
+<div class="highlight-python"><div class="highlight"><pre><span></span>for i in $ROOT/*.tar; do j=${i%.*}; echo $j; mkdir -p $j; tar -xf $i -C $j; done
rm $ROOT/*.tar
ls $ROOT | head
@@ -309,7 +307,7 @@ n00120010
<div class="section" id="remove-uncommon-classes-for-transfer-learning-optional">
<span id="remove-uncommon-classes-for-transfer-learning-optional"></span><h3>Remove uncommon classes for transfer learning (optional)<a class="headerlink" href="#remove-uncommon-classes-for-transfer-learning-optional" title="Permalink to this headline">¶</a></h3>
<p>A common reason to train a network on ImageNet data is to use it for transfer learning (including feature extraction or fine-tuning other models). According to <a class="reference external" href="https://arxiv.org/pdf/1608.08614v1.pdf">this</a> study, classes with too few images don’t help in transfer learning. So, we could remove classes with fewer than a certain number of images. The following code will remove classes with less than 500 images.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>BAK=${ROOT}_filtered
+<div class="highlight-python"><div class="highlight"><pre><span></span>BAK=${ROOT}_filtered
mkdir -p ${BAK}
for c in ${ROOT}/n*; do
count=`ls $c/*.JPEG | wc -l`
@@ -326,7 +324,7 @@ done
<div class="section" id="generate-a-validation-set">
<span id="generate-a-validation-set"></span><h3>Generate a validation set<a class="headerlink" href="#generate-a-validation-set" title="Permalink to this headline">¶</a></h3>
<p>To ensure we don’t overfit the data, we will create a validation set separate from the training set. During training, we will monitor loss on the validation set frequently. We create the validation set by picking fifty random images from each class and moving them to the validation set.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>VAL_ROOT=${ROOT}_val
+<div class="highlight-python"><div class="highlight"><pre><span></span>VAL_ROOT=${ROOT}_val
mkdir -p ${VAL_ROOT}
for i in ${ROOT}/n*; do
c=`basename $i`
@@ -343,28 +341,28 @@ done
<span id="pack-images-into-record-files"></span><h3>Pack images into record files<a class="headerlink" href="#pack-images-into-record-files" title="Permalink to this headline">¶</a></h3>
<p>While MXNet can read image files directly, it is recommended to pack the image files into a recordIO file for increased performance. MXNet provides a tool (tools/im2rec.py) to do this. To use this tool, MXNet and OpenCV’s python module needs to be installed in the system.</p>
<p>Set the environment variable <code class="docutils literal"><span class="pre">MXNET</span></code> to point to the MXNet installation directory and <code class="docutils literal"><span class="pre">NAME</span></code> to the name of the dataset. Here, we assume MXNet is installed at <code class="docutils literal"><span class="pre">~/mxnet</span></code></p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">MXNET</span><span class="o">=~/</span><span class="n">mxnet</span>
-<span class="n">NAME</span><span class="o">=</span><span class="n">full_imagenet_500_filtered</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>MXNET=~/mxnet
+NAME=full_imagenet_500_filtered
</pre></div>
</div>
<p>To create the recordIO files, we first create a list of images we want in the recordIO files and then use <code class="docutils literal"><span class="pre">im2rec</span></code> to pack images in the list into recordIO files. We create this list in <code class="docutils literal"><span class="pre">train_meta</span></code>. Training data is around 1TB. We split it into 8 parts, with each part roughly 100 GB in size.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>mkdir -p train_meta
+<div class="highlight-python"><div class="highlight"><pre><span></span>mkdir -p train_meta
python ${MXNET}/tools/im2rec.py --list True --chunks 8 --recursive True \
train_meta/${NAME} ${ROOT}
</pre></div>
</div>
<p>We then resize the images such that the short edge is 480 pixels long and pack the images into recordIO files. Since most of the work is disk I/O, we use multiple (16) threads to get the work done faster.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>python ${MXNET}/tools/im2rec.py --resize 480 --quality 90 \
+<div class="highlight-python"><div class="highlight"><pre><span></span>python ${MXNET}/tools/im2rec.py --resize 480 --quality 90 \
--num-thread 16 train_meta/${NAME} ${ROOT}
</pre></div>
</div>
<p>Once done, we move the rec files into a folder named <code class="docutils literal"><span class="pre">train</span></code>.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">mkdir</span> <span class="o">-</span><span class="n">p</span> <span class="n">train</span>
-<span class="n">mv</span> <span class="n">train_meta</span><span class="o">/*.</span><span class="n">rec</span> <span class="n">train</span><span class="o">/</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>mkdir -p train
+mv train_meta/*.rec train/
</pre></div>
</div>
<p>We do similar preprocessing for the validation set.</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>mkdir -p val_meta
+<div class="highlight-python"><div class="highlight"><pre><span></span>mkdir -p val_meta
python ${MXNET}/tools/im2rec.py --list True --recursive True \
val_meta/${NAME} ${VAL_ROOT}
python ${MXNET}/tools/im2rec.py --resize 480 --quality 90 \
@@ -390,14 +388,14 @@ mv val_meta/*.rec val/
<li><p class="first">Compile MXNet using <code class="docutils literal"><span class="pre">USE_DIST_KVSTORE=1</span></code> to enable distributed training.</p>
</li>
<li><p class="first">Create a hosts file in the master that contains the host names of all the machines in the cluster. For example,</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>$ head -3 hosts
+<div class="highlight-python"><div class="highlight"><pre><span></span>$ head -3 hosts
deeplearning-worker1
deeplearning-worker2
deeplearning-worker3
</pre></div>
</div>
<p>It should be possible to ssh into any of these machines from the master by invoking <code class="docutils literal"><span class="pre">ssh</span></code> with just a hostname from the file. For example,</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>$ ssh deeplearning-worker2
+<div class="highlight-python"><div class="highlight"><pre><span></span>$ ssh deeplearning-worker2
===================================
Deep Learning AMI for Ubuntu
===================================
@@ -412,7 +410,7 @@ ubuntu@ip-10-0-1-199:~$
<div class="section" id="run-training">
<span id="run-training"></span><h3>Run Training<a class="headerlink" href="#run-training" title="Permalink to this headline">¶</a></h3>
<p>After the cluster is setup, login to master and run the following command from ${MXNET}/example/image-classification</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span>../../tools/launch.py -n 16 -H $DEEPLEARNING_WORKERS_PATH python train_imagenet.py --network resnet \
+<div class="highlight-python"><div class="highlight"><pre><span></span>../../tools/launch.py -n 16 -H $DEEPLEARNING_WORKERS_PATH python train_imagenet.py --network resnet \
--num-layers 152 --data-train ~/data/train --data-val ~/data/val/ --gpus 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 \
--batch-size 8192 --model ~/data/model/resnet152 --num-epochs 1 --kv-store dist_sync
</pre></div>
@@ -420,8 +418,8 @@ ubuntu@ip-10-0-1-199:~$
<p>launch.py launches the command it is provided in all the machine in the cluster. List of machines in the cluster must be provided to launch.py using the <code class="docutils literal"><span class="pre">-H</span></code> switch. Here is description of options used for launch.py.</p>
<table border="1" class="docutils">
<colgroup>
-<col width="50%"></col>
-<col width="50%"></col>
+<col width="50%"/>
+<col width="50%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Option</th>
@@ -440,8 +438,8 @@ ubuntu@ip-10-0-1-199:~$
<p>train_imagenet.py trains the network provided by the <code class="docutils literal"><span class="pre">--network</span></code> option using the data provided by the <code class="docutils literal"><span class="pre">--data-train</span></code> and <code class="docutils literal"><span class="pre">--data-val</span></code> options. Here is description of the options used with train_imagenet.py.</p>
<table border="1" class="docutils">
<colgroup>
-<col width="50%"></col>
-<col width="50%"></col>
+<col width="50%"/>
+<col width="50%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Option</th>
@@ -488,10 +486,10 @@ ubuntu@ip-10-0-1-199:~$
<p>We fixed the batch size per GPU constant and doubled the number of GPUs for every subsequent test. Synchronized SGD (–kv-store dist_device_sync) was used. The CNNs used are located <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example/image-classification/symbols">here</a>.</p>
<table border="1" class="docutils">
<colgroup>
-<col width="25%"></col>
-<col width="25%"></col>
-<col width="25%"></col>
-<col width="25%"></col>
+<col width="25%"/>
+<col width="25%"/>
+<col width="25%"/>
+<col width="25%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"> </th>
@@ -516,10 +514,10 @@ ubuntu@ip-10-0-1-199:~$
<p>Number of images processed per second is shown in the following table:</p>
<table border="1" class="docutils">
<colgroup>
-<col width="25%"></col>
-<col width="25%"></col>
-<col width="25%"></col>
-<col width="25%"></col>
+<col width="25%"/>
+<col width="25%"/>
+<col width="25%"/>
+<col width="25%"/>
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Number of GPUs</th>
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